Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.

Long non-coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA's functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classification. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the first known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi-source relationships of lncRNAs and proteins with LPN's topological data. Second, the implemented masking strategy efficiently identifies LPN's key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model's potential in LPI prediction.

Assessing the Diagnostic Efficacy of Handgrip Dynamometry and Diaphragmatic Ultrasound in Intensive Care Unit-Acquired Weakness.

Objective: The aim of this study is to examine the diagnostic significance of using handgrip dynamometry and diaphragmatic ultrasound in intensive care unit-acquired weakness (ICU-AW). Methods: This study included patients who received mechanical ventilation in the ICU at the Fourth Hospital of Hebei Medical University from July to December 2020. We collected comprehensive demographic data and selected conscious patients for muscle strength and ICU-AW assessments. The evaluation comprised grip strength measurement and bedside ultrasound for diaphragmatic excursion (DE) and thickening fraction (DTF). Results were documented for comparative analysis between patient groups, focusing on the diagnostic efficacy of grip strength, DE, DTF, and their combined application in diagnosing ICU-AW. Results: A total of 95 patients were initially considered for inclusion in this study. Following the exclusion of 20 patients, a final cohort of 75 patients were enrolled, comprising of 32 patients (42.6%) diagnosed with ICU-AW and 43 patients (57.4%) classified as non-ICU-AW. Comparative analysis revealed that grip strength, DE, and DTF were significantly lower in the ICU-AW group (P < 0.05). Subgroup analysis specific to male patients demonstrated a noteworthy decrease in grip strength, DE, and DTF within the ICU-AW group (P < 0.05). Receiver operating characteristic curve analysis indicated statistically significant diagnostic value for ICU-AW with grip strength, DE, DTF, and grip strength and diaphragmatic ultrasound (P < 0.01). Furthermore, it was observed that the amalgamation of grip strength and diaphragmatic ultrasound significantly enhanced the diagnostic accuracy of ICU-AW in patients who are critically ill. Conclusion: Grip strength, DE, DTF, and the combined use of grip strength with diaphragm ultrasound demonstrated diagnostic efficacy in ICU-AW. Notably, the integration of grip strength with diaphragm ultrasound exhibited a heightened capacity to enhance the diagnostic value specifically in patients diagnosed who are critically ill with ICU-AW.

Modeling the Zigzag Curves of Transition Metal Ions' Charge Transition Levels in Crystals.

Predicting the defect levels of transition metal (TM) dopants in the band gap of crystals is critical in determining the charge states of TM dopants and explaining their electronic and optical properties. By analyzing the calculated charge transition levels and the crystal-field strengths of all the 3d-TM ions in several insulators, we demonstrate that the variation trend of the 3d-TM dopants in a crystal is a scaling of the variation of 3d-electron binding energies (ionization potential) of the free TM ions corrected by adding the contribution of the 3d-orbital's crystal-field splitting. We therefore develop a model to predict the relative location of TM ions' defect levels in the band gap from the defect level and crystal-field splitting of a reference TM ion in the host of concern. The model is applied to predict the defect levels of the series of TM ions in ß-Ga2O3 and ZnO, which have moderate to small band gaps, making some of the levels fall into the conduction or valence bands. These results show that the model may serve as a quick reference for related material design and optimization.

GAM-MDR: probing miRNA-drug resistance using a graph autoencoder based on random path masking.

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA-drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA-drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy.

Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.

ML-NPI: Predicting Interactions between Noncoding RNA and Protein Based on Meta-Learning in a Large-Scale Dynamic Graph.

Deep learning methods can accurately study noncoding RNA protein interactions (NPI), which is of great significance in gene regulation, human disease, and other fields. However, the computational method for predicting NPI in large-scale dynamic ncRNA protein bipartite graphs is rarely discussed, which is an online modeling and prediction problem. In addition, the results published by researchers on the Web site cannot meet real-time needs due to the large amount of basic data and long update cycles. Therefore, we propose a real-time method based on the dynamic ncRNA-protein bipartite graph learning framework, termed ML-GNN, which can model and predict the NPIs in real time. Our proposed method has the following advantages: first, the meta-learning strategy can alleviate the problem of large prediction errors in sparse neighborhood samples; second, dynamic modeling of newly added data can reduce computational pressure and predict NPIs in real-time. In the experiment, we built a dynamic bipartite graph based on 300000 NPIs from the NPInterv4.0 database. The experimental results indicate that our model achieved excellent performance in multiple experiments. The code for the model is available at https://github.com/taowang11/ML-NPI, and the data can be downloaded freely at http://bigdata.ibp.ac.cn/npinter4.

