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Abstract Introduction Treatment strategies for depression based on interventions for glucose and lipid metabolism disorders are receiving increasing attention. Investigating the mechanism of their antidepressant effect and exploring new diagnostic and therapeutic biomarkers have attracted increasing attention. Dulaglutide, a long‐acting GLP‐1 receptor agonist, has been reported to alleviate cognitive deficits and neuronal damage. However, the antidepressant effect of dulaglutide and, especially, the underlying mechanism are still poorly understood. In this study, we aimed to explore the underlying biomarkers of depression and potential modulatory targets of dulaglutide in chronic mild stress (CMS) mice. Methods Sixty mice were randomly divided into a control group (CON group), a CMS+Vehicle group (CMS+Veh group), a CMS+0.3 mg/kg dulaglutide group (Low Dula group), and a CMS+0.6 mg/kg dulaglutide group (High Dula group). Numerous behavioral tests, mainly the open field test, forced swimming test, and tail suspension test, were applied to evaluate the potential effect of dulaglutide treatment on anxiety‐ and depression‐like behaviors in mice exposed to chronic stress. Furthermore, a liquid chromatography–tandem mass spectrometry‐based metabolomics approach was utilized to investigate the associated mechanisms of dulaglutide treatment. Results Three weeks of dulaglutide treatment significantly reversed depressive‐like but not anxiety‐like behaviors in mice exposed to chronic stress for 4 weeks. The results from the metabolomics analysis showed that a total of 20 differentially expressed metabolites were identified between the CON and CMS+Veh groups, and 46 metabolites were selected between the CMS+Veh and High Dula groups in the hippocampus of the mice. Comprehensive analysis indicated that lipid metabolism, amino acid metabolism, energy metabolism, and tryptophan metabolism were disrupted in model mice that experienced depression and underwent dulaglutide therapy. Conclusion The antidepressant effects of dulaglutide in a CMS depression model were confirmed. We identified 64 different metabolites and four major pathways associated with metabolic pathophysiological processes. These primary data provide a new perspective for understanding the antidepressant‐like effects of dulaglutide and may facilitate the use of dulaglutide as a potential therapeutic strategy for depression.

The prevalence of patent foramen ovale (PFO) is 15–35% among adults. The role of right-to-left shunting through the PFO, anxiety, depression, and hypoxemia in the systemic circulation remains poorly understood. Herein, we present the case of a 52-year-old woman with no heart or lung disease, who was admitted due to anxiety for 5 months and had symptom exacerbation with dizziness for 4 days and presented with cyanosis. She was noted to have acute hypoxemia, with an oxygen saturation of 94.48% on room air, and arterial blood gas showed an oxygen tension of 65.64 mmHg. Agitated saline contrast echocardiography showed right-to-left shunting due to PFO. Arteriovenous fistula, pneumonia, pulmonary embolism, pulmonary hypertension, congestion peripheral cyanosis, ischemic peripheral cyanosis, and methemoglobin were excluded. Additionally, the patient improved by taking Paroxetine, Oxazepam, and Olanzapine. Her oxygen tension returned to 90.42 mmHg, and her symptoms resolved. In the case of severe anxiety and depression, right-to-left shunting through the PFO may cause acute systemic hypoxemia via a flow-driven mechanism, occasionally manifesting as cyanosis. When anxiety improved, hypoxia also improved. Thus, the treatment of anxiety and depression seems effective in improving hypoxemia. Notably, this is a rare report, and we hope to draw the attention of psychosomatic specialists, psychiatrists, and clinicians to seek the relationship between anxiety appearing as acute stress and PFO. This may be a new therapeutic method for treating severe anxiety disorder.

Abstract Background Most sows will experience negative energy balance during lactation resulting in impaired follicular development. This study aimed to treat 28-day lactating sows with altrenogest (ALT) to suppress follicle enlargement during lactation, and to assess the estrus and reproductive performance post-weaning. Methods In this study, we conducted two trials. In trial 1, we monitored the follicular development of lactating sows including 10 primiparous sows and 10 multiparous sows during the whole lactation to confirm the ALT administration time. In trial 2, a total of 42 primiparous and 111 multiparous sows were allocated to three treatments: Ctrl (control group, n = 51): no treatment; TAI (timed artificial insemination group, n = 51): sows were injected with equine chorionic gonadotropin (eCG) after weaning 24 h and gonadotropin-releasing hormone (GnRH) when they expressed estrus; and AT-TAI (ALT treatment-timed artificial insemination group, n = 51): base on the process of TAI group, the sows were fed with 20 mg ALT per day before weaning 10 days. All sows were artificially inseminated twice at 12 h and 36 h after estrus. The follicle size changes and serum hormone levels were explored in this process. Results Although the follicle size of multiparous sows was larger than primiparous sows during the whole lactation (P 0.05). Additionally, ALT treatment increases estrus concentration and the preovulatory follicle size (P 0.05). Conclusions There were significant differences in the size of follicles in the lactation between primiparous and multiparous sows. ALT treatment during the last ten days of lactation concentrated estrus expression leading to higher work efficiency of breeder in batch production, however, with no improvement in reproductive performance.

Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.