New insights into the pH-dependent removal of sulfamethoxazole in peracetic acid activation systems: From mechanistic exploration to practical application potentials.

Peracetic acid (PAA) as emerging oxidant in advanced oxidation processes (AOPs) has attracted widespread attention in purifying water pollution. In this research, the removal of target contaminant (sulfamethoxazole, SMX) was investigated through PAA activation by a facile catalyst (Co@C), and the active sites of catalyst were identified as sp3-C, Oads, and Co0 by correlation analysis. Especially, different pH adjustment strategies were designed, including System A (adjusting pH after adding PAA) and System B (adjusting pH before adding PAA), to investigate the impact of oxidant acidity and alkalinity on solution microenvironment as well as effect and mechanism of pollutant removal. The results showed that HO· and CH3C(O)OO· dominated in System A, while Co(IV)O2+ was also observed in System B. Both systems showed optimal SMX degradation (98 %). However, System A exhibited excellent water quality tolerance (efficiency > 78 %), superior sustained catalyst activation (efficiency > 80 % in 40 h), less ion leaching (41 µg L-1), and lower products toxicity. Moreover, the pH of solution after reaction in System B was intensely acidic, requiring costly pH adjustments for discharge. This study unveils the strategy of adjusting pH after adding PAA is preferable for water purification, enriching the emerging research of PAA-based AOPs for the remediation of environments.

Albumin-seeking dyes with adjustable assemblies in situ enable programmable imaging windows and targeting tumor imaging.

Cyanine dyes are widely used organic probes for in vivo imaging due to their tunable fluorescence. They can form complexes with endogenous albumin, resulting in enhanced brightness and photostability. However, this binding is uncontrollable and irreversible, leading to considerable nonspecific background signals and unregulated circulation time. Methods: Here, we connect varying numbers of 4-(4-iodophenyl) butanoic acid (IP) as albumin-binding moieties (ABM) to the cyanine dye, enabling dynamic and controllable binding with albumin. Meanwhile, we provide a blocking method to completely release the dye from covalent capture with albumin, resulting in specific targeting fluorescence. Furthermore, we evaluate the pharmacokinetics and tumor targeting of the developed dyes. Results: The engineered dyes can dynamically and selectively bind with multiple albumins to change the in situ size of assemblies and circulation time, providing programmable regulation over the imaging time window. The nucleophilic substitution of meso-Cl with water-soluble amino acids or targeting peptides for IP-engineered dye further addresses the nonspecific signals caused by albumin, allowing for adjustable angiography time and efficient tumor targeting. Conclusion: This study rationalizes the binding modes of dyes and proteins, applicable to a wide range of near-infrared (NIR) dyes for improving their in vivo molecular imaging.

Intelligent maneuver strategy for hypersonic vehicles in three-player pursuit-evasion games via deep reinforcement learning.

Aiming at the rapid development of anti-hypersonic collaborative interception technology, this paper designs an intelligent maneuver strategy of hypersonic vehicles (HV) based on deep reinforcement learning (DRL) to evade the collaborative interception by two interceptors. Under the meticulously designed collaborative interception strategy, the uncertainty and difficulty of evasion are significantly increased and the opportunity for maneuvers is further compressed. This paper, accordingly, selects the twin delayed deep deterministic gradient (TD3) strategy acting on the continuous action space and makes targeted improvements combining deep neural networks to grasp the maneuver strategy and achieve successful evasion. Focusing on the time-coordinated interception strategy of two interceptors, the three-player pursuit and evasion (PE) problem is modeled as the Markov decision process, and the double training strategy is proposed to juggle both interceptors. In reward functions of the training process, the energy saving factor is set to achieve the trade-off between miss distance and energy consumption. In addition, the regression neural network is introduced into the deep neural network of TD3 to enhance intelligent maneuver strategies' generalization. Finally, numerical simulations are conducted to verify that the improved TD3 algorithm can effectively evade the collaborative interception of two interceptors under tough situations, and the improvements of the algorithm in terms of convergence speed, generalization, and energy-saving effect are verified.

