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Lead is a common environmental pollutant which can accumulate in the kidney and cause renal injury. However, regulatory effects and mechanisms of Sparassis latifolia polysaccharide (SLP) on lipid metabolism abnormality in kidney exposed to lead are not clarified. In this study, mice were used to construct an animal model to observe the histopathological changes in kidney, measure lead content, damage indicators, differentially expressed metabolites (DEMs) and genes (DEGs) in key signaling pathways that cause lipid metabolism abnormalities based on lipidomics and transcriptomics, which were later validated using qPCR and western blotting. Co-treatment of Pb and N-acetylcysteine (NAC) were used to verify the link between SLP and oxidative stress. Our results indicated that treatment with SLP identified 276 DEMs (including metabolism of glycerophospholipid, sphingolipid, glycerolipid and fatty acid) and 177 DEGs (including genes related to oxidative stress, inflammation, autophagy and lipid metabolism). Notably, regulatory effects of SLP on abnormal lipid metabolism in kidney were mainly associated with oxidative stress, inflammation and autophagy; SLP could regulate abnormal lipid metabolism in kidney by reducing oxidative stress and affecting its downstream-regulated autophagy and inflammatory to alleviate renal injury caused by lead exposure. This study provides a theoretical basis for SLP intervention in lead injury., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The livestock sector represents major challenges to safeguarding environmental integrity. This study comprehensively analyzes ten environmental footprints of the livestock sector from 1995 to 2022, with projections until 2030, and juxtaposes them with the planetary boundaries. We quantify greenhouse gas emissions, land use, water use, particulate matter formation, and biochemical flows associated with the livestock sector. Our findings indicate that the livestock sector alone poses a significant challenge to planetary boundaries and has the potential to threaten several of these boundaries by 2030. Scenario modeling shows that a "one-size-fits-all" strategy for all countries can be suboptimal. Conversely, a region-specific strategy that requires developed regions to align meat consumption with the Eat-Lancet diet while developing regions focus on improvement of production efficiency is optimal for reducing livestock's global environmental footprints. These findings highlight the need for targeted policy measures and regional strategies to effectively mitigate the environmental impacts of the livestock sector and ensure sustainable practices., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Thapaliya, Ohib, Geenjaar, Liu, Calhoun and Plis.)

In the area of geochemical analyses of rock solutions, achieving a complete sample dissolution is a fundamental prerequisite for obtaining accurate, precise and reliable analytical results. The challenge posed by the presence of resistant minerals such as zircon, rutile, corundum, spinel, tourmaline, beryl, chromite, and cassiterite in different silicate rocks is a well-recognized challenge in geological studies. These minerals, due to their resilient nature, demand additional efforts to ensure complete dissolution during sample preparation. The prevailing conventional sample digestion methods require several days of laboratory work and the handling of large amounts of multiple types of acids, which also increase sample blanks. Until recently, there was a widely held belief that microwave-assisted digestion, where microwave radiation is transformed to heat, faced limitations in achieving complete dissolution of refractory minerals. This prevailing opinion led to skepticism about the applicability of microwave-assisted digestion for sample preparation of e.g. igneous rock samples containing these minerals. This study introduces a novel, universal and quick closed-vessel (pressurized) high-temperature microwave-assisted digestion method appropriate for dissolution of all major types of igneous silicate rock samples, including rocks containing refractory minerals. This streamlined and expeditious procedure, comprising three steps, requires only a total time of ∼9 h. The method proves its versatility by successfully dissolving both, mafic igneous samples (e.g., basalt) with low-content of resistant minerals, and felsic igneous samples (e.g., granite) with relatively high-content of resistant minerals. To validate the reliability of this procedure, 36 trace elements were analyzed: Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Pb, Th and U in several geological Certified Reference Materials (CRMs). The CRMs including basalts JB-3, BCR-2, BHVO-2; andesites JA-2, AGV-2; granodiorite GSP-2; granite JG-2 and alkaline granite MGL-OShBO, were digested and analyzed using triple quadrupole Inductively Coupled-Plasma-Mass Spectrometer (ICP-QQQ). The results of the analysis demonstrate remarkable consistency, closely aligning with both certified and literature values., Competing Interests: Declaration of competing interest The authors declare that they do not have any competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music-video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music-video editing.

Understanding the environmental fate of organic carbon associated with iron (OC-Fe) is critically important for investigating OC preservation in aquatic systems. Here, we first investigate 13 C and 14 C isotopes of OC-Fe within grain size-fractionated sediments retrieved from the East China Sea and estimate their sources and reactivities of OC-Fe through isotope-mixing models and thermal pyrolysis approaches in order to reveal the fate of OC-Fe on continental shelves influenced by hydrodynamic processes. Our results show that the OC-Fe proportion in total OC ( f OC-Fe ) in the sortable silt fractions (20-63 μm) is the highest among three grain size fractions, likely suggesting that hydrodynamics may enhance the iron protection on OC. In addition, Δ 14 C OC-Fe values fall within the range of from -358.73 to -64.03‰, and both Δ 14 C OC-Fe values and ancient OC-Fe% exhibit strong positive linear relationships with f OC-Fe . This emphasized that the hydrodynamic processes may cause the ancient OC to be tightly associated with Fe, accompanying OC-Fe aging. Our findings shed new light on the preservation of OC-Fe in marginal seas to advance the recognition of carbon "rusty sinks" in seafloor sediments.

