Functional nanomaterials have emerged as versatile nanotransducers for wireless neural modulation because of their minimal invasion and high spatiotemporal resolution. The nanotransducers can convert external excitation sources (e.g., NIR light, X-rays, and magnetic fields) to visible light (or local heat) to activate optogenetic opsins and thermosensitive ion channels for neuromodulation. The present review provides insights into the fundamentals of the mostly used functional nanomaterials in wireless neuromodulation including upconversion nanoparticles, nanoscintillators, and magnetic nanoparticles. We further discussed the recent developments in design strategies of functional nanomaterials with enhanced energy conversion performance that have greatly expanded the field of neuromodulation. We summarized the applications of functional nanomaterials-mediated wireless neuromodulation techniques, including exciting/silencing neurons, modulating brain activity, controlling motor behaviors, and regulating peripheral organ function in mice. Finally, we discussed some key considerations in functional nanotransducer-mediated wireless neuromodulation along with the current challenges and future directions.

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.
METHODS: In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method.
RESULTS: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94.
CONCLUSIONS: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.

Flavor alteration is a crucial factor affecting the quality of mushrooms during preservation. The dynamic variations of volatile profiles of fresh Hericium erinaceus with electron-beam generated X-ray irradiation were investigated by combining E-nose, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). E-nose analysis achieved rapid discrimination in all treatments over storage time. 65 and 73 volatile organic compounds (VOCs) were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. Thereinto, 1-octen-3-ol, 1-octen-3-one, and 2-octanone were screened out as the characteristic VOCs, which contents declined during storage. While the contents of (E)-2-octenal, (E)-2-nonenal, and 1-octanol increased. The flavor profile changes from distinct mushroom and floral odor to an intense alcohol and fatty odor. Notably, one-kGy irradiation remained more volatiles and denser mushroom odor after storage. Multivariate analysis further confirmed that 1.0 kGy irradiation contributed to the overall aroma retention during postharvest storage of H. erinaceus.

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

Objective.Digitally reconstructed radiography (DRR) plays an important role in the registration of intraoperative x-ray and preoperative CT images. However, existing DRR algorithms often neglect the critical isocentric fixed angle irradiation (IFAI) principle in C-arm imaging, resulting in inaccurate simulation of x-ray images. This limitation degrades registration algorithms relying on DRR image libraries or employing DRR images (DRRs) to train neural network models. To address this issue, we propose a novel IFAI-based DRR method that accurately captures the true projection transformation during x-ray imaging of the human body.Approach.By strictly adhering to the IFAI principle and utilizing known parameters from intraoperative x-ray images paired with CT scans, our method successfully simulates the real projection transformation and generates DRRs that closely resemble actual x-ray images.Main result.Experimental results validate the effectiveness of our IFAI-based DRR method by successfully registering intraoperative x-ray images with preoperative CT images from multiple patients who underwent thoracic endovascular aortic procedures.Significance. The proposed IFAI-based DRR method enhances the quality of DRR images, significantly accelerates the construction of DRR image libraries, and thereby improves the performance of x-ray and CT image registration. Additionally, the method has the generality of registering CT and x-ray images generated by large C-arm devices.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.
METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists.
RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong\'s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings.
CONCLUSIONS: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