NIR-II Protein-Escaping Dyes Enable High-Contrast and Long-Term Prognosis Evaluation of Flap Transplantation.

Real-time vascular positioning, postoperative flap monitoring, and vascular reconstruction assessment are of great importance in flap transplantation. Cyanine dyes offer the advantage of high resolution in the Near-infrared-II (NIR-II) imaging window. However, the nonspecific binding of many cyanine dyes to endogenous albumin leads to high organ accumulation and skin absorption, resulting in low-quality imaging and poor reproducibility of contrast during long-term (e.g., 7 days) postoperative monitoring. Here, a novel strategy is proposed that can be widely applied to prevent protein binding for NIR-I/II Cl-containing cyanine dyes. This strategy produces protein-escaping dyes, ensuring high fluorescence enhancement in the blood with rapid clearance and no residual fluorescence, allowing for short-term repeatable injections for vascular imaging. This strategy in the perioperative monitoring of pedicle perforator flap models in mice and rats is successfully applied. Furthermore, leveraging the universality of this strategy, multiple nonoverlapping protein-escaping probes that achieve dual-excitation (808 and 1064 nm) interference-free imaging of nerve-vessel and tumor-vessel simultaneously are designed and synthesized. These protein-escaping dyes enable long-term repeatable dual-color imaging of tumor localization, resection, and tumor-vessel reconstruction at the wound site.

Incidence of Stress-Induced Hyperglycemia in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.

The aim of this study was to systematically evaluate the incidence of stress-induced hyperglycemia (SIH) in acute ischemic stroke (AIS). Studies that reported SIH incidence in AIS and examined risk factors for SIH and non-SIH patients were systematically searched in PubMed, Embase, Cochrane Library, and Web of Science from the inception of each database to December 2021. Article screening and data extraction were performed by two independent reviewers according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS), and meta-analysis was performed using Stata. A total of 13 studies involving 4552 patients (977 in the SIH group and 3575 in the non-SIH group) were included. Meta-analysis showed that the incidence of SIH was 24% (95% CI: 21-27%) in the total population, 33% (14-52%) in North America, 25% (20-29%) in Europe, and 21% (12-29%) in Asia. Subgroup analysis by year of publication revealed that the pooled incidence of SIH was 27% (22-32%) in studies published before 2010 and 19% (14-24%) in those published after 2010. SIH is relatively common in AIS and poses a serious public health problem. Therefore, more emphasis should be placed on the prevention and control of SIH in AIS.

AppraisalCloudPCT: A Computational Model of Emotions for Socially Interactive Robots for Autistic Rehabilitation.

Computational models of emotions can not only improve the effectiveness and efficiency of human-robot interaction but also coordinate a robot to adapt to its environment better. When designing computational models of emotions for socially interactive robots, especially for robots for people with special needs such as autistic children, one should take into account the social and communicative characteristics of such groups of people. This article presents a novel computational model of emotions called AppraisalCloudPCT that is suitable for socially interactive robots that can be adopted in autistic rehabilitation which, to the best of our knowledge, is the first computational model of emotions built for robots that can satisfy the needs of a special group of people such as autistic children. To begin with, some fundamental and notable computational models of emotions (e.g., OCC, Scherer's appraisal theory, PAD) that have deep and profound influence on building some significant models (e.g., PRESENCE, iGrace, xEmotion) for socially interactive robots are revisited. Then, a comparative assessment between our AppraisalCloudPCT and other five significant models for socially interactive robots is conducted. Great efforts have been made in building our proposed model to meet all of the six criteria for comparison, by adopting the appraisal theories on emotions, perceptual control theory on emotions, a component model view of appraisal models, and cloud robotics. Details of how to implement our model in a socially interactive robot we developed for autistic rehabilitation are also elaborated in this article. Future studies should examine how our model performs in different robots and also in more interactive scenarios.

Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology. AIM: To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC. METHODS: In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability. RESULTS: Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 (P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis. CONCLUSION: AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.

