Significant Heterogeneity Research Articles

Prevalence of neurological symptoms in patients with SARS-CoV-2: Systematic Review and Meta-Analysis

Introduction: The novel coronavirus, dubbed SARS-CoV-2, which causes the COVID-19 disease, was detected on December 31, 2019, in Wuhan, China. Objective: To perform a systematic review with a meta-analysis about the prevalence of headache and myalgia in patients with SARS-CoV-2. Methods: High-sensitivity research was conducted on the main platforms (Medline by PubMed, Embase (Elsevier), and Cochrane Library). The entire versions of the manuscripts were analyzed by three independent researchers, and only observational studies describing the symptoms of the selected sample were included. Results: 1,782 studies were identified, which were submitted to exclusion criteria, remaining only 62 manuscripts to do qualitative synthesis and metanalysis. Of the 28,412 patients diagnosed with SARS-CoV-2, 12% (95% CI 9% - 16%) presented headache and 19% (95% CI 14% - 24%) presented myalgia, both with statistical significance and high heterogeneity (I2=97.0%, p<0.01 and I2=98.0%, p<0.01, respectively). Conclusion: It was noticed that headache and myalgia were the most ordinary neurological symptoms, but it is important to emphasize the prevalence of symptoms in different degrees of severity of the patients.

Postoperative bleeding outcome of fresh frozen plasma prime in pediatric cardiac surgery: A systematic review & meta-analysis.

Bleeding after cardiac surgeries holds risk of mortality and morbidity in pediatrics. This systematic review aimed to evaluate postoperative blood loss and blood transfusion requirements for pediatric patients undergoing cardiac surgery with fresh frozen plasma (FFP) priming. In 2024, the search reviewed four databases on randomized trials (RCTs) examining the impact of FFP prime intervention on 24-h postoperative blood loss and transfusion requirements in pediatric cardiac surgeries. The journals were appraised using Grading of Recommendation Assessment, Development, and Evaluation checklists, and random effects models estimated the effect size with a 95% confidence interval. Significance and study heterogeneity were indicated by p-values and I2. Of the screened 2070 articles, one high-quality and four moderate-quality RCTs involving 354 children were identified. No significant reduction in 24-h mean postoperative blood loss was found following FFP priming (mean difference MD: -0.78, 95% CI [-3.3 to 1.75], p = .55) in general pediatric cardiac surgeries. However, subgroup analysis showed significant decrease in blood loss for younger children (<7 months) or those with lower body weight (<6kg). There was no significant difference between groups in FFP (MD: -0.19, 95% CI [-0.42 to 0.05], p = .13) or red blood cell transfusion (MD: -0.25, 95% CI [-0.51 to 0.02], p = .07). Administering FFP as prime fluid in cardiac surgery did not reduce postoperative bleeding in general pediatric patients, but younger children (<7 months) and those with lower weight (<6kg) were the subjects who benefited from the FFP priming before surgery.

A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis

Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.

Clinical outcomes of different revascularization approaches for patients with multi-vessel coronary artery disease: A network meta-analysis.

As surgical techniques continue to evolve, the optimal approach for revascularizing multi-vessel coronary artery disease (CAD) remains a matter of ongoing debate. Accordingly, our objective was to compare and contrast various surgical techniques utilized in the management of multi-vessel CAD. A systematic literature review was performed using PubMed, Embase, and Cochrane central register of controlled trials from inception to May 2022. Random-effects network meta-analysis was performed for the primary outcome; target vessel revascularization (TVR), and secondary outcomes; mortality, major adverse cardiac and cerebrovascular events, postoperative myocardial infarction, new-onset atrial fibrillation, stroke, new-onset dialysis, in patients undergoing percutaneous coronary intervention (PCI) with a stent, off-pump coronary bypass graft, on-pump coronary artery bypass graft (ONCABG), hybrid coronary revascularization, minimally-invasive coronary artery bypass, or robot-assisted coronary artery bypass (RCAB) surgeries. A total of 8841 patients were included from 23 studies. The analysis showed that ONCABG had the highest freedom from TVR, with a mean (SD) absolute risk of 0.027 (0.029); although ONCABG was found to be superior to all other methods, it was only significantly better than first-generation stent PCI. While RCAB did not demonstrate significant superiority over other treatments, it showed a greater probability of preventing postoperative complications. Notably, no significant heterogeneity was calculated for any of the reported outcomes. ONCABG shows a better rank probability compared to all other techniques for preventing TVR, while RCAB offers greater freedom from most postoperative complications. However, given the absence of randomized controlled trials, these results should be interpreted with caution.

