MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm.

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species' potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen's kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Rationale and design of a Single-center Randomized Trial to Compare the Graft Patency between the Radial Artery and the No-touch Saphenous Vein in Coronary Artery Bypass Grafting Surgery (GRAFT-CAB Study).

BACKGROUND: Previous studies suggested only the radial artery and the No-touch (NT) technique were effective in reducing graft occlusion after coronary artery bypass grafting (CABG) surgery. However, there is no randomized trial comparing these two graft conduits. The optimum second conduit for CABG remains undetermined. MATERIALS AND METHODS: This study is a prospective, single-center randomized clinical trial, aiming to compare the graft patency between the radial artery and the NT vein graft. All patients undergoing isolated CABG with left internal mammary artery (LIMA) plus at least two additional grafts will be considered eligible. 774 cases (516 in the radial artery group and 258 in the NT vein group) will be enrolled in over 1 to 2 years. Participants will be randomized and allocated to two bypass strategies: the LIMA plus one radial artery and one conventional vein graft, or the LIMA plus two NT vein grafts. The primary outcome is graft occlusion at 1 year after CABG evaluated by CT angiography. The secondary outcomes include graft occlusion at 3 and 5 years and major adverse cardiac or cerebrovascular events at 1, 3 and 5 years follow-ups. DISCUSSION: This study will define whether or not the NT vein has a lower graft occlusion rate than the radial artery in short and mid-term follow-ups, and provide new evidence for the second conduit choice in CABG surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06014047. Registered on October 15th, 2023.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

A systematic review of antibiotics and antibiotic resistance genes (ARGs) in mariculture wastewater: Antibiotics removal by microalgal-bacterial symbiotic system (MBSS), ARGs characterization on the metagenomic.

Antibiotic residues in mariculture wastewater seriously affect the aquatic environment. Antibiotic Resistance Genes (ARGs) produced under antibiotic stress flow through the environment and eventually enter the human body, seriously affecting human health. Microalgal-bacterial symbiotic system (MBSS) can remove antibiotics from mariculture and reduce the flow of ARGs into the environment. This review encapsulates the present scenario of mariculture wastewater, the removal mechanism of MBSS for antibiotics, and the biomolecular information under metagenomic assay. When confronted with antibiotics, there was a notable augmentation in the extracellular polymeric substances (EPS) content within MBSS, along with a concurrent elevation in the proportion of protein (PN) constituents within the EPS, which limits the entry of antibiotics into the cellular interior. Quorum sensing stimulates the microorganisms to produce biological responses (DNA synthesis - for adhesion) through signaling. Oxidative stress promotes gene expression (coupling, conjugation) to enhance horizontal gene transfer (HGT) in MBSS. The microbial community under metagenomic detection is dominated by aerobic bacteria in the bacterial-microalgal system. Compared to aerobic bacteria, anaerobic bacteria had the significant advantage of decreasing the distribution of ARGs. Overall, MBSS exhibits remarkable efficacy in mitigating the challenges posed by antibiotics and resistant genes from mariculture wastewater.

Echelon grating refractive index sensor.

There are few reports on optical refractive index sensors that have both high resonant-wavelength resolution (RWR) and high refractive index sensitivity (RIS). Herein, based on an echelon grating, we design a refractive index sensor that combines the two advantages together. The principal fringe of echelon grating has a small full width at half maximum and a good signal-to-noise ratio, leading to a high RWR. The wavefront splitting interference makes the sensor have high RIS. The large free spectral range (FSR) of the principal fringes expands the dynamic range of the sensor. The experimentally realized RWR, RIS, and FSR are 2 × 10-2 nm, 1.14 × 104 nm/RIU (RIU: refractive index unit), and 130 nm, respectively. The detection limit of refractive index is 1.59 × 10-6 RIU. The dynamic range of the sensor is 1.14 × 10-2 RIU. In addition, there are schemes to improve RWR and RIS, which can further reduce the detection limit of refractive index. The echelon grating refractive index sensor features low detection limit, low cost, high stability, and good robustness.