Since its identification as a marker for advanced melanoma in the 1980s, CD146 has been found to have multiple functions in both physiological and pathological processes, including embryonic development, tissue repair and regeneration, tumor progression, fibrosis disease, and inflammations. Subsequent research has revealed that CD146 is involved in various signaling pathways as a receptor or co-receptor in these processes. This correlation between CD146 and multiple diseases has sparked interest in its potential applications in diagnosis, prognosis, and targeted therapy. To better comprehend the versatile roles of CD146, we have summarized its research history and synthesized findings from numerous reports, proposing that cell plasticity serves as the underlying mechanism through which CD146 contributes to development, regeneration, and various diseases. Targeting CD146 would consequently halt cell state shifting during the onset and progression of these related diseases. Therefore, the development of therapy targeting CD146 holds significant practical value., (© 2024. Science China Press.)

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10 -3 ; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H 2 S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Transplantation of bone marrow mesenchymal stem cells (BMSCs) has demonstrated promising clinical utility in the treatment of endometrial injury and the restoration of fertility. However, since the efficacy of BMSCs after transplantation is not stable, it is very important to find effective ways to enhance the utilisation of BMSCs. Electroacupuncture (EA) has some positive effects on the chemotaxis of stem cells and diseases related to uterine injury. In this study, we established the intrauterine adhesion (IUA) model of the Sprague-Dawley rat using lipopolysaccharide infection and mechanical scratching. Phosphate-buffered saline, BMSCs alone, and BMSCs combined with EA were randomly administered to the rats. Fluorescent cell labelling showed the migration of transplanted BMSCs. H&E staining, Masson staining, Western blot, immunohistochemistry, ELISA, and qRT-PCR were utilised to detect changes in endometrial morphology and expressions of endometrial receptivity-related factors, endometrial pro-inflammatory factors, and fibrosis factors. Finally, we conducted a fertility test to measure the recovery of uterine function. The results showed that EA promoted transplanted BMSCs to migrate into the injured uterus by activating the SDF-1/CXCR4 axis. Endometrial morphology showed the most significant improvement in the BMSC + EA group. The expressions of endometrial pro-inflammatory factors and fibrosis indexes in the BMSC + EA group were lower than those in the model and BMSC groups. Further studies revealed that the expression of endometrial receptivity-related factors and the number of embryos implanted on day 8 of gestation increased in the BMSC + EA group compared with the model group and the BMSC group., (© 2024. The Author(s), under exclusive licence to Society for Reproductive Investigation.)

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences., (© 2024. The Author(s).)

In space missions, heating films are crucial for uniformly heating onboard equipment for precise temperature control. This study develops an optimization method using surrogate models for lightweight anisotropic substrate thermal conductive heating films, meeting the requirements of uniform heating in thermal control for space applications. A feedforward neural network optimized by particle swarm optimization (PSO) was employed to create a surrogate model, mapping design parameters to the temperature uniformity of the heating film. This model served as the basis for applying the NSGA-II algorithm to quickly optimize both temperature uniformity and lightweight characteristics. In this study, the PSO-BP surrogate model was trained using heating film thermal simulation data, and the surrogate model demonstrated an accurate prediction of the mean square error (MSE) of the predicted temperature difference within 0.0168 s. The maximum temperature difference in the optimal model is 1.188 ℃, which is 30.5 times lower than before optimization, and the equivalent density is only increased by 3.9%. In summary, this optimization design method effectively captures the relationships among various parameters and optimization objectives. Its superior computational accuracy and design efficiency offer significant advantages in the design of devices such as heating films.

Objectives: Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device., Methods: The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC)., Results: A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993., Conclusions: Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality., (© 2024. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

Objective: Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children., Methods: This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score., Results: Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants., Conclusion: Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure., Significance: These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.

Liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS) is a widely utilized technique for in vivo pharmaceutical analysis. Ionization interference within electrospray ion source, occurring between drugs and metabolites, can lead to signal variations, potentially compromising quantitative accuracy. Currently, method validation often overlooks this type of signal interference, which may result in systematic errors in quantitative results without matrix-matched calibration. In this study, we conducted an investigation using ten different groups of drugs and their corresponding metabolites across three LC-ESI-MS systems to assess the prevalence of signal interference. Such interferences can potentially cause or enhance nonlinearity in the calibration curves of drugs and metabolites, thereby altering the relationship between analyte response and concentration for quantification. Finally, we established an evaluation scheme through a step-by-step dilution assay and employed three resolution methods: chromatographic separation, dilution, and stable labeled isotope internal standards correction. The above strategies were integrated into the method establishment process to improve quantitative accuracy., Competing Interests: The authors declare that there are no conflicts of interest., (© 2024 The Authors.)

