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In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs’ potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large populations. However, it is considered difficult to completely homogenize the environment and settings during the measurement of large populations. Therefore, it is necessary to appropriately deal with technical errors that are not related to the biological factors of interest. In this study, by analyzing a large cohort database, we have identified four major types of anomalies that occur during gait analysis using OpenPose in uncontrolled environments: anatomical, biomechanical, and physical anomalies and errors due to estimation. We have also developed a workflow for identifying and correcting these anomalies and confirmed that this workflow is reproducible through simulation experiments. Our results will help obtain a comprehensive understanding of the anomalies to be addressed during pre-processing for 2D video-based gait analysis of large populations.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist’s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Large language models (LLMs) such as ChatGPT have garnered considerable interest for their potential to aid non-native English-speaking researchers. These models can function as personal, round-the-clock English tutors, akin to how Prometheus in Greek mythology bestowed fire upon humans for their advancement. LLMs can be particularly helpful for non-native researchers in writing the Introduction and Discussion sections of manuscripts, where they often encounter challenges. However, using LLMs to generate text for research manuscripts entails concerns such as hallucination, plagiarism, and privacy issues; to mitigate these risks, authors should verify the accuracy of generated content, employ text similarity detectors, and avoid inputting sensitive information into their prompts. Consequently, it may be more prudent to utilize LLMs for editing and refining text rather than generating large portions of text. Journal policies concerning the use of LLMs vary, but transparency in disclosing artificial intelligence tool usage is emphasized. This paper aims to summarize how LLMs can lower the barrier to academic writing in English, enabling researchers to concentrate on domain-specific research, provided they are used responsibly and cautiously.

Inflammatory cytokines released by synovium after trauma disturb the gene regulatory network and have been implicated in the pathophysiology of osteoarthritis. A mechanistic understanding of how aging perturbs this process can help identify novel interventions. Here, we introduced network paradigms to simulate cytokine‐mediated pathological communication between the synovium and cartilage. Cartilage‐specific network analysis of injured young and aged murine knees revealed aberrant matrix remodeling as a transcriptomic response unique to aged knees displaying accelerated cartilage degradation. Next, network‐based cytokine inference with pharmacological manipulation uncovered IL6 family member, Oncostatin M (OSM), as a driver of the aberrant matrix remodeling. By implementing a phenotypic drug discovery approach, we identified that the activation of OSM recapitulated an “inflammatory” phenotype of knee osteoarthritis and highlighted high‐value targets for drug development and repurposing. These findings offer translational opportunities targeting the inflammation‐driven osteoarthritis phenotype. The network medicine paradigms introduced here simulated cytokine‐mediated pathological communication between the synovium and cartilage and uncovered IL6 family member, OSM, as a driver of aberrant matrix remodeling. Intriguingly, while OSM is an IL6 family cytokine, pharmacological manipulations suggested that the age‐related aberrant ECM remodeling is attributed, at least partly, to downstream signals of OSM, but not IL6. By implementing a phenotypic drug discovery approach, we identified that the activation of OSM recapitulated an “inflammatory” phenotype of KOA and highlighted high‐value targets for drug development and repurposing. ECM, extracellular matrix; KOA, knee osteoarthritis; OSM, Oncostatin M; OSMR, Oncostatin M receptor.

The treatment of human immunodeficiency virus (HIV-1) has evolved since the establishment of combination antiretroviral therapy (ART) in the 1990s, providing HIV-infected individuals with approaches that suppress viral replication, prevent acquired immunodeficiency syndrome (AIDS) throughout their lifetime with continuous therapy, and halt HIV transmission. However, despite the success of these regimens, the global HIV epidemic persists, prompting a comprehensive exploration of potential strategies for an HIV cure. Here, we offer a consolidated overview of cell-based therapies for HIV-1, focusing on CAR-T cell approaches, gene editing, and immune modulation. Persistent challenges, including CAR-T cell susceptibility to HIV infection, stability, and viral reservoir control, underscore the need for continued research. This review synthesizes current knowledge, highlighting the potential of cellular therapies to address persistent challenges in the pursuit of an HIV cure.

IntroductionThis was a prospective, first-in-human trial to evaluate the feasibility and safety of insertion of biopsy introducer needles with our robot during CT fluoroscopy–guided biopsy in humans.Materials and methodsEligible patients were adults with a lesion ≥ 10 mm in an extremity or the trunk requiring pathological diagnosis with CT fluoroscopy–guided biopsy. Patients in whom at-risk structures were located within 10 mm of the scheduled needle tract were excluded. Ten patients (4 females and 6 males; mean [range] age, 72 [52–87] years) with lesions (mean [range] maximum diameter, 28 [14–52] mm) in the kidney (n = 4), lung (n = 3), mediastinum (n = 1), adrenal gland (n = 1), and muscle (n = 1) were enrolled. The biopsy procedure involved robotic insertion of a biopsy introducer needle followed by manual acquisition of specimens using a biopsy needle. The patients were followed up for 14 days. Feasibility was defined as the distance of ≤ 10 mm between needle tip after insertion and the nearest lesion edge on the CT fluoroscopic images. The safety of robotic insertion was evaluated on the basis of machine-related troubles and adverse events according to the Clavien-Dindo classification.ResultsRobotic insertion of the introducer needle was feasible in all patients, enabling pathological diagnosis. There was no machine-related trouble. A total of 11 adverse events occurred in 8 patients, including 10 grade I events and 1 grade IIIa event.ConclusionInsertion of biopsy introducer needles with our robot was feasible at several locations in the human body.Key Points• Insertion of biopsy introducer needles with our robot during CT fluoroscopy–guided biopsy was feasible at several locations in the human body.

Background Although interactions between amyloid-beta and tau proteins have been implicated in Alzheimer's disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein–protein interaction data, with an aim to uncover factors that moderate the effects of the Aβ-tau interaction on mild cognitive impairment (MCI) and early-stage AD. Methods Proteomic data from the ROSMAP cohort were integrated with protein–protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini–Hochberg correction (p < 0.05). Results Our results suggested that astrocytes and GPNMB + microglia moderate the Aβ-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis. Conclusions These findings suggest that GPNMB + microglia moderate the Aβ-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap ).

There has been accumulating evidence for the clinical benefit of chemoradiation therapy (CRT), whereas mechanisms in CRT-recurrent clones derived from the primary tumor are still elusive. Herein, we identified an aberrant BUB1B/BUBR1 expression in CRT-recurrent clones in bladder cancer (BC) by comprehensive proteomic analysis. CRT-recurrent BC cells exhibited a cell-cycle-independent upregulation of BUB1B/BUBR1 expression rendering an enhanced DNA repair activity in response to DNA double-strand breaks (DSBs). With DNA repair analyses employing the CRISPR/cas9 system, we revealed that cells with aberrant BUB1B/BUBR1 expression dominantly exploit mutagenic nonhomologous end joining (NHEJ). We further found that phosphorylated ATM interacts with BUB1B/BUBR1 after ionizing radiation (IR) treatment, and the resistance to DSBs by increased BUB1B/BUBR1 depends on the functional ATM. In vivo, tumor growth of CRT-resistant T24R cells was abrogated by ATM inhibition using AZD0156. A dataset analysis identified FOXM1 as a putative BUB1B/BUBR1-targeting transcription factor causing its increased expression. These data collectively suggest a redundant role of BUB1B/BUBR1 underlying mutagenic NHEJ in an ATM-dependent manner, aside from the canonical activity of BUB1B/BUBR1 on the G2/M checkpoint, and offer novel clues to overcome CRT resistance.