High-reliability infrared broadband thin-film polarizing beam splitter with ZnSe compensation layers.

Thin-film polarizing beam splitters (PBSs) fulfill a pivotal role in laser beam splitting, modulation, shaping and isolation. In this study, a high-reliability infrared broadband thin-film PBS was developed. To correct for tensile stress in Ge/YbF3 multilayer coatings, ZnSe compensation layers were incorporated in the multilayer design. The effects of different symmetrical periods on the spectral properties of the infrared PBS were systematically discussed. The infrared PBS operated at 45° and in the long-wave infrared (LWIR) band. Using the percent of optical extrema monitoring (POEM) strategy combined with the high-temperature optical constants (HTOC) of Ge film, the infrared PBS was precisely fabricated on ZnSe substrates. Subsequently, the spectral performance and film reliability of the infrared PBS were carefully characterized. Specifically, the transmittance of p-polarization surpassed 96%, while the extinction ratio exceeded 100:1 within the 10.6 ± 0.15 µm band. The infrared PBS demonstrated commendable environmental reliability, in addition to exhibiting excellent spectral characteristics.

Precisely Self-Cooperative Nanoassembly Enables Photothermal/Ferroptosis Synergistic Tumor Eradication.

Ferroptosis is identified as a potential target for anticancer therapy. However, most conventional ferroptosis inducers not only fail to trigger intracellular lipid peroxidation storm, but are also prone to cause ferroptosis-related toxicity through off-target destruction of intracellular antioxidant defense systems. Therefore, a potent and highly tumor-specific ferroptosis induction modality is desired. Herein, a self-cooperative nanomedicine for imaging-guided photothermal ferrotherapy, which is fabricated based on molecular nanoassembly (NA) of DiR (a photothermal probe) and ferrocene (Fc, a reactant of the Fenton reaction), is elaborately exploited. DiR-elicited hyperthermia induces both photothermal therapy (PTT) and a significant acceleration of the kinetics of the Fc-involved Fenton reaction, collaboratively causing a lipid peroxidation storm in tumor cells. In turn, plenty of lipid peroxides boost PTT through the downregulation of heat shock protein 90. As expected, such a self-cooperative NA demonstrates synergetic tumor eradication in the 4T1 breast tumor-bearing mice xenograft model. This study offers a novel nanotherapeutic paradigm for precise multimodal cancer therapy.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning.

RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA, and mRNA. It influences various cellular functions, including mRNA stability and rRNA biosynthesis. Wet-lab detection of ac4C modification sites is highly resource-intensive and costly. Therefore, various machine learning and deep learning techniques have been employed for computational detection of ac4C modification sites. The known ac4C modification sites are limited for training an accurate and stable prediction model. This study introduces GANSamples-ac4C, a novel framework that synergizes transfer learning and generative adversarial network (GAN) to generate synthetic RNA sequences to train a better ac4C modification site prediction model. Comparative analysis reveals that GANSamples-ac4C outperforms existing state-of-the-art methods in identifying ac4C sites. Moreover, our result underscores the potential of synthetic data in mitigating the issue of data scarcity for biological sequence prediction tasks. Another major advantage of GANSamples-ac4C is its interpretable decision logic. Multi-faceted interpretability analyses detect key regions in the ac4C sequences influencing the discriminating decision between positive and negative samples, a pronounced enrichment of G in this region, and ac4C-associated motifs. These findings may offer novel insights for ac4C research. The GANSamples-ac4C framework and its source code are publicly accessible at http://www.healthinformaticslab.org/supp/.

Discriminating the Active Ru Species Towards the Selective Generation of Singlet Oxygen from Peroxymonosulfate: Nanoparticles Surpass Single-Atom Catalysts.