Previous observational studies have found that the gut microbiota is closely related to the pathogenesis of gastroesophageal reflux disease (GERD), while their causal relationship is unclear. A two-sample multivariate Mendelian randomization analysis was implemented to estimate the causal effect of gut microbiota on GERD. The gut microbiota aggregated statistics were derived from a meta-analysis of the largest available genome-wide association studies (GWAS) conducted by the MiBioGen alliance ( n  = 13 266). GERD aggregated statistics were derived from published GWAS (129 080 cases and 473 524 controls). A bidirectional two-sample Mendelian randomization study was conducted to explore the causal relationship between gut microbiota and GERD using the inverse variance weighted (IVW), Mendelian randomization Egger, single model, weighted median, and weighted model. To verify the stability of the main results of Mendelian randomization analysis, we performed sensitivity analysis. Based on the results of IVW, we found that Anaerostipes was causally associated with an increased risk of GERD [odds ratio (OR): 1.09, P  = 0.018]. Eight gut microbiota taxa ( Actinobacteria, Bifidobacteriales, Bifidobacteriaceae, Clostridiales vadin BB60 group, Rikenellaceae, Lachnospiraceae UCG004, Methanobrevibacter , and unknown genus id.1000000073 ) are predicted to act causally in suppressing the risk of GERD ( P  < 0.05). In addition, reverse Mendelian randomization analyses revealed that the abundance of 15 gut microbiota taxon was found to be affected by GERD. No significant estimation of heterogeneity or pleiotropy is detected. Our study presents a complicated causal relationship between gut microbiota and GERD that offers guidance on the selection of appropriate probiotics as clinical interventions for GERD., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Long non-coding RNAs (lncRNAs) represent a new group of host factors involved in viral infection. Current study identified an intergenic lncRNA, LINC08148, as a proviral factor of Zika virus (ZIKV) and Dengue virus 2 (DENV2). Knockout (KO) or silencing of LINC08148 decreases the replication of ZIKV and DENV2. LINC08148 mainly acts at the endocytosis step of ZIKV but at a later stage of DENV2. RNA-seq analysis reveals that LINC08148 knockout downregulates the transcription levels of five endocytosis-related genes including AP2B1 , CHMP4C , DNM1 , FCHO1 , and Src . Among them, loss of Src significantly decreases the uptake of ZIKV. Trans -complementation of Src in the LINC08148 KO cells largely restores the caveola-mediated endocytosis of ZIKV, indicating that the proviral effect of LINC08148 is exerted through Src. Finally, LINC08148 upregulates the Src transcription through associating with its transcription factor SP1. This work establishes an essential role of LINC08148 in the ZIKV entry, underscoring a significance of lncRNAs in the viral infection., Importance: Long non-coding RNAs (lncRNAs), like proteins, participate in viral infection. However, functions of most lncRNAs remain unknown. In this study, we performed a functional screen based on microarray data and identified a new proviral lncRNA, LINC08148. Then, we uncovered that LINC08148 is involved in the caveola-mediated endocytosis of ZIKV, rather than the classical clathrin-mediated endocytosis. Mechanistically, LINC08148 upregulates the transcription of Src, an initiator of caveola-mediated endocytosis, through binding to its transcription factor SP1. This study identifies a new lncRNA involved in the ZIKV infection, suggesting lncRNAs and cellular proteins are closely linked and cooperate to regulate viral infection., Competing Interests: The authors declare no conflict of interest.

Renal ischemia-reperfusion injury (IRI) is one of the most important causes of acute kidney injury (AKI). Interleukin (IL)-37 has been suggested as a novel anti-inflammatory factor for the treatment of IRI, but its application is still limited by its low stability and delivery efficiency. In this study, we reported a novel engineered method to efficiently and easily prepare neutrophil membrane-derived vesicles (N-MVs), which could be utilized as a promising vehicle to deliver IL-37 and avoid the potential side effects of neutrophil-derived natural extracellular vesicles. N-MVs could enhance the stability of IL-37 and targetedly deliver IL-37 to damaged endothelial cells of IRI kidneys via P-selectin glycoprotein ligand-1 (PSGL-1). In vitro and in vivo evidence revealed that N-MVs encapsulated with IL-37 (N-MV@IL-37) could inhibit endothelial cell apoptosis, promote endothelial cell proliferation and angiogenesis, and decrease inflammatory factor production and leukocyte infiltration, thereby ameliorating renal IRI. Our study establishes a promising delivery vehicle for the treatment of renal IRI and other endothelial damage-related diseases., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Prostate cancer (PCa) ranks as the sixth most serious male malignant disease globally. While docetaxel (DTX) chemotherapy is the standard treatment for advanced PCa patients with distant metastasis, some individuals exhibit insensitivity or resistance to DTX. Cancer-associated fibroblasts (CAFs) play a pivotal role as stromal cells within the tumor microenvironment, influencing tumor development, progression, and drug resistance through exosomes. Ferroptosis, a novel form of programmed cell death, is characterized by intracellular iron accumulation that triggers lipid peroxidation, ultimately leading to cell demise. To delve into the potential mechanisms of chemotherapy resistance in prostate cancer, our research delved into the impact of CAF-derived exosomes on ferroptosis. Our findings revealed that CAF exosomes hindered the buildup of lipid reactive oxygen species (ROS) in prostate cancer cells induced by erastin, as well as mitigated erastin-induced mitochondrial damage, thereby impeding iron-induced cell death in prostate cancer cells. Furthermore, miR-432-5p was identified to diminish glutathione (GSH) consumption by targeting CHAC1, consequently inhibiting ferroptosis in prostate cancer cells. Our study found that miR-432-5p, originating from cancer-associated fibroblast (CAF) exosomes, suppresses ferroptosis by targeting CHAC1, thereby increasing resistance to docetaxel (DTX) in PCa. This research introduces a novel approach to address resistance to DTX., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Thapaliya, Ray, Farahdel, Suresh, Sapkota, Holla, Mahadevan, Chen, Vaidya, Perrone-Bizzozero, Benegal, Schumann, Calhoun and Liu.)