Innovative insight into the mechanism of enantioselective skin permeation for chiral drugs.

OBJECTIVES: A profound comprehension of the molecular mechanisms underpinning the enantioselective transdermal permeation of chiral drugs is critical in the design and assessment of transdermal preparations. The primary objective of this study is to investigate the distinct skin permeation behaviors exhibited by enantiomers of non-steroidal anti-inflammatory drugs (NSAIDs) and elucidate the intricate molecular mechanism at play. METHODS: In vitro and in vivo transdermal permeation studies of chiral NSAIDs were performed using transdermal patch and solution system. Chiral interaction between NSAIDs enantiomers and synthesized chiral ceramide present in the skin was characterized to clarify the different transdermal behaviors. RESULTS: The S-enantiomers of NSAIDs exhibited higher permeability through the skin than R-enantiomer in vitro (1.5-fold) and in vivo (2.0-fold), which was attributed to a stronger interaction between S-enantiomer and ceramide caused by more favorable spatial conformations. S-enantiomer required lower activation energy (24.4 kJ/mol) and Gibbs energy (43.3 kJ/mol), which was favorable in forming the H-bond with ceramide in the skin, resulting in more permeation. CONCLUSION: This research furnished an innovative comprehension of the molecular underpinnings governing the enantioselective permeation of drug enantiomers through the skin, fostering the minimization of undesired enantiomer ingestion (distomers) and amplifying therapeutic efficiency.

A tetrasulfide bond-bridged mesoporous organosilica-based nanoplatform for triple-enhanced chemodynamic therapy combined with chemotherapy and H2S therapy.

The high glutathione (GSH) concentration and insufficient H2O2 content in tumor cells strongly constrict the efficacy of Fenton reaction-based chemodynamic therapy (CDT). Despite numerous efforts, it still remains a formidable challenge for achieving satisfactory efficacy using CDT alone. Herein, an intelligent tetrasulfide bond-bridged mesoporous organosilica-based nanoplatform that integrates GSH-depletion, H2S generation, self-supplied H2O2, co-delivery of doxorubicin (DOX) and Fenton reagent Fe2+ is presented for synergistic triple-enhanced CDT/chemotherapy/H2S therapy. Because the tetrasulfide bond is sensitive to GSH, the nanoplatform can effectively consume GSH, leading to ROS accumulation and H2S generation in the GSH-overexpressed tumor microenvironment. Meanwhile, tetrasulfide bond-induced GSH-depletion triggers the degradation of nanoparticles and the release of DOX and Fe2+. Immediately, Fe2+ catalyzes endogenous H2O2 to highly toxic hydroxyl radicals (˙OH) for CDT, and H2S induces mitochondria injury and causes energy deficiency. Of note, H2S can also decrease the decomposition of H2O2 to augment CDT by downregulating catalase. DOX elicits chemotherapy and promotes H2O2 production to provide a sufficient substrate for enhanced CDT. Importantly, the GSH depletion significantly weakens the scavenging effect on the produced ˙OH, guaranteeing the enhanced and highly efficient CDT. Based on the synergistic effect of triple-augmented CDT, H2S therapy and DOX-mediated chemotherapy, the treatment with this nanoplatform gives rise to a superior antitumor outcome.

The enantioselective enhancing effect and mechanistic insights of chiral enhancers in transdermal drug delivery.

Overlook of chiral consideration in transdermal drug delivery increases administrated dose and risk of side effects, decreasing therapeutical effects. To improve the transdermal delivery efficiency of eutomer, this work focused on investigating the law and mechanism of enantioselective enhancing effects of chiral permeation enhancers on drug enantiomers. Chiral nonsteroidal anti-inflammatory drugs and terpene permeation enhancers were selected as model drug and enhancers. The results indicated that the L-isomer of permeation enhancers increased the skin absorption of S-enantiomer of drug and D-isomer improve the permeation of R-enantiomer, in which the enhancement effect (ER) of L-menthol on S-enantiomer (ER = 3.23) was higher than that on R-enantiomer (ER = 1.49). According to the pharmacokinetics results, L-menthol tended to enhance the permeation of S-enantiomer better than R-enantiomer (2.56 fold), and showed excellent in vitro/in vivo correlations. The mechanism study showed that L-isomer of permeation enhancers improved the permeation of S-enantiomer by increasing the retention, but the D-isomer by improving partition for better permeation. Enantioselective mechanism indicated that the weaker chiral H-bond interaction between drug-chiral enhancers was caused by the enantiomeric conformation. Additionally, stronger chiral enhancers-skin interaction between L-isomer and S-conformation of ceramide produced better enhancing effects. In conclusion, enantioselective interaction of chiral drug-chiral enhancers and chiral enhancers-chiral skin played a critical role in transdermal drug delivery, rational utilization of which contributed to improving the uptake of eutomer and inhibiting distomers to decrease a half of dose and side effects, increasing transdermal therapeutical efficiency.

