DataSheet1_Gender expression and gender identity in virtual reality: avatars, role-adoption, and social interaction in VRChat.docx

Introduction: This study examines the complex relationship between gender, virtual reality, and social interaction.Methods: Utilizing unobtrusive observations and interviews within the VRChat platform, this research explored avatar choices, interactions, and full-body tracking (FBT) technology utilization as they related to users’ expressions and perceptions of gender in virtual reality (VR).Results: The findings revealed that cultural background plays a significant role in shaping gender expression and perception. Results demonstrated the fluidity of gender expression in virtual environments, highlighting how users can challenge and subvert traditional gender norms, and the potential of virtual reality as a tool for experiential learning, fostering cross-cultural understanding, and promoting inclusive and diverse gender expressions.Discussion: This study contributes to the emerging body of literature on virtual reality and gender, providing insights that can inform future research and technology development.</p

Presentation1_Gender expression and gender identity in virtual reality: avatars, role-adoption, and social interaction in VRChat.pdf

Introduction: This study examines the complex relationship between gender, virtual reality, and social interaction.Methods: Utilizing unobtrusive observations and interviews within the VRChat platform, this research explored avatar choices, interactions, and full-body tracking (FBT) technology utilization as they related to users’ expressions and perceptions of gender in virtual reality (VR).Results: The findings revealed that cultural background plays a significant role in shaping gender expression and perception. Results demonstrated the fluidity of gender expression in virtual environments, highlighting how users can challenge and subvert traditional gender norms, and the potential of virtual reality as a tool for experiential learning, fostering cross-cultural understanding, and promoting inclusive and diverse gender expressions.Discussion: This study contributes to the emerging body of literature on virtual reality and gender, providing insights that can inform future research and technology development.</p

Nonparametric Additive Models for Billion Observations

The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not well-equipped to handle massive data with billions of observations. To address this challenge, we develop a scalable element-wise subset selection method, referred to as Core-NAM, for fitting penalized regression spline based NAMs. Specifically, we first propose an approximation of the penalized least squares estimation, based on which we develop an efficient variant of generalized cross-validation (GCV) to select the smoothing parameter and approximate the Bayesian confidence intervals for statistical inference. Theoretically, we show that the proposed estimator approximately minimizes an upper bound of the estimation mean squared error. Moreover, we provide a non-asymptotic approximation guarantee for the proposed estimator and establish the asymptotic optimality of the proposed variant of GCV. Extensive simulations demonstrate the superior accuracy and efficiency of the Core-NAM method. We also apply the proposed method to a total column ozone dataset containing nearly one billion observations, and the results indicate a speed-up by almost a thousand times with comparable performance compared to the full data approach. Supplementary materials for this article are available online.</p

Data_Sheet_1_Development and validation of a risk prediction model for incident liver cancer.docx

ObjectiveWe aimed to develop and validate a risk prediction model for liver cancer based on routinely available risk factors using the data from UK Biobank prospective cohort study.MethodsThis analysis included 359,489 participants (2,894,807 person-years) without a previous diagnosis of cancer. We used the Fine-Gray regression model to predict the incident risk of liver cancer, accounting for the competing risk of all-cause death. Model discrimination and calibration were validated internally. Decision curve analysis was conducted to quantify the clinical utility of the model. Nomogram was built based on regression coefficients.ResultsGood discrimination performance of the model was observed in both development and validation datasets, with an area under the curve (95% confidence interval) for 5-year risk of 0.782 (0.748–0.816) and 0.771 (0.702–0.840) respectively. The calibration showed fine agreement between observed and predicted risks. The model yielded higher positive net benefits in the decision curve analysis than considering either all participants as being at high or low risk, which indicated good clinical utility.ConclusionA new risk prediction model for liver cancer composed of routinely available risk factors was developed. The model had good discrimination, calibration and clinical utility, which may help with the screening and management of liver cancer for general population in the public health field.</p

A Bayesian phase I–II clinical trial design to find the biological optimal dose on drug combination