Heterogeneity, Jumps and Co-Movements in Transmission of Volatility Spillovers Among Cryptocurrencies

Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.

Smoking and Its Impact on Orthodontic Treatment/Management Modalities: A Systematic Review and Meta-Analysis.

We aimed to review studies that evaluated the effect of cigarette smoking on orthodontic treatment methods and determine whether the smoke affected appliances in a way that could impair the effectiveness of the overall treatment strategy. PubMed, Google Scholar, Cochrane Library, and Web of Science were scoured using pertinent keywords, reference searches, and citation searches in accordance with the PRISMA protocol regarding articles published from 2008 till 2022. Ultimately, 7 papers were chosen for further analysis at the end of the selection protocol. Overall pooled odds ratio (OR) for the impact of cigarette smoking on orthodontic treatment was 0.25 (95% CI: 0.15, 0.43), with high statistical significance (P<0.00001) but also high heterogeneity (I2 = 81%). The relative risk (RR) was 0.50 (95% CI: 0.38, 0.66), indicating a 50% greater risk of noticeable impact, with high statistical significance (P<0.00001) and high heterogeneity (I2 = 79%), and the risk difference (RD), which was -0.33 (95% CI: -0.45, -0.21), suggesting a 33% higher risk of noticeable impact, with high statistical significance (P<0.00001) and high heterogeneity (I2 = 81%). The high heterogeneity in all measures indicates significant variability in the results across the included studies. All the 7 studies selected for our systematic review exhibited significant detrimental associations between smoking and orthodontic appliances and other modalities that were exposed to cigarette smoke. However, more studies need to be done in this regard, since the literature currently available on this relationship is quite poor and lacking in concrete evidence.

Cerebral small vessel disease modifies outcomes after minimally invasive surgery for intracerebral haemorrhage

BackgroundMinimally invasive surgery (MIS) for spontaneous supratentorial intracerebral haemorrhage (ICH) is controversial but may be beneficial if end-of-treatment (EOT) haematoma volume is reduced to ≤15 mL. We explored whether MRI...

The present and future of micro, small, and medium enterprises from a holistic perspective

The study of micro, small, and medium enterprises (msmes) reveals a significant het-erogeneity in approaches, reflected in multiple perspectives such as social, administra-tive, economic, among others. This diversity has driven the application of a wide range of techniques and methodologies, from qualitative research to quantitative studies. Research on msmes in a post-pandemic scenario is multidisciplinary and adaptable to the complexities of the sector, enriching our current and future understanding of these enterprises. Furthermore, the global perspective of business internationalization stands out in a context of value chain integration and nearshoring, while local issues such as family business management and support for social enterprises are also crucial for their development. This duality highlights the need for ongoing dialogue among different academic studies on msmes to effectively address the challenges and oppor-tunities in this constantly evolving sector.

A Multinational Cohort Study Examining Sex Differences in Excess Risk of Death With Graft Function After Kidney Transplant.

Kidney transplant recipients show sex differences in excess overall mortality risk that vary by donor sex and recipient age. However, whether the excess risk of death with graft function (DWGF) differs by recipient sex is unknown. In this study, we combined data from 3 of the largest transplant registries worldwide (Scientific Registry of Transplant Recipient, Australia and New Zealand Dialysis and Transplant Registry, and Collaborative Transplant Study) using individual patient data meta-analysis to compare the excess risk of DWGF between male and female recipients of a first deceased donor kidney transplant (1988-2019), conditional on donor sex and recipient age. Among 463 895 individuals examined, when the donor was male, female recipients aged 0 to 12 y experienced a higher excess risk of DWGF than male recipients (relative excess risk 1.68; 95% confidence interval, 1.24-2.29); there were no significant differences in other age intervals or at any age when the donor was female. There was no statistically significant between-cohort heterogeneity. Given the lack of sex differences in the excess risk of DWGF (other than in prepubertal recipients of a male donor kidney) and the known greater excess overall mortality risk for female recipients compared with male recipients in the setting of a male donor, future study is required to characterize potential sex-specific causes of death after graft loss.

