Door to needle time trends after transition to tenecteplase: A Multicenter Texas stroke registry.

BACKGROUND: Tenecteplase (TNK) is considered a promising option for the treatment of acute ischemic stroke (AIS) with the potential to decrease door-to-needle times (DTN). This study investigates DTN metrics and trends after transition to tenecteplase. METHODS: The Lone Star Stroke (LSS) Research Consortium TNK registry incorporated data from three Texas hospitals that transitioned to TNK. Subject data mapped to Get-With-the-Guidelines stroke variables from October 1, 2019 to March 31, 2023 were limited to patients who received either alteplase (ALT) or TNK within the 90 min DTN times. The dataset was stratified into ALT and TNK cohorts with univariate tables for each measured variable and further analyzed using descriptive statistics. Logistic regression models were constructed for both ALT and TNK to investigate trends in DTN times. RESULTS: In the overall cohort, the TNK cohort (n = 151) and ALT cohort (n = 161) exhibited comparable population demographics, differing only in a higher prevalence of White individuals in the TNK cohort. Both cohorts demonstrated similar clinical parameters, including mean NIHSS, blood glucose levels, and systolic blood pressure at admission. In the univariate analysis, no difference was observed in median DTN time within the 90 min time window compared to the ALT cohort [40 min (30-53) vs 45 min (35-55); P = .057]. In multivariable models, DTN times by thrombolytic did not significantly differ when adjusting for NIHSS, age (P = .133), or race and ethnicity (P = .092). Regression models for the overall cohort indicate no significant DTN temporal trends for TNK (P = .84) after transition; nonetheless, when stratified by hospital, a single subgroup demonstrated a significant DTN upward trend (P = 0.002). CONCLUSION: In the overall cohort, TNK and ALT exhibited comparable temporal trends and at least stable DTN times. This indicates that the shift to TNK did not have an adverse impact on the DTN stroke metrics. This seamless transition is likely attributed to the similarity of inclusion and exclusion criteria, as well as the administration processes for both medications. When stratified by hospital, the three subgroups demonstrated variable DTN time trends which highlight the potential for either fatigue or unpreparedness when switching to TNK. Because our study included a multi-ethnic cohort from multiple large Texas cities, the stable DTN times after transition to TNK is likely applicable to other healthcare systems.

Optimal modularity and memory capacity of neural reservoirs.

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that a neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information-spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization.

Collective Dynamics of Belief Evolution under Cognitive Coherence and Social Conformity.

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework may offer explanations for how social transitions can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We argue that the inclusion of cognitive factors into a social model could provide a more complete picture of collective human dynamics.