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I–II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose–response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.</p

Table2_Agranulocytosis and secondary infection related to JAK inhibitors and IL-6 receptor blockers: a disproportionality analysis using the US Food and drug administration adverse event reporting system.DOCX

Background: Given that the fight against coronavirus disease 2019 (COVID-19) is not over, we aimed to explore the occurrence of agranulocytosis and infectious complications in patients with and without COVID-19 following immunoregulatory therapy based on real-world data.Methods: This was a retrospective disproportionality analysis based on the US Food and Drug Administration Adverse Event Reporting System (FAERS). All cases reported between the first quarter of 2004 and the fourth quarter of 2022 about Janus kinase inhibitors (baricitinib, tofacitinib, ruxolitinib) and interleukin-6 receptor blockers (tocilizumab, sarilumab) were collected. Disproportionality analyses were conducted by reporting odds ratio (ROR) and information component (IC).Results: A total of 211,363 cases were recognized from the FDA Adverse Event Reporting System database. Data analysis showed that tocilizumab (reporting odds ratio: 3.18, 95% CI: 3.18–3.29; information component: 1.37, 95% CI: 1.31–1.42), sarilumab (ROR: 1.64, 95% CI: 1.55–1.73; IC: 0.61, 95% CI: 0.43–0.79), baricitinib (ROR: 3.42, 95% CI: 3.19–3.67; IC: 1.43, 95% CI: 1.21–1.65), tofacitinib (ROR: 2.53, 95% CI: 2.49–2.57; IC: 1.11, 95% CI: 1.05–1.16), and ruxolitinib (ROR: 1.87, 95% CI: 1.83–1.91; IC: 0.77, 95% CI: 0.70–0.84) were all associated with secondary infection. The association in the combination group was higher than that in the monotherapy group (ROR: 4.69, 95% CI: 4.53–4.86; IC: 1.73, 95% CI: 1.62–1.84). As for agranulocytosis, tocilizumab (ROR: 1.61, 95% CI: 1.53–1.69; IC: 0.67, 95% CI: 0.50–0.84) and ruxolitinib (ROR: 2.32, 95% CI: 2.21–2.43; IC: 1.18, 95% CI: 1.02–1.33) showed the significant signals. The association was higher in the combination group than in the monotherapy group (ROR: 2.36, 95% CI: 2.15–2.58; IC: 1.20, 95% CI: 0.90–1.51). Secondary infection after treatment with tofacitinib (ROR: 1.37, 95% CI: 1.02–1.84), tocilizumab (ROR: 1.46, 95% CI: 1.01–2.09), and sarilumab (ROR: 2.46, 95% CI: 1.10–5.50) was reported more frequently in COVID-19 than in non–COVID-19 patients.Conclusion: Both Janus kinase inhibitors and interleukin-6 receptor blockers are significantly associated with secondary infection and agranulocytosis, and the combined treatment further increases the association. The correlation with secondary infection in patients treated with tofacitinib, tocilizumab, and sarilumab is higher in COVID-19 than in non–COVID-19 patients.</p

Metal-Free [2 + 2 + 2] Cycloaddition of Ynamides with Nitriles to Construct 2,4-Diaminopyridines

We present a metal-free [2 + 2 + 2] cycloaddition of ynamides with nitriles that enables highly efficient access to 2,4-diaminopyridines. This catalytic protocol is more environmentally friendly and allows for a concomitant construction of C–C and C–N bonds between ynamides and nitriles, exhibiting excellent chemoselectivity, regioselectivity, and wide functional groups tolerance

Table1_Agranulocytosis and secondary infection related to JAK inhibitors and IL-6 receptor blockers: a disproportionality analysis using the US Food and drug administration adverse event reporting system.XLSX

