Ubiquitin ligase TRIM15 promotes the progression of pancreatic cancer via the upregulation of the IGF2BP2-TLR4 axis.

BACKGROUND: The tripartite motif family, predominantly characterized by its E3 ubiquitin ligase activities, is involved in various cellular processes including signal transduction, apoptosis and autophagy, protein quality control, immune regulation, and carcinogenesis. Tripartite Motif Containing 15 (TRIM15) plays an important role in melanoma progression through extracellular signal-regulated kinase activation; however, data on its role in pancreatic tumors remain lacking. We previously demonstrated that TRIM15 targeted lipid synthesis and metabolism in pancreatic cancer; however, other specific regulatory mechanisms remain elusive. METHODS: We used transcriptomics and proteomics, conducted a series of phenotypic experiments, and used a mouse orthotopic transplantation model to study the specific mechanism of TRIM15 in pancreatic cancer in vitro and in vivo. RESULTS: TRIM15 overexpression promoted the progression of pancreatic cancer by upregulating the toll-like receptor 4. The TRIM15 binding protein, IGF2BP2, could combine with TLR4 to inhibit its mRNA degradation. Furthermore, the ubiquitin level of IGF2BP2 was positively correlated with TRIM15. CONCLUSIONS: TRIM15 could ubiquitinate IGF2BP2 to enhance the function of phase separation and the maintenance of mRNA stability of TLR4. TRIM15 is a potential therapeutic target against pancreatic cancer.

Characterization of Pancreatic Fistula after Post-pancreatectomy Acute Pancreatitis.

OBJECTIVE: This study aimed to investigate the clinical significance and risk factors of postoperative pancreatic fistula (POPF) after post-pancreatectomy acute pancreatitis (PPAP) in patients who underwent pancreaticoduodenectomy (PD). SUMMARY BACKGROUND DATA: PPAP has been recognized as a critical factor in the pathophysiology of POPF after PD. METHODS: A total of 817 consecutive patients who underwent elective PD between January 2020 and June 2022 were included. PPAP and POPF were defined in accordance with the International Study Group for Pancreatic Surgery (ISGPS) definitions. Multivariate logistic analyses were performed to investigate the risk factors for POPF. Comparisons between PPAP-associated POPF and non-PPAP-associated POPF were made to further characterize this intriguing complication. RESULTS: Overall, 159 (19.5%) patients developed POPF after PD, of which 73 (45.9%) occurred following PPAP, and the remaining 86 (54.1%) had non-PPAP-associated POPF. Patients with PPAP-associated POPF experienced significantly higher morbidity than patients without POPF. Multivariate analyses revealed distinct risk factors for each POPF type. For PPAP-associated POPF, independent risk factors included estimated blood loss >200 mL (OR 1.93), MPD ≤3 cm (OR 2.88), and soft pancreatic texture (OR 2.01), largely overlapping with FRS (Fistula Risk Score) elements. On the other hand, non-PPAP-associated POPF was associated with age >65 years (OR 1.95), male (OR 2.10), and MPD ≤3 cm (OR 2.57). Notably, among patients with PPAP, the incidence of POPF consistently hovered around 50% regardless of the FRS stratification. CONCLUSIONS: PPAP-associated POPF presents as a distinct pathophysiology in the development of POPF after PD, potentially opening doors for future prevention strategies targeting the early postoperative period.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures.

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

The Value of VR-PVEP in Objective Evaluation of Monocular Refractive Visual Impairment.