Objective: This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms., Methods: mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments., Results: A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients., Conclusion: This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis., Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest., (© Indian Orthopaedics Association 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Objective: To evaluate the predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragm thickening fraction (RR/DTF) for noninvasive-invasive mechanical ventilation transition timing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), through ultrasound evaluation of diaphragm movement indicators., Methods: Sixty-four patients diagnosed with AECOPD and undergoing non-invasive ventilation (NIV), who were admitted to the department of critical care medicine of the First Affiliated Hospital of Jinzhou Medical University from January 2022 to July 2023 were enrolled. They were divided into NIV successful group and NIV failure group based on the outcome of NIV within 24 hours. Clinical indicators such as RR/DTF, diaphragmatic excursion (DE), tidal volume (VT), respiratory rate (RR), pH value, partial pressure of carbon dioxide (PaCO 2 ), and sputum excretion disorder were compared between the two groups after 2 hours of NIV. The factors influencing NIV failure were included in binary Logistic regression analysis, and an RR/DTF oriented risk prediction model was established. Receiver operator characteristic curve (ROC curve) analysis was used to assess the predictive value of this model for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients., Results: Among 64 patients with AECOPD, with 43 in the NIV successful group and 21 in the NIV failure group. There were no statistically significant differences in baseline data such as age, gender, body mass index (BMI), oxygenation index (P/F), smoking history, and acute physiological and chronic health evaluation II (APACHE II) between the two groups of patients, indicating comparability. Compared to the NIV successful group, the NIV failure group showed a significantly increase in RR/DTF, RR, PaCO 2 , and sputum retention, while VT and DE were significantly decreased [RR/DTF (%): 1.00±0.18 vs. 0.89±0.22, RR (bpm): 21.64±3.13 vs. 19.62±2.98, PaCO 2 (mmHg, 1 mmHg ≈ 0.133 kPa): 70.82±8.82 vs. 65.29±9.47, sputum retention: 57.1% vs. 30.2%, VT (mL): 308.09±14.89 vs. 324.48±23.82, DE (mm): 19.91±2.94 vs. 22.05±3.30, all P < 0.05]. Binary Logistic regression analysis showed that RR/DTF [odds ratio (OR) = 147.989, 95% confidence interval (95%CI) was 3.321-595.412, P = 0.010], RR (OR = 1.296, 95%CI was 1.006-1.670, P = 0.045), VT (OR = 0.966, 95%CI was 0.935-0.999, P = 0.044), PaCO 2 (OR = 1.086, 95%CI was 1.006~1.173, P = 0.035), and sputum retention (OR = 4.533, 95%CI was 1.025-20.049, P = 0.046) were independent risk factors for predicting NIV failure in AECOPD patients. ROC curve analysis showed that the area under the curve (AUC) of 0.713 with a 95%CI of 0.587-0.839 (P = 0.005). The sensitivity was 72.73%, the specificity was 88.10%, the Youden index was 0.394, and the optimal cut-off value was 0.87., Conclusions: The RR/DTF risk prediction model has good predictive value for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients.

In this study, we extracted the polysaccharides from C. militaris fruiting bodies (CFIPs), mycelial intracellular polysaccharides (CMIPs), and fermentation broth extracellular polysaccharides (CFEPs) to investigate their physicochemical properties, antioxidant capacities, and effects on oxazolone-induced zebrafish ulcerative colitis (UC). Our results revealed differences in monosaccharide composition and surface structure among CFIPs, CMIPs, and CFEPs. The molar ratios of glucose to mannose in CFIPs, glucose to xylose in CMIPs, and xylose to glucose in CFEPs were 7.57: 1.6, 7.26: 1.81, and 5.44: 2.98 respectively. Moreover, CFEPs exhibited significantly (p < 0.05) higher chemical antioxidant capacity compared to CMIPs and CFIPs. Surprisingly, CFEP treatment didn't show a significant effect in protecting against H 2 O 2 -induced oxidative damage in RAW 264.7 cells. After 3 d of treatment, the levels of ROS, MDA, and MPO in the CFIPs group exhibited a significant (p < 0.05) reduction by 37.82 %, 68.15 %, and 22.77 % respectively. Additionally, the ACP and AKP increased by 60.33 % and 96.99 %. Additionally, C. militaris polysaccharides (CMPs) were found to effectively improve UC by activating the MyD88/NF-κB signaling pathway in vivo. These findings confirm the distinct physicochemical properties of these three types of CMP and their potential for development into antioxidant-rich anti-inflammatory health foods., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024. Published by Elsevier Ltd.)

