Tailoring near-infrared amyloid-ß probes with high-affinity and low background based on CN and amphipathic regulatory strategies and in vivo imaging of AD mice.

Amyloid-ß (Aß) species (Aß fibrils and Aß plaques), as one of the typical pathological markers of Alzheimer's disease (AD), plays a crucial role in AD diagnosis. Currently, some near-infrared I (NIR I) Aß probes have been reported in AD diagnosis. However, they still face challenges such as strong background interference and the lack of effective probe design. In this study, we propose molecular design strategy that incorporates CN group and amphiphilic modulation to synthesize a series of amphiphilic NIR I Aß probes, surpassing the commercial probe ThT and ThS. Theoretical calculations indicate that these probes exhibit stronger interaction with amino acid residues in the cavities of Aß. Notably, the probes containing CN group display the ability of binding two distinct sites of Aß, which dramatically enhanced the affinity to Aß species. Furthermore, these probes exhibit minimal fluorescence in aqueous solution and offer ultra-high signal-to-noise ratio (SNR) for in vitro labeling, even in wash-free samples. Finally, the optimal probe DM-V2CN-PYC3 was utilized for in vivo imaging of AD mice, demonstrating its rapid penetration through the blood-brain barrier and labelling to Aß species. Moreover, it enabled long-term monitoring for a duration of 120 min. These results highlight the enhanced affinity and superior performance of the designed NIR I Aß probe for AD diagnosis. The molecular design strategy of CN and amphiphilic modulation presents a promising avenue for the development Aß probes with low background in vivo/in vitro imaging for Aß species.

Identification and expression analysis of bZIP transcription factors in Setaria italica in response to dehydration stress.

Among the largest transcription factor families in plants, bZIPs are crucial for various developmental and physiological processes, particularly abiotic stress resistance. Setaria italica has become a model for understanding stress resistance mechanisms. In this study, we identified 90 bZIP transcription factors in the Setaria italica genome. SibZIPs were classified into 13 groups based on references to Arabidopsis bZIPs. Members in the same group shared similar motifs and gene structure pattern. In addition, gene duplication analysis indenfied 37 pairs of segmental duplicated genes and none tandem duplicated genes in S. italica suggesting segmental duplication contributed to the expansion of the S. italica bZIP gene family. Moreover, the number of SibZIPs genes (39) exhibiting higher expression in roots was significantly more than that in other organs. Twelve SibZIP genes were upregulated in response to dehydration stress. In conclusion, our study advances the current understanding of SibZIP genes and provide a number of candidates for functional analysis of drought tolerance in S. italica.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA-disease association.

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA-disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA-disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.

Two new types of structures from soft coral-associated epiphytic fungus Aspergillus versicolor CGF9-1-2.

Global Natural Products Social (GNPS) molecular networking platform was applied to discovery the undescribed compounds from the common marine fungi Aspergillus versicolor CGF9-1-2, ultimately resulting in isolation of four new polyketides, decumbenone E (1), decumbenone F (2), 2'-epi-8-O-methylnidurufin (6), (-)-phomoindene A (7), one new nucleoside, 3-methyl-9-(2-methylbutene)-xanthine (8), and five known analogues. Their structures were elucidated based on 1D/2D NMR spectroscopic and HRESIMS data analyses, meanwhile, the absolute configurations of new compounds were established based on the X-ray crystallographic experiments, as well as the electronic circular dichroism (ECD) analysis. All compounds were predicted pharmaceutical chemistry with ten commonly disease-related proteins by molecular docking. In addition, all compounds against TDP1 were performed in vitro, which was consistent with the docking result, and compound 6 shown a weak inhibitory activity.

Drug repositioning via Multi-view Representation Learning with Heterogeneous Graph Neural Network.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, current computational drug repositioning methods face challenges in accurately predicting drug-disease associations. These challenges include only considering drugs and diseases to construct a heterogeneous graph without including other biological nodes associated with the disease or drug for a more comprehensive heterogeneous graph, as well as not fully utilizing the local structure of heterogeneous graphs and rich semantic features. To address these problems, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformerbased semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.

HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network.

Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.

Self-supported porous wood carbon electrode with a MoC/carbon nanocage composite for application in a high-performance supercapacitor.

The rational design and synthesis of carbon nanocages with highly complex porous structures are continuously facing challenges in the development of high-performance supercapacitors (SCs). The electrochemical performance characteristics of electrodes rely on their compositions and fabrication methods. Here, we propose a universal and efficient approach for the in-situ synthesis of zeolitic imidazolate framework-8 (ZIF-8) on porous carbonized wood, where the selective utilization of hexacarbonyl molybdenum protects the structural integrity of the ZIF-8 precursor, preventing collapse during thermal treatment. The subsequent pyrolysis process leads to the formation of small-sized molybdenum carbide (MoC) which are embedded in carbon nanocages (CN). The composite electrode consists of MoC/CN embedded in a porous carbonized wood (PCW), and it shows area-specific capacity of 9.7F cm-2 and 9.4 F cm-2 at 5 mA cm-2 and 30 mA cm-2, respectively. Subsequently, the symmetric supercapacitor, with two MoC/CN@PCW electrodes exhibits a areal specific capacitance of 2.7 F cm-2 at 5 mA cm-2. Moreover, this supercapacitor maintains an capacitance retention rate of 98.5 % after 12,000 discharge cycles. The supercapacitor exhibits a power density of 6.5 mW cm-2, resulting in an energy density of 0.864 mWh cm-2. Therefore, the utilization of wood-based electrodes holds promise for energy storage systems.

High performance supercapacitors deploying cube-templated tracheid cavities of wood-derived carbon.

Wood-derived carbon, with its strong tracheid array structure, is an ideal material for use as a self-supporting electrode in supercapacitors. By leveraging the inherent through pore structure and surface affinity found in wood tracheids, we successfully engineered a highly spatially efficient cube-templated porous carbon framework inside carbonized wood tracheid cavities through precise control over precursor crystallization temperatures. This innovative cubic channel architecture effectively maximizes up to (79 ± 1)% of the cavity volume in wood-derived carbon while demonstrating exceptional hydrophilicity and high conductivity properties, facilitating the development of supercapacitors with enhanced areal/volumetric capacitances (2.65F cm-2/53.0F cm-3 at 5.0 mA cm-2) as well as superior areal/volumetric energy densities (0.37 mWh cm-2/7.36 mWh cm-3 at 2.5 mW cm-2). The fabrication of these cube-templated channels with high cube filling content is not only simple and precisely controllable, but also environmentally friendly. The proposed method eliminates the conventional acid-base treatment process for pore formation, facilitating the rapid development and practical implementation of thick electrodes with superior performance in supercapacitors. Moreover, it offers a universal research approach for the commercialization of wood-derived thick electrodes.

Validated enhancement and temperature modulated absorbance of a WS2 monolayer based on a planar structure.

Transition-metal dichalcogenides (TMDCs), as emerging optoelectronic materials, necessitate the establishment of an experimentally viable system to study their interaction with light. In this study, we propose and analyze a WS2/PMMA/Ag planar Fabry-Perot (F-P) cavity, enabling the direct experimental measurement of WS2 absorbance. By optimizing the structure, the absorbance of A exciton of WS2 up to 0.546 can be experimentally achieved, which matches well with the theoretical calculations. Through temperature and thermal expansion strain induced by temperature, the absorbance of the A exciton can be tuned in situ. Furthermore, temperature-dependent photocurrent measurements confirmed the consistent absorbance of the A exciton under varying temperatures. This WS2/PMMA/Ag planar structure provides a straightforward and practical platform for investigating light interaction in TMDCs, laying a solid foundation for future developments of TMDC-based optoelectronic devices.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.

Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.

Beyond Conventional Charge Density Wave for Strongly Enhanced 2D Superconductivity in 1H-TaS2 Superlattices.

Noncentrosymmetric transition metal dichalcogenide (TMD) monolayers offer a fertile platform for exploring unconventional Ising superconductivity (SC) and charge density waves (CDWs). However, the vulnerability of isolated monolayers to structural disorder and environmental oxidation often degrade their electronic coherence. Herein, an alternative approach is reported for fabricating stable and intrinsic monolayers of 1H-TaS2 sandwiched between SnS blocks in a (SnS)1.15TaS2 van der Waals (vdW) superlattice. The SnS block layers not only decouple individual 1H-TaS2 sublayers to endow them with monolayer-like electronic characteristics, but also protect the 1H-TaS2 layers from electronic degradation. The results reveal the characteristic 3 × 3 CDW order in 1H-TaS2 sublayers associated with electronic rearrangement in the low-lying sulfur p band, which uncovers a previously undiscovered CDW mechanism rather than the conventional Fermi surface-related framework. Additionally, the (SnS)1.15TaS2 superlattice exhibits a strongly enhanced Ising-like SC with a layer-independent Tc of ≈3.0 K, comparable to that of the isolated monolayer 1H-TaS2 sample, presumably attributed to their monolayer-like characteristics and retained Fermi states. These results provide new insights into the long-debated CDW order and enhanced SC of monolayer 1H-TaS2, establishing bulk vdW superlattices as promising platforms for investigating exotic collective quantum phases in the 2D limit.

Engineering Faradaic Electrode Materials for High-Efficiency Water Desalination.

Water desalination technologies play a key role in addressing the global water scarcity crisis and ensuring a sustainable supply of freshwater. In contrast to conventional capacitive deionization, which suffers from limitations such as low desalination capacity, carbon anode oxidation, and co-ion expulsion effects of carbon materials, the emerging faradaic electrochemical deionization (FDI) presents a promising avenue for enhancing water desalination performance. These electrode materials employed faradaic charge-transfer processes for ion removal, achieving higher desalination capacity and energy-efficient desalination for high salinity streams. The past decade has witnessed a surge in the advancement of faradaic electrode materials and considerable efforts have been made to explore optimization strategies for improving their desalination performance. This review summarizes the recent progress on the optimization strategies and underlying mechanisms of faradaic electrode materials in pursuit of high-efficiency water desalination, including phase, doping and vacancy engineering, nanocarbon incorporation, heterostructures construction, interlayer spacing engineering, and morphology engineering. The key points of each strategy in design principle, modification method, structural analysis, and optimization mechanism of faradaic materials are discussed in detail. Finally, this work highlights the remaining challenges of faradaic electrode materials and present perspectives for future research.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis.

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

PB2 residue 473 contributes to the mammalian virulence of H7N9 avian influenza virus by modulating viral polymerase activity via ANP32A.

