Individual Modalities Research Articles

Microbiome as a modulator of immunotherapy response in pancreatic cancer

Pancreatic adenocarcinoma is widely regarded as one of the most lethal malignancies due to its rapid progression and the limited success of early detection methods and therapeutic interventions. While immunotherapy has emerged as an effective treatment option for various solid tumors, it has not demonstrated comparable efficacy in pancreatic cancer. Further research is required to evaluate the safety and efficacy of different immunotherapy modalities, including immune checkpoint inhibitors, T-cell transfer therapy, chimeric antigen receptor T-cell therapy, neoantigen vaccines, and epigenome-targeting treatments, specifically in the context of pancreatic cancer. Emerging evidence highlights the crucial role of the microbiome in modulating cancer cells’ responses to immunotherapy. Studies have increasingly implicated the gut microbiota composition as a direct influencer of tumorigenesis in pancreatic cancer. Certain microbial species have been shown to exert immunostimulatory or immunosuppressive effects on pancreatic cancer cells, thereby directly enhancing or suppressing their response to immunotherapeutic regimens. Despite these findings, there remains a paucity of comprehensive reviews on microbiome studies specific to individual immunotherapy modalities in pancreatic cancer. This review highlights the exciting potential of the microbiome in modulating pancreatic cancer responses across various immunotherapy subtypes and emphasizes the clinical need for further research in the field.

Efficacy of high-intensity interval training in individuals with type 2 diabetes mellitus: An umbrella review of systematic reviews and meta-analyses.

High-intensity interval training (HIIT) has gained attention as a potentially effective alternative to traditional exercise modalities for individuals with type 2 diabetes mellitus (T2DM). Previous studies have evaluated this exercise strategy with various regimens, comparator groups and outcomes, limiting the generalisability of findings. We performed a novel umbrella review to generate an up-to-date synthesis of the available evidence regarding the effect of HIIT on glycaemic control and other clinically relevant cardiometabolic health outcomes in individuals with T2DM, as compared with traditional moderate-intensity continuous training (MICT) and/or non-exercise control (CON). This umbrella review followed the Preferred Reporting Items for Overviews of Reviews guideline. Seven databases were searched until August 2024. Systematic reviews with meta-analyses comparing HIIT with MICT and/or CON were included. Literature search, data extraction and methodological quality assessment (A MeaSurement Tool to Assess systematic Reviews 2 [AMSTAR-2]) were conducted independently by two reviewers. Ten systematic reviews with meta-analyses, encompassing 76 primary studies and 2954 unique participants, met the inclusion criteria. The data indicated that HIIT significantly improves glycosylated haemoglobin and cardiorespiratory fitness compared with CON (weighted mean difference [WMD]: -0.83% to -0.39% and 3.35-6.38 mL/kg/min) and MICT (WMD: -0.37% to -0.07% and 1.68-4.12 mL/kg/min) in individuals with T2DM. HIIT is also effective in improving other glycaemic parameters, including fasting blood glucose, fasting blood insulin and HOMA-IR. Improvement in body composition, lipid profiles and blood pressure has also been observed following HIIT. Most systematic reviews received moderate to low AMSTAR-2 score. This umbrella review supports HIIT as an efficacious exercise strategy for improving glycaemic control and certain relevant cardiometabolic health outcomes in individuals with T2DM. Our findings offer a comprehensive basis that may potentially contribute to informing physical activity recommendations for incorporating HIIT into T2DM management strategies.

Healthcare Industry and Environmental Sustainability: Radiology's Next Biggest Opportunity for Meaningful Change.

Climate change has widespread impacts on patient health, affecting most body organs. At the same time, healthcare systems are a large contributor to global greenhouse emissions and other environmental harms, yet very few such organizations have taken concrete steps to encourage sustainable practices. Radiology should drive sustainable change because we are one of the most energy intensive and one of the fastest growing specialties within healthcare. While most current efforts focus on decreasing carbon emissions and other impacts of individual modalities, radiologists ought to broaden their perspectives. Incentives and education for trainees and clinicians to reduce unnecessary imaging is paramount to decrease radiology's environmental impact. A three-pronged approach guides change: increasing sustainability of essential studies, leveraging education to decrease low-value imaging, and expanding equitable access to preventative (high-value) imaging services. If radiology takes the lead, other specialties may follow.

