Synergistic anticancer effects of co-delivery of linc-RoR siRNA and curcumin using polyamidoamine dendrimers against breast cancer.

Breast cancer resistance to chemotherapy necessitates novel combination therapeutic approaches. Linc-RoR is a long intergenic noncoding RNA that regulates stem cell differentiation and promotes metastasis and invasion in breast cancer. Herein, we report a dual delivery system employing polyamidoamine dendrimers to co-administer the natural compound curcumin and linc-RoR siRNA for breast cancer treatment. Polyamidoamine dendrimers efficiently encapsulated curcumin and formed complexes with linc-RoR siRNA at an optimal N/P ratio. In MCF-7 breast cancer cells, the dendriplexes were effectively internalized and the combination treatment synergistically enhanced cytotoxicity, arresting the cell cycle at the G1 phase and inducing apoptosis. Linc-RoR gene expression was also significantly downregulated. Individual treatments showed lower efficacy, indicating synergism between components. Mechanistic studies are warranted to define the molecular underpinnings of this synergistic interaction. Our findings suggest dual delivery of linc-RoR siRNA and curcumin via dendrimers merits further exploration as a personalized therapeutic approach for overcoming breast cancer resistance.

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition. Significance statement: Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys' lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.

Dissociable contributions of basolateral amygdala and ventrolateral orbitofrontal cortex to flexible learning under uncertainty.

Reversal learning measures the ability to form flexible associations between choice outcomes with stimuli and actions that precede them. This type of learning is thought to rely on several cortical and subcortical areas, including highly interconnected orbitofrontal cortex (OFC) and basolateral amygdala (BLA), and is often impaired in various neuropsychiatric and substance use disorders. However, unique contributions of these regions to stimulus- and action-based reversal learning have not been systematically compared using a chemogenetic approach and particularly before and after the first reversal that introduces new uncertainty. Here, we examined the roles of ventrolateral OFC (vlOFC) and BLA during reversal learning. Male and female rats were prepared with inhibitory DREADDs targeting projection neurons in these regions and tested on a series of deterministic and probabilistic reversals during which they learned about stimulus identity or side (left or right) associated with different reward probabilities. Using a counterbalanced within-subject design, we inhibited these regions prior to reversal sessions. We assessed initial and pre-post reversal changes in performance to measure learning and adjustments to reversals, respectively. We found that inhibition of vlOFC, but not BLA, eliminated adjustments to stimulus-based reversals. Inhibition of BLA, but not vlOFC, selectively impaired action-based probabilistic reversal learning, leaving deterministic reversal learning intact. vlOFC exhibited a sex-dependent role in early adjustment to action-based reversals, but not in overall learning. These results reveal dissociable roles for BLA and vlOFC in flexible learning and highlight a more crucial role for BLA in learning meaningful changes in the reward environment.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

Efficacy and safety of low dose oral ketamine for controlling pain and distress during intravenous cannulation in children: a double-blind, randomized, placebo-controlled trial.

Background: Ketamine is widely used in infants and young children for procedural sedation and anesthesia. The aim of this study was to evaluate the efficacy and safety of low dose oral ketamine to control pain and distress in children during intravenous (IV) cannulation. Methods: This is a prospective, randomized, double-blind study, including children aged between 3 and 6 years requiring a non-emergent IV-line placement. Children were randomly assigned to two groups, treated either with oral ketamine or a placebo. All patients were monitored for vital signs. Pain was assessed using the Children's Hospital of Eastern Ontario Pain Scale (CHEOPS) and Wong-Baker Faces Pain Rating Scale (WBFS) scales and sedation using a 5-point sedation score. The facility of IV-line placement was measured by a 3-point scale. Adverse effects were recorded after 1 and 24 hours. Results: A total of 79 and 81 children were entered in the ketamine and placebo groups, respectively. The heart and respiratory rates increased significantly in the placebo group. The median CHEOPS 4 (95% confidence interval [CI]: 3, 4, P < 0.001) and WBFS 6 (95% CI: 4, 6, P < 0.001) scores decreased statistically in the ketamine group. IV-line placement was 50% easier in the ketamine group (95% CI: 37%, 63%, P < 0.001). No serious adverse effects were observed in all cases. Conclusions: Low dose oral ketamine effectively decreased the pain and distress during IV cannulation in children without any significant adverse reactions.

Prediction of COVID-19 manipulation by selective ACE inhibitory compounds of Potentilla reptant root: In silico study and ADMET profile.