Given the widespread production and use of plastics, poor biodegradability, and inadequate recycling, micro/nanoplastics (MNPs) have caused widespread environmental pollution. As a result, humans inevitably ingest MNPs through various pathways. However, there is still no consensus on whether exposure to MNPs has adverse effects on humans. This article aims to provide a comprehensive overview of the knowledge of MNPs and the potential mechanisms of their impact on the central nervous system. Numerous in vivo and in vitro studies have shown that exposure to MNPs may pass through the blood-brain barrier (BBB) and lead to neurotoxicity through impairments in oxidative and inflammatory balance, neurotransmitter alternation, nerve conduction-related key enzymes, and impact through the gut-brain axis. It is worth noting that MNPs may act as carriers and have more severe effects on the body when co-exposed with other substances. MNPs of smaller sizes cause more severe harm. Despite the scarcity of reports directly relevant to humans, this review brings together a growing body of evidence showing that exposure to MNPs disturbs neurons and has even been found to alter the memory and behavior of organisms. This effect may lead to further potential negative influence on the central nervous system and contribute to the development of other diseases such as central nervous system inflammation and Parkinson 's-like neurodegenerative disorders. There is a need further to investigate the threat of MNPs to human health., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Pterostilbene, a stilbene compound, demonstrates neuroprotective effects through its antioxidant and anti-inflammatory properties. However, pterostilbene exhibits low bioavailability. We developed a pterostilbene nanoemulsion with better release stability and particle size. Behavioral tests, including the Y maze, new object recognition, and water maze, revealed that the pterostilbene nanoemulsion demonstrated a more significant effect on improving learning and memory function than pterostilbene. Immunofluorescence analysis revealed that pterostilbene nanoemulsion was more potent in safeguarding hippocampal neurons and inhibiting apoptosis and oxidative stress than pterostilbene. Further results from the Western blot and quantitative reverse transcription polymerase chain reaction indicated that the enhanced efficacy of pterostilbene nanoemulsion may be attributed to its stronger promotion of the nuclear factor erythroid 2-related factor 2 signaling pathway. Hence, enhanced drug delivery efficiency decreased dosage requirements and increased the bioavailability of pterostilbene, thereby potentially providing a safe, effective, and convenient treatment option for patients with Alzheimer's disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: Evidence from previous experimental and observational research demonstrates that the gut microbiota is related to circulating adipokine concentrations. Nevertheless, the debate as to whether gut microbiome composition causally influences circulating adipokine concentrations remains unresolved. This study aimed to take an essential step in elucidating this issue., Methods: We used two-sample Mendelian randomization (MR) to causally analyze genetic variation statistics for gut microbiota and four adipokines (including adiponectin, leptin, soluble leptin receptor [sOB-R], and plasminogen activator inhibitor-1 [PAI-1]) from large-scale genome-wide association studies (GWAS) datasets. A range of sensitivity analyses was also conducted to assess the stability and reliability of the results., Results: The composite results of the MR and sensitivity analyses revealed 22 significant causal associations. In particular, there is a suggestive causality between the family Clostridiaceae1 (IVW: β = 0.063, P = 0.034), the genus Butyrivibrio (IVW: β = 0.029, P = 0.031), and the family Alcaligenaceae (IVW: β=-0.070, P = 0.014) and adiponectin. Stronger causal effects with leptin were found for the genus Enterorhabdus (IVW: β=-0.073, P = 0.038) and the genus Lachnospiraceae (NK4A136 group) (IVW: β=-0.076, P = 0.01). Eight candidate bacterial groups were found to be associated with sOB-R, with the phylum Firmicutes (IVW: β = 0.235, P = 0.03) and the order Clostridiales (IVW: β = 0.267, P = 0.028) being of more interest. In addition, the genus Roseburia (IVW: β = 0.953, P = 0.022) and the order Lactobacillales (IVW: β=-0.806, P = 0.042) were suggestive of an association with PAI-1., Conclusion: This study reveals a causal relationship between the gut microbiota and circulating adipokines and may help to offer novel insights into the prevention of abnormal concentrations of circulating adipokines and obesity-related diseases., (© 2024. The Author(s), under exclusive licence to Hellenic Endocrine Society.)