Radiomics in Addition to Computed Tomography-Based Body Composition Nomogram May Improve the Prediction of Postoperative Complications in Gastric Cancer Patients.

OBJECTIVES: The study aimed to determine the impact of computed tomography (CT)-based body composition and radiomics nomogram on the prediction of postoperative complications in gastric cancer. METHODS: The clinical data of 457 individuals with surgically confirmed gastric cancer, 320 patients in the training cohort (TC) and 137 patients in the validation cohort (VC), were retrospectively analyzed. Body composition data were measured using CT. Postoperative complications were graded using the Clavien-Dindo system. Dedicated radiomics prototype software was used to segment lesions and extract characteristics from preoperative portal venous-phase CT images. Clinical, radiomics, and combined models were developed using logistic regression analysis. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic curve, and the prediction ability of the optimal model was evaluated using calibration curves and decision curve analysis (DCA). RESULTS: Nutritional Risk Screening 2002 (NRS2002) score, sarcopenia, and blood loss were independent predictors of postoperative complications in gastric cancer. A radiomics signature was created using 19 conserved radiomics features. The nomogram based on both the clinical model and radiomics signature showed the greatest predictive performance, with AUCs of 0.763 (95% confidence interval [CI], 0.708-0.817) and 0.748 (95% CI: 0.667-0.818) in the TC and VC, respectively. The calibration curve and DCA revealed that the nomogram was beneficial in clinical practice for the preoperative prediction of postoperative complications. CONCLUSIONS: The combined model consisting of NRS2002 score, sarcopenia, blood loss, and a radiomics signature holds potential application value for the individualized prediction of postoperative complications in gastric cancer patients.

Intravoxel incoherent motion to assess brain microstructure and perfusion in patients with end-stage renal disease.

BACKGROUND AND PURPOSE: This study aimed to investigate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in evaluating the brain microstructure and perfusion changes in end-stage renal disease (ESRD) patients. METHODS: The routine head MRI sequences and IVIM were performed on 40 ESRD patients and 30 healthy subjects. The IVIM was executed with 10 b-values varying from 0 to 1000 seconds/mm2 . All subjects were evaluated on neuropsychological test. Laboratory tests were conducted for ESRD patients. RESULTS: Compared with the control group, increased slow apparent diffusion coefficient values (ADCslow ) were found in the left frontal lobe, hippocampus, bilateral temporal lobe, and the right occipital lobe (p < .05), and increased fast ADC values (ADCfast ) were found in all regions of interest (all p < .001) in ESRD patients. In ESRD patients, ADCfast in right frontal lobe (p = .041) and insular lobe (p = .045) was negatively correlated with the Montreal Cognitive Assessment score (MoCA), and ADCfast in the right parietal lobe (p = .009) and hippocampus (p = .041) had positive correlation with hemoglobin levels. Using receiver operating characteristics (ROC) analysis, ADCfast in the right frontal lobe, insular lobe, hippocampus, and parietal lobe separately showed fair to good efficacy in differentiating ESRD patients from healthy subjects, with the area under the ROC ranging from .853 to .903. CONCLUSIONS: The microstructure and perfusion of the brain were impaired in ESRD patients. ADCfast of the right frontal lobe, insular lobe, hippocampus, and parietal lobe could be effective biomarker for evaluating cognitive impairment in ESRD patients.

FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder.

Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3-8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies.

The value of multi-parameter diffusion and perfusion magnetic resonance imaging for evaluating epithelial-mesenchymal transition in rectal cancer.