In the novel SARS-CoV-2 (COVID-19) as a global emergency event, the main reason of the cardiac injury from COVID-19 is angiotensin-converting enzyme 2 (ACE2) targeting in SARS-CoV-2 infection. The inhibition of ACE2 induces an increase in the angiotensin II (Ang II) and the angiotensin II receptor type 1 (AT1R) leading to impaired cardiac function or cardiac inflammatory responses. The ethyl acetate fraction of Potentilla reptans L. root can rescue heart dysfunction, oxidative stress, cardiac arrhythmias and apoptosis. Therefore, isolated components of P. reptans evaluated to identify natural anti-SARS-CoV-2 agents via molecular docking. In silico molecular docking study were carried out using the Auto Dock software on the isolated compounds of Potentilla reptans root. The protein targets of selective ACE and others obtained from Protein Data Bank (PDB). The best binding pose between amino acid residues involved in active site of the targets and compounds was discovered via molecular docking. Furthermore, ADMET properties of the compounds were evaluated. The triterpenoids of P. reptans showed more ACE inhibitory potential than catechin in both domains. They were selective on the nACE domain, especially compound 5. Also, the compound 5 & 6 had the highest binding affinity toward active site of nACE, cACE, AT1R, ACE2, and TNF-α receptors. Meanwhile, compound 3 showed more activity to inhibit TXA2. Drug likeness and ADMET analysis showed that the compounds passed the criteria of drug likeness and Lipinski rules. The current study depicted that P. reptans root showed cardioprotective effect in COVID-19 infection and manipulation of angiotensin II-induced side effects.

Safety and efficacy of aprotinin versus tranexamic acid for reducing absolute blood loss and transfusion in pediatric patients undergoing craniosynostosis surgery: a randomized, double-blind, three-arm controlled trial.

OBJECTIVE: Craniosynostosis surgery is associated with considerable blood loss and need for transfusion. Considering the lower estimated blood volume (EBV) of children compared to adults, excessive blood loss may quickly lead to hypovolemic shock. Therefore, reducing blood loss is important in craniosynostosis surgery. This study was conducted to evaluate the efficacy of aprotinin or tranexamic acid (TXA) in blood loss reduction in these patients. METHODS: In the current randomized controlled trial, 90 eligible pediatric patients with craniosynostosis were randomly divided into three groups to receive either aprotinin, TXA, or no intervention. The absolute blood loss and transfusion amount were assessed for all patients both intraoperatively and 2 and 8 hours postoperatively. RESULTS: Although crude values of estimated blood loss were not significantly different between groups (p = 0.162), when adjusted to the patient's weight or EBV, the values reached the significance level (p = 0.018), particularly when the aprotinin group was compared to the control group (p = 0.0154). The EBV losses 2 hours and 8 hours postoperatively significantly dropped in the TXA and aprotinin groups compared to the control group (p = 0.001 and p < 0.001, respectively). Rates of postoperative blood transfusion were significantly higher in the control group (p = 0.024). Hemoglobin and hematocrit 8 hours postoperatively were lower in the control group than in the TXA or aprotinin treatment groups (p < 0.002 and p < 0.001, respectively). There were no serious adverse events associated with the interventions in this study. CONCLUSIONS: Aprotinin and TXA can reduce blood loss and blood transfusion without serious complications and adverse events in pediatric patients undergoing craniosynostosis surgery.

Computational models of adaptive behavior and prefrontal cortex.

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

Computational mechanisms of distributed value representations and mixed learning strategies.

Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

Entropy-based metrics for predicting choice behavior based on local response to reward.

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Unique features of stimulus-based probabilistic reversal learning.

Reversal learning paradigms are widely used assays of behavioral flexibility with their probabilistic versions being more amenable to studying integration of reward outcomes over time. Prior research suggests differences between initial and reversal learning, including higher learning rates, a greater need for inhibitory control, and more perseveration after reversals. However, it is not well-understood what aspects of stimulus-based reversal learning are unique to reversals, and whether and how observed differences depend on reward probability. Here, we used a visual probabilistic discrimination and reversal learning paradigm where male and female rats selected between a pair of stimuli associated with different reward probabilities. We compared accuracy, rewards collected, omissions, latencies, win-stay/lose-shift strategies, and indices of perseveration across two different reward probability schedules. We found that discrimination and reversal learning are behaviorally more unique than similar: Fit of choice behavior using reinforcement learning models revealed a lower sensitivity to the difference in subjective reward values (greater exploration) and higher learning rates for the reversal phase. We also found latencies to choose the better option were greater in females than males, but only for the reversal phase. Further, animals employed more win-stay strategies during early discrimination and increased perseveration during early reversal learning. Interestingly, a consistent reward probability group difference emerged with a richer environment associated with longer reward collection latencies than a leaner environment. Future studies should systematically compare the neural correlates of fine-grained behavioral measures to reveal possible dissociations in how the circuitry is recruited in each phase. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Timescales of Cognition in the Brain.

We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.

Investigations of adsorption behavior and anti-inflammatory activity of glycine functionalized Al12N12 and Al12ON11 fullerene-like cages.