UNASSIGNED: To explore the effectiveness of irreducible intertrochanteric femoral fracture in the elderly by treating with folding top technique and right-angle pliers prying and pulling under G-arm X-ray fluoroscopy.
UNASSIGNED: The clinical data of 74 elderly patients with irreducible intertrochanteric femoral fracture admitted between February 2016 and December 2022 and met the selection criteria were retrospectively analyzed. Among them, 38 cases were treated with folding top technique combined with right-angle pliers prying and pulling under G-arm X-ray fluoroscopy and intramedullary nailing fixation (study group), and 36 cases were treated with limited open reduction combined with other reduction methods and intramedullary nailing fixation (control group). There was no significant difference in baseline data between the two groups, such as age, gender, cause of injury, affected side and classification of fractures, complicated medical diseases, and time from injury to operation ( P>0.05). The operation time, intraoperative blood loss, hospital stay, fracture reduction time, fracture healing time, and complications of the two groups were recorded and compared. The quality of fracture reduction was evaluated by Baumgaertner et al. and Chang et al. fracture reduction standards.
UNASSIGNED: Patients in both groups were followed up 10-14 months, with an average of 12 months. The operation time and intraoperative blood loss in the study group were significantly less than those in the control group ( P<0.05), there was no significant difference in hospital stay between the two groups ( P>0.05). At 2 days after operation, according to the fracture reduction standards of Baumgaertner et al. and CHANG Shimin et al., the quality of fracture reduction in the study group was better than that in the control group, and the fracture reduction time in the study group was shorter than that in the control group, with significant differences ( P<0.05). After operation, the fractures of the two groups all healed, and there was no significant difference in healing time between the two groups ( P>0.05). During the follow-up, there was no complication such as incision infection, internal fixation failure, deep venous thrombosis of lower limbs, intramedullary nail breakage, spiral blade cutting, or hip varus in the two groups, except for 2 cases of coxa vara in the control group.
UNASSIGNED: For the irreducible intertrochanteric femoral fracture, using folding top technique combined with right-angle pliers prying and pulling under G-arm X-ray fluoroscopy can obviously shorten the operation time, reduce the intraoperative blood loss, and improve the quality of fracture reduction.
UNASSIGNED: 探讨在G臂X线机透视下采用折顶技术联合直角钳撬拉辅助复位治疗老年难复性股骨转子间骨折的临床疗效。.
UNASSIGNED: 回顾分析2016年2月—2022年12月收治且符合选择标准的74例老年难复性股骨转子间骨折患者临床资料。其中38例术中应用折顶技术联合直角钳撬拉进行复位并髓内钉固定（研究组），36例术中应用有限切开复位联合其他复位方法并髓内钉固定（对照组）。两组患者年龄、性别、致伤原因、骨折侧别及分型、合并内科疾病、受伤至手术时间等基线资料比较，差异均无统计学意义（ P>0.05）。记录并比较两组患者手术时间、术中出血量、住院时间、骨折复位时间、骨折愈合时间及并发症发生情况；以Baumgaertner等及张世民等的骨折复位标准评定骨折复位质量。.
UNASSIGNED: 两组患者均获随访，随访时间10～14个月，平均12个月。研究组手术时间、术中出血量均少于对照组，差异有统计学意义（ P<0.05）；两组住院时间比较差异无统计学意义（ P>0.05）。术后2 d根据Baumgaertner等及Chang等的骨折复位标准评定骨折复位质量，研究组均优于对照组，且研究组骨折复位时间亦明显短于对照组，差异均有统计学意义（ P<0.05）。术后两组患者骨折均愈合，愈合时间比较差异无统计学意义（ P>0.05）。随访期间除对照组2例发生髋内翻畸形外，两组其余患者术后均无切口感染、内固定失效、下肢深静脉血栓形成以及髓内钉断裂、螺旋刀片切割、髋内翻等并发症发生。.
UNASSIGNED: 对于老年难复性股骨转子间骨折，采用G臂X线机透视下折顶技术联合直角钳撬拉辅助复位治疗，可明显缩短手术时间、减少术中出血量、提高骨折复位质量。.

Photo-crosslinking polymerization stands as a fundamental pillar in the domains of chemistry, biology, and medicine. Yet, prevailing strategies heavily rely on ultraviolet/visible (UV/Vis) light to elicit in situ crosslinking. The inherent perils associated with UV radiation, namely the potential for DNA damage, coupled with the limited depth of tissue penetration exhibited by UV/Vis light, severely restrict the scope of photo-crosslinking within living organisms. Although near-infrared light has been explored as an external excitation source, enabling partial mitigation of these constraints, its penetration depth remains insufficient, particularly within bone tissues. In this study, we introduce an approach employing X-ray activation for deep-tissue hydrogel formation, surpassing all previous boundaries. Our approach harnesses a low-dose X-ray-activated persistent luminescent phosphor, triggering on demand in situ photo-crosslinking reactions and enabling the formation of hydrogels in male rats. A breakthrough of our method lies in its capability to penetrate deep even within thick bovine bone, demonstrating unmatched potential for bone penetration. By extending the reach of hydrogel formation within such formidable depths, our study represents an advancement in the field. This application of X-ray-activated polymerization enables precise and safe deep-tissue photo-crosslinking hydrogel formation, with profound implications for a multitude of disciplines.

Among the least studied portion of the pterosaur skeleton is the palate, which tends to be poorly preserved and commonly only visible from one side (the ventral portion). Even in well-preserved specimens, the bones tend to be fused, with the limits of individual palatal elements obscured. To shed new light on this region, we employed advanced X-ray imaging techniques on the non-pterodactyloid Kunpengopterus (Wukongopteridae), and the pterodactyloids Dsungaripterus (Dsungaripteridae), Hongshanopterus (Istiodactylidae), and Hamipterus (Hamipteridae). Our analyses revealed the presence of sutures between palatal bones in Dsungaripterus and Kunpengopterus, which resulted in different interpretations of the relation between palatine, ectopterygoid, and pterygoid, leading to a new identification of the palatal openings. Furthermore, our study shows six main observations such as the variation of the angle between the palatine rami and the variation in the relative sizes of the palatal openings. We also point out that the presence of a maxillopalatine fenestra (previously identified as postpalatine fenestra), is unique within Diapsida. Although much more work needs to be done, we showed that advanced X-ray imaging techniques open a window for understanding pterosaur cranial anatomy and provide a new perspective for investigating the evolutionary history of these flying reptiles.