Clinical Setting Research Articles

Membraneless organelles in health and disease: exploring the molecular basis, physiological roles and pathological implications

AbstractOnce considered unconventional cellular structures, membraneless organelles (MLOs), cellular substructures involved in biological processes or pathways under physiological conditions, have emerged as central players in cellular dynamics and function. MLOs can be formed through liquid-liquid phase separation (LLPS), resulting in the creation of condensates. From neurodegenerative disorders, cardiovascular diseases, aging, and metabolism to cancer, the influence of MLOs on human health and disease extends widely. This review discusses the underlying mechanisms of LLPS, the biophysical properties that drive MLO formation, and their implications for cellular function. We highlight recent advances in understanding how the physicochemical environment, molecular interactions, and post-translational modifications regulate LLPS and MLO dynamics. This review offers an overview of the discovery and current understanding of MLOs and biomolecular condensate in physiological conditions and diseases. This article aims to deliver the latest insights on MLOs and LLPS by analyzing current research, highlighting their critical role in cellular organization. The discussion also covers the role of membrane-associated condensates in cell signaling, including those involving T-cell receptors, stress granules linked to lysosomes, and biomolecular condensates within the Golgi apparatus. Additionally, the potential of targeting LLPS in clinical settings is explored, highlighting promising avenues for future research and therapeutic interventions.

Benefits and Risks of AI in Health Care: Narrative Review

Background The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non–peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher’s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

Real-world clinical outcomes and rationale for initiating abatacept as a first-line biologic for patients with anticitrullinated protein antibody- and rheumatoid factor-positive rheumatoid arthritis

Aim: In rheumatoid arthritis (RA), seropositivity for both anticitrullinated protein antibody (ACPA) and rheumatoid factor (RF) is associated with disease severity and therapeutic response. Biologic (b) disease-modifying antirheumatic drugs (DMARDs) such as abatacept are recommended after inadequate response or contraindication to conventional synthetic DMARDs. This retrospective cohort study aimed to describe changes in Clinical Disease Activity Index (CDAI) measures over 12 months among patients with ACPA+ and RF+ RA with an inadequate response to methotrexate treated with abatacept as a first-line bDMARD. Patients & methods: Patient data were abstracted from medical records by treating rheumatologists. Analyses included McNemar tests for paired proportions or paired t -tests to assess longitudinal changes in CDAI scores, and Kaplan–Meier methods for time-to-event outcomes. Serious AEs and rationale for initiating treatment were recorded. Results: Overall, 296 patients were included. Mean CDAI scores improved (decreased) by 34.0, 61.0 and 74.0% (all p < 0.001) from baseline to 3–6 months, 6–12 months and ≥12 months after abatacept initiation, respectively. Of 279 patients not in CDAI low disease activity (LDA) or remission at baseline, 24.7% of patients achieved it within 6 months, 56.3% within 12 months and 71.0% at any point during follow-up after abatacept initiation. Median time to CDAI LDA/remission was 10.2 months. Serious AEs were reported in 2.4% of patients. Common reasons reported by rheumatologists for initiating abatacept were effectiveness/efficacy (52.7%), safety (31.4%) and patient preference (25.3%). Conclusion: In this analysis of patients with ACPA+ and RF+ RA treated with abatacept as a first-line bDMARD in a clinical practice setting, clinical outcomes and remission rates were improved at all time points, providing real-world evidence to further support the use of abatacept in this patient population.