OBJECTIVES: To study the virtual reality-pattern visual evoked potential (VR-PVEP) P100 waveform characteristics of monocular visual impairment with different impaired degrees under simultaneous binocular perception and monocular stimulations. METHODS: A total of 55 young volunteers with normal vision (using decimal recording method, far vision ≥0.8 and near vision ≥0.5) were selected to simulate three groups of monocular refractive visual impairment by interpolation method. The sum of near and far vision ≤0.2 was Group A, the severe visual impairment group; the sum of near and far vision <0.8 was Group B, the moderate visual impairment group; and the sum of near and far vision ≥0.8 was Group C, the mild visual impairment group. The volunteers' binocular normal visions were set as the control group. The VR-PVEP P100 peak times measured by simultaneous binocular perception and monocular stimulation were compared at four spatial frequencies 16×16, 24×24, 32×32 and 64×64. RESULTS: In Group A, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 24×24, 32×32 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group B, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 16×16, 24×24 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group C, there was no significant difference between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at all spatial frequencies (P>0.05). There was no significant difference in the P100 peak times measured at all spatial frequencies between simulant visual impairment eyes and simultaneous binocular perception in the control group (P>0.05). CONCLUSIONS: VR-PVEP can be used for visual acuity evaluation of patients with severe and moderate monocular visual impairment, which can reflect the visual impairment degree caused by ametropia. VR-PVEP has application value in the objective evaluation of visual function and forensic clinical identification.

A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.

It is important to measure scars in forensic and clinical medicine. In practice, scars are mostly manually measured, and the results are diverse and influenced by various subjective factors. With the development of digital image technology and artificial intelligence, noncontact and automatic photogrammetry has been gradually used in some practical applications. In this article, we propose an automatic method for measuring the length of linear scars based on multiview stereo and deep learning, which combines the 3D reconstruction algorithm of structure from motion and the image segmentation algorithm based on a convolutional neural network. With a few pictures taken by a smart phone, automatic segmentation and measurement of scars can be realized. The reliability of the measurement was first demonstrated through simulation experiments on five artificial scars, giving errors of length <5%. Then, experiment results on 30 clinical scar samples showed that our measurements were in high agreement with manual measurements, with an average error of 3.69%. Our study demonstrates that the application of photogrammetry in scar measurement is effective and that the deep learning technique can realize the automation of scar measurement with high accuracy.

Predicting visual acuity with machine learning in treated ocular trauma patients.

AIM: To predict best-corrected visual acuity (BCVA) by machine learning in patients with ocular trauma who were treated for at least 6mo. METHODS: The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y. The initial visual acuity was 0.99 logMAR. The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized. Four different machine-learning algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, and random forest regressor) were used to predict BCVA, and four algorithms (Extreme Gradient Boosting, support vector machine, logistic regression, and random forest classifier) were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer. Clinical features were obtained from outpatient records, and ocular parameters were extracted from optical coherence tomography images and fundus photographs. These features were put into different machine-learning models, and the obtained predicted values were compared with the actual BCVA values. The best-performing model and the best variable selected were further evaluated in the test dataset. RESULTS: There was a significant correlation between the predicted and actual values [all Pearson correlation coefficient (PCC)>0.6]. Considering only the data from the traumatic group (group A) into account, the lowest mean absolute error (MAE) and root mean square error (RMSE) were 0.30 and 0.40 logMAR, respectively. In the traumatic and healthy groups (group B), the lowest MAE and RMSE were 0.20 and 0.33 logMAR, respectively. The sensitivity was always higher than the specificity in group A, in contrast to the results in group B. The classification accuracy and precision were above 0.80 in both groups. The MAE, RMSE, and PCC of the test dataset were 0.20, 0.29, and 0.96, respectively. The sensitivity, precision, specificity, and accuracy of the test dataset were 0.83, 0.92, 0.95, and 0.90, respectively. CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.

Correlation between Elbow Flexor Muscle Strength and Needle Electromyography Parameters after Musculocutaneous Nerve Injury.

