Search Results (7)

This study introduces the Audit Risk Sentiment Value (ARSV), a novel audit risk proxy that leverages sentiment analysis to address limitations in traditional audit risk measures such as audit fees (LNFEE), audit hours (LNHOUR), and discretionary accruals (|MJDA|). Traditional proxies primarily capture quantitative dimensions, overlooking qualitative insights embedded in audit report narratives. By systematically analyzing sentiment and tone, ARSV captures nuanced audit risk dimensions that reflect the auditor’s risk perception. The study validates ARSV using a dataset of South Korean firms listed on the KOSPI from 2018 to 2023. The results demonstrate the ARSV’s superior explanatory power, as confirmed through the Vuong test, showing consistent performance across binary and continuous measures of explanatory language. ARSV bridges the gap between qualitative and quantitative audit risk assessments, offering significant benefits to auditors, regulators, and investors. Its ability to enhance the interpretability of audit reports improves transparency and trust in financial reporting, addressing stakeholder demands for actionable, forward-looking information. Furthermore, ARSV aligns with global trends emphasizing sustainability and accountability by integrating qualitative insights into audit practices. While this study provides robust evidence supporting ARSV effectiveness, its focus on South Korean firms may limit its generalizability. Future research should explore ARSV application in diverse regulatory and cultural contexts and refine the sentiment analysis tools using advanced machine learning techniques. Expanding ARSV to include other unstructured data, such as management commentary, could further enhance its applicability. This study marks a significant step toward modernizing audit methodologies, aligning them with evolving demands for comprehensive and transparent financial reporting. The empirical analysis reveals that ARSV outperforms traditional audit risk proxies with significantly higher explanatory power. Specifically, ARSV achieved a pseudo R² of 0.786, compared to 0.608 for LNFEE, 0.604 for LNHOUR, and 0.578 for |MJDA|. The Vuong test results further validate ARSV superiority, with Z-statistics of −12.168, −12.492, and −9.775 when compared against LNFEE, LNHOUR, and |MJDA|, respectively. The model incorporating ARSV demonstrated a 62.454 F-value and an Adjusted R² of 0.599, highlighting its robustness and reliability in audit risk assessment. These quantitative metrics underscore ARSV’s effectiveness in capturing qualitative audit risk dimensions, offering a more precise and informative measure for stakeholders. Full article

In this paper, we carry out a comprehensive comparison of Gaussian generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In particular, we investigate their performance using the physical measure (also known as the real-world probability measure) for risk management purposes and risk-neutral measures for derivative pricing purposes. Under the physical measure, after fitting the historical return sequence, we calculate the likelihoods and test the normality for the error terms of these two models. In addition, two robust loss functions, the MSE and QLIKE, are adopted for a comparison of the one-step-ahead volatility forecasts. The empirical results show that the ARSV(1) model outperforms the GARCH(1, 1) model in terms of the in-sample and out-of-sample performance under the physical measure. Under the risk-neutral measure, we explore the in-sample and out-of-sample average option pricing errors of the two models. The results indicate that these two models are considerably close when pricing call options, while the ARSV(1) model is significantly superior to the GARCH(1, 1) model regarding fitting and predicting put option prices. Another finding is that the implied versions of the two models, which parameterize the initial volatility, are not robust for out-of-sample option price predictions. Full article

Background/Objectives: Respiratory viruses, including Influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs. RSV, control vs. Influenza A, control vs. COVID-19, control vs. all respiratory viruses, and COVID-19 vs. Influenza A/RSV. Method: We proposed a stacking-based ensemble technique, integrating the top three best-performing ML models from the initial results to enhance prediction accuracy by leveraging the strengths of multiple base learners. Key techniques such as feature ranking, standard scaling, and SMOTE were used to address class imbalances, thus enhancing model robustness. SHAP analysis identified crucial metabolites influencing positive predictions, thereby providing valuable insights into diagnostic markers. Results: Our approach not only outperformed existing methods but also revealed top dominant features for predicting COVID-19, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, Tyrosine, and Aspartic Acid (Asp). Conclusions: This study demonstrates the effectiveness of leveraging nasopharyngeal metabolome data and stacking-based ensemble techniques for predicting respiratory virus scenarios. The proposed approach enhances prediction accuracy, provides insights into key diagnostic markers, and offers a robust framework for managing respiratory infections. Full article

