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Wireless capsule endoscopy (WCE) offers a non-invasive evaluation of the digestive system, eliminating the need for sedation and the risks associated with conventional endoscopic procedures. Its significance lies in diagnosing gastrointestinal tissue irregularities, especially in the small intestine. However, existing commercial WCE devices face limitations, such as the absence of autonomous lesion detection and treatment capabilities. Recent advancements in micro-electromechanical fabrication and computational methods have led to extensive research in sophisticated technology integration into commercial capsule endoscopes, intending to supersede wired endoscopes. This Review discusses the future requirements for intelligent capsule robots, providing a comparative evaluation of various methods’ merits and disadvantages, and highlighting recent developments in six technologies relevant to WCE. These include near-field wireless power transmission, magnetic field active drive, ultra-wideband/intrabody communication, hybrid localization, AI-based autonomous lesion detection, and magnetic-controlled diagnosis and treatment. Moreover, we explore the feasibility for future “capsule surgeons”. future requirements for intelligent wireless capsule endoscopy, providing a comparative evaluation of various methods’ merits and disadvantages, and highlighting recent developments in six technologies.

Waste dumps generated during open-pit mining directly affect operational safety, and their hazards increase with dump height. During the stacking of high-bench dumps composed of soil-rock mixtures, particle rolling along the slope commonly produces particle-size grading, yielding gradation patterns that differ markedly from those of conventional dumps. Based on a field case, an improved cellular automaton (CA) was coupled with the finite difference method (FDM) to clarify how spatially disordered particle arrangements influence the distribution of shear-strength parameters in soil-rock mixtures. A slope-stability analysis framework tailored to high-bench dumps was developed, and the effect of particle grading on stability was assessed. Owing to spatial variability in particle distribution, strength parameters exhibited significant scatter even for identical overall gradation; both cohesion and internal friction angle were found to follow normal distributions. Particle grading along the slope was shown to reduce the deformation and volume of the tensile plastic zone, raise the slip-surface position, and increase the factor of safety. These findings advance the understanding of soil-rock mixture mechanics and provide a practical reference for slope stability analysis of complex particulate materials.

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds. •A 3D convolutional neural network was applied to accurately quantify susceptibility.•Increasing the patch receptive field and cropping the output reduced the error.•Generative adversarial networks improved the quality of the susceptibility maps.•Our algorithm resulted in more accurate maps compared to common nonlearning methods.

To improve the safety of ramp vehicles changing lane and to shorten the merging distance, this paper explores the dynamic game interaction properties of vehicles merging and the consistency of vehicles’ decision-making behaviors at the macro-microscopic levels. Using the exiD dataset and evolutionary game theory, the merging behavior of ramp vehicles is modeled to explore the effects of different driving states on the evolutionary convergence of strategies. Based on the game cost theory, the lane choice behavior of mainline vehicles is modeled. Validated by SUMO software, the results show that the model in this paper can significantly improve the safety of vehicle merging and reduce the merging distance. The mainline vehicles are more inclined to change lane and cut out in advance when facing the ramp vehicles under the influence of the change of advantage in the subsequent game.

To compare the clinical outcomes and learning curve characteristics of unilateral biportal endoscopic lumbar interbody fusion (UBE-TLIF) and percutaneous uniportal full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) in patients with single-segment lumbar degenerative diseases (LDD). A retrospective study was conducted from January 2022 to July 2023, involving a total of 95 patients with single-segment LDD, who were divided into two groups: the Endo-TLIF group and the UBE-TLIF group. The demographic characteristics, radiographic and clinical outcomes, as well as complications were meticulously recorded and analyzed in both groups. The mean operation time of Endo-TLIF group was 224.08 ± 58.90 min, which was significantly longer than that of UBE-TLIF group (169.93 ± 30.86 min) ( P  < 0.05). The perspective times were significantly shortened in the UBE-TLIF group compared with the Endo-TLIF group ( P  < 0.05). The Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores showed significant improvement post-operation in both groups ( P  < 0.05). There were no significant differences in VAS, ODI and modified Macnab criteria during the last follow-up periods ( P  > 0.05). Both groups exhibited similar complication rates and fusion rates ( P  > 0.05). CUSUM analysis indicated that the stabilization of operation time occurred after 23 cases for Endo-TLIF and 19 cases for UBE-TLIF, respectively. The safety and efficacy of both Endo-TLIF and UBE-TLIF for the treatment of LDD have been demonstrated. As the number of surgeries increased, the operation time for both procedures decreased. Specifically, after 23 surgeries, the operation time for Endo-TLIF reached a relative stability, while for UBE-TLIF it was achieved after 19 surgeries.