OBJECTIVES: To explore the changes of elbow flexor muscle strength after musculocutaneous nerve injury and its correlation with needle electromyography (nEMG) parameters. METHODS: Thirty cases of elbow flexor weakness caused by unilateral brachial plexus injury (involving musculocutaneous nerve) were collected. The elbow flexor muscle strength was evaluated by manual muscle test (MMT) based on Lovett Scale. All subjects were divided into Group A (grade 1 and grade 2, 16 cases) and Group B (grade 3 and grade 4, 14 cases) according to their elbow flexor muscle strength of injured side. The biceps brachii of the injured side and the healthy side were examined by nEMG. The latency and amplitude of the compound muscle action potential (CMAP) were recorded. The type of recruitment response, the mean number of turns and the mean amplitude of recruitment potential were recorded when the subjects performed maximal voluntary contraction. The quantitative elbow flexor muscle strength was measured by portable microFET 2 Manual Muscle Tester. The percentage of residual elbow flexor muscle strength (the ratio of quantitative muscle strength of the injured side to the healthy side) was calculated. The differences of nEMG parameters, quantitative muscle strength and residual elbow flexor muscle strength between the two groups and between the injured side and the healthy side were compared. The correlation between elbow flexor manual muscle strength classification, quantitative muscle strength and nEMG parameters was analyzed. RESULTS: After musculocutaneous nerve injury, the percentage of residual elbow flexor muscle strength in Group B was 23.43% and that in Group A was 4.13%. Elbow flexor manual muscle strength classification was significantly correlated with the type of recruitment response, and the correlation coefficient was 0.886 (P<0.05). The quantitative elbow flexor muscle strength was correlated with the latency and amplitude of CMAP, the mean number of turns and the mean amplitude of recruitment potential, and the correlation coefficients were -0.528, 0.588, 0.465 and 0.426 (P<0.05), respectively. CONCLUSIONS: The percentage of residual elbow flexor muscle strength can be used as the basis of muscle strength classification, and the comprehensive application of nEMG parameters can be used to infer quantitative elbow flexor muscle strength.

A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis.

Objective: Inguinal lymph node metastasis (ILNM) is significantly associated with poor prognosis in patients with squamous cell carcinoma of the penis (SCCP). Patient prognosis could be improved if the probability of ILNM incidence could be accurately predicted at an early stage. We developed a predictive model based on machine learning combined with big data to achieve this. Methods: Data of patients diagnosed with SCCP were obtained from the Surveillance, Epidemiology, and End Results Program Research Data. By combing variables that represented the patients' clinical characteristics, we applied five machine learning algorithms to create predictive models based on logistic regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor. Model performance was evaluated by ten-fold cross-validation receiver operating characteristic curves, which were used to calculate the area under the curve of the five models for predictive accuracy. Decision curve analysis was conducted to estimate the clinical utility of the models. An external validation cohort of 74 SCCP patients was selected from the Affiliated Hospital of Xuzhou Medical University (February 2008 to March 2021). Results: A total of 1,056 patients with SCCP from the SEER database were enrolled as the training cohort, of which 164 (15.5%) developed early-stage ILNM. In the external validation cohort, 16.2% of patients developed early-stage ILNM. Multivariate logistic regression showed that tumor grade, inguinal lymph node dissection, radiotherapy, and chemotherapy were independent predictors of early-stage ILNM risk. The model based on the eXtreme Gradient Boosting algorithm showed stable and efficient prediction performance in both the training and external validation groups. Conclusion: The ML model based on the XGB algorithm has high predictive effectiveness and may be used to predict early-stage ILNM risk in SCCP patients. Therefore, it may show promise in clinical decision-making.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases.

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Research Progress of Age Estimation in the Living by Knee Joint MRI.

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.

Development and validation of a novel risk model in newly diagnosed de novo bone metastatic prostate cancer (M1b): a retrospective study.