Singlet oxygen (1O2) is an exceptional reactive oxygen species in advanced oxidation processes for environmental remediation. Despite single-atom catalysts (SACs) representing the promising candidate for the selective generation of 1O2 from peroxymonosulfate (PMS), the necessity to meticulously regulate the coordination environment of metal centers poses a significant challenge in the precisely-controlled synthetic method. Another dilemma to SACs is their high surface free energy, which results in an inherent tendency for the surface migration and aggregation of metal atoms. We here for the first time reported that Ru nanoparticles (NPs) synthesized by the facile pyrolysis method behave as robust Fenton-like catalysts, outperforming Ru SACs, towards efficient activation of PMS to produce 1O2 with nearly 100 % selectivity, remarkably improving the degradation efficiency for target pollutants. Density functional theory calculations have unveiled that the boosted PMS activation can be attributed to two aspects: (i) enhanced adsorption of PMS molecules onto Ru NPs, and (ii) decreased energy barriers by offering adjacent sites for promoted dimerization of *O intermediates into adsorbed 1O2. This study deepens the current understanding of PMS chemistry, and sheds light on the design and optimization of Fenton-like catalysts.

Facile Friedel-Crafts alkylation of arenes under solvent-free conditions.

The Friedel-Crafts alkylation of arenes is an important part of electrophilic aromatic substitution reactions. However, the reactivity of arenes is weakened by electron-withdrawing substituents, leading to limited substrate scopes and applications. Herein, we developed an efficient HOTf-promoted Friedel-Crafts alkylation reaction of broad arenes with α-aryl-α-diazoesters under metal-free and solvent-free conditions.

Research on accurate pipetting complementation model for high-throughput molecular detection platform.

The incidence of infectious diseases has risen in recent years, leading to a significant surge in the demand for medical molecular detection. High-throughput molecular detection platforms play a crucial role in facilitating rapid and efficient molecular detection. Among the various techniques employed in high-throughput molecular detection, microliquid transfer stands out as one of the most frequently utilized methods. However, ensuring the accuracy of liquid transfer poses a challenge due to variations in the physical and chemical properties of different samples and reagents. In this study, a pipetting complementation model was developed specifically for the serum, paraffin oil, and throat swabs. The aim was to enhance the transfer accuracy of diverse liquids in the context of high-throughput molecular detection, ultimately ensuring detection reliability and stability. The experimental findings revealed notable improvements in pipetting accuracy after compensating for the three liquids. In particular, the pipetting error rates decreased by 52.5, 96, and 71.4% for serum, paraffin oil, and throat swabs, respectively. These results underscore the model's effectiveness in providing reliable support for the precise transfer of liquids on the high-throughput molecular detection platform.

Mitochondrial KATP channel-mediated autophagy contributes to angiotensin II-induced vascular dysfunction in mice.

BACKGROUND AND AIM: The present study aimed to investigate whether the mitochondrial KATP channel contributes to angiotensin II (Ang II)-induced vascular dysfunction, the development of hypertension, and atherosclerosis. METHODS AND RESULTS: ApoE (-/-) mice fed a high-fat diet were chronically infused with Ang II for eight weeks and concomitantly treated with losartan (ARB), apocynin, or 5-hydroxy decanoate (5-HD), or 3-methyladenine (3-MA). Systolic blood pressure was measured, and pathological changes of aortic or liver tissue were observed. Nitric oxide (NO), superoxide dismutase 2 (SOD2) levels and vasorelaxation rate were measured, and protein and mRNA expressions were examined by western blot and RT-PCR. Ang II-induced development of hypertension was suppressed not only by ARB, and apocynin but also by 5-HD or 3-MA. Ang II infusion decreased aortic NO production and relaxation, as well as SOD2 activity in liver, which were improved by all treatments. In addition, Ang II-induced activation of autophagy was suppressed by 5-HD in aortic tissue, furthermore, Ang II increases the atherosclerotic index in plasma and exacerbates the development of atherosclerosis by increases of fat deposition in the aorta and liver. Lipid metabolism-related mRNA expressions (LXR-α, LDLR, SRBI, Acca, and FASN) were changed by Ang II. Similarly, not only ARB, and apocynin, but also 5-HD and 3-MA suppressed Ang II-induced these changes. CONCLUSIONS: Our present findings evidence that mitochondrial KATP channel-mediated autophagy contributes to Ang II-induced vascular dysfunction, development of hypertension, and atherosclerosis.