Blood Gene Expression Profiling and Donor-derived Cell-free DNA to Noninvasively Diagnose Clinical and Subclinical Kidney Transplant Rejection: A Real-life Appraisal Study

Background. Peripheral blood biomarkers aim to noninvasively diagnose kidney allograft rejection, but most lack robust independent validation. TruGraf is intended to exclude subclinical cellular rejection (TCMR), whereas donor-derived cell-free DNA Viracor-TRAC has proven value in excluding antibody-mediated rejection (AMR). We aim to validate both biomarkers for accurate rejection diagnosis in a real-world clinical setting. Methods. We prospectively included 230 unselected, consecutive kidney transplants from 6 centers undergoing for-cause and protocol biopsies with paired blood samples from December 2021 to 2022. TruGraf and Viracor-TRAC were blindly run by a central laboratory. Results. The incidence of rejection was 22.6% (17.3% surveillance; 27% for-cause biopsies). Inflammation was associated with higher TRAC levels, with AMR/mixed and microvascular inflammation (MVI) showing the highest levels (P < 0.05). TruGraf did not associate with any specific allograft injury. No biomarkers, individually or combined, accurately diagnosed any rejection (area under the receiver operating characteristic curve [AUROC] < 0.65). However, high TRAC levels, when combined with DSA in for-cause biopsies, predicted AMR/mixed rejection or MVI (AUROC = 0.817; P < 0.001), outperforming serum creatinine and DSA (AUROC < 0.65). Conclusions. In this large, prospective, observational real-life study, we were unable to validate TruGraf and TRAC to diagnose rejection but found a useful context of use for TRAC to noninvasively diagnose AMR/mixed or MVI in conjunction with DSA in dysfunctioning graft.

Incremental clinical value of intraplaque neovascularization in predicting recurrent ischemic stroke

AbstractObjectiveCarotid intraplaque neovascularization (IPN) detected by contrast‐enhanced ultrasound (CEUS) is a risk factor for recurrent ischemic stroke. However, it is still unclear whether IPN can be used to accurately identify patients with recurrent ischemic stroke in clinical practice. Herein, we investigated the clinical predictive value of IPN for recurrent ischemic stroke in a real‐world setting.MethodsWe enrolled 200 patients with ischemic stroke and atherosclerotic carotid stenosis who were followed up for 2 years. The endpoint was recurrent ischemic stroke. Cox regression and subgroup analyses were employed to assess whether treatment affected the relationship between IPN and recurrent ischemic stroke. The net classification index (NRI) and integrated discriminant improvement index (IDI) were used to validate the additional clinical value of IPN in identifying recurrent ischemic stroke.ResultsDuring the 2‐year follow‐up, 36 patients experienced recurrent ischemic stroke. Cox regression analyses showed that IPN (grade 2), hypoechoic plaque, high homocysteine levels, and smoking were independent risk factors for recurrent ischemic stroke. Additional IPN evaluation may increase the NRI (0.512; 95% confidence interval [CI]: 0.083–0.624) and IDI (0.151; 95% CI: 0.010–0.213) for identifying high‐risk patients with recurrent ischemic stroke. In addition, in the subgroup undergoing revascularization, the proportion of IPN (grade 2) was significantly higher in patients with recurrent ischemic stroke than in patients with nonrecurrent ischemic stroke (p = 0.001).InterpretationIn clinical settings, IPN, assessed by CEUS, may provide additional clinical value for predicting recurrent ischemic stroke, helping to identify patients with ischemic stroke who require close follow‐up.

Diagnostic Performance of Artificial Intelligence in Chest Radiographs Referred from the Emergency Department

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice.

Abstract A027: Acceleration of drug metabolism mediated by CYP3A4 enzyme activation provides a bona fide environmental resistance mechanism to targeted therapies