Adherence to the multi-target stool DNA test in the US Hispanic population from 2016-2024.

100 Background: With colorectal cancer (CRC) as the second most common cause of cancer-related mortality in the US, adherence to CRC screening is a vital component to alleviating the cancer burden. Statistics show that only 52% of Hispanic or Latino individuals ≥45 years were up-to-date with CRC screening in 2021. A guideline-recommended, at-home CRC screening option, such as the multi-target stool DNA test (mt-sDNA), could be an efficient modality for screening average-risk individuals. Using a large national claims database, we examined CRC screening adherence with mt-sDNA in the US Hispanic population. Methods: Data was sourced from a database of over 165 million individuals linked with Exact Sciences Laboratories data. Hispanic persons who received mt-sDNA orders and resided in the US were retrospectively identified from 2016 to 2024. The analyses included persons who were ≥45 years of age, new to mt-sDNA and average risk for CRC during the 12-month pre-index period. Primary outcome of interest was adherence, which was defined as the mt-sDNA kit return rate within 365-days from shipment. All patients received a mailed letter along with digital outreach through the accompanying patient navigation program, according to their communication preference. The modes of digital outreach included: SMS, email, or both. Baseline demographics collected included age, sex, ordering provider, residential geography, payor, and outreach preference. Adjusted analyses were used to examine effects of covariates on adherence. Results: A total of 447,968 mt-sDNA orders were shipped during the study period and the overall adherence rate was 62.5%. Average time to test return was 28.5 days. Women comprised the majority of the cohort (59.9%), resided mostly in the Southern US (46.9%), aged 50-64 (60.9%), resided in a metropolitan area (91.2%), and received their mt-sDNA order from their primary care provider (62.7%). Across evaluated age groups, adherence was above 60% for all: ages 45-49 [60.5%]; 50-64 [62.6%], 65-75 [62.6%]), and ≥ 76 years [66.8]. By payor, Medicare-insured patients demonstrated the highest adherence (65.5%). Digital SMS was the preferred contact method (51.2%), and those opting for SMS + Email had the highest adherence rate (64.2%). Of provider specialties, individuals having their tests ordered by GIs produced the shortest time to adherence (23.2 days). Regression analyses revealed that older age (≥ 76 years), living in a rural area, and receiving fully or partially digital outreach were associated with increased mt-sDNA adherence. Conclusions: This large, national analysis reveals high mt-sDNA adherence amongst Hispanic individuals in the US, despite reports of low national CRC screening rates in this population. These data support the important role of mt-sDNA in facilitating screening engagement, while emphasizing the importance of preference-based navigation in facilitating screening test completion.

The aging human larynx, presbyphonia, and voice quality: The evolutionary and social effects on listeners.

The human voice is a major signal transmission modality for our species. Accordingly, perception of its quality serves as an intraspecific window into the status of an individual's health, robusticity, and even stature in the group. Diminished quality-as may occur in aging or certain diseases-is a major feature in the lessening of an individual's communication modality. Here, we review the underlying anatomy and function that comprise the voice producing elements-the vocal tract sensu lato-from the lungs through the larynx and supralaryngeal modifying elements. Aspects of the aging larynx and constituent elements such as the vocal folds ("true vocal cords") and intrinsic laryngeal joints, are discussed within the context of how their aging or disease degradation may affect an individual's speech and concomitant perception and interpretation by listeners. The aging voice is considered within the context of the overall effects of aging sensory systems in both humans and other mammals.

Comparisons among radiologist, MR findings and radiomics-clinical models in predicting placenta accreta spectrum disorders: a multicenter study.

