AI Article Synopsis

  • The text indicates that there is a correction to a previously published article.
  • The specific article being corrected can be identified by its DOI: 10.1039/D4SC03219E.
  • This correction is likely aimed at clarifying errors or providing updated information related to the original article.

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Introduction: Patients with bipolar disorder (BD) demonstrate episodic memory deficits, which may be hippocampal-dependent and may be attenuated in lithium responders. Induced pluripotent stem cell-derived CA3 pyramidal cell-like neurons show significant hyperexcitability in lithium-responsive BD patients, while lithium nonresponders show marked variance in hyperexcitability. We hypothesize that this variable excitability will impair episodic memory recall, as assessed by cued retrieval (pattern completion) within a computational model of the hippocampal CA3.

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Designing cost-effective electrocatalysts with fast reaction kinetics and high stability is an outstanding challenge in green hydrogen generation through overall water splitting (OWS). Layered double hydroxide (LDH) heterostructure materials are promising candidates to catalyze both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), the two OWS half-cell reactions. This work develops a facile hydrothermal route to synthesiz hierarchical heterostructure MoS@NiFeCo-LDH and MoS@NiFeCo-Mo(doped)-LDH electrocatalysts, which exhibit extremely good OER and HER performance as witnessed by their low IR-corrected overpotentials of 156 and 61 mV with at a current density of 10 mA cm under light assistance.

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Objective: This study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.

Methods: A cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS).

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[Statistical methods for extremely unbalanced data in genome-wide association study (2)].

Zhonghua Liu Xing Bing Xue Za Zhi

January 2025

Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing211166, China China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing211166, China.

Extremely unbalanced data refers to datasets with independent or dependent variables showing severe imbalances in proportions, which might lead to deviation of classical test statistics from theoretical distribution and difficulties in controlling type Ⅰ error. The increased availability of genome-wide resources from large population cohorts has highlighted the growing demand for efficient and accurate statistical methods for the process of extremely unbalanced data to improve the development of genetic statistical methods. This paper introduces two widely used correction methods in current genome-wide association study for extremely unbalanced data, i.

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Anti-neutrophil cytoplasmic antibody-associated vasculitides (AAV) represent a heterogeneous multisystem group of disorders typified by necrotising inflammation of smaller blood vessels, classically yielding a pauci-immune, crescentic glomerulonephritis. Without prompt treatment, there is a significant risk of irreversible damage and ensuing renal impairment.Diagnosis is often challenging, exacerbated by the disorder's often vague and insidious presentation.

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