Prevalence and Temporal Trends of Attention Deficit Hyperactivity Disorder Medication Fills During Pregnancy and Breastfeeding in Denmark.

Background And Objectives: Females of reproductive age are increasingly using attention deficit hyperactivity disorder (ADHD) medication, but its use during pregnancy and breastfeeding is largely unknown. The aim of this study is to examine the prevalence of ADHD medication fills during pregnancy and breastfeeding, including characteristics of these females and cohort differences over time.

Methods: We conducted a descriptive study using Danish nationwide registers.

Transforming an ATP-dependent enzyme into a dissipative, self-assembling system.

Nucleoside triphosphate (NTP)-dependent protein assemblies such as microtubules and actin filaments have inspired the development of diverse chemically fueled molecular machines and active materials but their functional sophistication has yet to be matched by design. Given this challenge, we asked whether it is possible to transform a natural adenosine 5'-triphosphate (ATP)-dependent enzyme into a dissipative self-assembling system, thereby altering the structural and functional mode in which chemical energy is used. Here we report that FtsH (filamentous temperature-sensitive protease H), a hexameric ATPase involved in membrane protein degradation, can be readily engineered to form one-dimensional helical nanotubes.

Ionic Liquid-Functionalized Defective MOFs for Membrane-Based CO Separation: A Dual Optimization Approach for Interface and Transport.

Defective MOFs have been identified as promising candidates for efficient membrane-based separation applications. However, the utilization of defective MOFs in membrane gas separation is still in its infancy due primarily to the inefficient molecular differentiation induced by structural defects. Herein, we report a strategic combination of ionic liquid (IL) and defective UiO-66-NH MOF to ameliorate the CO/N selectivity within the highly permeable PIM-1 polymer.

MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.

Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites.