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[This corrects the article DOI: 10.3389/fncel.2023.1226580.].
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With global warming, heat stress has become an important factor that seriously affects crop yield and quality. Therefore, understanding plant responses to heat stress is important for agricultural practice, but the molecular mechanism of high-temperature tolerance in garlic remains unclear. In this study, 'Xusuan No. 6' was used as the experimental material. After heat stress for 0 (CK), 2 and 24 h, transcriptome sequencing was used to screen metabolic pathways and differentially expressed genes (DEGs) closely related to heat stress and was further verified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 86,110 unigenes obtained from the raw transcriptome sequencing data were spliced. After 2 h of heat treatment, the expression levels of 8898 genes increased, and 3829 genes were decreased in leaves. After 24 h, the expression levels of 7167 genes were upregulated, and 3176 genes were downregulated. Gene Ontology enrichment analysis showed that DEGs were mainly enriched in seven categories: cellular processes, metabolic processes, binging, catalytic activity, cellular anatomical entity and protein-containing complex response to stimulus. Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that DEGs are involved in protein processing in the endoplasmic reticulum, plant hormone signal transduction, phenylpropanoid biosynthesis, and photosynthetic antenna proteins. Six genes were selected and further verified by qRT-PCR. In this study, the full-length transcriptome of garlic was constructed, and the regulatory genes related to the heat resistance of garlic were studied. Taken together, these findings can provide a theoretical basis for the cloning of heat resistance genes in garlic and for the analysis of heat resistance mechanisms.
Subject(s)
Garlic , Gene Expression Profiling , Gene Expression Regulation, Plant , Heat-Shock Response , Transcriptome , Garlic/genetics , Garlic/metabolism , Heat-Shock Response/genetics , Gene Ontology , Plant Proteins/genetics , Plant Proteins/metabolismABSTRACT
BACKGROUND AND AIMS: Nuclear protein testis (NUT) carcinoma (NC) is a rare and highly aggressive tumour characterised by chromosomal rearrangement of the nuclear protein testis family member 1 (NUTM1) gene, also known as the NUT gene. NC occurs mainly in the head and neck, mediastinum and lung. In general, primary NC in the oral cavity is extremely rare and reported sporadically. METHODS: A total of 111 formalin-fixed and paraffin-embedded specimens of poorly differentiated oral and oropharyngeal tumours were collected from 10 hospitals. NUT protein IHC staining was performed on these samples, and fluorescence in-situ hybridisation (FISH) and RNA sequencing detection were further carried out for NUT IHC-positive cases. RESULTS: The expression of NUT protein in tumour cells was detected in five cases (five of 111, 4.5%). The tumours in these cases were located in the oral floor, lip, base of the tongue, gingiva and hard palate. FISH detection results showed BRD4::NUT rearrangement in three patients and a non-BRD4::NUT rearrangement pattern in two patients. RNA sequencing results confirmed BRD4::NUT rearrangement in two cases. CONCLUSIONS: To our knowledge, this is the first and largest retrospective study of oral NC, and we found that NC is easily misdiagnosed as poorly differentiated oral squamous cell carcinoma (SCC) or poorly differentiated carcinoma. The morphology and immunophenotype of four NC cases were similar to SCC, and abrupt keratinisation was observed in three cases. Therefore, it is necessary to detect NUT protein for NC screening in oral malignant tumours with these morphologies, especially for young patients who are more likely to be misdiagnosed with other types of cancer.
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Microenvironment regulation near the catalyst surface plays a critical role in heterogeneous electrocatalytic reactions. The local concentration of reactants and intermediates significantly affects the reaction kinetics and product selectivity. Herein, we propose an innovative strategy of utilizing the spatial confinement effect in a sandwich-structured C/Cu/C assembly to regulate kinetic mass transport during the electrocatalytic CO2 reduction reaction. The sandwich C/Cu/C assembly catalyst was successfully prepared using a simple bidirectional freezing and freeze-drying method. The sandwich structure changes the free diffusion pathway of the CO intermediate within the sandwich interlayer and helps confine CO with locally increased CO concentration near the catalyst surface, which in turn promotes C-C coupling and thus improves the reaction activity and doubles the C2 product selectivity compared to its disordered mixture counterpart. This kinetics regulation in the sandwich structure may provide a new insight into the catalyst design and inspire the understanding of the structure-performance relationship in electrocatalysis.