The adsorption behavior of the amino acid, glycine (Gly), via the carboxyl, hydroxyl, and amino groups onto the surfaces of Al12N12 and Al16N16 fullerene-like cages were computationally evaluated by the combination of density functional theory (DFT) and molecular docking studies. It was found that Gly can chemically bond with the Al12N12 and Al16N16 fullerene-like cages as its amino group being more favorable to interact with the aluminum atoms of the adsorbents compared to carboxyl and hydroxyl groups. Oxygen and carbon doping were reported to reduce steric hindrance for Glycine interaction at Al site of Al12ON11/Gly and Al12CN11/Gly complexes. Interaction was further enhanced by oxygen doping due to its greater electron withdrawing effect. Herein, the Al12ON11/Gly complex where two carbonyl groups of Gly are bonded to the aluminum atoms of the Al12N12 fullerene-like cage is the most stable interaction configuration showing ∆adsH and ∆adsG values of -81.74 kcal/mol and -66.21 kcal/mol, respectively. Computational studies also revealed the frequency shifts that occurred due to the interaction process. Molecular docking analysis revealed that the Al12N12/Gly (-11.7 kcal/mol) and the Al12ON11/Gly (-9.2 kcal/mol) complexes have a good binding affinity with protein tumor necrosis factor alpha (TNF-α). TNF-α was implicated as a key cytokine in various diseases, and it has been a validated therapeutic target for the treatment of rheumatoid arthritis. These results suggest that the Al12N12/Gly complex in comparison with the Al16N16/Gly, Al12ON11/Gly, and the Al12CN11/Gly complexes could be efficient inhibitors of TNF-α.

Separable Influences of Reward on Visual Processing and Choice.

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remain unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the end points of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that, in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, although the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options, whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for the integration of reward outcomes.

Learning from Others, but with What Confidence?

A recent study by Zhang and Gläscher (2020) in humans examines learning from one's own versus others' actions under reward uncertainty. Comparing findings from this and non-human studies on learning under perceptual uncertainty suggests a unified role for confidence in learning under different types of uncertainty across mammalian brains.

Comparison of the Laryngoscopic View using Macintosh and Miller Blades in Children Less than Four Years Old.

This study aimed to compare Miller and Macintosh laryngoscopes in zero to 4-year-old children. A total of 72 children with a score of I and II, according to the American Society of Anesthesiologists (ASA) physical status classification, who were candidates for elective surgery with general anesthesia and tracheal intubation were enrolled in the study. The children were divided into two equal groups (36 persons) according to used laryngoscope: Miller laryngoscope (group 1) and Macintosh laryngoscope (group 2). Observations and all laryngoscopies were performed by a single experienced anesthesiologist. Heart rate, systolic blood pressure, non-invasive arterial blood pressure, and hemoglobin saturation were measured and recorded. The number of endotracheal intubation attempts and complications were also recorded for both groups. In terms of gender, the first group consisted of 88.9% boys and 11.1% girls, and the second group consisted of 66.6% boys and 33.3% girls (p-value=0.05). The mean age was 16.7 months in the first group and 17.7 months in the second group (p-value=0.5). The mean weight of the children was 16988.5 g and 16300 g in the Miller and Macintosh groups, respectively (p-value=0.9). Regarding the Cormack-Lehane classification system, 5 patients were classified as grade 1 (13.9%), 14 patients as grade 2 (38.9%), 15 patients as grade 3 (41.7%), and 2 patients as grade 4 (5.6%) in the Macintosh group. In contrast, in the Miller group, 5 patients were classified as grade 1 (13.9%), 27 patients as grade 2 (75%), and 4 patients as grade 3 (11.1%) (p-value=0.004). These results can provide more data about the tracheal intubation method with the Macintosh and Miller laryngoscopes, the ease of intubation, and the best laryngoscopic view with each blade.

Learning arbitrary stimulus-reward associations for naturalistic stimuli involves transition from learning about features to learning about objects.

Most cognitive processes are studied using abstract or synthetic stimuli with specific features to fully control what is presented to subjects. However, recent studies have revealed enhancements of cognitive capacities (such as working memory) when processing naturalistic versus abstract stimuli. Using abstract stimuli constructed from distinct visual features (e.g., color and shape), we have recently shown that human subjects can learn multidimensional stimulus-reward associations via initially estimating reward value of individual features (feature-based learning) before gradually switching to learning about reward value of individual stimuli (object-based learning). Here, we examined whether similar strategies are adopted during learning about naturalistic stimuli that are clearly perceived as objects (instead of a combination of features) and contain both task-relevant and irrelevant features. We found that similar to learning about abstract stimuli, subjects initially adopted feature-based learning more strongly before transitioning to object-based learning. However, there were three key differences between learning about naturalistic and abstract stimuli. First, compared with abstract stimuli, the initial learning strategy was less feature-based for naturalistic stimuli. Second, subjects transitioned to object-based learning faster for naturalistic stimuli. Third, unexpectedly, subjects were more likely to adopt feature-based learning for naturalistic stimuli, both at the steady state and overall. These results suggest that despite the stronger tendency to perceive naturalistic stimuli as objects, which leads to greater likelihood of using object-based learning as the initial strategy and a faster transition to object-based learning, the influence of individual features on learning is stronger for these stimuli such that ultimately the object-based strategy is adopted less. Overall, our findings suggest that feature-based learning is a general initial strategy for learning about reward value of all types of multi-dimensional stimuli.