Major depressive disorder: hypothesis, mechanism, prevention and treatment.

Worldwide, the incidence of major depressive disorder (MDD) is increasing annually, resulting in greater economic and social burdens. Moreover, the pathological mechanisms of MDD and the mechanisms underlying the effects of pharmacological treatments for MDD are complex and unclear, and additional diagnostic and therapeutic strategies for MDD still are needed. The currently widely accepted theories of MDD pathogenesis include the neurotransmitter and receptor hypothesis, hypothalamic-pituitary-adrenal (HPA) axis hypothesis, cytokine hypothesis, neuroplasticity hypothesis and systemic influence hypothesis, but these hypothesis cannot completely explain the pathological mechanism of MDD. Even it is still hard to adopt only one hypothesis to completely reveal the pathogenesis of MDD, thus in recent years, great progress has been made in elucidating the roles of multiple organ interactions in the pathogenesis MDD and identifying novel therapeutic approaches and multitarget modulatory strategies, further revealing the disease features of MDD. Furthermore, some newly discovered potential pharmacological targets and newly studied antidepressants have attracted widespread attention, some reagents have even been approved for clinical treatment and some novel therapeutic methods such as phototherapy and acupuncture have been discovered to have effective improvement for the depressive symptoms. In this work, we comprehensively summarize the latest research on the pathogenesis and diagnosis of MDD, preventive approaches and therapeutic medicines, as well as the related clinical trials.

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning.

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

QTL Mapping for Adult-Plant Resistance to Leaf Rust in Italian Wheat Cultivar Libellula.

Wheat leaf rust (Lr), which is caused by Puccinia triticina Eriks. (Pt), is one of the most important wheat diseases affecting wheat production globally. Using resistant wheat cultivars is the most economical and environmentally friendly way to control leaf rust. The Italian wheat cultivar Libellula has demonstrated good resistance to Lr in field studies. To identify the genetic basis of Lr resistance in 'Libellula', 248 F6 recombinant inbred lines from the cross 'Libellula'/'Huixianhong' was phenotyped for Lr severity in seven environments: the 2014/2015, 2016/2017, 2017/2018, and 2018/2019 cropping seasons at Baoding, Hebei Province, and the 2016/2017, 2017/2018, and 2018/2019 crop seasons at Zhoukou, Henan Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult-plant resistance in the population. Six QTLs were consequently detected and designated as QLr.hebau-1AL and QLr.hebau-1AS that were presumed to be new and QLr.hebau-1BL, QLr.hebau-3AL, QLr.hebau-4BL, and QLr.hebau-7DS that were identified at similar physical positions as previously reported QTLs. Based on chromosome positions and molecular marker tests, QLr.hebau-1BL and QLr.hebau-7DS share similar flanking markers with Lr46 and Lr34, respectively. Lr46 and Lr34 are race nonspecific adult plant resistance (APR) genes for leaf rust and stripe rust and powdery mildew. QLr.hebau-4BL showed multiple disease resistance to leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTL identified in this study, as well as their closely linked markers, may potentially be used in marker-assisted selection in wheat breeding.

Geographical verification of Pleuropterus multiflorus thunb. by functional compounds, stable isotope ratios, and multielement combined with machine learning methods.

The dry roots of Pleuropterus multiflorus Thunb. (PM) have been traditionally utilized as functional foods and medicines China and various Asian countries. They are extensively cultivated in multiple provinces in China, with variations in prices and qualities. This study aims to investigate the regional characteristics of PM by 4 stable isotope ratios, 40 multielement and 16 functional compounds contents, using a total of 357 samples from 8 different geographical origins. Machine learning methods were developed to authenticating the geographical origins of PM, yielding the accuracy range from 94.44 % to 100 % in the test set. Notably, the protected designation of origin, Deqing PM, exhibited a high accuracy of 100 % in most models, A total of 30 significant prediction variables, encompassing 16 functional compounds, δ2H, 12 rare earth elements, and Cu, were identified. Additionally, the study identified altitude, high temperature, and dry or moisture index as the primary influenced environmental factors.