To assess and compare the diagnostic accuracy of radiologist, MR findings, and radiomics-clinical models in the diagnosis of placental implantation disorders. Retrospective collection of MR images from patients suspected of having placenta accreta spectrum (PAS) was conducted across three institutions: Institution I (n = 505), Institution II (n = 67), and Institution III (n = 58). Data from Institution I were utilized to form a training set, while data from Institutions II and III served as an external test set. Radiologist diagnosis was performed by radiologists of varying levels of experience. The interpretation of MR findings was conducted by two radiologists with 10-15years of experience in pelvic MR diagnosis, following the guidelines for diagnosis. Radiomics analysis extracted features from sagittal T2-weighted images and combined them with prenatal clinical features to construct predictive models. These models were then evaluated for discrimination and calibration to assess their performance. As measured by the area under the receiver operating characteristic curve (AUC), the diagnostic efficacy was 0.587 (0.542-0.630) for junior radiologists from Institution I, 0.568 (0.441-0.689) from Institution II, and 0.507 (0.373-0.641) from Institution III. The AUC was 0.623 (0.580-0.666) for senior radiologists from Institution I, 0.635 (0.508-0.749) from Institution II, and 0.632 (0.495-0.755) from Institution III. The diagnostic efficacy of MR findings was 0.648 (0.601-0.695) for Institution I, 0.569 (0.429-0.709) for Institution II, and 0.588 (0.442-0.735) for Institution III. The diagnostic efficacy of the radiomics-clinical model was significantly higher, with an AUC of 0.794 (0.754-0.833) for Institution I, 0.783 (0.664-0.903) for Institution II, and 0.816 (0.704-0.927) for Institution III. The diagnostic efficacy of the Fusion model was significantly higher, with an AUC of 0.867 (0.836-0.899) for Institution I, 0.849 (0.753-0.944) for Institution II, and 0.823 (0.708-0.939) for Institution III. The fusion models demonstrated superior diagnostic efficacy compared to radiologists, MR findings, and the radiomics-clinical models. Furthermore, the diagnostic accuracy of PAS was notably higher when utilizing the radiomics-clinical models than when relying solely on radiologist diagnosis or MR findings. Radiomics analysis substantially augments the diagnostic precision in PAS, providing a significant enhancement over conventional radiologist and MRI findings. The diagnostic efficacy of the fusion model is notably superior to that of individual diagnostic modalities.

Spatio-Temporal Attention for Text-Video Retrieval

Text-video retrieval, a fundamental task for associating textual descriptions with video content, has become increasingly important in the video domain. Most existing methods focus on the single-modality features only considering the knowledge within individual video or text modalities, often neglecting cross-modal interactions. However, a text description corresponds to a specific spatio-temporal content within a video, involving a certain segment of a frame sequence and distinct sub-regions within these frames. Therefore, we focus on the text-conditioned video features to bridge the modality gap. In this paper, we propose Spatio-Temporal Attention for video-text retrieval, termed STAttn, which utilizes textual information to focus on the spatio-temporal video content. Our final text-conditioned video features are generated from the text-related video frames and the text-related regions within these frames. First, we propose the Spatial Text-Attention Module (STAM) to learn the spatial information within video frames. STAM introduces the text-related salient patches to capture more fine-grained details. Second, we propose the Temporal Text-Attention Module (TTAM) to learn the temporal relationships between video frames. Temporal Triplet loss is proposed in TTAM to enhance the attention towards the text-related frames. Thus, the two modules learn the text-related spatio-temporal content from both intra-frame and inter-frame aspects. Extensive experiments on three benchmark datasets, MSRVTT, ActivityNet, and DiDeMo, demonstrate that our STAttn outperforms state-of-the-art methods.

The Integration of AI and Metaverse in Education: A Systematic Literature Review

The use of the metaverse in educational environments has grown significantly in recent years, particularly following the shift of major tech companies towards virtual worlds and immersive technologies. Virtual reality and augmented reality technologies are employed to construct immersive learning environments. The metaverse is generally understood as a vast digital ecosystem or virtual space, facilitating the transition of individuals from physical to virtual environments, and is applicable to educational domains where practical experiments are challenging or fraught with risks, such as space exploration, chemical experimentation, and flight simulation training. In addition, the integration of artificial intelligence with the metaverse within educational contexts has significantly enriched the learning environment, giving rise to AI-driven teaching systems tailored to each student’s individual pace and learning modalities. As a result, a number of research articles have been conducted to explore the applications of the metaverse and artificial intelligence in education. This paper provides a systematic literature review following the PRISMA methodology to analyze and investigate the significance and impact of the metaverse in education, with a specific focus on the integration of AI with the metaverse. We address inquiries regarding the applications, challenges, academic disciplines, and effects of integrating AI and the metaverse in education that have not yet been explored in most research articles. Additionally, we study the AI techniques used in the metaverse in education and their roles. The review affirms that the integration of the metaverse in education, with the utilization of AI applications, will enrich education by improving students’ understanding and comprehension across diverse academic disciplines.