Research on the impact of digital economic development level on ecological environment

PurposeExploring the intrinsic connection between the ecological environment and the digital economy and empirically testing how the level of digital economic development affects the ecological environment. Using the entropy weighting method to analyze the weights of the indicators in the digital economic development level and ecological environment system to explore the factors that have the greatest impact on the ecological environment in the indicator system of the digital economic development level so as to deepen the theoretical understanding of the relationship between the level of development of the digital economy and the ecological environment. Explore the regional heterogeneity of the level of development of the digital economy to promote the healthy development of China’s ecological environment proving the difference in the level of development of the digital economy in the east west and central regions of China and the difference in the effect on the ecological environment.Design/methodology/approachBased on the panel data of 30 provinces in China from 2013 to 2021 this paper fits the index system of digital economy development level with three factors. A digital infrastructure digital industry and digital application combines environmental pollution and energy consumption to construct ecological environment indicators and explored the impact of digital economy development level on the ecological environment by using the entropy weight method and the random effect model.FindingsThe findings indicate that the degree of digital economic development has a positive and significant impact on promoting the healthy development of the ecological environment, in which the digital industry has the greatest impact on the ecological environment. Meanwhile, the improvement of industrial structure also has a positive effect on the improvement of the ecological environment, whereas the level of human capital inhibits the healthy development of the ecological environment, and the governmental support fails to effectively and significantly promote the improvement of the ecological environment. Furthermore, the empirical research indicates that the level of digital economy development has obvious regional heterogeneity on the healthy development of the ecological environment: the eastern and central regions have a significant effect, while the western region has a less significant effect.Originality/valueAlthough domestic and foreign scholars and experts have conducted sufficient studies on the ecological environment and the development level of digital economy respectively, there are few studies on the empirical analysis of the positive significance and regional heterogeneity of the impact of the development level of digital economy on the ecological environment, which can be supplemented and referred to in this study. At the same time, it also provides intellectual support for our country to achieve high-quality development of digital economy and efficient governance of ecological environment.

Statin Therapy and Flow-Mediated Dilation: A Systematic Review and Dose-Response Meta-Analysis Using the GRADE of Data from Randomized Controlled Trials.

A previous meta-analysis reported the positive effects of statin therapy on endothelial function. However, the obtained result had several limitations that necessitated updating the information in this field. Therefore, a systematic and meta-analysis review was conducted to determine whether statin therapy could improve endothelial function, as assessed by flow-- mediated dilation (FMD). MEDLINE, SciVerse Scopus, and Clarivate Analytics Web of Science were searched to identify randomized placebo-controlled trials assessing the impact of statin therapy on FMD. A random-effects model was used for meta-analysis to calculate the mean difference in weight. Meta- regression and subgroup analyses were used to identify sources of heterogeneity. In addition, nonlinear dose-response, quality of evidence, influence analysis, and publication bias evaluation were assessed using standard methods. Thirty-five trials (41 arms) involving 2178 participants were included in the meta-analysis study. Statin treatment significantly improved FMD [weighted mean difference (WMD): 1.7%, 95% CI: 1.3-2.2, p < 0.001). However, significant heterogeneity was observed (I2=97.9%, p < 0.001). The results of the subgroup analysis showed that health status can contribute to heterogeneity. Non-linear dose-response analysis revealed the most significant improvement in FMD with atorvastatin at a dose of 20 mg/day and simvastatin at 80 mg/day. Statin therapy significantly improved endothelial function, as assessed by FMD. These changes are clinically significant, but their use should be approached with caution.

Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review.

As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths.

A systematic review and meta-analysis of synthetic cathinone use and psychosis.

