X-ray-activated polymerization expanding the frontiers of deep-tissue hydrogel formation.

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.

X-ray-guided self-expandable metal stent (SEMS) implantation in oesophageal malignancy as an alternative treatment.

<b><br>Indroduction:</b> Significant dysphagia, aspiration pneumonia, and impossible oral nutrition in patients with unresectable or recurrent gastroesophageal malignancy or bronchial cancer invading the oesophagus with a tracheoesophageal fistula lead to cachexia. Dehiscence of the esophago-jejunal or gastroesophageal anastomosis may cause severe oesophageal haemorrhage. We believe that X-ray-guided oesophageal stent implantation (SEMS) is an alternative palliative method for microjejunostomy or full parenteral nutrition.</br> <b><br>Aim:</b> The aim of this paper was to assess the safety and efficacy of a novel X-ray-guided oesophageal stent implantation technique.</br> <b><br>Materials and methods:</b> This retrospective analysis included 54 patients (35 men and 19 women) treated for malignant dysphagia, gastroesophageal/gastrointestinal anastomotic fistula or bronchoesophageal fistula in two Surgical Units between 2010 and 2019, using a modified intravascular approach to oesophageal stent implantation.</br> <b><br>Results:</b> The presented modified intravascular method of oesophageal stent implantation was successfully performed in all described patients requiring oral nutrition restoration immediately following oesophageal stent implantation. Two patients with oesophageal anastomotic dehiscence died on postoperative days 7 and 9 due to circulatory and respiratory failure. One patient was reimplanted due to a recurrent fistula. Two patients with ruptured thoracic aneurysm and thoracic stent graft implantation due to oesophageal haemorrhage, who were implanted with an oesophageal stent, died on postoperative days 4 and 14.</br> <b><br>Conclusions:</b> The modified intravascular X-ray-guided SEMS technique may be a palliative treatment for patients with unresectable oesophageal malignancies.</br>.

[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].

Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.

[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].

Objective: A deep learning-based method for evaluating the quality of pediatric pelvic X-ray images is proposed to construct a diagnostic model and verify its clinical feasibility. Methods: Three thousand two hundred and forty-seven children with anteroposteric pelvic radiographs are retrospectively collected and randomly divided into training datasets, validation datasets and test datasets. Artificial intelligence model is conducted to evaluate the reliability of quality control model. Results: The diagnostic accuracy, area under ROC curve, sensitivity and specificity of the model are 99.4%, 0.993, 98.6% and 100.0%, respectively. The 95% consistency limit of the pelvic tilt index of the model is -0.052-0.072. The 95% consistency threshold of pelvic rotation index is -0.088-0.055. Conclusion: This is the first attempt to apply AI algorithm to the quality assessment of children's pelvic radiographs, and has significantly improved the diagnosis and treatment status of DDH in children.

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

New insights into pterosaur cranial anatomy: X-ray imaging reveals palatal structure and evolutionary trends.

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.

RT-SRTS: Angle-agnostic real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection.

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.

Pneumopericardium in a child - "halo sign" on chest x-ray: A Case Report.

A 5 year old boy with acute ly mphoblastic leukaemia on chemotherapy presented with chest pain and vomiting for two days after an elective procedure under general anaesthesia. H is ches t x-ray was remarkabl e for a promin ent halo sign, an air gap surrounding the he art indicat ing a large pneumope ricardium. Alth ough the pneu mo pericardium could not be appre ciated on an echocardiogram, the child developed clinical sig ns of cardiac t amponade. Attem pts to evacu ate the pneumopericardium were unsuccessful leading to death.

[Features of detection and interpretation of intravital and postmortem changes according to the results of traditional X-ray and X-ray computed tomography of objects from historical graves and artefacts]. / Osobennosti vyyavleniya i interpretatsii prizhiznennykh i posmertnykh izmenenii po rezul'tatam traditsionnoi rentgenografii i rentgenovskoi komp'yuternoi tomografii ob"ektov iz istoricheskikh zakhoronenii i artefaktov.

