The role of APOBEC3C in modulating the tumor microenvironment and stemness properties of glioma: evidence from pancancer analysis

BackgroundIt is now understood that APOBEC3 family proteins (A3s) are essential in tumor progression, yet their involvement in tumor immunity and stemness across diverse cancer types remains poorly understood.MethodsIn the present study, comprehensive genome-wide statistical and bioinformatic analyses were conducted to elucidate A3 family expression patterns, establishing clinically relevant correlations with prognosis, the tumor microenvironment(TME), immune infiltration, checkpoint blockade, and stemness across cancers. Different experimental techniques were applied, including RT–qPCR, immunohistochemistry, sphere formation assays, Transwell migration assays, and wound-healing assays, to investigate the impact of A3C on low-grade glioma (LGG) and glioblastoma multiforme (GBM), as well as its function in glioma stem cells(GSCs).ResultsDysregulated expression of A3s was observed in various human cancer tissues. The prognostic value of A3 expression differed across cancer types, with a link to particularly unfavorable outcomes in gliomas. A3s are associated with the the TME and stemness in multiple cancers. Additionally, we developed an independent prognostic model based on A3s expression, which may be an independent prognostic factor for OS in patients with glioma. Subsequent validation underscored a strong association between elevated A3C expression and adverse prognostic outcomes, higher tumor grades, and unfavorable histology in glioma. A potential connection between A3C and glioma progression was established. Notably, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses implicated A3C in immune system-related diseases, with heightened A3C levels contributing to an immunosuppressive tumor microenvironment (TME) in glioma. Furthermore, in vitro experiments substantiated the role of A3C in sustaining and renewing glioma stem cells, as A3C deletion led to diminished proliferation, invasion, and migration of glioma cells.ConclusionThe A3 family exhibits heterogeneous expression across various cancer types, with its expression profile serving as a predictive marker for overall survival in glioma patients. A3C emerges as a regulator of glioma progression, exerting its influence through modulation of the tumor microenvironment and regulation of stemness

Prediction of the location of the pyramidal tract in patients with thalamic or basal ganglia tumors.

BACKGROUND: Locating the pyramidal tract (PT) is difficult in patients with thalamic or basal ganglia tumors, especially when the surrounding anatomical structures cannot be identified using computed tomography or magnetic resonance images. Hence, we objected to find a way to predict the location of the PT in patients with thalamic and basal ganglia tumors METHODOLOGY/PRINCIPAL FINDINGS: In 59 patents with thalamic or basal ganglia tumors, the PTs were constructed by with diffusion tensor imaging (DTI)-based fiber tracking (FT). In axial slices crossing the foramen of Monro, the tumor position was classified according to three lines. Line 1 was vertical and crossed the vertex point of the anterior limbs of the internal capsule. Lines 2 and line 3 were horizontal and crossed the foramen of Monro and joint of the middle and lateral thirds of the posterior limbs, respectively. Six (10.17%) patients were diagnosed with type 1 tumor, six (10.17%) with type 2, seven (11.86%) with type 3a, five (8.47%) with type 3b, 17 (28.81%) with type 4a, six (10.17%) with type 4b, three (5.08%) with type 5, and nine (15.25%) with type 6. In type 1 tumors, the PTs were located at the 12 o'clock position of the tumor, type 2 at six o'clock, type 3a between nine and 12 o'clock, type 3 between six and nine o'clock, type 4a between 12 and three o'clock, type 4b at three o'clock, type 5 between six and nine o'clock, and type 6 between three and six o'clock. CONCLUSIONS/SIGNIFICANCE: The position of the PT relative to the tumor could be determined according to the tumor location. These results could prove helpful in determining the location of the PT preoperatively