Synthetic cathinones (SC), commonly referred to as "bath salts", are stimulants resembling the natural alkaloid cathinone found in the khat plant. These substances have the potential to induce serious health risks such as hallucinations, delusions, paranoia and agitation which can lead to substance-induced psychotic disorders. Despite growing concerns, there is a limited understanding of the association between SC consumption and the devolvement of such psychopathologies. We conducted a systematic review to investigate the frequency of substance-induced psychotic disorder (SIPD) and associated conditions in humans following synthetic cathinone consumption. We qualitatively and quantitatively analyzed SC exposure cases. A total of 32 studies were included, with a diverse range of demographics, synthetic cathinone types, and consumption patterns. The proportion of individuals developing psychotic symptoms was reported at 0.380 (Random-effects model, 95% CI 0.289 - 0.475). Additionally, the significant heterogeneity in diagnostic approaches limited our ability to provide a precise estimate of prevalence. Synthetic cathinone consumption is associated with the risk of developing psychotic symptoms as indicated by the prevalence of hallucinations and/or delusions. Due to the lack of information on classifying factors, particularly duration of symptoms, we are unable to conclude synthetic cathinone-induced psychosis. Further research is warranted to elucidate the underlying mechanism linking synthetic cathinone consumption and psychosis. This review underscores the urgency of addressing the growing health risks posed by synthetic cathinone use. Additionally, it highlights the necessity of proper quantification of psychotic symptoms through scales and reporting of classification criteria to accurately diagnose SIPD.

Comparative Efficacy, Tolerability, and Acceptability of Donanemab, Lecanemab, Aducanumab, Melatonin, and Aerobic Exercise for a Short Time on Cognitive Function in Mild Cognitive Impairment and Mild Alzheimer's Disease: A Systematic Review and Network Meta-Analysis.

The Food and Drug Administration (FDA) has approved lecanemab and aducanumab and is reviewing donanemab, but they have questionable efficacy, serious side effects and are costly, whereas melatonin administration and aerobic exercise for a short time may overcome these problems. We aim to compare the efficacy on cognitive function, tolerability and acceptability of melatonin administration and aerobic exercise for a short time with donanemab, lecanemab, and aducanumab in people with mild AD and MCI. We systematically reviewed relevant randomized placebo-controlled trials (RCTs) in PubMed, the Cochrane Library, CINHAL, and ClinicalTrials.gov and performed network meta-analyses. The analysis included 10 randomized placebo-controlled trials with 4,599 patients. Although melatonin and aerobic exercise for a short time were significantly more effective than donanemab, lecanemab, aducanumab and placebo in the primary analysis, there was significant heterogeneity. In the sensitivity analysis excluding exercise, melatonin was significantly more effective than donanemab, lecanemab, aducanumab and placebo, with no significant heterogeneity. Aerobic exercise for a short time was significantly less acceptable than donanemab, aducanumab and placebo. Donanemab, lecanemab, and aducanumab were significantly less tolerable than placebo and donanemab and lecanemab were significantly less acceptable than placebo. Melatonin may be a better potential disease-modifying treatment for cognitive decline in mild AD and MCI. Aerobic exercise for a short time might also be better than donanemab, lecanemab and aducanumab if continued, as it is well tolerated and more effective, although less valid due to heterogeneity. Another limitation is the small number of participants.

Genetic Insights into the Association and Causality Between Blood Metabolites and Alzheimer's Disease.

Alzheimer's disease (AD) is an increasing public health concern with the aging of the global population. Understanding the genetic correlation and potential causal relationships between blood metabolites and AD may provide important insights into the metabolic dysregulation underlying this neurodegenerative disorder. The aim of this study was to investigate the causal relationship between blood metabolites and AD using Mendelian randomization (MR) analysis. Association data were obtained from three large-scale genome-wide association studies of 486 blood metabolites (N = 7,824), AD (71,880 cases and 383,378 controls), early-onset AD (N = 303,760), and late-onset AD (N = 307,112). Causal associations between blood metabolites and AD were assessed using inverse variance weighting (IVW), MR-Egger, and weighted median methods. Bidirectional two-sample MR analysis was used to identify causal blood metabolites. MR-PRESSO, MR-Egger, and Cochran-Q were used to quantify instrumental variable heterogeneity and horizontal pleiotropy. Using MR and sensitivity analysis, we identified 40 blood metabolites with potential causal associations with AD. After applying false discovery rate (FDR) correction, two metabolites, gamma-glutamylphenylalanine (OR = 1.15, 95% CI: 1.06-1.24, p = 3.88×10-4, q = 0.09) and X-11317 (OR = 1.16, 95% CI: 1.08-1.26, p = 1.14×10-4, q = 0.05), retained significant associations with AD. Reverse MR analysis indicated no significant causal effect of AD on blood metabolites. No significant instrumental variable heterogeneity or horizontal pleiotropy was found. This two-sample MR study provides compelling evidence for a potential causal relationship between blood metabolic dysregulation and susceptibility to AD. Further investigation of the biological relevance of the identified metabolites to AD and additional supporting evidence is warranted.