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Synthesizing uniform functional covalent organic framework (COF) microspheres is the prerequisite of applying COFs as novel stationary phases for liquid chromatography. However, the synthesis of functionalized COF microspheres is challenging due to the difficulty in maintaining microspheric morphology when conferring functions. Here, a facile and universal "self-limited dynamic linker exchange" strategy is developed to achieve surface functionalization of uniform COF microspheres. Six different types of COF microspheres are constructed, showing the universality and superiority of the strategy. The library of COF microspheres' stationary phases can be further enriched on demand by varying different functional building blocks. The "self-limited dynamic linker exchange" is attributed to the result of a delicate balance of reaction thermodynamics and molecular diffusion energy barrier. As a demonstration, the chiral functional COF microspheres are used as stationary phases of chiral chromatography and realized effective enantioseparation.
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Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive neoplasm with biological heterogeneity. Mutations in multiple genes have been identified in LCNEC. However, associations between gene alterations, histopathological characteristics, and prognosis remain ambiguous. Here, we investigated the clinicopathologic, immunohistochemical, and genomic characteristics of 19 patients with LCNEC and 9 patients with atypical carcinoid (AC). We revealed high mutation frequencies of TP53 (89.5 %), RB1 (42.1 %), APC (31.6 %), and MCL1 (31.6 %) in LCNEC, while genetic alterations were rarely found in AC. APC alterations mainly occurred to the exon 16 and were only identified in LCNEC with wild-type RB1. The 19 LCNEC were further subgrouped into APC wild-type (LCNEC-APCMT, 6/19) and APC-mutated (LCNEC-APCWT, 13/19) subgroups. In comparison with LCNEC-APCWT, LCNEC-APCMT displayed lower TMB (median: 12.64 vs 4.20, P = 0.045), and relatively mild cytologic atypia. In addition, LCNEC-APCMT distinguished itself from AC and LCNEC-APCWT by obviously downregulated expression of neuroendocrine markers (CD56 and Syn, P < 0.01) and significantly altered expression of genes downstream of APC (ß-catenin migrating into the cytoplasm and nucleus, P < 0.001; c-Myc upregulating, P = 0.005). The OS of LCNEC-APCMT was numerically intermediate between AC and LCNEC-APCWT. We first proposed that APC alterations were common in LCNEC with wild-type RB1 and that LCNEC-APCMT was associated with lower TMB and better OS in comparison with LCNEC-APCWT.
Subject(s)
Carcinoma, Large Cell , Carcinoma, Neuroendocrine , Lung Neoplasms , Mutation , Humans , Male , Female , Carcinoma, Neuroendocrine/genetics , Carcinoma, Neuroendocrine/pathology , Middle Aged , Prognosis , Lung Neoplasms/genetics , Lung Neoplasms/pathology , Lung Neoplasms/mortality , Lung Neoplasms/diagnosis , Aged , Carcinoma, Large Cell/genetics , Carcinoma, Large Cell/pathology , DNA Mutational Analysis , Adult , Biomarkers, Tumor/genetics , Adenomatous Polyposis Coli Protein/genetics , Aged, 80 and overABSTRACT
The intricate protonation process in carbon dioxide reduction usually makes the product unpredictable. Thus, it is significant to control the reactive intermediates to manipulate the reaction steps. Here, we propose that the synergistic La-Ti active sites in the N-La2Ti2O7 nanosheets enable the highly selective carbon dioxide photoreduction into methane. In the photoreduction of CO2 over N-La2Ti2O7 nanosheets, in situ Fourier transform infrared spectra are utilized to monitor the *CH3O intermediate, pivotal for methane production, whereas such monitoring is not conducted for La2Ti2O7 nanosheets. Also, theoretical calculations testify to the increased charge densities on the Ti and La atoms and the regulated formation energy barrier of *CO and *CH3O intermediates by the constructed synergistic active sites. Accordingly, the methane formation rate of 7.97 µL h-1 exhibited by the N-La2Ti2O7 nanosheets, along with an electron selectivity of 96.6%, exceeds that of most previously reported catalysts under similar conditions.
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The impact of emotions on health, especially stress, is receiving increasing attention. It is important to provide a non-invasive affect detection system that can be continuously monitored for a long period of time. Multi-sensor fusion strategies can better improve the performance of affect detection models, but there are also problems such as insufficient feature extraction and poor spatiotemporal feature fusion. Therefore, this study proposes a feature-level fusion method based on long short-term memory and one-dimensional convolutional neural network to extract temporal and spatial features of electrocardiogram, electromyogram, electrical activity, temperature, accelerator and response data, respectively, and then fuse them in a summation fashion for affect and stress detection. In particular, we added the tanh activation function before feature fusion, which can improve the model's performance. We used the wearable affect and stress detection dataset to train the model, which includes three different emotion states (neutral, stress, and amusement) for three-class emotion classification with accuracy and F1-scores of 87.82% and 86.68%, respectively. Due to the importance of stress, we also studied binary classification for stress detection, where neutral and amusement were combined as non-stress, with accuracy and F1-scores of 94.9% and 94.98%, respectively. The performance of the proposed model outperforms other control models and can effectively improve the performance of affect and stress detection, and promote medical care, health care and elderly care.