Background: Previous studies suggested that bone metastasis has a significant effect on the time of progression to metastatic castration-resistant prostate cancer (CRPC) for newly diagnosed de novo bone metastatic hormone-sensitive prostate cancer (mHSPC). Nevertheless, the effect of different bone metastasis sites was not fully evaluated. This study aimed to develop and validate a novel bone metastatic risk model. Methods: We enrolled 122 patients who were newly diagnosed with de novo bone metastatic prostate cancer following primary androgen deprivation based therapy at our institution from January 2008 to June 2021. The metastatic bone sites were classified into six sites: skull; cervical, thoracic, and lumbar vertebrae; chest (ribs and sternum); pelvis; upper limbs; and lower limbs. We calculated the bone metastatic score (BMS) for each site: 0 points were assigned for non-metastasis and 1 point was assigned for metastasis. The X-tile was adopted to acquire optimal cutoff points of BMS. We defined high-risk group (HRG) as BMS ≥ 3 and low-risk group (LRG) as BMS < 3. The new bone risk stratification was validated by calculating the area under the receiver operating characteristic curve (AUC). Subsequently, the relevant clinical prognostic variables were added to construct a predictive nomogram for predicting CRPC. Results: The median patient age was 73 years. Most patients had Gleason score ≤8 (93 cases, 76.2%). The median follow-up duration was 11.5 months (range: 2-92 months). Eighty-six patients progressed to CRPC during the follow-up. The most common bone metastatic site was the pelvis (90.2%). The median BMS was 4. Seventy-six patients had HRG, while forty-six had LRG. The 1-, 2-, and 3-year AUCs for H/LRG were 0.620, 0.754, and 0.793, respectively. The HRG was associated with earlier time to CRPC. A nomogram based on four parameters (Gleason score, H/LRG, prostate-specific antigen [PSA] nadir, and time to PSA nadir) was developed to predict CRPC. Internal validation using bootstrapping demonstrated good accuracy for predicting the CRPC (C-index: 0.727). The calibration analysis demonstrated that the model performed well. Conclusion: We established a novel H/LRG risk model for newly diagnosed de novo bone metastatic prostate cancer, which provided evidence to support clinical decision-making.

Retrospective validation of bone risk stratification criteria for men with de novo metastatic hormone-naive prostate cancer in China.

Background: Bone metastasis has been suggested to be a significant impactor on the prognosis of newly diagnosed de novo metastatic hormone-sensitive prostate cancer (mHSPC), and some risk stratification models have been proposed on the basis of this hypothesis. However, the effectiveness of these risk stratification criteria has not been fully evaluated in China. This study aimed to evaluate the effectiveness of the risk stratification models in China. Methods: A total of 140 patients who were newly diagnosed with metastatic prostate cancer followed by primary androgen deprivation-based therapy from January 2008 to June 2021 at our institution were enrolled in this study. The patients were divided into different groups on the basis of high- and low-volume disease (H/LVD) criteria, high-and low-risk disease (H/LRD) criteria, extremity bone metastasis criteria (EBM), and extent of disease (EOD) criteria. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) were used to compare the validity and net benefit of these models. Using the Cox proportional hazards model, we performed univariable and multivariable analyses of the factors influencing overall survival (OS) and the time of progression to metastatic castration-resistant prostate cancer (CRPC). Results: The median patient age was 72 years. Most patients had a Gleason score ≥8 (102 cases, 72.9%) and clinical T stage >2 (75 cases, 53.6%). The median follow-up time was 25 months (range, 2-95 months). Ninety-two patients progressed to CRPC and fifty-seven patients died during the follow-up. The AUC of OS and CRPC showed that the EOD model had higher validity than the other risk stratification models. DCA shows that the net benefit of the EOD model on OS was better than that of the other risk stratification models. As for CRPC, the net benefit of the EOD model was second only to that of the H/LRD model when the threshold was <0.5; however, when the threshold was >0.5, the EOD model outperformed the other models. The effectiveness of EOD as an independent prognostic variable was verified through univariable and multivariable analyses. Conclusion: The EOD model yields reasonable risk stratification for use in Chinese mHSPC patients, providing further evidence supporting its role in clinical decision-making.

Preoperative Systemic Inflammatory Markers as a Significant Prognostic Factor After TURBT in Patients with Non-Muscle-Invasive Bladder Cancer.