OBJECTIVE: To study emergence mechanism, physical nature, pattern of intravital and postmortem changes of biological and non-biological objects originated in the period from 1550 to 1918 yr. using traditional X-ray and X-ray computed tomography. MATERIAL AND METHODS: The relics of Saint Macarius the Roman of Novgorod, the remains of the First Reverend of the Resurrection Novodevichy Convent in Saint Petersburg Mother Superior Theophania, damages on the chair leg on which Tsesarevich Alexey sat during the shooting of Russian Emperor Nicholas II, his family and entourage in 1918 in Yekaterinburg were stidued. RESULTS AND CONCLUSION: The application of highly informative methods of traditional X-ray and X-ray computed tomography of biological and non-biological objects showed their high informativity and allowed to correctly interpret the emergence mechanism, physical nature, pattern of intravital and postmortem changes of skeleton bones and historical artefact (chair legs) originated long ago. The necessity of special professional training and advanced training of experts in forensic radiology to prevent possible diagnostic and expert errors has been substantiated.

[Review of age assessment methods of children and adolescents by teeth X-rays]. / Obzor metodov otsenki vozrasta detei i podrostkov po rentgenogrammam zubov.

Interest in the topic of age assessment for forensic medical identification of personality has not decreased for over the past decade. Establishing an exact age have a critical importance for law enforcement authorities, for example in case of wrongdoing by illegal migrants without identity documents. The search and systemic analysis of published researches devoted to age assessment by dental status in children and adolescents with subsequent updating of the directions of development in this scientific subject theme and the possibility of their realization in practice in the Russian Federation were carried out in order to have an objective concept of used methods of dental status assessment in the world practice.

[Treatment of irreducible intertrochanteric femoral fracture in elderly by folding top technique combined with right-angle pliers prying and pulling under G-arm X-ray fluoroscopy].

Objective: 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. Methods: 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. Results: 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. Conclusion: 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.

X-ray structure and enzymatic study of a bacterial NADPH oxidase highlight the activation mechanism of eukaryotic NOX.

NADPH oxidases (NOX) are transmembrane proteins, widely spread in eukaryotes and prokaryotes, that produce reactive oxygen species (ROS). Eukaryotes use the ROS products for innate immune defense and signaling in critical (patho)physiological processes. Despite the recent structures of human NOX isoforms, the activation of electron transfer remains incompletely understood. SpNOX, a homolog from Streptococcus pneumoniae, can serves as a robust model for exploring electron transfers in the NOX family thanks to its constitutive activity. Crystal structures of SpNOX full-length and dehydrogenase (DH) domain constructs are revealed here. The isolated DH domain acts as a flavin reductase, and both constructs use either NADPH or NADH as substrate. Our findings suggest that hydride transfer from NAD(P)H to FAD is the rate-limiting step in electron transfer. We identify significance of F397 in nicotinamide access to flavin isoalloxazine and confirm flavin binding contributions from both DH and Transmembrane (TM) domains. Comparison with related enzymes suggests that distal access to heme may influence the final electron acceptor, while the relative position of DH and TM does not necessarily correlate with activity, contrary to previous suggestions. It rather suggests requirement of an internal rearrangement, within the DH domain, to switch from a resting to an active state. Thus, SpNOX appears to be a good model of active NOX2, which allows us to propose an explanation for NOX2's requirement for activation.

Location, speciation, and quantification of carbon in silica phytoliths using synchrotron scanning transmission X-ray microspectroscopy.

Phytoliths of biogenic silica play a vital role in the silicon biogeochemical cycle and occlude a fraction of organic carbon. The location, chemical speciation, and quantification of this carbon within phytoliths have remained elusive due to limited direct experimental evidence. In this work, phytoliths (bilobate morphotype) from the sugarcane stalk epidermis are sectioned with a focused ion beam to produce lamellas (≈10 × 10 µm2 size, <500 nm thickness) and probed by synchrotron scanning transmission X-ray microspectroscopy (≈100-200 nm pixel size; energies near the silicon and carbon K-absorption edges). Analysis of the spectral image stacks reveals the complementarity of the silica and carbon spatial distributions, with carbon found at the borders of the lamellas, in islands within the silica, and dispersed in extended regions that can be described as a mixed silica-carbonaceous matrix. Carbon spectra are assigned mainly to lignin-like compounds as well as to proteins. Carbon contents of 3-14 wt.% are estimated from the spectral maps of four distinct phytolith lamellas. The results provide unprecedented spatial and chemical information on the carbon in phytoliths obtained without interference from wet-chemical digestion.

[Clinical study of ear keloids with surgical excision and intraoperative low-energy X-ray irradiation therapy].

