MRI-based radiomics signatures for preoperative prediction of Ki-67 index in primary central nervous system lymphoma.

PURPOSE: The aim of this study was to develop and validate radiomics signatures based on MRI for preoperative prediction of Ki-67 proliferative index (PI) expression in primary central nervous system lymphoma (PCNSL). METHODS: A total of 341 patients with PCNSL were retrospectively analyzed, including 286 patients in one center as the training set and 55 patients in another two centers as the external validation set. Radiomics features were extracted and selected from preoperative contrast-enhanced T1-weighted images, fluid attenuation inversion recovery to build radiomics signatures according to the Ki-67 PI. The predictive performances of the radiomics model were evaluated using four classifiers including random forest, K-Nearest Neighbors, Neural Network and Decision Tree. A combined model was built by incorporating radiomics signature, clinical variables and MRI radiological characteristics using multivariate logistic regression analysis, and a nomogram was established to predict the expression of Ki-67 individually. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: Radiomics signatures were independent predictors of the expression level of Ki-67 (OR: 2.523, P < 0.001). RF radiomics models had the highest accuracy (0.934 in the training set and 0.811 in the external validation set) and F1 Score (0.920 in the training set and 0.836 in the external validation set). The clinic-radiologic-radiomics nomogram showed better predictive performance with AUCs of 0.877(95 % CI: 0.837-0.918) in the training set and 0.866(95 % CI: 0.774-0.957) in the external validation set. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram. CONCLUSIONS: Nomograms integrating MRI-based radiomics and clinical-radiological characteristics could effectively predict Ki-67 PI in primary PCNSL.

Prognostic Assessment in Patients With Primary Diffuse Large B-Cell Lymphoma of the Central Nervous System Using MRI-Based Radiomics.

BACKGROUND: Primary central nervous system lymphoma (PCNSL) carries a poor prognosis. Radiomics may hold potential value in prognostic assessment. PURPOSE: To develop and validate an MRI-based radiomics model and combine it with clinical factors to assess progression-free survival (PFS) and overall survival (OS) of patients with PCNSL. STUDY TYPE: Retrospective and prospective. POPULATION: Three hundred seventy-nine patients (179 female, 53 ± 7 years) from 2014 to 2022. FIELD STRENGTH/SEQUENCE: T2/fluid-attenuated inversion recovery, contrast-enhanced T1WI and diffusion-weighted echo-planar imaging sequences on 3.0 T. ASSESSMENT: Radiomics features were extracted from enhanced tumor regions on preoperative multi-sequence MRI. Using a least absolute shrinkage and selection operator (LASSO) Cox regression model to select radiomic signatures in training cohort (N = 169). Cox proportional hazards models were constructed for clinical, radiomics, and combined models, with internal (N = 72) and external (N = 32) cohorts validating model performance. STATISTICAL TESTS: Chi-squared, Mann-Whitney, Kaplan-Meier, log-rank, LASSO, Cox, decision curve analysis, time-dependent Receiver Operating Characteristic, area under the curve (AUC), and likelihood ratio test. P-value <0.05 was considered significant. RESULTS: Follow-up duration was 28.79 ± 22.59 months (median: 25). High-risk patients, determined by the median radiomics score, showed significantly lower survival rates than low-risk patients. Compared with NCCN-IPI, conventional imaging and clinical models, the combined model achieved the highest C-index for both PFS (0.660 internal, 0.802 external) and OS (0.733 internal, 0.781 external) in validation. Net benefit was greater with radiomics than with clinical alone. The combined model exhibited performance with AUCs of 0.680, 0.752, and 0.830 for predicting 1-year, 3-year, and 5-year PFS, and 0.770, 0.789, and 0.863 for OS in internal validation, with PFS AUCs of 0.860 and 0.826 and OS AUCs of 0.859 and 0.748 for 1-year and 3-year survival in external validation. DATA CONCLUSION: Incorporating a multi-sequence MR-based radiomics model into clinical models enhances the assess accuracy for the prognosis of PCNSL. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Multiscale Heterogeneities-Based Piezoresistive Interfaces with Ultralow Detection Limitation and Adaptively Switchable Pressure Detectability.