Introduction: Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) have been widely proposed to have predictive value for the patient prognosis of many malignancies, including bladder cancer. However, the predictive value of their combination in non-muscle-invasive bladder cancer (NMIBC) is unclear. Methods: Cases of NMIBC patients who underwent transurethral resection of the bladder tumor were recruited from two tertiary public medical centers. A systemic inflammatory marker (SIM) score was calculated based on comprehensive consideration of NLR, PLR, and LMR. Recurrence-free survival (RFS) and progression-free survival (PFS) were estimated by Kaplan-Meier analysis. The Log rank test was used to compare differences between the groups. Cox regression was used to screen risk factors affecting RFS and PFS. Nomogram models were established and validated, and patients were stratified based on the model scores. Results: The study dataset was grouped according to a 7:3 randomization, with the training cohort consisting of 292 cases and the validation cohort consisting of 124 cases. Cox regression analysis showed that SIM score is an independent predictor of RFS and PFS in NMIBC patients. The novel models were established based on the SIM score and other statistically significant clinicopathological features. The area under the curve (AUC) for predicting 1-, 2-, and 3-year RFS was 0.667, 0.689, and 0.713, respectively. The AUC for predicting 1-, 2-, and 3-year PFS was 0.807, 0.775, and 0.862, respectively. Based on the risk stratification, patients at high risk of recurrence and progression could be accurately identified. The established models were applied to the patient evaluation of the validation cohort, which proved the great performance of the novel models. Conclusion: The novel models based on the SIM score and clinicopathological characteristics can accurately predict the survival prognosis of NMIBC patients, and the models can be used by clinicians for individualized patient assessment and to assist in clinical decision-making.

Preoperative systemic immune-inflammation index as a significant prognostic factor after TURBT in patients with non-muscle-invasive bladder cancer: A retrospective study based on propensity score matching analysis.

OBJECTIVE: To investigate the association of the preoperative systemic immune-inflammation index (SII) with recurrence-free survival (RFS) after transurethral resection of the bladder tumor (TURBT) of non-muscle-invasive bladder cancer (NMIBC) using propensity score matching (PSM) analysis. METHODS: The clinicopathological characteristics and follow-up data of NMIBC patients were collected retrospectively from two tertiary medical centers. A 1:1 PSM analysis was carried out using the nearest-neighbor method (caliper size: 0.02). Cox regression analysis was used to identify the risk factors associated with RFS. RESULTS: A total of 416 NMIBC patients were included in this study. Before and after matching, patients with increased SII had worse RFS (p < 0.0001 and p = 0.027, respectively). Multivariate Cox analysis identified SII as an independent predictor of RFS before (HR [95% CI]: 1.789 [1.232, 2.599], p = 0.002) and after matching (HR [95% CI]: 1.646 [1.077, 2.515], p = 0.021). In the matched subgroup analysis, an elevated SII had a significant association with postoperative worse RFS in the T1 stage (p = 0.025), primary status (p = 0.049), high-grade (p = 0.0015), and multiple lesions (p = 0.043) subgroups. CONCLUSION: SII could accurately stratify the prognosis of NMIBC patients before and after PSM analysis. An elevated SII was significantly associated with worse RFS in NMIBC patients.

The value of PMCTA in the diagnosis of coronary atherosclerosis in isolated human hearts.

