The poor drug delivery to cerebral ischemic regions is a key challenge of ischemic stroke treatment. Inspired by the intriguing blood−brain barrier (BBB)-penetrating ability of 4T1 cancer cells upon their brain metastasis, we herein designed a promising biomimetic nanoplatform by camouflaging a succinobucol-loaded pH-sensitive polymeric nanovehicle with a 4T1 cell membrane (MPP/SCB), aiming to promote the preferential targeting of cerebral ischemic lesions to attenuate the ischemia/ reperfusion injury. In transient middle cerebral artery occlusion (tMCAO) rat models, MPP/SCB could be preferentially delivered to the ischemic hemisphere with a 4.79-fold higher than that in the normal hemisphere. Moreover, MPP/SCB produced notable enhancement of microvascular reperfusion in the ischemic hemisphere, resulting in a 69.9% reduction of infarct volume and showing remarkable neuroprotective effects of tMCAO rats, which was superior to the counterpart uncamouflaged nanovehicles (PP/SCB). Therefore, this design provides a promising nanoplatform to target the cerebral ischemic lesions for ischemic stroke therapy.

Glioma is the most frequent form of malignant brain tumors. Surgical debulking is a major strategy for glioma treatment. However, there is a great challenge for the neurosurgeons to intraoperatively identify the true margins of glioma because of its infiltrative nature. Tumor residues or microscopic satellite foci left in the resection bed are the main reasons leading to early recurrence as well as poor prognosis. In this study, a surface-enhanced resonance Raman scattering (SERRS) probe was developed to intraoperatively guide glioma resection. In this probe, molecular reporters with absorptive maxima at the near-infrared wavelength range were covalently functionalized on the surface of gold nanostars. This SERRS probe demonstrated an ultrahigh sensitivity with a detection limit of 5.0 pM in aqueous solution. By the development of glioma xenografts in a mouse dorsal skin window chamber, extravasation of this probe from leaky tumor vasculature as functions of time and distance to tumor boundary was investigated. Importantly, the invasive margin of the tumor xenograft was demarcated by this probe with a high signal-to-background ratio. Preoperative magnetic resonance imaging (MRI) first defined the position of orthotopic glioma xenografts in the brain of rat models, and the craniotomy plan was designed. The brain tumor was then excised intraoperatively step-by-step with the assistance of a handheld Raman scanner till the Raman signals of the probe completely disappeared in the resection bed. Notably, longitudinal MRI showed that SERRS-guided surgery significantly reduced the tumor recurrence rate and improved the overall survival of rat models compared with the white light-guided surgery. Overall, this work demonstrates the prognostic benefit of SERRS-guided glioma surgery in animal models. Because delineation of tumor-invasive margins is a common challenge faced by the surgeons, this SERRS probe with a picomolar detection limit holds the promise in improving the surgical outcome of different types of infiltrated tumors.

Chemical exchange saturation transfer (CEST) MRI is versatile for measuring the dilute labile protons and microenvironment properties. However, the use of insufficiently long RF saturation duration (Ts) and relaxation delay (Td) may underestimate the CEST measurement. This study proposed a quasi-steady-state (QUASS) CEST analysis for robust CEST quantification. Methods: The CEST signal evolution was modeled as a function of the longitudinal relaxation rate during Td and spin-lock relaxation rate during Ts, from which the QUASS-CEST effect is derived. Numerical simulation and in vivo rat glioma MRI experiments were conducted at 11.7 T to compare the apparent and QUASS-CEST results obtained under different Ts/Td of 2 seconds/2 seconds and 4 seconds/4 seconds. Magnetization transfer and amide proton transfer effects were resolved using a multipool Lorentzian fitting and evaluated in contralateral normal tissue and tumor regions. Results: The simulation showed the dependence of the apparent CEST effect on Ts and Td, and such reliance was mitigated with the QUASS algorithm. Animal experiment results showed that the apparent magnetization transfer and amide proton transfer effects and their contrast between contralateral normal tissue and tumor regions increased substantially with Ts and Td. In comparison, the QUASS magnetization transfer and amide proton transfer effects and their difference between contralateral normal tissue and tumor exhibited little dependence on Ts and Td. In addition, the apparent magnetization transfer and amide proton transfer were significantly smaller than the corresponding QUASS indices (P < .05). |ZHANG et Al. | INTRODUCTIONChemical exchange saturation transfer MRI has emerged as a sensitive method for detecting dilute labile protons and microenvironment properties such as pH and temperature. [1][2][3][4] It has shown promise in measuring endogenous metabolites and compounds (eg, glucose, glycogen, amide protons) and exogenous diamagnetic/paramagnetic CEST agents, offering a novel means to image a host of disorders, including acute stroke, tumor, and epilepsy. [5][6][7][8][9][10] Although CEST MRI is versatile, its contrast is quite complicated. The measurable CEST effect depends not only on the labile proton concentration and exchange rate, but also on experimental parameters, 11,12 such as the duration of RF irradiation and the relaxation delay (Td) between RF saturation. [13][14][15][16] Indeed, the CEST effect reflects two competing processes, namely, the signal reduction due to saturation transfer from irradiated labile protons and the signal recovery through relaxation. The CEST-MRI effect has a complicated dependence on bulk water T 1 . 17 This is further confounded in the presence of T 1 changes in diseased tissues. 18 Recently, Tanoue et al demonstrated the effect of RF saturation duration (Ts) on the CEST effect at 11.7 T. 19 It takes prolonged saturation and recovery time to reach the steady state. A trade-off has to be made between the magnitudes of the CEST effect and scan tim...

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Surgeons face challenges in intraoperatively defining margin of brain tumors due to its infiltrative nature. Extracellular acidosis caused by metabolic reprogramming of cancer cells is a reliable marker for tumor infiltrative regions. Although the acidic margin‐guided surgery shows promise in improving surgical prognosis, its clinical transition is delayed by having the exogenous probes approved by the drug supervision authority. Here, an intelligent surface‐enhanced Raman scattering (SERS) navigation system delineating glioma acidic margins without administration of exogenous probes is reported. With assistance of this system, the metabolites at the tumor cutting edges can be nondestructively transferred within a water droplet to a SERS chip with pH sensitivity. Homemade deep learning model automatically processes the Raman spectra collected from the SERS chip and delineates the pH map of tumor resection bed with increased speed. Acidity correlated cancer cell density and proliferation level are demonstrated in tumor cutting edges of animal models and excised tissues from glioma patients. The overall survival of animal models post the SERS system guided surgery is significantly increased in comparison to the conventional strategy used in clinical practice. This SERS system holds the promise in accelerating clinical transition of acidic margin‐guided surgery for solid tumors with infiltrative nature.

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

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In the prognosis of radar transmitter degradation fault, there are some problems, such as the total sample size and fault sample size of sensor monitoring data are small, and the monitoring data can not reach the fault threshold. To solve these problems, a prediction model combining the multivariate long short-term memory networks with multivariate Gaussian distribution is proposed, in which the long short-term memory networks predict the subsequent time step of multi-sensor monitoring data, and the multivariate Gaussian distribution model constructed by few fault samples is used to realize the prediction of degraded faults. The key parameters of the model are determined by the data processing experiments of double monitoring points, and the validity of the model is verified by the data processing experiments of multiple monitoring points. The experimental results show that the degradation fault can be predicted effectively within 10% of the total time series of monitoring data. Compared with the traditional radar warning after failure, the model can effectively predict the degradation fault of the transmitter when the fault sample size is low and the fault threshold is not reached.INDEX TERMS Multivariate long short-term memory network, multivariate gaussian distribution, prognostic method, degradation fault of radar transmitter.