Since the first human infection reported in 2013, H7N9 avian influenza virus (AIV) has been regarded as a serious threat to human health. In this study, we sought to identify the virulence determinant of the H7N9 virus in mammalian hosts. By comparing the virulence of the SH/4664 H7N9 virus, a non-virulent H9N2 virus, and various H7N9-H9N2 hybrid viruses in infected mice, we first pinpointed PB2 as the primary viral factor accounting for the difference between H7N9 and H9N2 in mammalian virulence. We further analyzed the in vivo effects of individually mutating H7N9 PB2 residues different from the closely related H9N2 virus and consequently found residue 473, alongside the well-known residue 627, to be critical for the virulence of the H7N9 virus in mice and the activity of its reconstituted viral polymerase in mammalian cells. The importance of PB2-473 was further strengthened by studying reverse H7N9 substitutions in the H9N2 background. Finally, we surprisingly found that species-specific usage of ANP32A, a family member of host factors connecting with the PB2-627 polymorphism, mediates the contribution of PB2 473 residue to the mammalian adaption of AIV polymerase, as the attenuating effect of PB2 M473T on the viral polymerase activity and viral growth of the H7N9 virus could be efficiently complemented by co-expression of chicken ANP32A but not mouse ANP32A and ANP32B. Together, our studies uncovered the PB2 473 residue as a novel viral host range determinant of AIVs via species-specific co-opting of the ANP32 host factor to support viral polymerase activity.IMPORTANCEThe H7N9 avian influenza virus has been considered to have the potential to cause the next pandemic since the first case of human infection reported in 2013. In this study, we identified PB2 residue 473 as a new determinant of mouse virulence and mammalian adaptation of the viral polymerase of the H7N9 virus and its non-pathogenic H9N2 counterparts. We further demonstrated that the variation in PB2-473 is functionally linked to differential co-opting of the host ANP32A protein in supporting viral polymerase activity, which is analogous to the well-known PB2-627 polymorphism, albeit the two PB2 positions are spatially distant. By providing new mechanistic insight into the PB2-mediated host range determination of influenza A viruses, our study implicated the potential existence of multiple PB2-ANP32 interfaces that could be targets for developing new antivirals against the H7N9 virus as well as other mammalian-adapted influenza viruses.

Prevalence and risk factors for abnormal glucose metabolism in first-episode and drug-naïve major depressive disorder outpatients with comorbid anxiety: A cross-sectional study from a large sample of the Chinese Han population.

BACKGROUND: Both abnormal glucose metabolism and anxiety have been reported to be common in major depressive disorder (MDD). However, few studies have explored glucose disturbances in first-episode and drug-naive (FEDN) MDD patients with anxiety. The purpose of this study was to examine the prevalence and risk factors of glucose disturbance in FEND MDD patients comorbid with anxiety. METHODS: 1718 FEDN MDD patients were included in this study. The positive subscale of the Positive and Negative Syndrome Scale (PANSS), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Rating Scale (HAMD) were used to measure psychotic, anxiety and depressive symptoms respectively. Sociodemographic and biochemical indicators were also collected. RESULTS: The prevalence of glucose disorders in MDD patients combined with anxiety was 15.7 %, significantly higher than in MDD patients without anxiety symptoms (7.1 %). Glucose disturbances were associated with HAMD score, HAMA score, thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), anti-thyroglobulin (TGAb), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL-C), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), suicide attempts, and psychotic symptoms. Further logistic regression showed that illness duration, TSH, TGAb, and TPOAb levels were correlates of glucose disturbances in MDD patients with anxiety. LIMITATIONS: No causal relationship could be drawn due to the cross-sectional design. CONCLUSIONS: Our findings suggest that TSH, TGAb and TPOAb may be promising biomarkers of glucose disturbances in MDD comorbid with anxiety, suggesting the importance of regular assessment of thyroid function parameters for abnormal glucose metabolism prevention.

Integrating evolutionarily conserved mechanism of response to radiation for exploring novel Caenorhabditis elegans radiation-responsive genes for estimation of radiation dose associated with spaceflight.