Postmortem computed tomography (PMCT) has a limited value in investigating coronary artery disease, despite several obvious advantages over the conventional autopsy. To address this issue, postmortem computed tomography angiography (PMCTA) has been introduced into various studies, where it has been used to investigate natural and unnatural deaths involving vascular damage, occlusion, or other pathologies of the vascular system. To investigate the application value of PMCTA in the diagnosis of coronary artery stenosis in ex situ hearts, the water-based contrast media were injected into isolated hearts, scaned, and finally compared with gold standards (autopsy and histology findings of the coronary artery). This study involved 16 subjects from the Academy of Forensic Science who were suspected to have died of sudden death without traumatic injuries. Unenhanced PMCT was performed first, followed by PMCTA using a water-based contrast agent, injected into the coronary arteries of isolated hearts using a self-designed angiography device. The image data were reconstructed into three-dimensional (3D) angiography images using software in the angiography facility. The 3D images were recorded and evaluated by two radiologists and then statistically analysed. The results of PMCTA were consistent with the gold standards for the diagnosis of coronary artery stenosis (P > 0.05). However, water-based contrast media can only be used to examine the pathological changes of blood vessels, which may have limitations in the diagnosis of causes of death such as myocardial oedema. PMCTA can be used as a new method to evaluate the degree of coronary atherosclerosis in addition to traditional autopsy. The 3D reconstruction technique reveals the coronary artery lesions more objectively and vividly and provides the opportunity to re-read the data at any time. Key points: The methods and parameters for coronary angiography in isolated human hearts were standardized based on the previous researcher.PMCTA in isolated human hearts is including the 3D reconstruction technique that reveals the coronary artery lesions more objectively and vividly, and provides the opportunity to re-read the data at anytime.PMCTA could only be used to examine the pathological changes of blood vessels, which might have limitations for the diagnosis of causes of death.PMCTA in isolated human hearts can be viewed as an auxiliary method for establishing the cause of death, which can provide an assessment of degree and extent of arterial stenosis and accurately help determine the abnormal location.

Development and external validation of a novel nomogram model for predicting postoperative recurrence-free survival in non-muscle-invasive bladder cancer.

Background: Transurethral resection of the bladder tumor with or without adjuvant intravesical instillation (IVI) has been the standard treatment for non-muscle-invasive bladder cancer (NMIBC), whereas a high percentage of patients still experience local tumor recurrence and disease progression after receiving the standard treatment modalities. Unfortunately, current relevant prediction models for determining the recurrent and progression risk of NMIBC patients are far from impeccable. Methods: Clinicopathological characteristics and follow-up information were retrospectively collected from two tertiary medical centers between October 2018 and June 2021. The least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were used to screen potential risk factors affecting recurrence-free survival (RFS) of patients. A nomogram model was established, and the patients were risk-stratified based on the model scores. Both internal and external validation were performed by sampling the model with 1,000 bootstrap resamples. Results: The study included 299 patient data obtained from the Affiliated Hospital of Xuzhou Medical University and 117 patient data obtained from the First Affiliated Hospital of Guangxi Medical University. Univariate regression analysis suggested that urine red blood cell count and different tumor invasion locations might be potential predictors of RFS. LASSO-Cox regression confirmed that prior recurrence status, times of IVI, and systemic immune-inflammation index (SII) were independent factors for predicting RFS. The area under the curve for predicting 1-, 2-, and 3-year RFS was 0.835, 0.833, and 0.871, respectively. Based on the risk stratification, patients at high risk of recurrence and progression could be accurately identified. A user-friendly risk calculator based on the model is deposited at https://dl0710.shinyapps.io/nmibc_rfs/. Conclusion: Internal and external validation analyses showed that our model had excellent predictive discriminatory ability and stability. The risk calculator can be used for individualized assessment of survival risk in NMIBC patients and can assist in guiding clinical decision-making.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS: In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

HDAC5 Loss Enhances Phospholipid-Derived Arachidonic Acid Generation and Confers Sensitivity to cPLA2 Inhibition in Pancreatic Cancer.

HDAC5 is a class IIa histone deacetylase member that is downregulated in multiple solid tumors, including pancreatic cancer, and loss of HDAC5 is associated with unfavorable prognosis. In this study, assessment of The Cancer Genome Atlas pancreatic adenocarcinoma dataset revealed that expression of HDAC5 correlates negatively with arachidonic acid (AA) metabolism, which has been implicated in inflammatory responses and cancer progression. Nontargeted metabolomics analysis revealed that HDAC5 knockdown resulted in a significant increase in AA and its downstream metabolites, such as eicosanoids and prostaglandins. HDAC5 negatively regulated the expression of the gene encoding calcium-dependent phospholipase A2 (cPLA2), the key enzyme in the production of AA from phospholipids. Mechanistically, HDAC5 repressed cPLA2 expression via deacetylation of GATA1. HDAC5 knockdown in cancer cells enhanced sensitivity to genetic or pharmacologic inhibition of cPLA2 in vitro and in vivo. Fatty acid supplementation in the diet reversed the sensitivity of HDAC5-deficient tumors to cPLA2 inhibition. These data indicate that HDAC5 loss in pancreatic cancer results in the hyperacetylation of GATA1, enabling the upregulation of cPLA2, which contributes to overproduction of AA. Dietary management plus cPLA2-targeted therapy could serve as a viable strategy for treating HDAC5-deficient pancreatic cancer patients. SIGNIFICANCE: The HDAC5-GATA1-cPLA2-AA signaling axis regulates sensitivity to fat restriction plus cPLA2 inhibition in pancreatic ductal adenocarcinoma, proposing dietary management as a feasible strategy for treating a subset of patients with pancreatic cancer.