PURPOSE: Epithelial-mesenchymal transition (EMT) is characterized by a decreased expression of E-cadherin and an increased expression of vimentin, which is associated with poor prognosis in rectal cancer. This study aimed to explore the feasibility of using multi-parameter diffusion and perfusion magnetic resonance imaging (MRI) to evaluate the expression of E-cadherin and vimentin. METHODS: One hundred and ten patients with rectal cancer, who underwent preoperative multi-parameter diffusion and perfusion MRI and subsequent radical resection of rectal carcinoma, were included in this prospective study. The expression of E-cadherin, vimentin, vascular endothelial growth factor (VEGF), and Ki67 was identified by immunohistochemical test and MRI morphology features; quantitative parameters were analyzed and combined diagnostic models were created by binary logistic regression. The receiver operating characteristics curve with area under the curve (AUC) was used to evaluated diagnostic performance. Interobserver agreement for MRI parameters was evaluated by intraclass correlation coefficient. RESULTS: In the low expression E-cadherin group, the VEGF expression was significantly higher than the high expression group (60.5% vs 37.3%, P < 0.05), and the Ki67 expression had no significant difference (P > 0.05). In the high expression vimentin group, the Ki67 expression was significantly higher than the low expression group (77.3% vs 56.1%, P < 0.05), and the VEGF expression was not significantly significant (P > 0.05). MRI morphological features had no significant diagnostic efficacy for E-cadherin and vimentin expression in rectal cancer (P > 0.05). There were positive and negative correlations between apparent diffusion coefficient (ADC) values with E-cadherin and vimentin expression (P < 0.05), the Ktrans and Kep values were negatively and positively correlated with the expression of E-cadherin and vimentin (P < 0.05). The ADC value (b = 3000 s/mm2) had mild significant diagnostic efficiencies for low E-cadherin expression and high vimentin expression (AUC = 0.63 and 0.644, P < 0.05), the Ktrans and Kep values had significant diagnostic efficiencies (AUC = 0.801 and 0.724, P < 0.05; AUC = 0.722 and 0.628, P < 0.05, respectively). The combined model of ADC, Ktrans, and Kep values had good diagnostic efficiencies for low E-cadherin expression and high vimentin expression (AUC = 0.814 and 0.728, P < 0.05). CONCLUSION: The quantitative parameters of diffusion (with ultra-high b-values) and perfusion MRI can assess the major biomarkers of EMT in rectal cancer, and the combined diagnostic model could further improve the evaluating efficiency for EMT, which possibly can provide valuable information for non-invasive and preoperative assessment of the tumor microenvironment.

Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer.

PURPOSE: The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. METHODS: This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. RESULTS: 110 participants with the age of 60.7 ± 12.5 years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000 s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000 s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). CONCLUSION: Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.

ARMC Subfamily: Structures, Functions, Evolutions, Interactions, and Diseases.

Armadillo repeat-containing proteins (ARMCs) are widely distributed in eukaryotes and have important influences on cell adhesion, signal transduction, mitochondrial function regulation, tumorigenesis, and other processes. These proteins share a similar domain consisting of tandem repeats approximately 42 amino acids in length, and this domain constitutes a substantial platform for the binding between ARMCs and other proteins. An ARMC subfamily, including ARMC1∼10, ARMC12, and ARMCX1∼6, has received increasing attention. These proteins may have many terminal regions and play a critical role in various diseases. On the one hand, based on their similar central domain of tandem repeats, this ARMC subfamily may function similarly to other ARMCs. On the other hand, the unique domains on their terminals may cause these proteins to have different functions. Here, we focus on the ARMC subfamily (ARMC1∼10, ARMC12, and ARMCX1∼6), which is relatively conserved in vertebrates and highly conserved in mammals, particularly primates. We review the structures, biological functions, evolutions, interactions, and related diseases of the ARMC subfamily, which involve more than 30 diseases and 40 bypasses, including interactions and relationships between more than 100 proteins and signaling molecules. We look forward to obtaining a clearer understanding of the ARMC subfamily to facilitate further in-depth research and treatment of related diseases.

Short-term outcomes after totally laparoscopic total gastrectomy with esophagojejunostomy constructed by π-shaped method versus overlap method.