Background: Given that the fight against coronavirus disease 2019 (COVID-19) is not over, we aimed to explore the occurrence of agranulocytosis and infectious complications in patients with and without COVID-19 following immunoregulatory therapy based on real-world data.Methods: This was a retrospective disproportionality analysis based on the US Food and Drug Administration Adverse Event Reporting System (FAERS). All cases reported between the first quarter of 2004 and the fourth quarter of 2022 about Janus kinase inhibitors (baricitinib, tofacitinib, ruxolitinib) and interleukin-6 receptor blockers (tocilizumab, sarilumab) were collected. Disproportionality analyses were conducted by reporting odds ratio (ROR) and information component (IC).Results: A total of 211,363 cases were recognized from the FDA Adverse Event Reporting System database. Data analysis showed that tocilizumab (reporting odds ratio: 3.18, 95% CI: 3.18–3.29; information component: 1.37, 95% CI: 1.31–1.42), sarilumab (ROR: 1.64, 95% CI: 1.55–1.73; IC: 0.61, 95% CI: 0.43–0.79), baricitinib (ROR: 3.42, 95% CI: 3.19–3.67; IC: 1.43, 95% CI: 1.21–1.65), tofacitinib (ROR: 2.53, 95% CI: 2.49–2.57; IC: 1.11, 95% CI: 1.05–1.16), and ruxolitinib (ROR: 1.87, 95% CI: 1.83–1.91; IC: 0.77, 95% CI: 0.70–0.84) were all associated with secondary infection. The association in the combination group was higher than that in the monotherapy group (ROR: 4.69, 95% CI: 4.53–4.86; IC: 1.73, 95% CI: 1.62–1.84). As for agranulocytosis, tocilizumab (ROR: 1.61, 95% CI: 1.53–1.69; IC: 0.67, 95% CI: 0.50–0.84) and ruxolitinib (ROR: 2.32, 95% CI: 2.21–2.43; IC: 1.18, 95% CI: 1.02–1.33) showed the significant signals. The association was higher in the combination group than in the monotherapy group (ROR: 2.36, 95% CI: 2.15–2.58; IC: 1.20, 95% CI: 0.90–1.51). Secondary infection after treatment with tofacitinib (ROR: 1.37, 95% CI: 1.02–1.84), tocilizumab (ROR: 1.46, 95% CI: 1.01–2.09), and sarilumab (ROR: 2.46, 95% CI: 1.10–5.50) was reported more frequently in COVID-19 than in non–COVID-19 patients.Conclusion: Both Janus kinase inhibitors and interleukin-6 receptor blockers are significantly associated with secondary infection and agranulocytosis, and the combined treatment further increases the association. The correlation with secondary infection in patients treated with tofacitinib, tocilizumab, and sarilumab is higher in COVID-19 than in non–COVID-19 patients.</p

A real-world pharmacovigilance study of severe cutaneous adverse reactions associated with antiepileptic drug combination therapy: data mining of FDA adverse event reporting system

The aim of this study was to evaluate the association between antiepileptic drug combination regimens and severe cutaneous adverse reactions (SCAR). We gathered cases indication with epilepsy based on the US Food and Drug Administration Adverse Event Reporting System (FAERS) database from 2004 to 2021. Disproportionality analyses were conducted by estimating the reporting odds ratio (ROR) and the information component (IC). Out of 128,262 reports were collected from the FAERS database, 104,278 cases were in the antiepileptic drugs group, and 23,984 cases were in the other primary suspected drugs group. A total of 20 combination regimens were associated with increased association of SCAR, top five of them were topiramate-phenytoin (ROR 57.62, 95% CI 30.93–107.34), lamotrigine-valproic acid (ROR 52.93, 95% CI 47.09–59.49), diazepam-phenobarbital (ROR 39.61, 95% CI 20.01–78.38), zonisamide-valproic acid (ROR 36.57, 95% CI 19.16–69.80), lamotrigine-diazepam (ROR 35.22, 95% CI 15.70–79.00). The antiepileptic agent combinations may increase the incidence of SCAR and should be carefully evaluated in clinical practice. It is recommended to choose the combination regimens which have lower SCAR reporting rate for patients.</p