Self-supervised deep learning for joint 3D low-dose PET/CT image denoising.

Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET) and low-dose computed tomography (LDCT) has been widely explored. However, previous methods have focused only on single modality denoising, neglecting the possibility of simultaneously denoising LDPET and LDCT using only one neural network, i.e., joint LDPET/LDCT denoising. Moreover, DL-based denoising methods generally require plenty of well-aligned LD-normal-dose (LD-ND) sample pairs, which can be difficult to obtain. To this end, we propose a self-supervised two-stage training framework named MAsk-then-Cycle (MAC), to achieve self-supervised joint LDPET/LDCT denoising. The first stage of MAC is masked autoencoder (MAE)-based pre-training and the second stage is self-supervised denoising training. Specifically, we propose a self-supervised denoising strategy named cycle self-recombination (CSR), which enables denoising without well-aligned sample pairs. Unlike other methods that treat noise as a homogeneous whole, CSR disentangles noise into signal-dependent and independent noises. This is more in line with the actual imaging process and allows for flexible recombination of noises and signals to generate new samples. These new samples contain implicit constraints that can improve the network's denoising ability. Based on these constraints, we design multiple loss functions to enable self-supervised training. Then we design a CSR-based denoising network to achieve joint 3D LDPET/LDCT denoising. Existing self-supervised methods generally lack pixel-level constraints on networks, which can easily lead to additional artifacts. Before denoising training, we perform MAE-based pre-training to indirectly impose pixel-level constraints on networks. Experiments on an LDPET/LDCT dataset demonstrate its superiority over existing methods. Our method is the first self-supervised joint LDPET/LDCT denoising method. It does not require any prior assumptions and is therefore more robust.

BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI.

Gastrointestinal (GI) cancer is a malignancy affecting the digestive organs. During radiation therapy, the radiation oncologist must precisely aim the X-ray beam at the tumor while avoiding unaffected areas of the stomach and intestines. Consequently, accurate, automated GI image segmentation is urgently needed in clinical practice. While the fully convolutional network (FCN) and U-Net framework have shown impressive results in medical image segmentation, their ability to model long-range dependencies is constrained by the convolutional kernel's restricted receptive field. The transformer has a robust capacity for global modeling owing to its inherent global self-attention mechanism. The TransUnet model leverages the strengths of both the convolutional neural network (CNN) and transformer models through a hybrid CNN-transformer encoder. However, the concatenation of high- and low-level features in the decoder is ineffective in fusing global and local information. To overcome this limitation, we propose an innovative transformer-based medical image segmentation architecture called BiFTransNet, which introduces a BiFusion module into the decoder stage, enabling effective global and local feature fusion by enabling feature integration from various modules. Further, a multilevel loss (ML) strategy is introduced to oversee the learning process of each decoder layer and optimize the use of globally and locally fused contextual features at different scales. Our method achieved a Dice score of 89.51% and an intersection-over-union (IoU) score of 86.54% on the UW-Madison Gastrointestinal Segmentation dataset. Moreover, our method attained a Dice score of 78.77% and a Hausdorff distance (HD) of 27.94% on the Synapse Multi-organ Segmentation dataset. Compared with the state-of-the-art methods, our proposed method achieves superior segmentation performance in gastrointestinal segmentation tasks. More significantly, our method can be easily extended to medical segmentation in different modalities such as CT and MRI. Our method achieves clinical multimodal medical segmentation and provides decision supports for clinical radiotherapy plans.

Recent Advances in Sensor Technology for Healthcare and Biomedical Applications (Volume II).

With remarkable progress being witnessed in recent years in the development of sensors, these advances in sensor technology provide unprecedented opportunities for (1) the early diagnosis and prevention of human diseases by detecting critical biomarkers; (2) health assessments by monitoring and analyzing human physiological signals in healthcare and biomedical applications; and (3) the efficient evaluation of human-health-relevant environmental factors by monitoring and measuring environmental determinants [...].