BACKGROUND: Regarding the overlap anastomosis and recently introduced π-shaped anastomosis, there is no consensus on which intracorporeal esophagojejunostomy (EJS) methods are preferred using linear stapler in totally laparoscopic total gastrectomy (TLTG). This study aims to evaluate the short-term outcomes using two methods. METHODS: Patients with upper gastric cancer underwent TLTG with either π-shaped (n = 48) or the modified overlap method using knotless barbed sutures (MOBS) (n = 37) were included in our study. Intraoperative and perioperative outcomes were compared. RESULTS: All patients achieved R0 resection margin. The overall esophagojejunal (E-J)-related complications rate was 7.06%. There was no significant difference between the two groups in terms of postoperative complications, margin distance, numbers of lymph nodes (LNs), length of stay. In the π-shaped group, anastomosis time (19.61 ± 7.17 min vs. 27.09 ± 3.59 min, p < 0.001) was significantly lower. The consumable costs for surgery were similar (44 507.74¥ [42 933.03-46 937.29] vs. 43 718.36¥ [42 743.25-47 256.06], p = 0.825). The first defection time was significantly longer in π-shaped group (131.00 h [93.75-171.25] vs. 100.00 h [85.00-120.00], p = 0.026), whereas the other postoperative recovery parameters were similar. No mortality was observed. CONCLUSIONS: Both methods showed similar short-term postoperative outcomes. The π-shaped technique was faster than the MOBS method without significantly increasing the supplies costs. Large prospective studies are warranted.

Disruption of myofibroblastic Notch signaling attenuates liver fibrosis by modulating fibrosis progression and regression.

The phenotypic transformation of hepatic myofibroblasts (MFs) is involved in the whole process of the progression and regression of liver fibrosis. Notch signaling has been demonstrated to modulate the fibrosis. In this study, we found that Notch signaling in MFs was overactivated and suppressed with the progression and regression of hepatic fibrosis respectively, by detecting Notch signaling readouts in MFs. Moreover, we inactivated Notch signaling specifically in MFs with Sm22αCreER-RBPjflox/flox mice (RBPjMF-KO), and identified that MFs-specific down-regulation of Notch signaling significantly alleviated CCl4-induced liver fibrosis during the progression and regression. During the progression of liver fibrosis, MFs-specific blockade of Notch signaling inhibited the activation of HSCs to MFs and increases the expression of MMPs to reduce the deposition of ECM. During the regression of fibrosis, blocking Notch signaling in MFs increased the expression of HGF to promote proliferation in hepatocytes and up-regulated the expression of pro-apoptotic factors, Ngfr and Septin4, to induce apoptosis of MFs, thereby accelerating the reversal of fibrosis. Collectively, the MFs-specific disruption of Notch signaling attenuates liver fibrosis by modulating fibrosis progression and regression, which suggests a promising therapeutic strategy for liver fibrosis.

Comparison of Conventional Gadoxetate Disodium-Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion.

OBJECTIVE. This study aimed to determine the best model for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using conventional gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (gadoxetate disodium)-enhanced MRI features and radiomics signatures with machine learning. MATERIALS AND METHODS. This retrospective study included 269 patients with a postoperative pathologic diagnosis of HCC. Gadoxetate disodium-enhanced MRI features were assessed, including T1 relaxation time, tumor margin, tumor size, peritumoral enhancement, peritumoral hypointensity, and ADC. Radiomics models were constructed and validated by machine learning. The least absolute shrinkage and selection operator (LASSO) was used for feature selection, and radiomics-based LASSO models were constructed with six classifiers. Predictive capability was assessed using the ROC AUC. RESULTS. Histologic examination confirmed MVI in 111 (41.3%) of the 269 patients. ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time showed diagnostic accuracy with AUC values of 0.850, 0.847, and 0.846, respectively (p < .05 for all). A total of 1395 quantitative imaging features were extracted. In the hepatobiliary phase (HBP) model, the support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) classifiers showed greater diagnostic efficiency for predicting MVI, with AUCs of 0.942, 0.938, and 0.936, respectively (p < .05 for all). CONCLUSION. ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time show high diagnostic accuracy for predicting MVI. Radiomics signatures with machine learning can further improve the ability to predict MVI and are best modeled during HBP. The SVM, XGBoost, and LR classifiers may serve as potential biomarkers to evaluate MVI.

Low expression of tRF-Pro-CGG predicts poor prognosis in pancreatic ductal adenocarcinoma.