Subject(s)
Electrocardiography , Neural Networks, Computer , Humans , Electromyography , TemperatureABSTRACT
BACKGROUND: Pulmonary alveolar proteinosis (PAP) is a rare pulmonary disease characterized by abnormal accumulation of pulmonary surfactant lipids in alveoli or terminal bronchioles, leading to increased infection risk and progressive respiratory failure. Approximately more than 90% of all cases are autoimmune PAP (aPAP). Since one of the predisposing factors has been identified as genes located within the major-histocompatibility-complex region, an investigation of human leukocyte antigen (HLA) alleles associated with the risk of aPAP is warranted. METHODS: We retrospectively studied 60 patients pathologically diagnosed with PAP from 2019 to 2022. Patients were divided into the aPAP group or secondary PAP (sPAP) group according to their clinical information. Qualified DNA was extracted from the paraffin-embedded tissue of 28 patients, and the PCR-sequence-based typing method was used for HLA-DRB1 genotyping. RESULTS: A similar HLA-DRB1 allele profile (including the HLA-DRB1*08:03) between the aPAP group and sPAP group was revealed, except that HLA-DRB1*14:54, which has never been reported in aPAP patients, was only detected in the aPAP group rather than the sPAP group (19.4% vs. 0.0%, p = 0.030). Under inhaled granulocyte-macrophage colony-stimulating factor therapy, more clinical remission was observed in HLA-DRB1*14:54 carriers rather than in HLA-DRB1*08:03 carriers (80.0% vs. 57.1%). CONCLUSIONS: Our real-world study revealed for the first time that a population with HLA-DRB1*14:54 was subject to aPAP, and HLA-DRB1*14:54 might imply a response in aPAP patients to inhaled granulocyte-macrophage colony-stimulating factor in aPAP patients.
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Normal brain development, function, and aging critically depend on unique characteristics of the cerebrovascular system. Growing evidence indicated that cerebrovascular defects can have irreversible effects on the brain, and these defects have been implicated in various neurological disorders, including autism spectrum disorder (ASD). ASD is a neurodevelopmental disorder with heterogeneous clinical manifestations and anatomical changes. While extensive research has focused on the neural abnormalities underlying ASD, the role of brain vasculature in this disorder remains poorly understood. Indeed, the significance of cerebrovascular contributions to ASD has been consistently underestimated. In this work, we discuss the neurovascular crosstalk during embryonic development and highlight recent findings on cerebrovascular alterations in individuals with ASD. We also discuss the potential of vascular-based therapy for ASD. Collectively, these investigations demonstrate that ASD can be considered a neurovascular disease.
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BACKGROUND: Deep brain stimulation (DBS) is an effective surgical treatment for essential tremor (ET), with the ventral intermediate nucleus (Vim) and posterior subthalamic area (PSA) as the most common targets. The stimulation efficacy of ET with Vim-PSA double-target DBS has been reported. Herein, we aim to propose surgical techniques for Vim-PSA double-target DBS surgery. METHODS: This study enrolled six patients with ET who underwent Vim-PSA double-target electrode implantation from October 2019 to May 2022. The targets were located and adjusted using coordinates and multimodality MRI images. A burr hole was accurately drilled in line with the electrode trajectory under the guidance of a stereotactic frame. Novel approaches were adopted during the electrode implantation process for pneumocephalus reduction, including "arachnoid piamater welding" and "water sealing". Electrophysiological recording was used to identify the implantation sites of the electrodes. A 3D reconstruction model of electrodes and nuclei was established to facilitate programming. RESULTS: The combination of coordinates and multimodality MRI images for target location and adjustment enabled the alignment of Vim and PSA. Postoperative CT scanning showed that the electrode was precisely implanted. Stereotactic guidance facilitated accurate burr hole drilling. "Arachnoid piamater welding" and "water sealing" were efficient in reducing pneumocephalus. Intraoperative electrophysiological verified the efficacy of Vim-PSA double-target DBS surgery. CONCLUSIONS: The methods for target location and adjustment, accurate drilling of the burr hole, reduction in pneumocephalus, and intraoperative electrophysiological verification are key issues in DBS surgery targeting both the Vim and PSA. This study may provide technical support for Vim-PSA DBS, especially for surgeons with less experience in functional neurosurgery.