During space exploration, space radiation is widely recognized as an inescapable perilous stressor, owing to its capacity to induce genomic DNA damage and escalate the likelihood of detrimental health outcomes. Rapid and reliable estimation of space radiation dose holds paramount significance in accurately assessing the health risks associated with spaceflight. However, the identification of space radiation-responsive genes, with their potential to serve as early indicators for diagnosing radiation dose associated with spaceflight, continues to pose a significant challenge. In this study, based on the evolutionarily conserved mechanism of radiation response, an in silico analysis method of homologous comparison was performed to identify the Caenorhabditis elegans orthologues of human radiation-responsive genes with possible roles in the major processes of response to radiation, and thereby to explore the potential C. elegans radiation-responsive genes for evaluating the levels of space radiation exposure. The results showed that there were 60 known C. elegans radiation-responsive genes and 211 C. elegans orthologues of human radiation-responsive genes implicated in the major processes of response to radiation. Through an investigation of all available transcriptomic datasets obtained from space-flown C. elegans, it was observed that the expression levels of the majority of these putative C. elegans radiation-responsive genes identified in this study were notably changed across various spaceflight conditions. Furthermore, this study indicated that within the identified genes, 19 known C. elegans radiation-responsive genes and 40 newly identified C. elegans orthologues of human radiation-responsive genes exhibited a remarkable positive correlation with the duration of spaceflight. Moreover, a noteworthy presence of substantial multi-collinearity among the majority of these identified genes was observed. This observation lends support to the possibility of treating each identified gene as an independent indicator of radiation dose in space. Ultimately, a subset of 15 potential radiation-responsive genes was identified, presenting the most promising indicators for estimation of radiation dose associated with spaceflight in C. elegans.

Efficacy and safety of zuranolone for the treatment of depression: A systematic review and meta-analysis.

Major depressive disorder (MDD) and postpartum depression (PPD) are common and burdensome conditions. This study aims to evaluate the efficacy and safety of zuranolone, a neuroactive steroid γ-aminobutyric acid type A receptors-positive allosteric modulator, in treating MDD and PPD. A comprehensive literature search was conducted until September 2023, identifying seven randomized controlled trials (RCTs). The results demonstrated that zuranolone significantly decreased Hamilton Rating Scale for Depression (HAM-D) scores in patients with PPD or MDD at day 15 (concluding the 14-day course) and day 42-45 (4 weeks after treatment cessation) compared with the placebo, albeit exhibiting a diminishing trend. Moreover, a higher percentage of patients with PPD or MDD achieved HAM-D response and remission with zuranolone treatment compared with placebo at day 15. However, zuranolone did not significantly increase the proportion of MDD patients achieving HAM-D remission at 42/43 days. Adverse events (AEs) such as somnolence, dizziness, and sedation were linked to zuranolone, with a higher but not statistically significant rate of discontinuation due to AEs in the zuranolone group. Overall, our findings support the rapid antidepressant effects of zuranolone in MDD and PPD, along with a relatively favorable safety and tolerability. Large-scale longitudinal RCTs are needed to evaluate the long-term efficacy of zuranolone.

Association of subclinical hypothyroidism with metabolic syndrome and its components among outpatients with first-episode drug-naïve major depressive disorder: a large-scale cross-sectional study.

Both metabolic syndrome (MetS) and subclinical hypothyroidism (SCH) are prevalent in major depressive disorder (MDD) patients. However, their relationship in this population remains unknown. The study assessed the association between SCH and MetS in 1706 first-episode drug-naïve (FEDN) MDD patients. We also compared the relationship between MetS and clinical symptoms in patients with and without comorbid SCH. The Positive and Negative Syndrome Scale positive subscale, the Hamilton Anxiety Rating Scale, and the Hamilton Depression Rating Scale were used to detect clinical symptoms. Serum levels of free triiodothyronine, free thyroxine, thyroid stimulating hormone (TSH), anti-thyroglobulin, thyroid peroxidases antibody, cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting glucose were measured. The Area Under the Curve (AUC) was used to test the performance of serum TSH in identifying MetS patients. The prevalence of MetS and SCH was 34.5% (n = 585) and 61% (n = 1034), respectively. The presence of SCH increased the risk of MetS, hyperglycemia, hypertension, obesity, and low HDL-C by 4.91, 3.51, 3.54, 2.02, and 2.34 times, respectively. Serum TSH had a nice ability to distinguish MetS patients from non-MetS patients (AUC value = 0.77). MetS and its components exhibited a positive association with clinical profiles only in SCH patients, but not in non-SCH patients. Taken together, our study suggested SCH was closely related to MetS and might play a vital role in the relationship between MetS and clinical symptoms. Regular thyroid function checks might help early detect MetS.