BACKGROUND & AIMS: tRFs (tRNA-derived RNA fragments) have been reported to facilitate cancer progression in multiple cancers. However, their role in pancreatic ductal adenocarcinoma (PDAC) remains to be determined. In this study, we mainly investigated the expression of tRF-Pro-CGG in pancreatic ductal adenocarcinoma and evaluated its relationship with the clinicopathology and survival time of patients. METHODS: 37 cases of pancreatic ductal adenocarcinoma, and 15 cases of normal pancreatic tissues were collected which were resected by surgery from January 2017 to June 2020 from the Department of Hepatobiliary and Pancreatic surgery of Changzhou second people's Hospital. The expression of tRF-Pro-CGG in paraffin-embedded tissues was detected by fluorescence in situ hybridization (FISH). The clinical data including age, sex, tumor location, tumor diameter, tumor clinical stage (TNM stage), depth of invasion, regional lymph node metastasis, serum CA199, and serum CEA were collected and analyzed retrospectively, whether the expression tRF-Pro-CGG was correlation with the pathological parameters and clinical outcomes of patients. RESULTS: The expression level of tRF-Pro-CGG was significantly downregulated in PDAC and associated with an advanced TNM stage (P=0.000) and the N stage (P=0.000) of patients. More importantly, low tRF-Pro-CGG expression predicted poor survival in PDAC patients (P=0.003). CONCLUSIONS: TRF-Pro-CGG is under-expressed in PDAC and is associated with short clinical survival and poor prognosis. tRF-Pro-CGG is an independent prognostic factor, which highlights its role as a potential biomarker for PDAC progression and therapy.

Associative memory model with arbitrary Hebbian length.

Conversion of temporal to spatial correlations in the cortex is one of the most intriguing functions in the brain. The learning at synapses triggering the correlation conversion can take place in a wide integration window, whose influence on the correlation conversion remains elusive. Here we propose a generalized associative memory model of pattern sequences, in which pattern separations within an arbitrary Hebbian length are learned. The model can be analytically solved, and predicts that a small Hebbian length can already significantly enhance the correlation conversion, i.e., the stimulus-induced attractor can be highly correlated with a significant number of patterns in the stored sequence, thereby facilitating state transitions in the neural representation space. Moreover, an anti-Hebbian component is able to reshape the energy landscape of memories, akin to the memory regulation function during sleep. Our work thus establishes the fundamental connection between associative memory, Hebbian length, and correlation conversion in the brain.

Eigenvalue spectrum of neural networks with arbitrary Hebbian length.

Associative memory is a fundamental function in the brain. Here, we generalize the standard associative memory model to include long-range Hebbian interactions at the learning stage, corresponding to a large synaptic integration window. In our model, the Hebbian length can be arbitrarily large. The spectral density of the coupling matrix is derived using the replica method, which is also shown to be consistent with the results obtained by applying the free probability method. The maximal eigenvalue is then obtained by an iterative equation, related to the paramagnetic to spin glass transition in the model. Altogether, this work establishes the connection between the associative memory with arbitrary Hebbian length and the asymptotic eigen-spectrum of the neural-coupling matrix.

Element-Specific Detection of Sub-Nanosecond Spin-Transfer Torque in a Nanomagnet Ensemble.

Spin currents can exert spin-transfer torques on magnetic systems even in the limit of vanishingly small net magnetization, as recently shown for antiferromagnets. Here, we experimentally show that a spin-transfer torque is operative in a macroscopic ensemble of weakly interacting, randomly magnetized Co nanomagnets. We employ element- and time-resolved X-ray ferromagnetic resonance (XFMR) spectroscopy to directly detect subnanosecond dynamics of the Co nanomagnets, excited into precession with cone angle ≳0.003° by an oscillating spin current. XFMR measurements reveal that as the net moment of the ensemble decreases, the strength of the spin-transfer torque increases relative to those of magnetic field torques. Our findings point to spin-transfer torque as an effective way to manipulate the state of nanomagnet ensembles at subnanosecond time scales.