Qure.AI, a leading company in artificial intelligence (AI) applied to healthcare, has developed a suite of innovative solutions to revolutionize medical diagnosis and treatment. With a plethora of FDA-approved tools for clinical use, Qure.AI continually strives for innovation in integrating AI into healthcare systems. This article delves into the efficacy of Qure.AI\'s chest X-ray interpretation tool, \"qXR,\" in medicine, drawing from a comprehensive review of clinical trials conducted by various institutions. Key applications of AI in healthcare include machine learning, deep learning, and natural language processing (NLP), all of which contribute to enhanced diagnostic accuracy, efficiency, and speed. Through the analysis of vast datasets, AI algorithms assist physicians in interpreting medical data and making informed decisions, thereby improving patient care outcomes. Illustrative examples highlight AI\'s impact on medical imaging, particularly in the diagnosis of conditions such as breast cancer, heart failure, and pulmonary nodules. AI can significantly reduce diagnostic errors and expedite the interpretation of medical images, leading to more timely interventions and treatments. Furthermore, AI-powered predictive analytics enable early detection of diseases and facilitate personalized treatment plans, thereby reducing healthcare costs and improving patient outcomes. The efficacy of AI in healthcare is underscored by its ability to complement traditional diagnostic methods, providing physicians with valuable insights and support in clinical decision-making. As AI continues to evolve, its role in patient care and medical research is poised to expand, promising further advancements in diagnostic accuracy and treatment efficacy.

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.

BACKGROUND: The umbilical venous catheter is a vital access device in neonatal intensive care units for preterm and critically ill infants. Correct positioning is crucial, as malpositioning can lead to severe complications. According to international guidelines, the position of the umbilical venous catheter tip must be assessed in real time; traditionally, the catheter is visualized with a thoracoabdominal X-ray, but one of the most effective and safest methods is therefore real-time ultrasound.
METHODS: This study compares real-time ultrasound and traditional X-ray methods for assessing umbilical venous catheter tip location in 461 cases. The rate of tip malposition was analyzed retrospectively. The secondary aim was to assess indwelling time of umbilical venous catheters and reasons of removal.
RESULTS: Real-time ultrasound tip location, found to be more reliable and efficient, demonstrated a significantly lower incidence of primary malpositioning compared to X-ray assessments (9.6 vs. 75.9%). The study also highlighted the association of real-time ultrasound with reduced catheter manipulation, fewer radiographs, and higher indwelling times of umbilical venous catheter. The multiple logistic regression showed a high probability of the central safe position of the umbilical venous catheter tip using real-time ultrasound tip location (odds ratio 29.5, 95% confidence interval: 17.4-49.4).
CONCLUSIONS: The findings support the adoption of real-time ultrasound in clinical settings to enhance umbilical venous catheter placement accuracy and minimize associated risks. A minimal training investment is needed to attain the proficiency to visualize the umbilical venous catheters, offering a substantial advantage in terms of both cost-effectiveness for the procedure and enhanced patient safety.

Activated guided irradiation by X-ray (AGuIX) nanoparticles are gadolinium-based agents that have the dual benefit of mimicking the effects of a magnetic resonance imaging (MRI) contrast agent used in a clinical routine and enhancing the radiotherapeutic activity of conventional X-rays (for cancer treatment). This \"theragnostic\" action is explained on the one hand by the paramagnetic properties of gadolinium and on the other hand by the generation of high densities of secondary radiation following the interaction of ionizing radiation and high-Z atoms, which leads to enhanced radiation dose deposits within the tumors where the nanoparticles accumulate. Here, we report the results of a phase I trial that aimed to assess the safety and determine the optimal dose of AGuIX nanoparticles in combination with chemoradiation and brachytherapy in patients with locally advanced cervical cancer. AGuIX nanoparticles were administered intravenously and appropriately accumulated within tumors on a dose-dependent manner, as assessed by T1-weighted MRI, with a rapid urinary clearance of uncaught nanoparticles. We show that the observed tumor accumulation of the compounds can support precise delineation of functional target volumes at the time of brachytherapy based on gadolinium enhancement. AGuIX nanoparticles combined with chemoradiation appeared well tolerated among the 12 patients treated, with no dose-limiting toxicity observed. Treatment yielded excellent local control, with all patients achieving complete remission of the primary tumor. One patient had a distant tumor recurrence. These results demonstrate the clinical feasibility of using theranostic nanoparticles to augment the accuracy of MRI-based treatments while focally enhancing the radiation activity in tumors.