Polychlorinated biphenyls (PCBs) are typical persistent organic pollutants that have been associated with type 2 diabetes (T2DM) in cohort studies. This review aims to comprehensively assess the molecular mechanisms of PCBs-induced T2DM. Recent progress has been made in the research of PCBs in liver tissue, adipose tissue, and other tissues. By influencing the function of nuclear receptors, such as the aryl hydrocarbon receptor (AhR), pregnancy X receptor (PXR), and peroxisome proliferator activated receptor γ (PPARγ), as well as the inflammatory response, PCBs disrupt the balance of hepatic glucose and lipid metabolism. This is associated with insulin resistance (IR) in the target organ of insulin. Through androgen receptor (AR), estrogen receptor α/β (ERα/β), and pancreato-duodenal-homeobox gene-1 (PDX-1), PCBs affect the secretion of insulin and increase blood glucose. Thus, this review is a discussion on the relationship between PCBs exposure and the pathogenesis of T2DM. It is hoped to provide basic concepts for diabetes research and disease treatment., (© 2024 Wiley Periodicals LLC.)

Smooth bromegrass ( Bromus inermis ) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha -1 ) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R 2 = 0.75, RMSE = 51.93 kg ha -1 , MAE = 29.43 kg ha -1 , and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: This study aimed to compare the efficacy and safety of the use of esketamine to reduce the risk for postpartum depression and pain after cesarean delivery., Data Sources: Literature searches were conducted in PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Wan fang from inception to August 2023., Study Eligibility Criteria: The eligibility criteria were all randomized controlled trials of people who underwent a cesarean delivery and who were randomized to receive esketamine interventions irrespective of age or ethnicity. The outcomes that were assessed included the incidence of postpartum depression and the Edinburgh Postnatal Depression Scale score within 7 days and at 28 to 42 days after delivery, the pain score (visual analog scale or numerical rating scale, 0-10), the consumption of opioids, and intraoperative and postoperative adverse events., Methods: The Cochrane collaboration's tool was used for quality appraisal of the included studies. Statistical analysis of the data was performed using Review Manager 5.3 software, and the results were expressed as mean differences with 95% confidence intervals. Assessments were pooled using a random-effects or fixed-effects model. Study heterogeneity was assessed using the standard I 2 statistic., Results: Among the 11 included randomized controlled trials that used the Edinburgh Postnatal Depression Scale for postpartum depression assessment, patients in esketamine group had a lower risk for postpartum depression within a week of surgery (risk ratio, 0.45; 95% confidence interval, 0.33-0.62). Intraoperative use of esketamine maintained a lower Edinburgh Postnatal Depression Scale score after surgery (mean difference, -1.64; 95% confidence interval, -2.14 to -1.14). Esketamine was associated with a beneficial effect in terms of the other outcomes, including a significant decline in pain score within 48 hours (mean difference, -0.71; 95% confidence interval, -0.89 to 0.52). Esketamine increased the risk for adverse neurologic and mental events during surgery without harming health, and there was no significant difference after delivery when compared with the control group., Conclusion: Esketamine may reduce the risk for postpartum depression among patients who are undergoing cesarean delivery in the short term. In addition, as an adjunct to reduce analgesia, esketamine also effectively assists in pain management. Because of the lack of more high-quality evidence, we need more compelling evidence to confirm the value of esketamine in improving postpartum recovery., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery., (© 2024 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.

Background: Polycystic ovary syndrome (PCOS) is one of the most complex endocrine disorders in women of reproductive age. Abnormal proliferation of granulosa cells (GCs) is an important cause of PCOS. This study aimed to explore the role of fatty acid-binding protein 5 (FABP5) in granulosa cell (GC) proliferation in polycystic ovary syndrome (PCOS) patients., Methods: The FABP5 gene, which is related to lipid metabolism, was identified through data analysis of the gene expression profiles of GSE138518 from the Gene Expression Omnibus (GEO) database. The expression levels of FABP5 were measured by quantitative real-time PCR (qRT‒PCR) and western blotting. Cell proliferation was evaluated with a cell counting kit-8 (CCK-8) assay. Western blotting was used to assess the expression of the proliferation marker PCNA, and immunofluorescence microscopy was used to detect Ki67 expression. Moreover, lipid droplet formation was detected with Nile red staining, and qRT‒PCR was used to analyze fatty acid storage-related gene expression., Results: We found that FABP5 was upregulated in ovarian GCs obtained from PCOS patients and PCOS mice. FABP5 knockdown suppressed lipid droplet formation and proliferation in a human granulosa-like tumor cell line (KGN), whereas FABP5 overexpression significantly enhanced lipid droplet formation and KGN cell proliferation. Moreover, we determined that FABP5 knockdown inhibited PI3K-AKT signaling by suppressing AKT phosphorylation and that FABP5 overexpression activated PI3K-AKT signaling by facilitating AKT phosphorylation. Finally, we used the PI3K-AKT signaling pathway inhibitor LY294002 and found that the facilitation of KGN cell proliferation and lipid droplet formation induced by FABP5 overexpression was inhibited. In contrast, the PI3K-AKT signaling pathway agonist SC79 significantly rescued the suppression of KGN cell proliferation and lipid droplet formation caused by FABP5 knockdown., Conclusions: FABP5 promotes active fatty acid synthesis and excessive proliferation of GCs by activating PI3K-AKT signaling, suggesting that abnormally high expression of FABP5 in GCs may be a novel biomarker or a research target for PCOS treatment., (© 2024. The Author(s).)