Multiple studies have demonstrated the importance of androgen receptor (AR) splice variants (SVs) in the progression of prostate cancer to the castration-resistant phenotype and their utility as a diagnostic. However, studies on AR expression in non-prostatic malignancies uncovered that AR-SVs are expressed in glioblastoma, breast, salivary, bladder, kidney, and liver cancers, where they have diverse roles in tumorigenesis. AR-SVs also have roles in non-cancer pathologies. In granulosa cells from women with polycystic ovarian syndrome, unique AR-SVs lead to an increase in androgen production. In patients with nonobstructive azoospermia, testicular Sertoli cells exhibit differential expression of AR-SVs, which is associated with impaired spermatogenesis. Moreover, AR-SVs have been identified in normal cells, including blood mononuclear cells, neuronal lipid rafts, and the placenta. The detection and characterization of AR-SVs in mammalian and non-mammalian species argue that AR-SV expression is evolutionarily conserved and that AR-SV-dependent signaling is a fundamental regulatory feature in multiple cellular contexts. These discoveries argue that alternative splicing of the AR transcript is a commonly used mechanism that leads to an expansion in the repertoire of signaling molecules needed in certain tissues. Various malignancies appropriate this mechanism of alternative AR splicing to acquire a proliferative and survival advantage. Full article

Background: Epigenetic modification influences androgen receptor (AR) activation, often resulting in prostate cancer (PCa) development and progression. Silencing histone-modifying enzymes (histone deacetylases-HDACs) either genetically or pharmacologically suppresses PCa proliferation in preclinical models of PCa; however, results from clinical studies were not encouraging. Similarly, PCa patients eventually become resistant to androgen ablation therapy (ADT). Our goal is to develop dual-acting small molecules comprising antiandrogen and HDAC-inhibiting moieties that may overcome the resistance of ADT and effectively suppress the growth of castration-resistant prostate cancer (CRPC). Methods: Several rationally designed antiandrogen-equipped HDAC inhibitors (HDACi) were synthesized, and their efficacy on CRPC growth was examined both in vitro and in vivo. Results: While screening our newly developed small molecules, we observed that SBI-46 significantly inhibited the proliferation of AR⁺ CRPC cells but not AR^- CRPC and normal immortalized prostate epithelial cells (RWPE1) or normal kidney cells (HEK-293 and VERO). Molecular analysis confirmed that SBI-46 downregulated the expressions of both AR⁺ and AR-splice variants (AR-SVs) in CRPC cells. Further studies revealed the downregulation of AR downstream (PSA) events in CRPC cells. The oral administration of SBI-46 abrogated the growth of C4-2B and 22Rv1 CRPC xenograft tumors that express AR or both AR and AR-SV in xenotransplanted nude mice models. Further, immunohistochemical analysis confirmed that SBI-46 inhibits AR signaling in xenografted tumor tissues. Conclusion: These results demonstrate that SBI-46 is a potent agent that inhibits preclinical models of CRPC by downregulating the expressions of both AR and AR-SV. Furthermore, these results suggest that SBI-46 may be a potent compound for treating CRPC. Full article

Androgen receptor splice variants (AR-SVs) contribute to the aggressive growth of castration-resistant prostate cancer (CRPC). AR-SVs, including AR-V7, are expressed in ~30% of CRPC, but minimally in treatment-naïve primary prostate cancer (PCa). Compared to Caucasian American (CA) men, African American (AA) men are more likely to be diagnosed with aggressive/potentially lethal PCa and have shorter disease-free survival. Expression of a truncated AR in an aggressively growing patient-derived xenograft developed with a primary PCa specimen from an AA patient led us to hypothesize that the expression of AR-SVs could be an indicator of aggressive growth both in PCa progression and at the CRPC stage in AA men. Tissue microarrays (TMAs) were created from formalin-fixed paraffin-embedded (FFPE) prostatectomy tumor blocks from 118 AA and 115 CA treatment-naïve PCa patients. TMAs were stained with AR-V7-speicifc antibody and with antibodies binding to the N-terminus domain (NTD) and ligand-binding domain (LBD) of the AR. Since over 20 AR-SVs have been identified, and most AR-SVs do not as yet have a specific antibody, we considered a 2.0-fold or greater difference in the NTD vs. LBD staining as indication of potential AR-SV expression. Two AA, but no CA, patient tumors stained positively for AR-V7. AR staining with NTD and LBD antibodies was robust in most patients, with 21% of patients staining at least 2-fold more for NTD than LBD, indicating that AR-SVs other than AR-V7 are expressed in primary treatment-naïve PCa. About 24% of the patients were AR-negative, and race differences in AR expression were not statistically significant. These results indicate that AR-SVs are not restricted to CRPC, but also are expressed in primary PCa at higher rate than previously reported. Future investigation of the relative expression of NTD vs. LBD AR-SVs could guide the use of newly developed treatments targeting the NTD earlier in the treatment paradigm. Full article

Hepatocellular carcinoma (HCC) is the predominant type of liver cancer and a leading cause of cancer-related death globally. It is also a sexually dimorphic disease with a male predominance both in HCC and in its precursors, non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH). The role of the androgen receptor (AR) in HCC has been well documented; however, AR-targeted therapies have failed to demonstrate efficacy in HCC. Building upon understandings of AR in prostate cancer (PCa), this review examines the role of AR in HCC, non-androgen-mediated mechanisms of induced AR expression, the existence of AR splice variants (AR-SV) in HCC and concludes by surveying current AR-targeted therapeutic approaches in PCa that show potential for efficacy in HCC in light of AR-SV expression. Full article