Feasibility and resource utilization of nurse-administered subcutaneous immunoglobulin therapy in antibody deficiency: A cross-sectional study.

Primary and secondary antibody deficiencies (PAD and SAD) are amongst the most prevalent immunodeficiency syndromes, often necessitating long-term immune globulin replacement therapy (IRT). Both intravenous immunoglobulin (IVIG) and subcutaneous immunoglobulin (SCIG) have demonstrated efficacy in antibody deficiency. Comparative analyses of these two routes of administration are limited to nurse-administered IVIG and home therapy with self-administered SCIG. A third programmatic approach, SCIG administered by nurses in hospital-based outpatient infusion clinics, combines certain advantages of IVIG and home SCIG such as the provision of nursing support and the reduced risk of systemic reactions associated with the subcutaneous route. This cross-sectional study aimed to compare the viability and resource utilization of nurse-administered SCIG with IVIG in patients with antibody deficiency. We hypothesized that nurse-administered SCIG would require a similar amount of infusion clinic time per month as IVIG, despite more frequent dosing, due to shorter individual appointments, while maintaining high patient satisfaction. Information on infusion duration, time in the infusion chair, direct nursing time, and treatment satisfaction using the Life Quality Index was collected. Time measures for each patient were expressed as minutes/4 weeks to account for the more frequent dosing of nurse-administered SCIG compared to IVIG. We determined that the total time, infusion time, and nursing time needed to provide nurse-administered SCIG was comparable to IVIG. The more frequent dosing of SCIG was offset by the shorter times required per infusion. Patients reported favorable treatment satisfaction with both nurse-administered SCIG and IVIG. We conclude that nurse-administered SCIG may be a useful treatment modality for well-selected individuals.

Feature and Decision Levels Fusion for the Synergistic Analysis of Facial Expressions and EEG Signals in the Context of Discrete Emotion Recognition

The undertaking of brain-computer interface emotion recognition represents a challenging task that demands meticulous thinking in order for machines to discern human emotions and respond appropriately. This work aims to improve the efficacy of individual modalities by incorporating multimodality for emotion recognition, which employs two distinct modalities. Electroencephalogram (EEG) data and facial expressions are used as independent modalities for emotion identification, with each modality evaluated separately. To combine the modalities, decision- and feature-level fusion algorithms are used. While EEG-derived emotions are primarily classified as continuous domains of valence and arousal, facial emotions are primarily classified as discrete emotions, which presents a considerable hindrance to the fusion process. Ashford Bird[10]. present the dataset used for EEG experiments at UKCI-2019.the CFEE dataset serves as the foundation for face emotion recognition for the proposed work[9]. EEG signals are analyzed for statistical properties such as mean, standard deviation, skewness, and kurtosis, while the face emotion dataset is used to identify various action units. Given the large number of features, redundant feature removal approaches are used to determine feature efficacy. Using the product rule, decision-level fusion obtains an accuracy of 80%. Accuracy in feature-level fusion is 94.48% for KNN and 98.66% for SVM classification which are comparatively higher than individual average accuracy of facial expression 88.04% and 90.73% for EEG signal.

Predicting Mental Health Treatment Outcomes using Latent Growth Mixture Models and Machine Learning in a Real-World Clinical Setting

Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population, subsequently developing machine learning models to predict clinical outcomes based on baseline characteristics and symptom measures. The study analyzed approximately 15,000 clients aged 18-89 in a real-world clinical setting, treated for up to 180-days between 2015 and 2020. Three distinct groups were identified: Low-Low (stable low scores), High-Low (improving scores), and High-High (stable high scores), representing 56%, 18%, and 26% of the cohort, respectively. Significant differences were observed in baseline factors and outcome assessments across these groups. The machine learning model demonstrated balanced accuracies of 0.67-0.93, and ROC-AUC values between 0.87-0.97 in predicting group membership from baseline data. Key predictors included baseline PHQ-9 scores, sex, age, and PTSD diagnosis. Prospective application of the model accurately categorized new client data, aligning predictions with observed outcomes. The study highlights the potential of machine learning in clinical settings to predict client outcomes, offering valuable insights based on initial data, potentially enhancing treatment personalization and efficacy. Further research should explore the impact of individual treatment modalities to validate the model’s clinical utility.