Mechanistic insights for regulating the site occupancy, valence states and optical transitions of Mn ions in yttrium-aluminum garnets via codoping.

The site-dependent photoluminescence of activators can be regulated by the sintering atmosphere, coexistence conditions, and especially cation codoping, which have been intensively studied for design and optimization of optical functional materials. Here, first-principles calculations are performed to determine the regulation of the site occupancy, valence states and optical transitions of Mn activators via codoping in yttrium aluminum garnets (YAGs), which contain three different cation sites. Without any codopants, Mnoct3+ dominates in defect concentration and photoluminescence, which can hardly be tuned by the sintering atmosphere or coexistence conditions of YAGs with other competing compounds. With the low formation energy of Ca2+, Be2+, Mg2+, and Sr2+ codopants and in an oxidation sintering atmosphere, the Fermi energy is lowered and the concentration and luminescence of Mnoct4+ are enhanced. Na+ and Li+ codopants with relatively high formation energy have little influence on tuning the Fermi energy. Then with the low formation energy of Ti4+, Si4+ codopants and in a reducing sintering atmosphere, the Fermi energy is lifted and the luminescence of Mndod2+ and Mnoct2+ is enhanced as a result of increased concentrations. The proposed first-principles scheme, with general applicability and encouraging predictive power, provides an effective approach for elucidating the effects of codoping impurities on the design and optimization of optical materials.

Comparison of early and delayed strategy for renal replacement therapy initiation for severe acute kidney injury with heart failure: a retrospective comparative cohort study.

Background: Determining the timing of renal replacement therapy (RRT) in patients with acute kidney injury (AKI) and heart failure (HF) can optimize the clinical management strategy. We compared the impact of "early" and "delayed" timing of RRT on the prognosis of patients with AKI and HF. Methods: Clinical data from September 2012 to September 2022 were retrospectively analyzed. Patients with AKI complicated by HF and undergoing RRT in the intensive care unit (ICU) were enrolled. Patients with stage 3 AKI and fluid overload present (FOP) or who met the emergency indications for RRT were assigned to the delayed RRT group. Patients with stage 1 AKI or stage 2 AKI and without urgent indications for RRT and patients with stage 3 AKI without FOP and without urgent indications for RRT were enrolled in the Early RRT group. At 90-day follow-up after initiation of RRT, the mortality was compared between the two groups. Logistic regression analysis was performed to adjust for confounding factors affecting 90-day mortality. Results: A total of 151 patients were enrolled, including 77 in the early RRT group and 74 in the delayed RRT group. For baseline characteristics, patients in the early RRT group had significantly lower acute physiology and chronic health evaluation-II (APACHE-II) score, sequential organ failure assessment (SOFA), serum creatinine (Scr) values and blood urea nitrogen (BUN) values on the day of ICU admission than those in the delayed RRT group (both P values <0.05), there were no significant differences in other baseline characteristics. The number of RRT-free days in the ICU was significantly longer in the early RRT group than in the delayed RRT group [1.69 (0.35-10.87) vs. 0.88 (0.20-4.55) days; P=0.046]. However, clinical outcomes (except for the number of RRT-free days) and complications showed no significant differences between these 2 groups (all P values >0.05). Multivariate binary logistic regression analysis showed early initiation of RRT was not an independent risk factor for increased 90-day mortality [odds ratio (OR): 0.671; 95% confidence interval (CI): 0.314-1.434; P=0.303]. Conclusions: Early initiation of RRT is not recommended to reduce mortality in AKI patients with HF.

The multi-modal fusion in visual question answering: a review of attention mechanisms.

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

Ultra-fast recovery of cathode materials from spent LiFePO4 lithium-ion batteries by novel electromagnetic separation technology.

The separation of electrode materials from current collectors plays a significant role in determining the leaching efficiency of different metals from spent lithium-ion batteries (LIBs). In the presented research, a highly efficient, environmentally sustainable, and cost-effective cathode materials separation strategy was proposed for spent LiFePO4 batteries. Based on the difference in the thermal expansion coefficient of the binder and aluminum foil, the electromagnetic induction system was examined to harvest cathode materials for the first time, which could provide a high heating rate to erase the mechanical interlocking force between Al foil and coated material, and breaking the chemical bond or Van der Waals forces of the binder. The process avoids the usage of any chemicals such as acids and alkalis, thus eliminating the emission of wastewater. Our system shows ultra-fast separation (3 min) and achieves high-purity of recovered electrode materials and Al foils (99.6% and 99.2%). Furthermore, the morphology and crystalline structure of delaminated electrode materials remain almost the same compared with the pristine materials, which provides a previously unexplored technology to realize sustainable spent battery recycling.

Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising.

Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples. However, unsupervised LDCT denoising algorithms are rarely used clinically due to their unsatisfactory denoising ability. In unsupervised LDCT denoising, the lack of paired samples makes the direction of gradient descent full of uncertainty. On the contrary, paired samples used in supervised denoising allow the parameters of networks to have a clear direction of gradient descent. To bridge the gap in performance between unsupervised and supervised LDCT denoising, we propose dual-scale similarity-guided cycle generative adversarial network (DSC-GAN). DSC-GAN uses similarity-based pseudo-pairing to better accomplish unsupervised LDCT denoising. We design a Vision Transformer-based global similarity descriptor and a residual neural network-based local similarity descriptor for DSC-GAN to effectively describe the similarity between two samples. During training, pseudo-pairs, i.e., similar LDCT samples and normal-dose CT (NDCT) samples, dominate parameter updates. Thus, the training can achieve equivalent effect as training with paired samples. Experiments on two datasets demonstrate that DSC-GAN beats the state-of-the-art unsupervised algorithms and reaches a level close to supervised LDCT denoising algorithms.

Multimodal Image Fusion for X-ray Grating Interferometry.

X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms-attenuation, refraction (differential phase-shift), and scattering (dark-field)-in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details.

Prolactin and glucocorticoid receptors in the prefrontal cortex are associated with anxiety-like behavior in prenatally stressed adolescent offspring rats.

Prenatal stress (PS) causes anxiety in mothers and their offspring and chewing is a commonly observed behavior during maternal stress. Prolactin (PRL) is an anti-anxiety factor that suppresses the hypothalamic-pituitary-adrenal axis. Here, we studied the roles of PRL, corticosterone (CORT), and their receptors in PS-induced anxiety-like behavior in dams and their offspring. We further investigated whether chewing during maternal stress could prevent PS-induced harmful consequences. Pregnant rats were randomly divided into PS, PS + chewing, and control groups. Anxiety-like behaviors of dams and their adolescent offspring were assessed using the open field test and elevated plus maze. Serum levels of PRL and CORT were measured by ELISA. Expression of mRNA and protein of PRLR and glucocorticoid receptor (GR) in the prefrontal cortex (PFC) were evaluated by qRT-PCR and western blotting, respectively. Compared to the control rats, dams and their female offspring, but not male offspring, in the PS group showed increased anxiety-like behaviors. The PS-affected rats had a lower serum PRL level and increased PRLR expression in the PFC. In contrast, these rats had a higher serum CORT level and decreased GR expression in the PFC. Chewing ameliorated anxiety-like behaviors and counteracted stress-induced changes in serum PRL and CORT, as well as the expression of their receptors in the PFC. Conclusion: PS-induced anxiety-like behavior is associated with changes in the serum levels of PRL and CORT and expression of their receptors in the PFC. Moreover, chewing blunts the hormonal and receptor changes and may serve as an effective stress-coping method for preventing PS-induced anxiety-like behavior.

Does Cr3+ Occupy Tetrahedral Sites and Luminesce in Oxides? A First-Principles Exploration.

The Cr3+ activators have been adopted to produce desired near-infrared broadband emission via ligand field engineering by choosing hosts with appropriate sites. First-principles calculations help to analyze the site, valence, and luminescent mechanism of the activators. Our calculations on Mg2Al4Si5O18:Cr elucidate that the activators are dominated by Cr3+ at tetrahedral Al and octahedral Mg sites, while the experimentally reported near-infrared emission previously assigned to tetrahedral sites is actually produced by Cr3+ at the octahedral site. Meanwhile, our results show that the emission energies of Cr3+ activators at octahedral sites can be well predicted. Moreover, further calculations show that the quenching of the 4T2 → 4T1 transition of Cr3+ at a tetrahedral site is general due to nonradiative relaxation pathways mediated by sublevels split off from the 4T1 multiplet states by intrinsic or Jahn-Teller distortions. Our work shows that the sophisticated first-principles calculations put together here can be effective in exploring Cr3+ and potentially more general activators in crystals, which benefit the design and optimization of luminescent materials.