Abstract Background: Targeted therapies against oncogenic signaling addictions, including one resulting from EML4-ALK fusions, can induce strong and durable clinical responses. However, these therapies are not curative in advanced cancers, as a subset of tumor cells avoid elimination, and these persisting populations evolve resistance that drives tumor relapse. Using a drug screen, we found that the evolving ALK inhibitor (ALKi) persisters display exquisite sensitivity to the histone demethylase inhibitor, JIB04 in vitro. Paradoxically, validation studies in mice revealed that instead of enhancing tumor sensitivity, JIB04 desensitizes tumors to ALKi, alectinib and lorlatinib, suggesting that it activates an unknown environmental modifier. While JIB04 is not used in the clinics, we reasoned that deciphering the underlying mechanism could uncover environment-mediated resistance to ALKi that might be triggered by clinically relevant factors. Results: We found that the resistance-promoting effect of JIB04 in mice is mediated by reducing systemic ALKi concentrations. This reduction is attributable to accelerated pharmacokinetics, due to the hyperactivation of the P450 family member, CYP3A4. Higher CYP3A4 activity leads to faster drug clearance and reduced drug exposure to tumors. Importantly, JIB04 triggers a similar resistance to other clinically relevant CYP3A4 substrates, the frontline EGFR inhibitor, osimertinib and the recently introduced KRASG12C inhibitor, sotorasib. Finally, the resistance- promoting effects of JIB04 could be phenocopied by chemically unrelated activators of CYP3A4, indicating a wide generalizability of this macro-environmental therapy resistance mechanism. Conclusions: Our study uncovered that acceleration of drug metabolism, triggered by CYP3A4 enzyme activators is a bona fide environmental resistance mechanism. Since P450 family liver detoxifying enzymes mediate the metabolism of many drugs, this resistance mechanism is likely to be relevant to a broad range of therapeutic contexts. Significance: While the importance of pharmacokinetic considerations is widely appreciated, modulation of drug metabolism is not typically considered as a candidate mechanism when therapy resistance is encountered in research and clinical settings. Multiple dietary, pharmacological and genetic modifiers are known to modulate P450 family enzymes. Moreover, substantial variability in drug plasma concentrations was documented in patients treated with identical doses of targeted therapies, and this variability strongly correlated with variability in clinical responses. While the direct link between variability in CYP3A4 activity and clinical outcomes in human patients has yet to be established, the mechanism uncovered in our study might represent a substantial but entirely overlooked contributor to resistance. Consequently, consideration of this mechanism and monitoring of CYP3A4 activity and systemic drug levels might improve our understanding of the biology of therapy resistance and enable an actionable path to improve clinical outcomes. Citation Format: Pragya Kumar, Robert Vander Velde, Malgorzata Weh, Andriy Marusyk. Acceleration of drug metabolism mediated by CYP3A4 enzyme activation provides a bona fide environmental resistance mechanism to targeted therapies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A027.

Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?

ABSTRACTBackground and ObjectivesClinical nomograms have been developed to predict sentinel lymph node (SLN) status in early‐stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well‐validated Melanoma Institute Australia (MIA) nomogram.MethodsWe developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial‐I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.ResultsOur model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661–0.751). Our model achieved an AUC of 0.715 (0.706–0.724) compared to 0.723 (0.715–0.731) with the MIA model, using the NCDB set.ConclusionOur model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.

Abstract IA020: Deciphering the mechanisms of neutrophil immune response in bone metastatic prostate cancer