Speed observation of linear induction motor based on extended Kalman filter

In order to solve the problem of lacking velocity feedback information in the velocity closed-loop control system for linear induction motors (LIM) after cancelling the velocity sensor, a velocity observer based on the extended Kalman filtering algorithm has been implemented, considering the end edge effect of LIM. Firstly, based on the mathematical model of three-phase linear induction motor considering edge effect, an extended Kalman filter observer with appropriate gain and covariance update matrix is derived. Based on the vector control system of LIM, the speed parameters identified by the observer are fed back to the speed closed-loop system. Then, the vector control system model of linear induction motor with speed observer is built in Simulink, and the identification speed of observer is compared with the actual speed of motor. Finally, the results show that the closed-loop control using the identification speed can ensure the stable operation of the system. Under three kinds of loads, the error between the predicted speed and the actual speed of the observer is form 0.51% to 2.34%. The various dynamic performance of the system reveals that the LIM vector control system based on the extended Kalman filter observer increases prediction velocity error and decreases thrust error with increasing load. However, the magnetic flux amplitude error slightly increases. Therefore, considering the end-edge effect, the observer based on the extended Kalman filter can replace the velocity sensor to achieve three-phase LIM control under both unloaded and loaded conditions

Tumors of the thalamus and basal ganglion were categorized into eight types according to three lines.

<p>From left to right: type 1 was situated below line 2 and type 2 above it, with their center lines nearly overlapping line 1. Type 3 was located in the inferior lateral region. Type 4 was located in the inferior medial region. Type 5 was in the upper lateral region. Type 6 was in the upper medial region. Type 3 and type 4 were further divided into two subgroups respectively. The main parts of tumors of type 3a or type 4a were above line 3, while type 3b and 4b were below line 3.</p

Three typical tumors and corresponding PT positions are shown for each type of tumor.

<p><b>A</b>. Type 1 to type 3b tumors. <b>B</b>. Type 4a to type 6 tumors. The left column lists the tumor types. The right column demonstrates the relative positions of the PTs relative to each type of tumor. The green circles indicate tumors. The purple circles indicate the PT location. The middle three columns are typical cases. The PT positions are depicted by the purple line. Certain tumor boundaries are depicted by the green lines or green color, which indicate the enhanced part of the tumor. In the first and second case of type 5 tumor, the white lines show the entire brain with abnormal signals, which were much larger than the enhanced portions. These two tumor types were judged according to the shapes of the white lines, which were mainly above line 2.</p

Demonstrating the method of determining the pyramidal tract position and how to draw three lines for classifying the tumors.

<p><b>A</b>. An axial T2 FLAIR image crossing the foramen of Monro. A tumor is shown at the right basal ganglion. <b>B</b>. The tumor was mirrored to the left. The white points around the tumor indicate the o'clock scale with the 12 o'clock ahead and nine o'clock on the medial side. The PT, depicted by the purple line, was located between 12 and nine o'clock. <b>C</b>. The short, white line indicates the anterior limb of the internal capsule. The vertical, black line indicates line 1, which crossed the white line's vertex point. The horizontal black line was line 2, which crossed the foramen of Monro. <b>D</b>. The short, white line indicates the posterior limb of the internal capsule, which was divided equally into three parts. The horizontal black line indicates line 3, which crossed the marker between the middle and lateral thirds of the white line.</p

A Low-Cost iPhone-Assisted Augmented Reality Solution for the Localization of Intracranial Lesions.

Precise location of intracranial lesions before surgery is important, but occasionally difficult. Modern navigation systems are very helpful, but expensive. A low-cost solution that could locate brain lesions and their surface projections in augmented reality would be beneficial. We used an iPhone to partially achieve this goal, and evaluated its accuracy and feasibility in a clinical neurosurgery setting.We located brain lesions in 35 patients, and using an iPhone, we depicted the lesion's surface projection onto the skin of the head. To assess the accuracy of this method, we pasted computed tomography (CT) markers surrounding the depicted lesion boundaries on the skin onto 15 patients. CT scans were then performed with or without contrast enhancement. The deviations (D) between the CT markers and the actual lesion boundaries were measured. We found that 97.7% of the markers displayed a high accuracy level (D ≤ 5mm). In the remaining 20 patients, we compared our iPhone-based method with a frameless neuronavigation system. Four check points were chosen on the skin surrounding the depicted lesion boundaries, to assess the deviations between the two methods. The integrated offset was calculated according to the deviations at the four check points. We found that for the supratentorial lesions, the medial offset between these two methods was 2.90 mm and the maximum offset was 4.2 mm.This low-cost, image-based, iPhone-assisted, augmented reality solution is technically feasible, and helpful for the localization of some intracranial lesions, especially shallow supratentorial intracranial lesions of moderate size