Computer-aided diagnosis (CAD) systems play vital roles in the early detection of pulmonary nodules for reducing lung cancer mortality rates. To provide better services for professional doctors, this paper proposes an efficient open-source CAD platform with flexible equipments, user-friendly interfaces, and completed functions for 3D CT pulmonary nodule analysis. For the platform's design and implementation, we fully consider application scenarios and system requirements. The platform supplies core functions for (1) Basic Image Processing, (2) Intelligent Image Analysis, (3) Multi-View Image Visualization, (4) Report Editing and Generation, (5) User Information Management, and (6) Inference Service Monitoring. Specifically, other state-of-the-art or user-defined algorithms can be integrated as plugin modules with no interference for system architecture. System evaluation with use-case testing demonstrates the effectiveness and universality of the proposed platform., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The construction land quota pricing mechanism with cost plus pricing method is not sufficient to reflect its intrinsic value. This diminishes the willingness of farmers to voluntarily reclaim abandoned residential and other rural construction land, leading to suboptimal efficiency in rural land utilization and an excessive squandering of rural land resources. Thus, a sequential auction model with two stages for complementary goods was constructed, which considered the synergic characteristics between the land and quota. Further, regret psychology of bidder was considered in the case of winning or losing. A rational pricing mechanism has been developed to allocate construction land quotas, aiming to enhance farmers' motivation to the vacant homesteads of reclamation and revitalizing the stock of rural construction land. The results revealed that the regret psychology in the case of winning would decrease the transaction price of the quota, i.e., the greater the perceived regret in the case of winning, the more significant the reduction in the bidding price offered. Moreover, the regret psychology in the event of losing/failure would increase the transaction price of quota. Furthermore, publishing only the winner's price after the auction leads to the highest price of the quota offered by the bidder. In contrast, publishing only the loser's bidding price leads to the lowest transaction price of the quota offered by the bidder. In addition, the fee for delayed construction would increase the bidding price of the construction land quota. Therefore, local governments should consider announcing only the winner of price after the quota auction has ended. In addition, imposing a fee for delayed construction would enhance the transaction price of land quota, increase farmers' revenue from land reclamation, and incentivize farmers to reclaim unused rural land., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To comprehensively evaluate the contributions of numerical chromosomal abnormality, copy number variant (CNV), and sequence variant (SV) to fetuses with small head circumference in a Chinese cohort using chromosome microarray analysis and whole exome sequencing., Methods: A total of 157 fetuses with small heads defined as head circumference < - 2 standard deviation (SD) were recruited between October 2014 and March 2023. We used the ultrasonic measurement parameter Z-score to define small head as possible microcephaly (3 < Z ≤ -2), microcephaly (-5 < Z ≤ -3), or pathologic microcephaly (Z ≤ -5). Ultrasound findings and genetic results were analyzed., Results: The overall diagnostic yield of chromosomal abnormalities by microarray analysis was 13 %. Whole exome sequencing revealed eight novel variants and two interesting candidate genes and provided a 25.4 % incremental yield compared with microarray analysis. Of the detected SVs, 56 % were de novo and the most common inheritance pattern was autosomal dominant inheritance presented in 11/16 fetuses. Compared with isolated small heads, non-isolated small heads had a significantly higher detection rate of chromosomal abnormalities (16 % vs. 3.0 %, P = 0.049) but not SVs (24 % vs. 5.5 %, P = 0.126). Subgroup analysis showed that intracranial anomalies had a similar high detection rate of SVs in fetuses with all small heads subgroups while no chromosomal abnormalities and causative SVs were found in fetuses with isolated possible microcephaly., Conclusions: Ultrasound findings of small fetal head circumference < 3 SD below the mean, especially those with intracranial structural abnormalities, indicate the need for genetic counseling. Genetic variants, mainly copy number variants and SV, may be responsible for the substantial proportion of small fetal head circumference, while most are de novo. Whole exome sequencing and microarray analysis are effective diagnostic approaches for this population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI) can only reflect the relationship between pairwise brain regions. Thus, the hyper-connectivity network (HCN) has been widely used to reveal high-order interactions among multiple brain regions. However, existing HCN models are essentially spatial HCN, which reflect the spatial relevance of multiple brain regions, but ignore the temporal correlation among multiple time points. Furthermore, the majority of HCN construction and learning frameworks are limited to using a single template, while the multi-template carries richer information. To address these issues, we first employ multiple templates to parcellate the rs-fMRI into different brain regions. Then, based on the multi-template data, we propose a spatio-temporal weighted HCN (STW-HCN) to capture more comprehensive high-order temporal and spatial properties of brain activity. Next, a novel deep fusion model of multi-template called spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN) is proposed to fuse the STW-HCN of multiple templates, which extracts the deep interrelation information between different templates. Finally, we evaluate our method on the ADNI-2 and ABIDE-I datasets for mild cognitive impairment (MCI) and autism spectrum disorder (ASD) analysis. Experimental results demonstrate that the proposed method is superior to the state-of-the-art approaches in MCI and ASD classification, and the abnormal spatio-temporal hyper-edges discovered by our method have significant significance for the brain abnormalities analysis of MCI and ASD.