Background Tetrandrine, a bisbenzylisoquinoline alkaloid that was isolated from the medicinal plant Stephania tetrandrine S. Moore, was recently identified as a novel chemotherapy drug. Tetrandrine exhibited a potential antitumor effect on multiple types of cancer. Notably, an enhanced therapeutic efficacy was identified when tetrandrine was combined with a molecularly targeted agent. H89 is a potent inhibitor of protein kinase A and is an isoquinoline sulfonamide. Methods The effects of H89 combined with tetrandrine were investigated in vitro with respect to cell viability, apoptosis and autophagy, and synergy was assessed by calculation of the combination index. The mechanism was examined by western blot, flow cytometry and fluorescence microscopy. This combination was also evaluated in a mouse xenograft model; tumor growth and tumor lysates were analyzed, and a TUNEL assay was performed. Results Combined treatment with H89 and tetrandrine exerts a mostly synergistic anti-tumor effect on human cancer cells in vitro and in vivo while sparing normal cells. Mechanistically, the combined therapy significantly induced cancer cell apoptosis and autophagy, which were mediated by ROS regulated PKA and ERK signaling. Moreover, Mcl-1 and c-Myc were shown to play a critical role in H89/tetrandrine combined treatment. Mcl-1 ectopic expression significantly diminished H89/tetrandrine sensitivity and amplified c-Myc sensitized cancer cells in the combined treatment. Conclusion Our findings demonstrate that the combination of tetrandrine and H89 exhibits an enhanced therapeutic effect and may become a promising therapeutic strategy for cancer patients. They also indicate a significant clinical application of tetrandrine in the treatment of human cancer. Moreover, the combination of H89/tetrandrine provides new selectively targeted therapeutic strategies for patients with c-Myc amplification.

Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R2 value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.

Nerves are important pathological elements of the microenvironment of tumors, including those in pancreatic, colon and rectal, prostate, head and neck, and breast cancers. Recent studies have associated perineural invasion with tumor progression and poor outcomes. In turn, tumors drive the reprogramming of neurons to recruit new nerve fibers. Therefore, the crosstalk between nerves and tumors is the hot topic and trend in current cancer investigations. Herein, we reviewed recent studies presenting direct supporting evidences for a better understanding of nerve–tumor interactions.

The uvula flapping is one of the most distinctive features of snoring and is critical in affecting airway aerodynamics and vibrations. This study aimed to elucidate the mechanism of pharyngeal vibration and pressure fluctuation due to uvula flapping employing fluid–structure interaction simulations. The followings are the methodology part: we constructed an anatomically accurate pediatric pharynx model and put attention on the oropharynx region where the greatest level of upper airway compliance was reported to occur. The uvula was assumed to be a rigid body with specific flapping frequencies to guarantee proper boundary conditions with as little complexity as possible. The airway tissue was considered to have a uniform thickness. It was found that the flapping frequency had a more significant effect on the airway vibration than the flapping amplitude, as the flapping uvula influenced the pharyngeal aerodynamics by altering the jet flow from the mouth. Breathing only through the mouth could amplify the effect of flapping uvula on aerodynamic changes and result in more significant oropharynx vibration.

Purpose Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease. Methods High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively. Results Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7). Conclusions Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.