Mechanical compliance and electrical enhancement are crucial for pressure sensors to promote performances when perceiving external stimuli. Here we propose a bioinspired multiscale heterogeneity-based interface to adaptively regulate its structure layout and switch to desirable piezoresistive behaviors with ultralow detection limitation. In such a multiscale heterogeneities system, the micro-/nanoscale spiny Ag-MnO2 heterostructure contributes to an ultralow detection limitation of 0.008 Pa and can perceive minor pressure increments under preloads with high resolution (0.0083%). The macroscale heterogeneous orientation of the cellular backbone enables anisotropic deformation, allowing the sensor to switch to rational sensitivity and working range (e.g., 580 kPa-1 for 0-20 kPa/54 kPa-1 for 60-140 kPa) as required. The sensor's stepwise activation progresses from the micro-/nanoscale heterostructure to the macroscale heterogeneous orientation, which can adaptively match diverse sensing tasks in complex applications scenarios. This multiscale heterogeneous and switchable design holds immense potential in the development of intelligent electromechanical devices, including wearable sensors, soft robotics, and smart actuators.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration.

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

Dual function of protein phosphatase 5 (PPP5C): An emerging therapeutic target for drug discovery.

Phosphorylation of proteins is reversibly controlled by the kinases and phosphatases in many posttranslational regulation patterns. Protein phosphatase 5 (PPP5C) is a serine/threonine protein phosphatase showing dual function by simultaneously exerting dephosphorylation and co-chaperone functions. Due to this special role, PPP5C was found to participate in many signal transductions related to various diseases. Abnormal expression of PPP5C results in cancers, obesity, and Alzheimer's disease, making it a potential drug target. However, the design of small molecules targeting PPP5C is struggling due to its special monomeric enzyme form and low basal activity by a self-inhibition mechanism. Through realizing the PPP5C's dual function as phosphatase and co-chaperone, more and more small molecules were found to regulate PPP5C with a different mechanism. This review aims to provide insights into PPP5C's dual function from structure to function, which could provide efficient design strategies for small molecules targeting PPP5C as therapeutic candidates.

Freestanding and Scalable Force-Softness Bimodal Sensor Arrays for Haptic Body-Feature Identification.

Tactile technologies that can identify human body features are valuable in clinical diagnosis and human-machine interactions. Previously, cutting-edge tactile platforms have been able to identify structured non-living objects; however, identification of human body features remains challenging mainly because of the irregular contour and heterogeneous spatial distribution of softness. Here, freestanding and scalable tactile platforms of force-softness bimodal sensor arrays are developed, enabling tactile gloves to identify body features using machine-learning methods. The bimodal sensors are engineered by adding a protrusion on a piezoresistive pressure sensor, endowing the resistance signals with combined information of pressure and the softness of samples. The simple design enables 112 bimodal sensors to be integrated into a thin, conformal, and stretchable tactile glove, allowing the tactile information to be digitalized while hand skills are performed on the human body. The tactile glove shows high accuracy (98%) in identifying four body features of a real person, and four organ models (healthy and pathological) inside an abdominal simulator, demonstrating identification of body features of the bimodal tactile platforms and showing their potential use in future healthcare and robotics.

Tactile Near-Sensor Analogue Computing for Ultrafast Responsive Artificial Skin.

Ultrafast artificial skin enables unprecedented tactile internet applications in prosthetics, robotics, and human-machine interactions. However, current artificial skin systems that rely on front-end interface electronics typically perform redundant data transfer and analogue-to-digital conversions for decision-making, causing long latency (milliseconds). Here, a near-sensor analogue computing system based on a flexible memristor array for artificial skin applications is reported. This system, which seamlessly integrates a tactile sensor array with a flexible hafnium oxide memristor array, can simultaneously sense and compute raw multiple analogue pressure signals without interface electronics. As a proof-of-concept, the system is used for real-time noise reduction and edge detection of tactile stimuli. One sensing-computing operation of this system takes about 400 ns and consumes on average 1000 times less power than a conventional interface electronic system. The results demonstrate that near-sensor analogue computing offers an ultrafast and energy-efficient route to large-scale artificial skin systems.

High-Yield Two-Dimensional Metal-Organic Framework Derivatives for Wideband Electromagnetic Wave Absorption.