Background: The aim of this study was to identify the risk factors for posttraumatic stress disorder in patients with myocardial infarction., Methods: Cohort, case-control, and cross-sectional studies on posttraumatic stress disorder (PTSD) in patients with myocardial infarction were searched from PubMed, Embase, Cochrane Library, Web of Science, China Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Technology Journal database. The Newcastle-Ottawa Quality Assessment Scale was used to score the quality of the included literature in the cohort and case-control studies, and the cross-sectional studies were scored using the American Agency for Health Care Quality and Research cross-sectional study quality evaluation criteria. The literature was screened independently by 2 researchers, and if there was no consensus, the inclusion was decided by a third party. The extraction content included first author, publication year, sample size, PTSD assessment tool, PTSD assessment time, PTSD incidence, influencing factors, and study type. Meta-analysis of data was performed using Stata17.0 software., Results: Ten studies were included, including 2 cohort studies, 7 cross-sectional studies, and 1 case-control study, with a total sample size of 2371 patients, including 26 influencing factors. The results of meta-analysis showed that the prevalence of PTSD in patients with myocardial infarction was 21.2%. Statistically significant influencing factors were gender (odd ratio [OR] = 3.124), neuroticism score (OR = 2.069), and age (OR = 0.913)., Conclusions: The prevalence of PTSD in patients with myocardial infarction in China is higher than that in other countries. Female and neurotic personality are risk factors for developing PTSD in patients with myocardial infarction, and old age is protective factor for developing PTSD in patients with myocardial infarction. Targeted measures should be taken to prevent and reduce the occurrence and development of PTSD in patients with myocardial infarction in the future., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure., Methods: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference., Results: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness., Conclusions: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment., (Copyright © 2023. Published by Elsevier Inc.)

Introduction: Breast cancer (BC) is the most common malignant tumor in the female population. Despite staging and treatment consensus guidelines, significant heterogeneity exists in BC patients' prognosis and treatment efficacy. Alterations in one-carbon (1C) metabolism are critical for tumor growth, but the value of the role of 1C metabolism in BC has not been fully investigated., Methods: To investigate the prognostic value of 1C metabolism-related genes in BC, 72 1C metabolism-related genes from GSE20685 dataset were used to construct a risk-score model via univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm, which was validated on three external datasets. Based on the risk score, all BC patients were categorized into high-risk and low-risk groups. The predictive ability of the model in the four datasets was verified by plotting Kaplan-Meier curve and receiver operating characteristic (ROC) curve. The candidate genes were then analyzed in relation to gene mutations, gene enrichment pathways, immune infiltration, immunotherapy, and drug sensitivity., Results: We identified a 7-gene 1C metabolism-related signature for prognosis and structured a prognostic model. ROC analysis demonstrated that the model accurately predicted the 2-, 3-, and 5-year overall survival rate of BC patients in the four cohorts. Kaplan-Meier analysis revealed that survival time of high-risk patients was markedly shorter than that of low-risk patients (p < 0.05). Meanwhile, high-risk patients had a higher tumor mutational burden (TMB), enrichment of tumor-associated pathways such as the IL-17 signaling pathway, lower levels of T follicular helper (Tfh) and B cells naive infiltration, and poorer response to immunotherapy. Furthermore, a strong correlation was found between MAT2B and CHKB and immune checkpoints., Discussion: These findings offer new insights into the effect of 1C metabolism in the onset, progression, and therapy of BC and can be used to assess BC patients' prognosis, study immune infiltration, and develop potentially more effective clinical treatment options., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Liu, Wang, Liu, Qu and Zhang.)

Background: As an important factor affecting personal health, anxiety has always been valued by people. Prior research has consistently shown that personality traits is associated with anxiety level,but little is known about the inner mechanism of this relationship. To fill the gap, the present research aims to explore the chain mediating role of general self-efficacy and academic burnout in the relationship between big five personality and anxiety., Methods: This cross-sectional study was performed from September to November 2022. Self-reported questionnaires including the Big Five Personality Questionnaire, General Self-Efficacy Scale, College Student's academic burnout Scale, Generalized Anxiety Scale and demographic characteristics were distributed to 2505 college students in a university in Hebei Province, of which 2,471 were valid. Statistical analysis was carried out through SPSS26.0 and SPSS PROCESS macro., Results: Results showed four of the big five personality characters (i.e., extraversion, agreeableness, conscientiousness, and openness) were negatively correlated with anxiety. Neuroticism was positively correlated with anxiety. Moreover, general self-efficacy was found to be negatively correlated with academic burnout and anxiety; academic burnout was positively correlated with anxiety. Finally, general self-efficacy and academic burnout mediated the relationship between personality traits (i.e., extraversion, agreeableness, neuroticism, openness) and anxiety., Conclusions: Personality traits (i.e., extraversion, agreeableness, neuroticism, and openness) could influence anxiety through the chain mediating effects of general self-efficacy and academic burnout. Interventions focusing on anxiety reduction may be successful in increasing general self-efficacy and decreasing students' academic burnout., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)