Abstract Metastatic disease of prostate cancer (PCa) is currently incurable. Bone is the most common tissue site for prostate cancer metastasis and progression in bone is largely dictated by tumor-stromal interactions in the bone microenvironment. We showed previously that bone marrow neutrophils inhibit bone metastatic PCa growth, however metastatic PCa becomes resistant to neutrophil response and progresses within the bone compartment. It is unclear how these findings translate to a clinical setting. Further, the mechanisms associated with tumor resistance to neutrophil immune response remain unknown and presents a new avenue for therapeutic intervention. Thus, the goals of this study were: 1) to define the impact of metastatic PCa on neutrophil function throughout prostate cancer progression and 2) to determine the potential of neutrophils as predictive biomarkers of PCa disease progression. To do this, peripheral blood polymorphonuclear neutrophils (PMNs) were collected from 4 patient cohorts (localized PCa, metastatic hormone-sensitive PCa (mHSPC), metastatic castration-resistant (mCRPC) and healthy men) and PMN molecular and functional properties assessed, including: cancer cell cytotoxicity and cell surface markers. There was a significant decrease with disease progression in PMN expression of CD10, a marker for differentiation, while in tandem, there was an increase in pro-inflammatory marker, CD88 (complement receptor) in mCRPC men compared to mHSPC. PMNs from all disease stages were equally cytotoxic to human PCa cell lines (LNCaP, C42B, and PC3). However, second generation androgen deprivation therapy (ADT) suppressed patient PMN cytotoxicity against mCRPC cells (C42B, PC3). This suppression was due to androgen receptor (AR)-mediated regulation of the pleotropic and immune suppressant cytokine, transforming growth factor beta receptor I (TβRI). PMNs from mCRPC patients who received 2nd generation ADT, expressed significantly more TβRI than patients who only received 1st line ADT. Further, we found that PMNs in CRPC patient bone metastases express more TβRI than PMNs in matched liver metastases, which express little to no TβRI. Treatment of mouse bone marrow neutrophils with enzalutamide, increased neutrophil TβRI expression in a dose dependent manner and blocked cytotoxicity against PCa cells. Exogenous testosterone treatment in vivo, pharmacologic inhibition (using Repsox, a small molecule kinase inhibitor of TβRI) or genetic deletion (using conditional TβRI knockout mice) rescued enzalutamide-mediated neutrophil suppression and restored neutrophil cytotoxicity. These data collectively suggest that AR inhibition suppresses anti-tumor neutrophil responses via TβRI however, this study highlights the ability to leverage standard-care ADT to generate neutrophil anti-tumor responses against bone metastatic PCa. Citation Format: Leah M Cook. Deciphering the mechanisms of neutrophil immune response in bone metastatic prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr IA020.

Expert consensus on the multifaceted utility of alginates in addressing diverse manifestations of acid reflux

Gastroesophageal reflux disease (GERD) is one of the most frequent reasons for seeking outpatient gastroenterology consultations. Current professional guidelines advocate the use of proton pump inhibitors (PPIs) as the primary medical approach to manage GERD. However, PPIs may not be as effective, especially for certain patients like those with non-erosive reflux disease (NERD). An alternative strategy for addressing symptomatic GERD involves obstructing the flow of acidic refluxate. Alginate-based pharmaceutical formulations have proven effective in alleviating symptoms of acid reflux for many years and provide rapid relief of symptoms with a long duration of action. Alginic acid derivatives, or alginates, combat acid reflux through a unique mechanism: they create a physical barrier that displaces the post-prandial acid pocket. Alginates have recently garnered renewed interest for promoting symptomatic relief especially when employed alongside antacids or PPIs. Here, the role of alginates is reviewed in the treatment of various profiles of acid reflux, including post-prandial acid reflux, nocturnal GERD, refractory GERD, and GERD during pregnancy, along with the opinion of expert gastroenterologists on the same. The experts believed that not all alginate formulations are equivalent and raft strength is the most important physicochemical property to be considered while selecting the alginate-based product. The physicochemical properties of the rafts eventually impact their effectiveness in relieving symptoms in clinical settings.

Identification of potential mechanisms of Schisandrin B in the treatment of idiopathic pulmonary fibrosis by integrating network pharmacology and experimental validation.