Evaluation of online videos and websites on inspiratory muscle training for individuals with chronic lung disease.

Inspiratory muscle training (IMT) is an effective rehabilitation modality for individuals with chronic lung disease. IMT can improve dyspnea, exercise capacity, and health-related quality of life. Online resources are common sources of health information for individuals. This study is the first to: 1) evaluate the content, reliability, quality, and comprehensibility of IMT-related videos and websites for individuals with chronic lung disease, and 2) determine the characteristics of these online resources. The search term "(respiratory muscle training) OR (inspiratory muscle training)" was used to evaluate the first 200 consecutive YouTube videos and 200 Google websites on IMT for chronic lung disease management. Online resources were evaluated using validated scoring metrics: modified DISCERN tool, Global Quality Scale (GQS), and Patient Education Materials Assessment Tools (PEMAT) understandability and actionability. Content comprising key IMT components was also scored. Forty videos and fourteen websites were included, with majority uploaded by for-profit organizations. Content scores (out of 25) were low (videos 7.7±4.4; websites 11.4±5.3, p=0.01). Benefits of IMT were often highlighted, but safety considerations were infrequently mentioned. Resources scored poorly on modified DISCERN (videos 2/5 IQR[1.0-3.0]; websites 3.5/5 IQR[2.0-4.0], p=0.001), and GQS scores (videos 2/5 IQR[2.0-3.0]; websites 3/5 IQR[2.8-3.3], p=0.003). Online resources met the PEMAT threshold (>70%) for understandability, but not actionability. Online IMT resources have mainly focused on the benefits of IMT and majority were developed by for-profit organizations. There is a need for high-quality, evidence-based online resources, as IMT is an important rehabilitation modality for chronic lung disease management.

Quantitative MRI Assessment of Post-Surgical Spinal Cord Injury Through Radiomic Analysis.

This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization, which are needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. A total of 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98), and localized lesions (MCC: 0.85; ACC: 0.90). Combined T1 and T2 features outperformed individual modalities across tasks with gradient boosting models showing the highest efficacy. The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration.

Causal Inference for Modality Debiasing in Multimodal Emotion Recognition

Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose Causal Inference in Multimodal Emotion Recognition (CausalMER), which leverages counterfactual reasoning and causal graphs to capture relationships between modalities and reduce direct modality effects contributing to bias. This allows CausalMER to make unbiased predictions while being easily applied to existing MER methods in a model-agnostic manner, without requiring any architectural modifications. We evaluate CausalMER on the IEMOCAP and CMU-MOSEI datasets, widely used benchmarks in MER, and compare it with existing methods. On the IEMOCAP dataset with the MulT backbone, CausalMER achieves an average accuracy of 83.4%. On the CMU-MOSEI dataset, the average accuracies with MulT, PMR, and DMD backbones are 50.1%, 48.8%, and 48.8%, respectively. Experimental results demonstrate that CausalMER is robust in missing modality scenarios, as shown by its low standard deviation in performance drop gaps. Additionally, we evaluate modality contributions and show that CausalMER achieves balanced contributions from each modality, effectively mitigating direct biases from individual modalities.

Equity-Focused Interventions Improve Interpreter Use in the Pediatric Intensive Care Unit.

Federal guidelines and equitable care mandate that patients who use a language other than English receive interpretation in their preferred language. Substantial variability exists in interpreter use in intensive care settings. We aimed to increase the rate of interpretations in our pediatric intensive care unit (PICU) through a series of targeted interventions. A multidisciplinary team developed a key driver diagram to identify areas for focused intervention. Each plan-do-study-act cycle informed the next cycle of interventions, targeting increasing interpreter (video, phone, and in-person) use. Interventions included standardizing technology, standardizing placement of interpretation devices in patient rooms, provider education, and creating accountability systems of interpreter use by care providers. We reviewed data from PICU encounters between January 2018 and January 2022 and used summary statistics and statistical process control methods to measure the impact of our interventions. We analyzed 882 patient encounters over the 4-year study period. Demographic characteristics were similar in the preintervention and postintervention periods. The total interpretation rate increased to 2.7 interpretations per patient per day from a baseline rate of 1.4. Each individual interpretation modality demonstrated increases in use. Average time spent interpreting via phone increased from 8 to 10.5 minutes per patient per day, and average time spent interpreting via video went from 9.5 to 22 minutes per patient per day. Iterative quality improvement methodology effectively identified barriers to equitable care, guided development of focused interventions, and improved interpreter use among pediatric patients who were critically ill.