Objective:To explore the clinical efficacy of surgical excision combined with low-energy X-ray irradiation in the treatment of ear keloids. Methods:Clinical data of 32 cases of ear keloid lesions that received surgical treatment alone or surgery combined with radiotherapy from March 2019 to November 2022 in the Department of Otorhinolaryngology Head and Neck Surgery of the Tianjin First Central Hospital were retrospectively analyzed. Among them, 10 cases received radiotherapy and 22 cases did not receive radiotherapy. The radiotherapy group received irradiation with a large divided dose of 50 kV low-energy X-rays. The mode of fractionation radiotherapy was as follows: the first was 10 Gy of intraoperative radiation therapy and the second was 8 Gy on the 3rd postoperative day for a total of 18 Gy. The local efficacy and skin radiation reaction were observed at a follow-up of 8-52 months. Results:The median follow-up was 26 months, and as of the date of the last follow-up, 9 cases were cured and 1 case was ineffective in the radiotherapy group, with an effective rate of 90.0%, while 9 cases were cured and 13 cases were ineffective in the no-radiotherapy group, with an effective rate of 40.9%. The recurrence of ear keloids was not related to the side, site, or etiology of the patient's onset（P>0.05）. Recurrence was related to whether or not the patients received radiotherapy（χ²=4.885, P<0.05）, and the recurrence rate in the radiotherapy group（10.0%） was significantly lower than that in the non-radiotherapy group（59.1%）. Conclusion:Surgical excision combined with low-energy X-ray irradiation therapy is an effective method of treating keloids in the ear, especially with intraoperative radiation therapy can achieve more satisfactory results.

Chest X-ray evaluation using machine learning to support the early diagnosis of pulmonary TB.

TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis..

Enhancing fracture diagnosis in pelvic X-rays by deep convolutional neural network with synthesized images from 3D-CT.

Pelvic fractures pose significant challenges in medical diagnosis due to the complex structure of the pelvic bones. Timely diagnosis of pelvic fractures is critical to reduce complications and mortality rates. While computed tomography (CT) is highly accurate in detecting pelvic fractures, the initial diagnostic procedure usually involves pelvic X-rays (PXR). In recent years, many deep learning-based methods have been developed utilizing ImageNet-based transfer learning for diagnosing hip and pelvic fractures. However, the ImageNet dataset contains natural RGB images which are different than PXR. In this study, we proposed a two-step transfer learning approach that improved the diagnosis of pelvic fractures in PXR images. The first step involved training a deep convolutional neural network (DCNN) using synthesized PXR images derived from 3D-CT by digitally reconstructed radiographs (DRR). In the second step, the classification layers of the DCNN were fine-tuned using acquired PXR images. The performance of the proposed method was compared with the conventional ImageNet-based transfer learning method. Experimental results demonstrated that the proposed DRR-based method, using 20 synthesized PXR images for each CT, achieved superior performance with the area under the receiver operating characteristic curves (AUROCs) of 0.9327 and 0.8014 for visible and invisible fractures, respectively. The ImageNet-based method yields AUROCs of 0.8908 and 0.7308 for visible and invisible fractures, respectively.

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

Coronary physiology instantaneous wave-free ratio (iFR) derived from x-ray angiography using artificial intelligence deep learning models: a pilot study.

OBJECTIVES: Coronary angiography (CAG)-derived physiology methods have been developed in an attempt to simplify and increase the usage of coronary physiology, based mostly on dynamic fluid computational algorithms. We aimed to develop a different approach based on artificial intelligence methods, which has seldom been explored. METHODS: Consecutive patients undergoing invasive instantaneous free-wave ratio (iFR) measurements were included. We developed artificial intelligence (AI) models capable of classifying target lesions as positive (iFR ≤ 0.89) or negative (iFR > 0.89). The predictions were then compared to the true measurements. RESULTS: Two hundred-fifty measurements were included, and 3 models were developed. Model 3 had the best overall performance: accuracy, negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity were 69%, 88%, 44%, 74%, and 67%, respectively. Performance differed per target vessel. For the left anterior descending artery (LAD), model 3 had the highest accuracy (66%), while model 2 the highest NPV (86%) and sensitivity (91%). PPV was always low/modest. Model 1 had the highest specificity (68%). For the right coronary artery, model 1's accuracy was 86%, NPV was 97%, and specificity was 87%, but all models had low PPV (maximum 25%) and low/modest sensitivity (maximum 60%). For the circumflex, model 1 performed best: accuracy, NPV, PPV, sensitivity, and specificity were 69%, 96%, 24%, 80%, and 68%, respectively. CONCLUSIONS: We developed 3 AI models capable of binary iFR estimation from CAG images. Despite modest accuracy, the consistently high NPV is of potential clinical significance, as it would enable avoiding further invasive maneuvers after CAG. This pivotal study offers proof of concept for further development.