Artificial intelligence approaches to enable early detection of CKD
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GluN2B-mediated regulation of silent synapses for receptor specification and addiction memory
Psychostimulants, including cocaine, elicit stereotyped, addictive behaviors. The reemergence of silent synapses containing only NMDA-type glutamate receptors is a critical mediator of addiction memory and seeking behaviors. Despite the predominant abundance of GluN2B-containing NMDA-type glutamate receptors in silent synapses, their operational mechanisms are not fully understood. Here, using conditional depletion/deletion of GluN2B in D1-expressing accumbal medium spiny neurons, we examined the synaptic and behavioral actions that silent synapses incur after repeated exposure to cocaine. GluN2B ablation reduces the proportion of silent synapses, but some of them can persist by substitution with GluN2C, which drives the aberrantly facilitated synaptic incorporation of calcium-impermeable AMPA-type glutamate receptors (AMPARs). The resulting precocious maturation of silent synapses impairs addiction memory but increases locomotor activity, both of which can be normalized by the blockade of calcium-impermeable AMPAR trafficking. Collectively, GluN2B supports the competence of cocaine-induced silent synapses to specify the subunit composition of AMPARs and thereby the expression of addiction memory and related behaviors.
Biomolecule sensors based on organic electrochemical transistors
Biosensors based on organic electrochemical transistors (OECTs) have been a research highlight in recent years owing to their remarkable biocompatibility, low operating voltage, and substantial signal amplification capability. Especially, as an emerging fundamental device for biosensing, OECTs show great potential for pH, ions, molecules, and biomarker sensing. This review highlights the research progress of biomolecule sensors based on OECTs, focusing on recent publications in the past 5 years. Specifically, OECT-based biomolecule sensors for small molecules (glucose, dopamine, lactate, etc. that act as signals or effectors), and macromolecules (DNA, RNA, proteins, etc. that are often used as markers in physiology and medicine), are summarized. Additionally, emerging technologies and materials used to enhance sensitivity, detection limits, and detection ranges are described comprehensively. Last, aspects of OECT-based biomolecule sensors that need further improvement are discussed along with future opportunities and challenges.
Myosteatosis is associated with adiposity, metabolic derangements and mortality in patients with chronic kidney disease
Myosteatosis has been associated with sarcopenia, and increased mortality risk in patients on hemodialysis. We aimed to explore the associations between myosteatosis, as assessed by computed tomography (CT), with demographic parameters, body composition metrics, muscle strength, metabolic parameters and mortality in patients with chronic kidney disease (CKD).
Photovoltaic bioelectronics merging biology with new generation semiconductors and light in biophotovoltaics photobiomodulation and biosensing
This review covers advancements in biosensing, biophotovoltaics, and photobiomodulation, focusing on the synergistic use of light, biomaterials, cells or tissues, interfaced with photosensitive dye-sensitized, perovskite, and conjugated polymer organic semiconductors or nanoparticles. Integration of semiconductor and biological systems, using non-invasive light-probes or -stimuli for both sensing and controlling biological behavior, has led to groundbreaking applications like artificial retinas. From fusion of photovoltaics and biology, a new research field emerges: photovoltaic bioelectronics.
An artificial market model for the forex market
As financial markets have transitioned toward electronic trading, there has been a corresponding increase in the number of algorithmic strategies and degree of transaction frequency. This move to high-frequency trading at the millisecond level, propelled by algorithmic strategies, has brought to the forefront short-term market reactions, like market impact, which were previously negligible in low-frequency trading scenarios. Such evolution necessitates a new framework for analyzing and developing algorithmic strategies in these rapidly evolving markets. Employing artificial markets stands out as a solution to this problem. This study aims to construct an artificial foreign exchange market referencing market microstructure theory, without relying on the assumption of information or technical traders. Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. Moreover, this work explores the importance of order flow and the underlying factors of stylized facts within the artificial market model.
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