Lipid-based nanoparticles for drug delivery in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder that predominantly affects dopaminergic neurons in the substantia nigra and ventral tegmental area, resulting in symptoms such as tremors, muscle rigidity, bradykinesia, and potential cognitive and affective disturbances. The effective delivery of pharmacological agents to the central nervous system is hindered by various factors, including the restrictive properties of the blood‒brain barrier and blood‒spinal cord barrier, as well as the physicochemical characteristics of the drugs. Traditional drug delivery methods may not provide the therapeutic concentrations necessary for functional restoration in PD patients. However, lipid-based nanoparticles (NPs) offer new possibilities for enhancing the bioavailability of established treatment regimens and developing innovative therapies that can modify the course of the disease. This review provides a concise overview of recent advances in lipid-based NP strategies aimed at mitigating specific pathological mechanisms relevant to PD progression. This study also explores the potential applications of nanotechnological innovations in the development of advanced treatment modalities for individuals with PD.

How Does the Dose and Type of Exercise Impact Acute Cardiovascular Function in Healthy Individuals?

During aerobic exercise, the cardiovascular system is tasked with delivering oxygen to active skeletal muscle via muscle blood flow while regulating mean arterial blood pressure. The impact of aerobic exercise on acute cardiovascular function may be modulated by the dose and type of exercise. Acutely, dose is the product of exercise intensity and time, whereas exercise type may refer to common aerobic modalities like cycling, running, swimming, or rowing. Each modality is unique for its medium of completion as well as the implications on blood flow arising from the position of active muscle mass relative to heart level. The purpose of this review was to address how an acute exercise dose influences cardiovascular function between prominent aerobic exercise modalities in healthy individuals. Across all modalities, all doses may transiently reduce both left and right ventricular systolic and diastolic function as well as both macro- and microvascular function. However, accurately quantifying and comparing exercise dose across the literature is challenging due to methodologic differences in exercise prescription and the cardiovascular demands imposed by differing modalities of exercise. Furthermore, the potential confounding influence of cardiovascular drift alongside variations in age, the composition of cohorts with respect to biological sex, and timing of cardiovascular measures further complicates interpretation. Future work should focus on exercise intensity prescription according to modality-specific physiologic thresholds to provide comparable doses. This approach may serve to standardize the physiologic stimulus and allow for objective assessments to be compared with confidence.

Unveiling rhabdomyosarcoma: A rare cause of ischialgia

Abstract Rhabdomyosarcoma (RMS) represents a rare subset of mesodermal malignancies characterized by skeletal muscle differentiation, exhibiting a notably low incidence among adults and demonstrating inferior prognosis compared to pediatric counterparts. The 5- and 10-year overall survival rates were determined to be 30% and 18%, respectively, with a median age of onset at 46.5 years and median overall survival duration of 2.3 years. Current challenges in RMS research encompass optimizing local control, managing systemic disease, refining risk stratification methods, and elucidating disease progression patterns. While aggressive therapeutic interventions remain imperative, novel and individualized treatment modalities are imperative to enhance long-term outcomes. This research reported an elderly female patient presenting persistent lower back pain, persisting over several months, despite seeking medical consultation from multiple sources. Subsequent diagnostic investigations confirmed the diagnosis of rhabdomyosarcoma, denoting the relatively rare etiology of said initial symptoms. Hence, it is imperative to reconsider many differential diagnoses in the case of ischialgia.

Tuned Manganese-Impregnated Mesoporous Silica Nanoparticles as a pH-Responsive Dual Imaging Probe.

The limitations of individual imaging modalities have led to significant interest in hybrid imaging methods that combine the advantages of multiple techniques. The development of diverse dual imaging agents, which offer the exceptional sensitivity of single-photon emission computed tomography (SPECT) and the high spatial resolution of magnetic resonance imaging (MRI), has been addressing the demand for more advanced diagnostic pharmaceuticals. In this study, 99mTc-labeled manganese oxide-loaded mesoporous silica nanoparticles (MSNs), conjugated with folic acid as the targeting moiety and the chelating agent H2pentapa-en-NH2 (99mTc-MnOx-MSN-FA-pa), were developed for targeted SPECT-MRI dual imaging. The toxicity of the nanoparticles was confirmed through an MTT assay, showing >90% viability in HEK-293 and MDA-MB-231 cells at concentrations up to 200 μg/mL, indicating nonsignificant toxicity. Cellular uptake studies showed that folic acid functionalization effectively accentuated tumor-specific intracellular uptake of nanoparticles in MDA-MB-231 cells through folate receptor-mediated endocytosis. Additionally, the radiolabeling yield of 99mTc-MnOx-MSN-FA-pa was found to be 99.6 ± 0.8% (n = 3), and the pH-responsive release of paramagnetic manganese ions increased the r1 relaxivity of the nanoprobe to 11.37 mM-1 s-1. In vivo SPECT imaging demonstrated rapid tracer accumulation in MDA-MB-231 xenografts, with a tumor-to-muscle ratio of 6.01 ± 0.51 at 2 h, and minimal uptake in nontargeted organs. In vivo MRI studies indicated the strongest tumor contrast at 2 h postinjection. Given its desirable contrast enhancement in T1 MRI and SPECT imaging, along with low toxicity, MnOx-MSN-FA-pa shows potential as an effective multifunctional nanoprobe for precise tumor imaging.

Abstract A016: Targeting metabolic vulnerability of non-small cell lung cancer with annonacin and 2-deoxy-d-glucose in individual and combination modality

Abstract Non-small cell lung cancer (NSCLC) is a primary type of lung cancer and a leading cause of cancer-related mortality worldwide. The Warburg effect, characterized by increased glycolysis and reduced oxidative phosphorylation, is a hallmark of NSCLC and presents a therapeutic target. Annonacin, a natural compound found in fruits from the Annonaceae family, interferes with oxidative phosphorylation by inhibiting mitochondrial complex I. 2-deoxy-d-glucose (2DG), a glucose analog, disrupts glycolysis. This study examines the effects of Annonacin and 2DG on NSCLC A549 cells, both individually and in combination, with the hypothesis that inhibiting both metabolic pathways simultaneously will lead to more effective cancer treatment. In addition, we explored how these treatments might alter the tumor microenvironment, oxidative stress, and effects on normal epithelial bronchial cells. A549 NSCLC cells were exposed to different concentrations of Annonacin (0, 1, 2, 4, 6 µM) and 2DG (0,1.25, 2.5, 5, 10 mM), separately and together. Cell viability was measured using the MTT assay. Genotoxicity was assessed through the Comet Assay, and oxidative stress was evaluated by measuring superoxide dismutase and glutathione peroxidase activities. The long-term ability of the cells to proliferate was tested using colony-forming assays. The NL20 bronchial epithelial cell line was tested as a parallel control. Both Annonacin and 2DG showed a dose-dependent ability to kill A549 cells. However, when used together, these substances had a stronger effect, significantly lowering the number of surviving cells compared to when each was used alone. The response of this combination treatment on genotoxicity using Comet Assay is being pursued. Changes in oxidative stress responses were found, as shown by the altered superoxide dismutase and glutathione peroxidase enzyme activities. The ability of A549 cells to form colonies was greatly reduced following combination treatment, indicating a reduction in their long-term proliferative capacity. The combination of Annonacin and 2DG effectively inhibits the growth of NSCLC A549 cells by targeting their metabolic weaknesses, which may lead to increased DNA damage and oxidative stress. These results suggest simultaneous targeting glycolysis and oxidative phosphorylation could be a promising new approach for treating NSCLC. Future in vivo studies will evaluate this combination in live models and can offer insights into efficacy and possible translation to clinical application. Citation Format: Bhoj Raj Bhattarai, Cora Teets, Avinash Tope. Targeting metabolic vulnerability of non-small cell lung cancer with annonacin and 2-deoxy-d-glucose in individual and combination modality [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 A016.