MiRNA-145 increases therapeutic sensibility to gemcitabine treatment of pancreatic adenocarcinoma cells.

Pancreatic adenocarcinoma is one of the most leading causes of cancer-related deaths worldwide. Although recent advances provide various treatment options, pancreatic adenocarcinoma has poor prognosis due to its late diagnosis and ineffective therapeutic multimodality. Gemcitabine is the effective first-line drug in pancreatic adenocarcinoma treatment. However, gemcitabine chemoresistance of pancreatic adenocarcinoma cells has been a major obstacle for limiting its treatment effect. Our study found that p70S6K1 plays an important role in gemcitabine chemoresistence. MiR-145 is a tumor suppressor which directly targets p70S6K1 for inhibiting its expression in pancreatic adenocarcinoma, providing new therapeutic scheme. Our findings revealed a new mechanism underlying gemcitabine chemoresistance in pancreatic adenocarcinoma cells

Effect of Modulating Activity of DLPFC and Gender on Search Behavior: A tDCS Experiment

Studies of search behavior have shown that individuals stop searching earlier and accept a lower point than predicted by the optimal, risk-neutral stopping rule. This behavior may be related to individual risk preferences. Studies have also found correlativity between risk preferences and the dorsolateral prefrontal cortex (DLPFC). As risk attitude plays a crucial role in search behavior, we studied whether modulating the activity of DLPFC, by using a transcranial direct current stimulation (tDCS) device, can change individual search behavior. We performed a sequential search task in which subjects decided when to accept a point randomly drawn from a uniform distribution. A total of 49 subjects (23 females, mean age = 21.84 ± 2.09 years, all right-handed) were recruited at Zhejiang University from May 2017 to September 2017. They repeated the task in 80 trials and received the stimulation at the end of the 40th trial. The results showed that after receiving right anodal/left cathodal stimulation, subjects increased their searching duration, which led to an increase in their accepted point from 778.17 to 826.12. That is, the subjects may have changed their risk attitude to search for a higher acceptable point and received a higher benefit. In addition, the effect of stimulation on search behavior was mainly driven by the female subjects rather than by the male subjects: the female subjects significantly increased their accepted point from 764.15 to 809.17 after right anodal/left cathodal stimulation, while the male subjects increased their accepted point from 794.18 to 845.49, but the change was not significant

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

IntroductionDecades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.MethodsUsing the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models—random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor—over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable’s importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.ResultsWith the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.DiscussionOur models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S

Does Gender Make a Difference in Deception? The Effect of Transcranial Direct Current Stimulation Over Dorsolateral Prefrontal Cortex

Neuroimaging studies have indicated a correlation between dorsolateral prefrontal cortex (DLPFC) activity and deceptive behavior. We applied a transcranial direct current stimulation (tDCS) device to modulate the activity of subjects’ DLPFCs. Causal evidence of the neural mechanism of deception was obtained. We used a between-subject design in a signaling framework of deception, in which only the sender knew the associated payoffs of two options. The sender could freely choose to convey the truth or not, knowing that the receiver would never know the actual payment information. We found that males were more honest than females in the sham stimulation treatment, while such gender difference disappeared in the right anodal/left cathodal stimulation treatment, because modulating the activity of the DLPFC using right anodal/left cathodal tDCS only significantly decreased female subjects’ deception

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Data_Sheet_1_Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.docx

IntroductionDecades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.MethodsUsing the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models—random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor—over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable’s importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.ResultsWith the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.DiscussionOur models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.</p

High Color Conversion Efficiency Realized in Graphene-Connected Nanorod Micro-Light-Emitting Diodes with Hybrid Ag Nanoparticles and Quantum Dots Using Non-Radiative Energy Transfer and Localized Surface Plasmons

As a medium for color conversion, quantum dots (QDs) can be employed in the display of full-color GaN-based Micro-Light-Emitting Diodes (µLEDs) arrays. Typically, in a system where QDs are excited by UV/blue µLEDs, QDs are coated onto the LED surface. However, due to inherent defects in QDs and significant energy loss associated with this method, the color conversion efficiency (CCE) is suboptimal. In this paper, we introduce an innovative approach where we etch a uniform nanorod (NR) array onto the surface of µLEDs. We then mix Ag nanoparticles (NPs) with QDs to fill the gaps between the nanorods. Simultaneously, we utilize the excellent conductivity, transparency, and high strength of graphene to create a transparent conductive electrode on the nanopillar surface. This electrode serves to connect individual nanorods and enhance current spreading. The nanorod array's structure significantly reduces the distance between the QDs and the quantum well (QW), reducing energy loss from the excitation light source through a non-radiative energy transfer (NRET) mechanism. Additionally, the Ag NPs function as local surface plasmons (LSPs) in luminescent systems, further enhancing the CCE of QDs via the NRET mechanism. In this study, we compare the effects of two types of Ag NPs with different absorption resonance peaks on device performance. Our results demonstrate that Ag NPs with absorption resonance peaks matching the emission wavelength of QDs play a more crucial role in the composite system. This configuration achieves a CCE of 77.78% for µLEDs with nanorod arrays, operating at a current of 10 mA. Compared to the conventional planar µLED structure with QDs spin-coated on the surface, our proposed method improves the CCE of µLEDs by an impressive 285%. This outcome underscores the significant contribution of the NR structure and LSPs in enhancing the CCE of QD- µLEDs

Multidimensional Branched Composite Binder via Covalent Grafting for High-Performance Silicon-Based Anodes

A binder plays an essential role in preserving the mechanical stability of electrodes and in enhancing the cycle life of Si anodes. Nevertheless, the typical binders normally are not effective enough to relieve the unavoidable volume expansion of Si during cycling. Herein, to make full use of the polar functional groups on the polymer backbone, the citric acid-grafted poly(acrylic acid) (CA-g-PAA) composite binder is rationally designed and prepared by a simple graft modification. The CA molecule not only provides more reactive groups but also connects with PAA to form a multidimensional branched structure. Due to the interaction of CA and PAA, the proposed binder (CA-g-PAA) demonstrates enhanced adhesion strength. Thus, the CA-g-PAA/Si electrode retains a high discharge specific capacity of 2482.3 mAh g–1 after 300 cycles at 840 mA g–1 and exhibits a better structural integrity. This study highlights the synergistic interaction of CA and PAA with Si particles and offers a simple path for the design of optimized binders