A systematic review of the prevalence of psychosis in people with tuberculosis

IntroductionTuberculosis is a bacterial infectious disease caused by Mycobacterium tuberculosis. This disease predominately affects the lungs but also affects other parts of the body, including the central nervous system. According to the the World Health Organization (2023), tuberculosis has an incidence of 6.4 million people in 2022, with 1.5 million deaths attributed to this disease. Psychosis describes a group of disorders that affects a person’s thought process and perception. It is a serious disorder that can have a profound impact on a person’s mental and physical health. As a result, psychosis symptoms and its treatment can complicate the management of tuberculosis.Objectives The aim of this systematic review is to explore the association between tuberculosis and psychosis. It has been shown that up to 70% of patients with tuberculosis also have comorbid mental illness, this is likely to include psychosis. There are also shared risk factors between tuberculosis and psychosis, including poverty and homelessness, substance abuse, HIV positive serology and isolation. Tuberculosis medication, including isoniazid and rifampicin have been shown to have adverse psychiatric effects and we will examine if this includes psychosis.MethodsA systematic review was pre-registered with PROSPRO and performed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, OVID and PsychINFO databases were searched from beginning of records to September 2023. This included hand-search of relevant reference lists. Observational and epidemiological studies were included along with population based registries.ResultsOver one thousand (1,154) articles were identified and screened. There was significant heterogenity in results and over half of studies were from Asia and Africa. Many studies reported cases of drug-induced psychosis from anti-tubercular agents. Studies also discussed the increased risk of TB incidence among patients with psychosis and other psychiatric disorders.ConclusionsThis study identifies the importance of training healthcare workers in rapid detection of co-morbid psychosis in patients with tuberculosis, along with neuropsychiatric side effects of antitubercular agents. Integration of psychiatric and medical care of these patients would be of benefit to improve outcomes in this patient population. More research is needed on co-morbidity of tuberculosis and psychosis.Disclosure of InterestNone Declared

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

The performance of artificial intelligence in prostate magnetic resonance imaging screening

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.

Abstract 2405: Harnessing liquid biopsy and genetic barcoding in evaluating diversity within triple-negative breast cancer