ITGA2 overexpression inhibits DNA repair and confers sensitivity to radiotherapies in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a dismal disease with a 5-year survival rate of less than 10%, despite the recent advances in chemoradiotherapy. The sensitivity of the PDAC patients to chemoradiotherapy varies widely, especially to radiotherapy, suggesting the need for more elucidation of the underlying mechanisms. In this study, a novel function of the nuclear ITGA2, the alpha subunit of transmembrane collagen receptor integrin alpha-2/beta-1, regulating the DNA damage response (DDR), was identified. First, analyzing The Cancer Genome Atlas (TCGA) PDAC data set indicated that the expression status of ITGA2 was negatively correlated with the genome stability parameters. The study further demonstrated that ITGA2 specially inhibited the activity of the non-homologous end joining (NHEJ) pathway and conferred the sensitivity to radiotherapy in PDAC by restraining the recruitment of DNA-dependent protein kinase catalytic subunit (DNA-PKcs) to Ku70/80 heterodimer during DDR. Considering the overexpression of ITGA2 and its associated with the poor prognosis of PDAC patients, this study suggested that the ITGA2 expression status could be used as an indicator for radiotherapy and DNA damage reagents, and the radiotherapy in combination with the overexpression of ITGA2 might be a viable treatment strategy for the PDAC patients.

Effects of methanol and formic acid on CRYB, ALDH2, and ATP5A1 of RGCs.

OBJECTIVE: To explore the toxicity of methanol and its metabolite, formic acid on αB-crystallin(CRYB), aldehyde dehydrogenase (ALDH2), and ATPsynthase (ATP5A1) of rat retinal ganglion cells (RGCs). METHODS: RGCs are cultured in vitro in a toxic environment with 15/30/60 mM methanol or formic acid, respectively. Then, the morphological changes of RGCs and protein and mRNA levels of ALDH2, ATP5A1, and CRYB in rat RGCs were evaluated. RESULTS: 1) Compared to the toxicity of 15 mM formic acid on RGCs, 30 mM of formic acid environment significantly promoted apoptosis, and cell death occurred in the 60-mM formic acid group 24 h later. The toxicity of methanol for inducing apoptosis was not as obvious as formic acid. 2) In the 15-mM group, the level of CRYB protein was down-regulated after stimulating with both methanol and formic acid for 48 h, and ATP5A1 protein level decreased significantly with formic but not methanol. No change in ALDH2 was observed in methanol or formic acid. With a prolonged duration (>7 d) or high concentration (>30 mM) stimulation, cells treated with both methanol and formic acid showed severe apoptosis, rendering it challenging to collect a sufficient number of cells for protein detection. 3) In the 48-h group, no significant effect was detected on the mRNA of CRYB, ATP5A1, and ALDH2 by both 15/30 mM formic acid and 15 mM methanol. Conversely, 30 mM methanol had a significant up-regulation effect on the expression of the three genes, while no significant effect was observed in the 7-d groups. CONCLUSIONS: Formic acid exerted stronger toxicity on CRYB, ATP5A1, and ALDH2 than methanol and played a regulatory role at the translation level, while the effect of methanol is still uncertain, needing additional investigation.