A general approach for inferring the ancestry of recent ancestors of an admixed individual.

The genome of an individual from an admixed population consists of segments originated from different ancestral populations. Most existing ancestry inference approaches focus on calling these segments for the extant individual. In this paper, we present a general ancestry inference approach for inferring recent ancestors from an extant genome. Given the genome of an individual from a recently admixed population, our method can estimate the proportions of the genomes of the recent ancestors of this individual that originated from some ancestral populations. The key step of our method is the inference of ancestors (called founders) right after the formation of an admixed population. The inferred founders can then be used to infer the ancestry of recent ancestors of an extant individual. Our method is implemented in a computer program called PedMix2. To the best of our knowledge, there is no existing method that can practically infer ancestors beyond grandparents from an extant individual's genome. Results on both simulated and real data show that PedMix2 performs well in ancestry inference.

RediscMol: Benchmarking Molecular Generation Models in Biological Properties.

Deep learning-based molecular generative models have garnered emerging attention for their capability to generate molecules with novel structures and desired physicochemical properties. However, the evaluation of these models, particularly in a biological context, remains insufficient. To address the limitations of existing metrics and emulate practical application scenarios, we construct the RediscMol benchmark that comprises active molecules extracted from 5 kinase and 3 GPCR data sets. A set of rediscovery- and similarity-related metrics are introduced to assess the performance of 8 representative generative models (CharRNN, VAE, Reinvent, AAE, ORGAN, RNNAttn, TransVAE, and GraphAF). Our findings based on the RediscMol benchmark differ from those of previous evaluations. CharRNN, VAE, and Reinvent exhibit a greater ability to reproduce known active molecules, while RNNAttn, TransVAE, and GraphAF struggle in this aspect despite their notable performance on commonly used distribution-learning metrics. Our evaluation framework may provide valuable guidance for advancing generative models in real-world drug design scenarios.

Current trends in gas-synergized phototherapy for improved antitumor theranostics.

Phototherapy, such as photothermal therapy (PTT) and photodynamic therapy (PDT), has been considered an elegant solution to eradicate tumors due to its minimal invasiveness and low systemic toxicity. Nevertheless, it is still challenging for phototherapy to achieve ideal outcomes and clinical translation due to its inherent drawbacks. Owing to the unique biological functions, diverse gases have attracted growing attention in combining with phototherapy to achieve super-additive therapeutic effects. Specifically, gases such as nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S) have been proven to kill tumor cells by inducing mitochondrial damage in synergy with phototherapy. Additionally, several gases not only enhance the thermal damage in PTT and the reactive oxygen species (ROS) production in PDT but also improve the tumor accumulation of photoactive agents. The inflammatory responses triggered by hyperthermia in PTT are also suppressed by the combination of gases. Herein, we comprehensively review the latest studies on gas-synergized phototherapy for cancer therapy, including (1) synergistic mechanisms of combining gases with phototherapy; (2) design of nanoplatforms for gas-synergized phototherapy; (3) multimodal therapy based on gas-synergized phototherapy; (4) imaging-guided gas-synergized phototherapy. Finally, the current challenges and future opportunities of gas-synergized phototherapy for tumor treatment are discussed. STATEMENT OF SIGNIFICANCE: 1. The novelty and significance of the work with respect to the existing literature. (1) Strategies to design nanoplatforms for gas-synergized anti-tumor phototherapy have been summarized for the first time. Meanwhile, the integration of various imaging technologies and therapy modalities which endow these nanoplatforms with advanced theranostic capabilities has been summarized. (2) The mechanisms by which gases synergize with phototherapy to eradicate tumors are innovatively and comprehensively summarized. 2. The scientific impact and interest. This review elaborates current trends in gas-synergized anti-tumor phototherapy, with special emphases on synergistic anti-tumor mechanisms and rational design of therapeutic nanoplatforms to achieve this synergistic therapy. It aims to provide valuable guidance for researchers in this field.