Novel pathogenesis of post-traumatic stress disorder studied in transgenic mice.

Posttraumatic stress disorder (PTSD) is very common after exposure to trauma, mental stress or violence. Because objective biological markers for PTSD are lacking, exactly diagnosing PTSD is a challenge for clinical psychologists. In-depth research on the pathogenesis of PTSD is a key for solving this problem. In this work, we used male Thy1-YFP transgenic mice, in which neurons are fluorescently labeled, to research the effects of PTSD on neurons in vivo. We initially discovered that pathological stress associated with PTSD increased the activation of glycogen synthesis kinase-beta (GSK-3ß) in neurons and induced the translocation of the transcription factor forkhead box-class O3a (FoxO3a) from the cytoplasm to the nucleus, which decreased the expression of uncoupling protein 2 (UCP2) and increased mitochondrial production of reactive oxygen species (ROS) to trigger neuronal apoptosis in the prefrontal cortex (PFC). Furthermore, the PTSD model mice showed increased freezing and anxiety-like behaviors and more severe decrease of memory and exploratory behavior. Additionally, leptin attenuated neuronal apoptosis by increasing the phosphorylation of signal transducer and activator of transcription 3 (STAT3), which further elevated the expression of UCP2 and inhibited the mitochondrial production of ROS induced by PTSD, thus reducing neuronal apoptosis and ameliorating PTSD-related behaviors. Our study is expected to promote the exploration of PTSD-related pathogenesis in neural cells and the clinical effectiveness of leptin for PTSD.

A novel murine model of mania.

Neuropathological mechanisms of manic syndrome or manic episodes in bipolar disorder remain poorly characterised, as the research progress is severely limited by the paucity of appropriate animal models. Here we developed a novel mania mice model by combining a series of chronic unpredictable rhythm disturbances (CURD), which include disruption of circadian rhythm, sleep deprivation, exposure to cone light, with subsequent interference of followed spotlight, stroboscopic illumination, high-temperature stress, noise disturbance and foot shock. Multiple behavioural and cell biology tests comparing the CURD-model with healthy controls and depressed mice were deployed to validate the model. The manic mice were also tested for the pharmacological effects of various medicinal agents used for treating mania. Finally, we compared plasma indicators of the CURD-model mice and the patients with the manic syndrome. The CURD protocol produced a phenotype replicating manic syndrome. Mice exposed to CURD presented manic behaviours similar to that observed in the amphetamine manic model. These behaviours were distinct from depressive-like behaviours recorded in mice treated with a depression-inducing protocol of chronic unpredictable mild restraint (CUMR). Functional and molecular indicators in the CURD mania model showed multiple similarities with patients with manic syndrome. Treatment with LiCl and valproic acid resulted in behavioural improvements and recovery of molecular indicators. A novel manic mice model induced by environmental stressors and free from genetic or pharmacological interventions is a valuable tool for research into pathological mechanisms of mania.

Leptin Attenuates Fear Memory by Inhibiting Astrocytic NLRP3 Inflammasome in Post-traumatic Stress Disorder Model.

Accumulating evidence suggests that the activation of nucleotide-binding domain and leucine-rich repeat protein-3 (NLRP3) inflammasome contributes to the pathophysiology of post-traumatic stress disorder (PTSD). Astrocytes, the homeostatic cells of the central nervous system are intimately involved into pathophysiology of various mental disorders including PTSD. We demonstrated previously that leptin exerts neuroprotection and ameliorates chronic sleep deprivation-induced depressive-like behaviours. Here, we extended the study of therapeutic effects of leptin to PTSD model mice. We discovered that PTSD is associated with significant activation of NLRP3 inflammasome in astrocytes sorted from GFAP-GFP transgenic mice, while administration of leptin markedly suppressed the activation of astrocytic NLRP3 inflammasome. Leptin effectively improved PTSD-associated behavioural alterations including fear memory, cognitive impairments, and depressive-like behaviours. Therapeutic effects of leptin were mediated by the signal transducer and activator of transcription 3 (STAT3) in astrocytes. In addition, the PTSD-related activation of NLRP3 inflammasome impairs astrocytic mitochondria suppressing ATP synthesis and leading to an increased ROS production. Leptin reversed mitochondrial inhibition by stimulating STAT3 in astrocytes. We propose leptin as a novel candidate for the pharmacological treatment of PTSD.

Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds.

Coix seed (CS, Coix lachryma-jobi L. var. ma-yuen (Roman.) Stapf) has rich nutrients, including starch, protein and oil. The geographical origin with a protected geographical indication and high levels of nutrient contents ensures the quality of CS, but non-destructive and rapid methods for predicting these quality indicators remain to be explored. This paper proposed hyperspectral imaging (HSI) assisted with the integrated deep learning models of attention mechanism (AM), convolutional neural networks, and long short-term memory. The method achieved the effective wavelengths selection, the highest prediction accuracy for production region discrimination and the lowest mean absolute error and root mean squared error for nutrient contents prediction. Moreover, the wavelengths selected via the AM model were explicable and reliable for predicting the geographical origins and nutrient contents. The proposed combination of HSI with integrated deep learning models has great potential in the quality evaluation of CS.

Ageing related thyroid deficiency increases brain-targeted transport of liver-derived ApoE4-laden exosomes leading to cognitive impairment.

Alzheimer's disease (AD) is the prevalent cause of dementia in the ageing world population. Apolipoprotein E4 (ApoE4) allele is the key genetic risk factor for AD, although the mechanisms linking ApoE4 with neurocognitive impairments and aberrant metabolism remains to be fully characterised. We discovered a significant increase in the ApoE4 content of serum exosomes in old healthy subjects and AD patients carrying ApoE4 allele as compared with healthy adults. Elevated exosomal ApoE4 demonstrated significant inverse correlation with serum level of thyroid hormones and cognitive function. We analysed effects of ApoE4-containing peripheral exosomes on neural cells and neurological outputs in aged or thyroidectomised young mice. Ageing-associated hypothyroidism as well as acute thyroidectomy augmented transport of liver-derived ApoE4 reach exosomes into the brain, where ApoE4 activated nucleotide-binding oligomerisation domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome by increasing cholesterol level in neural cells. This, in turn, affected cognition, locomotion and mood. Our study reveals pathological potential of exosomes-mediated relocation of ApoE4 from the periphery to the brain, this process can represent potential therapeutic target.

Synthesis of AuAg/Ag/Au open nanoshells with optimized magnetic plasmon resonance and broken symmetry for enhancing second-harmonic generation.

The cooperation of magnetic and electric plasmon resonances in cup-shaped metallic nanostructures exhibits significant capability for second-harmonic generation (SHG) enhancement. Herein, we report an approach for synthesizing Au open nanoshells with tunable numbers and sizes of openings on a template of six-pointed PbS nanostars. The morphology of Au nanoshells is controlled by adjusting the amount of HAuCl4, and the characteristic shapes of pointed nanocaps, open nanoshells, and hollow nanostars are obtained. Owing to the collaboration of electric and magnetic plasmon resonance modes, the Au nanoshells exhibit significantly broadened and highly tunable optical responses. Furthermore, the morphology-dependent SHG of the Au nanoshells shows two maximal SHG intensities, corresponding to four-opening and one-opening Au nanoshells with appropriate opening sizes. Ag/Au and AuAg/Ag/Au open nanoshells were further investigated to achieve enhanced SHG. By adjusting the thickness of the Ag shell, the SHG intensity of Ag/Au open nanoshells reaches a maximum due to the gradient field at the AuAg bimetallic interface. After replacing the Ag shells with Au shells, the SHG intensity of AuAg/Ag/Au open nanoshells reaches a maximum due to further symmetry breaking. These findings provide a strategy to prepare colloidal metal nanocrystals with prospective applications ranging from nonlinear photonic nanodevices to biospectroscopy and photocatalysis.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

BACKGROUND: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. RESULTS: In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. CONCLUSION: This study provides the guide to the development of computational methods for the drug-target interaction prediction.