Idiopathic pulmonary fibrosis (IPF) is a worsening fibrotic condition characterized by a short survival rate and limited treatment options. This study evaluates the potential anti-fibrotic properties of Schisandrin B (Sch B) through network pharmacology and experimental validation. A mouse model of bleomycin-induced pulmonary fibrosis was established, and the modeled mice were treated with Sch B at three doses (20 mg/kg/day, 40 mg/kg/day, and 80 mg/kg/day). A fibrotic model was developed in NIH/3T3 cells by treating them with TGF-β (10 ng/mL) and administering Sch B at various concentrations (10, 20, and 40 µM). The results revealed that Sch B treatment delayed the development of bleomycin-induced pulmonary fibrosis and substantially decreased the transcription levels of collagen I and α-SMA in TGF-β-induced fibroblasts. Core targets were screened with protein-protein interaction network analysis, molecular complex detection (MCODE), and CytoHubba plugin. The application of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and molecular docking highlighted the significance of the HIF-1α signaling pathway in the potential mechanism of Sch B in IPF therapy. Western blot, PCR, and immunofluorescence were performed to validate the effects of Sch B on HIF-1α. In vivo and in vitro, Sch B administration reduced HIF-1α expression. These outcomes provide valuable insights into the potential mechanism by which Sch B delays IPF development, with HIF-1α potentially serving as a key target. However, further investigation is warranted to assess the safety and efficacy of Sch B in clinical settings.

Occupational stress trajectories and metabolic dysfunction-associated steatotic liver disease among female nurses: a prospective Cohort Study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prominent cause of chronic liver disease, and occupational stress may serve as a potential risk factor. This study aims to assess the association between occupational stress trajectories and incident MASLD among Chinese female nurses. We conducted a prospective longitudinal study using data from the Nurse' Health Cohort Study, involving 1,113 female nurses, free of MASLD at baseline (2018). Occupational stress was measured using the Chinese Nurse Job Stress Scale at four time points. Group-based trajectory modeling was used to identify distinct stress trajectories. Through doctors' diagnoses, we assessed incident MASLD over a subsequent 6-year period from 2019 to 2024. Cox proportional hazards regression models evaluated the association of stress trajectories and MASLD risk, adjusting for demographics, work-related factors, and medical conditions. During follow-up, 256 nurses reported incident physician-diagnosed MASLD. Three occupational stress trajectories were identified: moderate decreasing (36.4%), moderate stable (55.9%), and moderate increasing (7.7%). Participants in the moderate increasing stress trajectory had a significantly higher risk of developing MASLD (adjusted HR: 3.14, 95% CI: 2.19-4.49, p < .001) compared to those in the moderate stable trajectory. This association between stress trajectory and MASLD risk was not modified by age or BMI (pinteraction>0.50). The study concludes that increasing stress levels over time are associated with a higher incidence of MASLD. These findings underscore the importance of stress management interventions in reducing the risk of MASLD progression. Further research is needed to explore the underlying mechanisms and to develop targeted strategies for stress reduction in clinical settings.

Electronic patient-reported outcomes as digital therapeutics for patients with cancer: a narrative review of current practices and future directions.

Improved cancer treatment outcomes have increased the demand for medical care that considers the quality of life of patients with cancer. Patient-reported outcomes (PROs) help assess the quality of life because they involve direct evaluation of the patients. Recently, electronic PROs (ePROs) have been used in clinical cancer care settings in Europe and the United States. Electronic PROs positively affected communication between patients with cancer and healthcare providers, enhanced education, optimized self-management, contributed to healthcare economics, assisted in monitoring adverse events, and improved prognosis. However, challenges such as adherence, burden on healthcare providers, lack of personalized formats, low digital literacy, and implementation costs remain. Therefore, carefully selecting the items to be recorded by ePROs in alignment with specific objectives is essential. Additionally, developing systems using lifelogs-digital records of daily activities-and creating mechanisms that automatically encourage patient behavioral changes based on the reported data are crucial. This review delineates the advantages and challenges of ePROs according to their history and proposes the prospects of ePRO.

Machine learning approach for detection of MACE events within clinical trial data.

