A Retrospective Study on Subgaleal Fluid Collection After Titanium Mesh and Polyetheretherketone Cranioplasty.

Objective: Cranioplasty is a common neurosurgical procedure aimed at providing structural protection to cerebral tissues and enhancing neurological function. The choice of implant material, particularly polyetheretherketone (PEEK) and titanium mesh, significantly influences postoperative outcomes, including the incidence of subgaleal fluid collections (SFC). This study investigates the incidence of SFC associated with PEEK and titanium mesh in cranioplasty, identifying risk factors and implications for clinical practice.

Total Water-Soluble Flavonoids From (Hance) Chun (Sweet Tea) Improve Glucose Homeostasis Through Multitarget Signalling in GDM Mice.

The oral safety of (Hance) Chun (sweet tea) that has antihyperglycemic potential has been verified. However, its specific application and action mechanism in the treatment of gestational diabetes mellitus (GDM) are still unclear. Total water-soluble flavonoids extracted from (Hance) Chun (sweet tea) were applied to GDM mice.

Unsupervised domain adaptation for remote sensing semantic segmentation with the 2D discrete wavelet transform.

There would be the differences in spectra, scale and resolution between the Remote Sensing datasets of the source and target domains, which would lead to the degradation of the cross-domain segmentation performance of the model. Image transfer faced two problems in the process of domain-adaptive learning: overly focusing on style features while ignoring semantic information, leading to biased transformation results, and easily overlooking the true transfer characteristics of remote sensing images, resulting in unstable model training. To address these issues, we proposes a novel dual-space generative adversarial domain adaptation segmentation framework, DS-DWTGAN, to minimize the differences between the source domain and the target domain.

Dynamically aggregating MLPs and CNNs for skin lesion segmentation with geometry regularization.

Background And Objective: Melanoma is a highly malignant skin tumor. Accurate segmentation of skin lesions from dermoscopy images is pivotal for computer-aided diagnosis of melanoma. However, blurred lesion boundaries, variable lesion shapes, and other interference factors pose a challenge in this regard.

Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation.

The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors. Semi-supervised models (e.g.

MAD-Net: Multi-attention dense network for functional bone marrow segmentation.

Radiotherapy is the main treatment modality for various pelvic malignancies. However, high intensity radiation can damage the functional bone marrow (FBM), resulting in hematological toxicity (HT). Accurate identification and protection of the FBM during radiotherapy planning can reduce pelvic HT.

[Research on three-dimensional skull repair by combining residual and informer attention].

Cranial defects may result from clinical brain tumor surgery or accidental trauma. The defect skulls require hand-designed skull implants to repair. The edge of the skull implant needs to be accurately matched to the boundary of the skull wound with various defects.

Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation.

Breast cancer detection largely relies on imaging characteristics and the ability of clinicians to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast tumors has recently shown great promise for enabling the automatic identification of breast tumors. Nevertheless, state-of-the-art MRI-based algorithms utilizing deep learning techniques are still limited in their ability to accurately separate tumor and healthy tissue.

Improved U-Net3+ with stage residual for brain tumor segmentation.

Background: For the encoding part of U-Net3+,the ability of brain tumor feature extraction is insufficient, as a result, the features can not be fused well during up-sampling, and the accuracy of segmentation will reduce.

Methods: In this study, we put forward an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to solve the vanishing gradient problem caused by the increasing in network depth, and enhances the feature extraction ability of the encoder which is instrumental in full feature fusion when up-sampling in the network.

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L-Regularization.

Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets.

BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction.

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features.

Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition.

Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task.

Antihyperglycemic and antioxidant effect of the total flavones of Potentilla kleiniana Wight et Arn. in streptozotocin induced diabetic rats.

To investigate the antihyperglycemic and antioxidant activity of the total flavones of Potentilla kleiniana Wight et Arn. (TFP) in streptozotocin (STZ) induced diabetic rats. STZ-induced diabetic rats were treated with TFP weekly for 4 weeks at three doses (100 mg/kg, 200 mg/kg and 400 mg/kg).

[Study on impact of ethanol extracts from Sedum sarmentosum in inhibiting STAT-3 signaling and inducing apoptosis of human hepatocellular carcinoma cell line HepG2].

Objective: To investigate the impact of ethanol extracts from Sedum sarmentosum (ESB) on STAT-3 signaling and its probable molecular mechanism in inducing apoptosis.

Method: MTT assay was used to detect the impact of ESB on HepG2 cell proliferation. FITC-Annexin V-FITC /PI double-labeling were used to investigate the impact on hepatoma carcinoma cell apoptosis.

Synergistic activities of MET/RON inhibitor BMS-777607 and mTOR inhibitor AZD8055 to polyploid cells derived from pancreatic cancer and cancer stem cells.

Tyrosine kinase inhibitor BMS-777067 is an inhibitor of RON/MET receptor tyrosine kinases currently under clinical trials. Here, we report the synergistic activity of BMS-777607 in combination with mTOR inhibitor AZD8055 in killing chemoresistant pancreatic cancer and cancer stem cells. Treatment of pancreatic cancer L3.

Oncogenic variant RON160 expression in breast cancer and its potential as a therapeutic target by small molecule tyrosine kinase inhibitor.

Aberrant expression of the RON receptor tyrosine kinase contributes to breast cancer malignancy. Although clinical trials of RON targeting are underway, the intriguing issue is the diversity of RON expression as evident by cancer cells expressing different variants including oncogenic RON160. The current study determines aberrant RON160 expression in breast cancer and its potential as a target for breast cancer therapy.

Small-molecule inhibitor BMS-777607 induces breast cancer cell polyploidy with increased resistance to cytotoxic chemotherapy agents.

The RON receptor tyrosine kinase is a therapeutic target for cancer treatment. Here, we report therapeutic effect and phenotypic change of breast cancer cells in response to BMS-777607, a RON tyrosine kinase inhibitor. Treatment of breast cancer cells with BMS-777607 at therapeutic doses inhibited cancerous clonogenic growth but had only minimal effect on cell apoptosis.