Two-dimensional metal-organic frameworks (2D-MOFs) and their derivatives are promising for catalysis, energy storage, gas separation, etc. due to their unique microstructure and physicochemical properties. Many efforts have been devoted to fabricating 2D-MOFs with challenges remaining in yield and fine control of their thickness and lateral size. Here a versatile strategy has been used involving epitaxial, anisotropic, and confined growth of CoNi-MOF-71 nanosheet arrays, giving rise to excellent quantity and controllability of the 2D-MOFs. Electromagnetic (EM) wave absorption performance has been investigated for the resultant 2D Co/Ni/C derivatives. Compared with the bulk counterpart, significantly increased surface area, conductivity, and shape anisotropy for the 2D derivatives result in enhanced interfacial polarization, conductive loss, and magnetic resonance. As such, optimum EM wave absorption of minimum reflection loss RLmin = -49.8 dB and an ultrawide effective adsorption bandwidth EAB = 7.6 GHz can be achieved at a thickness of 2.6 mm. This work not only sheds light on the performance enhancement for 2D absorbers via synergistic effects of multiple attenuation mechanisms but also provides an effective fabrication route of ultrathin MOFs with high yield and uniform size for extended applications in catalysis, electrochemistry, and optoelectronics fields.

Prokaryotic Expression of Phosphoenolpyruvate Carboxylase Fragments from Peanut and Analysis of Osmotic Stress Tolerance of Recombinant Strains.

Phosphoenolpyruvate carboxylase (PEPC) is a ubiquitous cytosolic enzyme that catalyzes the irreversible ß-carboxylation of phosphoenolpyruvate (PEP) in presence of HCO3- to produce oxaloacetate (OAA) during carbon fixation and photosynthesis. It is well accepted that PEPC genes are expressed in plants upon stress. PEPC also supports the biosynthesis of biocompatible osmolytes in many plant species under osmotic stress. There are five isoforms of PEPC found in peanut (Arachis hypogaea L.), namely, AhPEPC1, AhPEPC2, AhPEPC3, AhPEPC4, and AhPEPC5. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis revealed that the gene expression patterns of these AhPEPC genes were different in mature seeds, stems, roots, flowers, and leaves. The expression of all the plant type PEPC (PTPCs) (AhPEPC1, AhPEPC2, AhPEPC3, and AhPEPC4) was relatively high in roots, while the bacterial type PEPC (BTPC) (AhPEPC5) showed a remarkable expression level in flowers. Principal component analysis (PCA) result showed that AhPEPC3 and AhPEPC4 are correlated with each other, indicating comparatively associations with roots, and AhPEPC5 have a very close relationship with flowers. In order to investigate the function of these AhPEPCs, the fragments of these five AhPEPC cDNA were cloned and expressed in Escherichia coli (E. coli). The recombinant proteins contained a conserved domain with a histidine site, which is important for enzyme catalysis. Results showed that protein fragments of AhPEPC1, AhPEPC2, and AhPEPC5 had remarkable expression levels in E. coli. These three recombinant strains were more sensitive at pH 9.0, and recombinant strains carrying AhPEPC2 and AhPEPC5 fragments exhibited more growth than the control strain with the presence of PEG6000. Our findings showed that the expression of the AhPEPC fragments may enhance the resistance of transformed E. coli to osmotic stress.

Essential oil of Perilla frutescens-induced change in hippocampal expression of brain-derived neurotrophic factor in chronic unpredictable mild stress in mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Perilla frutescens (Perilla leaf), a traditional Chinese medicinal herb, has been used for centuries to treat various conditions including depression. A previous study of the authors demonstrated that essential oil of Perilla frutescens (EOPF) attenuated the depressive-like behavior in mice. AIM OF THE STUDY: This study was undertaken to explore the dynamic change of behaviors and brain-derived neurotrophic factor (BDNF) expression induced by chronic unpredictable mild stress (CUMS), and improved by EOPF. MATERIALS AND METHODS: Four separate CUMS experimental groups (1-week, 2-week, 3-week and 4-week treatment) were treated with EOPF (3 mg/kg and 6 mg/kg, p.o.) or fluoxetine (20 mg/kg, p.o.), followed by sucrose preference, locomotor activity, immobility and hippocampal BDNF measurement. RESULTS: EOPF, as well as fluoxetine, restored the CUMS-induced decreased sucrose preference and increased immobility time, without affecting body weight gain and locomotor activity. Furthermore, CUMS (3 or 4-week) produced a reduction in both BDNF mRNA and protein expression in the hippocampus, which were ameliorated by EOPF (4-week) and fluoxetine (3 or 4-week) treatment. CONCLUSION: These results presented here show that BDNF is expressed depending on length of CUMS procedure and EOPF administration. And this study might contribute to the underlying reason for the slow onset of antidepressant activity in clinic.