Randomized controlled trials (RCTs) are the gold standard for clinical research but may not accurately reflect the impact of medicines in real-world settings. Supplementing RCTs with insights from real-world data (RWD) can address known limitations by including more diverse patient populations, additional types of sites-of-care, and practices more representative of the care most people receive. One current challenge in using RWD is the lack of an algorithmic approach to identifying outcomes. To address this, machine learning models for identifying a frequently used outcome, Major Adverse Cardiovascular Events (MACE), were developed in Clinical Trial Data (CTD). Anonymized CTD sourced from the Medidata Enterprise Data Store were used to develop model features on the condition that they would be useful for labelling MACE events and that they could also be found in RWD. These features were used to train three random forest models to identify each component of 3-point MACE in a patient's clinical trial journey. Performance metrics for the models are presented (recall = 0.72 [0.07], precision = 0.68 [0.12] - mean, [SD]) along with the top contributing features. We show that the models can be tuned specifically to replicate the adjudication panels' results and present a cost-benefit analysis for deploying such models in clinical trial settings. We demonstrate the viability of using advanced algorithms for identifying clinical outcomes in prospective clinical trials. Deployment of such models could reduce the resources required to conduct RCTs. Extending such models to RWD would facilitate approval of pragmatic clinical trials for regulatory submissions.

Managing uterine artery pseudoaneurysm post-hysteroscopic surgery: Clinical insights and future directions

This editorial discusses the case report by Kakinuma et al , which details a rare occurrence of uterine artery pseudoaneurysm following hysteroscopic surgery. The case highlights diagnostic challenges and management strategies for this uncommon complication. The editorial explores the implications for clinical practice, emphasizing the importance of early recognition and appropriate intervention to prevent potential severe outcomes. Future research directions to increase the understanding and management of uterine artery pseudoaneurysm in similar clinical settings.

Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to evaluate the effectiveness of AI-based tools in diagnosing prostate cancer using MRI, with a focus on accuracy, specificity, sensitivity, and clinical utility compared to conventional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Embase, Ovid MEDLINE, Web of Science, Cochrane Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies published between 2019 and 2024. Inclusion criteria focused on full-text, English-language studies involving AI for Magnetic Resonance Imaging (MRI) -based prostate cancer diagnosis. Diagnostic performance metrics such as area under curve (AUC), sensitivity, and specificity were analyzed, with risk of bias assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: Seven studies met the inclusion criteria, employing various AI techniques, including deep learning and machine learning. These studies reported improved diagnostic accuracy (with AUC scores of up to 97%) and moderate sensitivity, with performance varying based on training data quality and lesion characteristics like Prostate Imaging Reporting and Data System (PI-RADS) scores. Conclusions: AI has significant potential to enhance prostate cancer diagnosis, particularly when used for second opinions in MRI interpretations. While these results are promising, further validation in diverse populations and clinical settings is necessary to fully integrate AI into standard practice.

HC-HA/PTX3 from Human Amniotic Membrane Induced Differential Gene Expressions in DRG Neurons: Insights into the Modulation of Pain

Background: The biologics derived from human amniotic membranes (AMs) demonstrate potential pain-inhibitory effects in clinical settings. However, the molecular basis underlying this therapeutic effect remains elusive. HC-HA/PTX3 is a unique water-soluble regenerative matrix that is purified from human AMs. We examined whether HC-HA/PTX3 can modulate the gene networks and transcriptional signatures in the dorsal root ganglia (DRG) neurons transmitting peripheral sensory inputs to the spinal cord. Methods: We conducted bulk RNA-sequencing (RNA-seq) of mouse DRG neurons after treating them with HC-HA/PTX3 (15 µg/mL) for 10 min and 24 h in culture. Differential gene expression analysis was performed using the limma package, and Gene Ontology (GO) and protein–protein interaction (PPI) analyses were conducted to identify the networks of pain-related genes. Western blotting and in vitro calcium imaging were used to examine the protein levels and signaling of pro-opiomelanocortin (POMC) in DRG neurons. Results: Compared to the vehicle-treated group, 24 h treatment with HC-HA/PTX3 induced 2047 differentially expressed genes (DEGs), which were centered on the ATPase activity, receptor–ligand activity, and extracellular matrix pathways. Importantly, PPI analysis revealed that over 50 of these DEGs are closely related to pain and analgesia. Notably, HC-HA/PTX3 increased the expression and signaling pathway of POMC, which may affect opioid analgesia. Conclusions: HC-HA/PTX3 induced profound changes in the gene expression in DRG neurons, centered around various neurochemical mechanisms associated with pain modulation. Our findings suggest that HC-HA/PTX3 may be an important biological active component in human AMs that partly underlies its pain inhibitory effect, presenting a new strategy for pain treatment.