Abstract Introduction: While liquid biopsy shows promise as a non-invasive precision medicine tool, its ability to comprehensively represent the complexity of a tumor or only a subset of its clones remains uncertain. This issue is particularly crucial in the context of triple-negative breast cancer (TNBC), a type of cancer known for its significant heterogeneity. The goal of this study is to examine the clonal diversity within Circulating Biomaterials (CBMs): namely circulating tumor cells (CTCs), extracellular vesicles RNA (evRNA), and circulating tumor DNA (ctDNA), employing genetic barcoding and patient-derived TNBC xenografts. Methodology: Cells from TNBC patients' resected tumors were tagged with a unique barcode sequence through vector transduction. These barcoded cells were subsequently introduced into immunodeficient mice to form xenografts. When these xenografts grew to a certain size (15mm in diameter), the mice were humanely euthanized, and the tumor tissues as well as CBMs from their blood and plasma were collected. CTCs were isolated through negative enrichment and DEPArray technology; evRNA was extracted using ultracentrifugation and TRIzol™ Plus RNA Purification method; ctDNA was retrieved using the QIAamp DNA Micro Kit. Tumor tissues were processed for RNA and DNA using RNeasy micro kit and Qiamp DNA micro kit, respectively. RNA sequencing of the tumor tissues was carried out using the Lexogen - CORALL Total RNA-Seq Kit and Illumina NextSeq high output reagents. Results: Analysis of xenografts from four patients showed that ctDNA was the predominant genetic material detected through liquid biopsy. Crucially, clonal analysis using Next Generation Sequencing (NGS) demonstrated that ctDNA provides an accurate reflection of the primary tumor's clonal heterogeneity. The method efficiently identified both main tumor clones and subclones, even those constituting as little as 1% of the tumor, with a 50% detection probability. Transcriptomic analysis of the tumor tissues uncovered a distinct mesenchymal and pro-metastatic signature, more pronounced in tumors with higher ctDNA release. Clonal analysis of CTCs and evRNA is still ongoing. Conclusions: This investigation affirms the effectiveness of ctDNA analysis in liquid biopsy for identifying subclonal diversity in TNBC. Our results suggest that liquid biopsy via ctDNA is capable of managing TNBC's extensive intra-tumoral heterogeneity, including the identification of smaller subclones. Furthermore, a link was identified between the release of ctDNA and a mesenchymal, prometastatic gene expression pattern in the tumors. Citation Format: Davide Ceresa, Barbara Cardinali, Antonio Daga, Roberta Tasso, Andrea Sciutto, Irene Appolloni, Daniela Marubbi, Franca Carli, Piero Fregatti, Paolo Malatesta, Lucia Del Mastro. Harnessing liquid biopsy and genetic barcoding in evaluating diversity within triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2405.

Abstract 7567: In-depth mass cytometry analysis reveals different immune cell composition of unique molecular subtypes in extensive stage small-cell lung cancer

Abstract Background: A novel molecular subtype classification was fashionable for small cell lung cancer (SCLC). However, the distribution of SCLC molecular subtype classification in Chinese patients were unknown, especially extensive stage (ES)-SCLC. It should validate and define their associated microenvironments across subtypes in ES-SCLC. Method: We used immunohistochemical (IHC) to evaluate SCLC markers of the subtypes including ASCL1, NEUROD1, POU2F3, SCLC-A/N/P in 135 ES-SCLC tumors. Peripheral blood of 24 ES-SCLC patients was test by mass cytometry using time-of-flight (CyTOF) to evaluate the differences in immune subsets regard to the subtype tumor microenvironment (TME). Validation was performed using flow cytometry and multiplex IF. Results: ASCL1, NEUROD1, POU2F3, and Inflamed were account for 60.7%, 28.9%, 7.4%, and 18.5% of SCLC tumors, respectively. Significant intratumoral heterogeneity was observed with 15.6% of the tumors being both positive for SCLC-A and -N subtype markers, respectively. The SCLC-I type possessed more CD8+ T-cells infiltration and had higher frequency an ‘inflamed’ immunophenotyped. CyTOF data showed greater CD161+CD127+CD8+T cells proportion in SCLC-I than SCLC-N subtypes before and after anti-immunotherapy. Furthermore, we validated that lung tissue of SCLC-I were infiltrated more CD161+CD127+CD8+T cells compared to SCLC-N subpopulation(P=0.0281). Flow cytometry further showed the stronger immune activation of CD161+CD127+CD8+T cells in SCLC-I types induced higher expression of GZMB and GNLY and lower-expression of T-cell exhaustion markers PD-1,TIGIT and LAG3 in comparison with SCLC-N types. We further confirmed that the high level of CD161+CD127+CD8+T associated with a favorable treatment response and prolonged overall survival. Conclusion: The present study validated the molecular subtypes in ES-SCLC patients. Furthermore, SCLC-I subtype has longer PFS and OS than SCLC-A, -N and -P patients. Our research not only provides an insight into the immune landscape of ES-SCLC but also sheds light on a special subset of CD161+CD127+CD8+T cells with prognostic and therapeutic significance. Citation Format: Yuekang Li, Jingjing Qu, Binggen Wu, Qian Shen, Wenjia Sun, Lijun Chen, Bo Wang, Lixiong Ying, Jianya Zhou, Jianying Zhou. In-depth mass cytometry analysis reveals different immune cell composition of unique molecular subtypes in extensive stage small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7567.