This paper reevaluates the first report of X-ray-induced somatic gene mutations. It was undertaken by John Patterson, Department Chair of Hermann Muller, using the same biological model, methods and equipment of Muller. Patterson reported X-ray induced mutation frequencies for X-chromosome-linked (sex-linked) recessive gene mutations in somatic cells of Drosophila melanogaster that resulted in color changes in the ommatidia of the eyes. Results were based on color changes detected in both male and female offspring irradiated while in egg, larval or pupal stages and for unirradiated controls. Patterson claimed that the observed dose response displayed linearity, with a clear implication that the linear response extended to background exposure levels of unirradiated controls. This reanalysis disputes Patterson\'s interpretation, showing that the dose response in the low-dose zone strongly supported a threshold model. The doses in the experiment, which were not clearly presented, were so high that it would preclude the assumption that the experiment provided any information of relevance to radiation exposures of humans at low doses, or even at high doses delivered at low-dose rates. Induced phenotypical changes that occurred at the higher doses, especially in female offspring, overwhelmingly resulted from X-ray-induced chromosome breaks instead of point mutations as initially expected by Patterson. The Patterson findings and linearity interpretations were an important contributory factor in the acceptance of the linear non-threshold (LNT) model during the formative time of concept consolidation. It is rather shocking now to see that the actual data provided no support for the LNT model.

Hypoxia poses a significant challenge to the effectiveness of radiotherapy in head and neck squamous cell carcinoma (HNSCC) patients, and it is imperative to discover novel approaches to overcome this. In this study, we investigated the underlying mechanisms contributing to x-ray radioresistance in HPV-negative HNSCC cells under mild hypoxic conditions (1% oxygen) and explored the potential for autophagy modulation as a promising therapeutic strategy. Our findings show that HNSCC cells exposed to mild hypoxic conditions exhibit increased radioresistance, which is largely mediated by the hypoxia-inducible factor (HIF) pathway. We demonstrate that siRNA knockdown of HIF-1α and HIF-1β leads to increased radiosensitivity in HNSCC cells under hypoxia. Hypoxia-induced radioresistance was not attributed to differences in DNA double strand break repair kinetics, as these remain largely unchanged under normoxic and hypoxic conditions. Rather, we identify autophagy as a critical protective mechanism in HNSCC cells following irradiation under mild hypoxia conditions. Targeting key autophagy genes, such as BECLIN1 and BNIP3/3L, using siRNA sensitizes these cells to irradiation. Whilst autophagy\'s role in hypoxic radioresistance remains controversial, this study highlights the importance of autophagy modulation as a potential therapeutic approach to enhance the effectiveness of radiotherapy in HNSCC.

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.

The regulation of proteolytic enzymes by protease inhibitors is crucial for maintaining the balance between protein synthesis and degradation, preventing uncontrolled proteolysis and fine-tuning cellular processes essential for optimal function and survival of the plants. It is known that the plant protease inhibitors activities are induced in defense of biotic as well as abiotic stresses. Thus, beyond their fundamental physiological functions, their involvement in stress responses, such as drought, cold, and salinity, is of equally significant. The X-ray film contact print method is an effective method for assessing various protease inhibitors exposed to stress conditions. In this approach, initially plant protease inhibitors will be separated using electrophoresis, and then the gel is treated with trypsin, which inhibits protease inhibitors. This gel when placed on X-ray film, the trypsin will digest the gelatin layer present on the film and the gelatinolytic activity stalled at the premises of protease inhibitors. This will provide the impression of the differentially expressed protease inhibitors in stress-treated plants.

OBJECTIVE: This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration.
METHODS: We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class.
RESULTS: Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios.
CONCLUSIONS: Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance.
CONCLUSIONS: This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes.
CONCLUSIONS: Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.