A universal powder-laden crosslinked chitosan microneedle patch for high-dose controllable drug delivery.

In this study, we constructed a novel powder-laden core-shell crosslinked chitosan microneedle patch for high-dose and controllable delivery of various drugs, including both macromolecular biological drugs and small-molecule chemical drugs. Direct loading of drug powders greatly improved drug loading capacity and minimized degradation. The results of the in vitro drug release study suggested that the release behaviors of the most tested drugs (both macromolecular drugs and small-molecule drugs) can be tuned by adjusting the crosslink density of the microneedle shell to achieve either rapid or sustained release of the loaded drug. The in vivo hypoglycemic efficacy test in streptozotocin-induced diabetic mice further proved that the onset and duration of the insulin-laden patch can be customized by adjusting the crosslink density. Furthermore, a combination of microneedle patches with different crosslink densities not only rapidly reduced blood glucose levels to normoglycemic levels (within 1 h) but also maintained normoglycemia for up to 36 h. The insulin loaded in the patch also showed good stability during storage at 40 °C for 6 months. Our results suggest that this powder-laden patch represents a strong candidate for addressing the multiple challenges in the preparation and application of polymeric microneedles and shows promise in clinical applications.

Individuals with higher suicide risk showed more anger and disgust during rest.

Previous research showed that suicide risk was associated with the anger trait and the facial expression of anger when advising on life dilemmas. We investigated if suicide risk was associated with the facial expression of anger during rest, a state when individuals often reflect upon their lives. Participants took a 1-min rest before being assessed for suicide risk. We measured 147 participants' frontal-view facial expressions during their rest 1475-3694 times using automated facial expression analysis technology. Participants' suicide risk was significantly positively correlated with their anger and disgust during the rest, which may be related to psychological pain and death-related thoughts among individuals with suicide risk. Therefore, rest for clinical patients should not be seen simply as a "rest" for the mind. Rather, for counselors, rest may open a window to look into patients' inner thoughts that may be important to their lives.

Protective factors for children with autism spectrum disorder during COVID-19-related strict lockdowns: a Shanghai autism early developmental cohort study.

BACKGROUND: COVID-19 lockdowns increased the risk of mental health problems, especially for children with autism spectrum disorder (ASD). However, despite its importance, little is known about the protective factors for ASD children during the lockdowns. METHODS: Based on the Shanghai Autism Early Developmental Cohort, 188 ASD children with two visits before and after the strict Omicron lockdown were included; 85 children were lockdown-free, while 52 and 51 children were under the longer and the shorter durations of strict lockdown, respectively. We tested the association of the lockdown group with the clinical improvement and also the modulation effects of parent/family-related factors on this association by linear regression/mixed-effect models. Within the social brain structures, we examined the voxel-wise interaction between the grey matter volume and the identified modulation effects. RESULTS: Compared with the lockdown-free group, the ASD children experienced the longer duration of strict lockdown had less clinical improvement (ß = 0.49, 95% confidence interval (CI) [0.19-0.79], p = 0.001) and this difference was greatest for social cognition (2.62 [0.94-4.30], p = 0.002). We found that this association was modulated by parental agreeableness in a protective way (-0.11 [-0.17 to -0.05], p = 0.002). This protective effect was enhanced in the ASD children with larger grey matter volumes in the brain's mentalizing network, including the temporal pole, the medial superior frontal gyrus, and the superior temporal gyrus. CONCLUSIONS: This longitudinal neuroimaging cohort study identified that the parental agreeableness interacting with the ASD children's social brain development reduced the negative impact on clinical symptoms during the strict lockdown.