Deep learning-based algorithm for automatic quantification of nigrosome-1 and Parkinsonism classification using susceptibility map-weighted MRI.

To develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism using susceptibility map-weighted imaging (SMwI). We retrospectively collected 450 participants (210 with idiopathic Parkinson's disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced Parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multi-echo GRE. Diagnostic performance for diagnosing IPD was assessed using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) model. Reference standard for IPD was based on 18F-FP-CIT PET finding. Additionally, the correlation between the H&Y stage and volume of nigral hyperintensity in patients with IPD was assessed. Quantification of nigral hyperintensity using Heuron NI showed AUC of 0.915 (95% CI, 0.872-0.947) and 0.928 (95% CI, 0.887-0.957) on the left and right, respectively. Classification of nigral hyperintensity abnormality using Heuron IPD showed AUC of 0.967 (95% CI, 0.881-0.991) and 0.976 (95% CI, 0.948-0.992) on the left and right, respectively. H&Y score ≥ 3 showed significant smaller nigral hyperintensity volume (1.43 ± 1.19 mm3) compared to H&Y score 1-2.5 (1.98 ± 1.63 mm3; p = 0.008). Our deep learning-based model proves rapid, accurate automatic quantification of nigral hyperintensity, facilitating IPD diagnosis, symptom severity prediction, and patient stratification for personalized therapy. Further study is warranted to validate the findings across various clinical settings. IPD = Idiopathic Parkinson's disease; SN = substantia nigra; SMwI = susceptibility map weighted imaging; QSM = quantitative susceptibility mapping; CNN = convolutional neural network; ICV = intracranial volume.

Effects on personal and cultural wellbeing of attending free community outreach concerts given by a professional orchestra

Despite hundreds of investigations into the benefits of arts engagement for health and wellbeing, much of what we know comes from research in clinical settings, and little is understood about the positive effects of attending live musical performances. As many professional orchestras have begun re-examining their purpose and mission statements, they also seek ways to measure their impact beyond ticket sales and donations in hopes of highlighting positive effects on concertgoers and their community. The purpose of this study was to explore the impacts of a free community outreach concert series provided by a professional orchestra on individual and community wellbeing in a large city in the United States. The researchers conducted semi-structured interviews, paired interviews, and a focus group with 18 participants between the ages of 18 and 77 years. Abductive analysis allowed us to consider emerging themes and pre-existing theories of wellbeing concurrently. The results showed three primary areas of impact on concert attendees’ wellbeing: emotional, social, and cultural. Concert attendance fostered positive emotional states and reduced negative emotions, led to personal feelings of connection with the orchestra and across the community, and contributed to personal- and local-cultural wellbeing. In addition to reinforcing much of the prior scholarship on the effects on wellbeing of arts participation, particularly in the emotional and social domains, the results highlight new possible connections between community outreach programs and increased cultural wellbeing.

Stem cell exosomes: New hope for recovery from diabetic brain hemorrhage

Recent advancements in stem cell–derived exosome therapy for diabetic brain hemorrhage are discussed in this editorial, which highlights this therapy’s potential for revolutionizing diabetic brain hemorrhage treatment. The paper offers compelling evidence that exosomes can effectively reduce neuroinflammation and promote recovery from diabetic brain hemorrhage. Although these findings are promising, further research is warranted to fully understand the underlying mechanisms and to validate the therapeutic potential of exosomes in clinical settings. The findings of this study indicate that continued exploration should be conducted into exosome-based therapies as a novel approach to managing diabetic brain hemorrhage.