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LncRNA (long non-coding RNA) and mRNA form a competitive endogenous RNA (ceRNA) network by competitively binding to common miRNAs. This network regulates various processes of plant growth and development at the post-transcriptional level. Somatic embryogenesis is an effective means of plant virus-free rapid propagation, germplasm conservation, and genetic improvement, which is also a typical process to study the ceRNA regulatory network during cell development. Garlic is a typical asexual reproductive vegetable. Somatic cell culture is an effective means of virus-free rapid propagation in garlic. However, the ceRNA regulatory network of somatic embryogenesis remains unclear in garlic. In order to clarify the regulatory role of the ceRNA network in garlic somatic embryogenesis, we constructed lncRNA and miRNA libraries of four important stages (explant stage: EX; callus stage: AC; embryogenic callus stage: EC; globular embryo stage: GE) in the somatic embryogenesis of garlic. It was found that 44 lncRNAs could be used as precursors of 34 miRNAs, 1511 lncRNAs were predicted to be potential targets of 144 miRNAs, and 45 lncRNAs could be used as eTMs of 29 miRNAs. By constructing a ceRNA network with miRNA as the core, 144 miRNAs may bind to 1511 lncRNAs and 12,208 mRNAs. In the DE lncRNA-DE miRNA-DE mRNA network of adjacent stages of somatic embryo development (EX-VS-CA, CA-VS-EC, EC-VS-GE), by KEGG enrichment of adjacent stage DE mRNA, plant hormone signal transduction, butyric acid metabolism, and C5-branched dibasic acid metabolism were significantly enriched during somatic embryogenesis. Since plant hormones play an important role in somatic embryogenesis, further analysis of plant hormone signal transduction pathways revealed that the auxin pathway-related ceRNA network (lncRNAs-miR393s-TIR) may play a role in the whole stage of somatic embryogenesis. Further verification by RT-qPCR revealed that the lncRNA125175-miR393h-TIR2 network plays a major role in the network and may affect the occurrence of somatic embryos by regulating the auxin signaling pathway and changing the sensitivity of cells to auxin. Our results lay the foundation for studying the role of the ceRNA network in the somatic embryogenesis of garlic.
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Garlic , MicroRNAs , RNA, Long Noncoding , Garlic/genetics , RNA, Long Noncoding/genetics , Plant Growth Regulators , Gene Regulatory Networks , MicroRNAs/genetics , RNA, Messenger/genetics , Indoleacetic AcidsABSTRACT
Aims: This study analyzes the impact of frailty, mild cognitive impairment, and cognitive frailty on adverse outcomes in community-dwelling older adults. Methods: This systematic review and meta-analysis were conducted using the PRISMA guidelines and MOOSE statement. We developed a specific search strategy for each electronic database and searched PubMed, Web of Science, MEDLINE, and Embase from initial records to July 2021. The studies on adverse outcomes of frailty, pre-frailty, mild cognitive impairment, and mild cognitive impairment with pre-frailty and cognitive frailty were included. Two researchers independently extracted data based on a spreadsheet and assessed the risk of bias. The primary outcomes were mortality, dementia, disability, and hospitalization. The second outcome included quality of life and falls. All analysis was conducted by using Review Manager (RevMan) 5.3 software. Results: A total of 22 cohort studies (71,544 older adults with mean age ranging from 65.1 to 93.6 years) were included with a low risk of bias and high methodological quality with a NOS score ≥7. Compared to robust elders, individuals identified as frailty were associated with a higher risk of mortality (RR = 2.11, 95% CI: 1.57-2.83) and disability (RR = 5.91, 95% CI: 2.37-14.74). Mild cognitive impairment with pre-frailty was associated with mortality (RR = 1.74, 95% CI: 1.48-2.05) and dementia (RR = 4.15, 95% CI: 1.87-9.20). Pre-frailty was associated with a higher risk of mortality (RR = 1.29, 95% CI: 1.11-1.50). Cognitive frailty was associated with higher risk of incident mortality (RR = 2.41, 95% CI: 1.97-2.94), dementia (RR = 3.67, 95% CI: 2.81-4.78), disability (RR = 11.32, 95% CI: 4.14-30.97), and hospitalization (RR = 2.30, 95% CI: 1.60-3.32), as well as poor quality of life. Conclusion: Cognitive frailty could be a comprehensive psychosomatic predictor for adverse outcomes among older people. Interactions between frailty, mild cognitive impairment, and cognitive frailty on adverse outcomes must be further explored. Systematic review registration: [https://inplasy.com/inplasy-2022-5-0064/], identifier [INPLASY202250064].
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In the post-COVID-19 era, significant changes have taken place regarding the epidemic, the economy, family and social life. However, it remains unclear how these changes encompass the psychological symptoms of college students. We carried out a cross-sectional study to investigate anxiety, depression, and post-traumatic stress disorder (PTSD) symptoms among college students from 10 November 2020, to 16 November 2020. The questionnaire included a self-designed canvas, Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), and Impact of Event Scale (IES-R). Factors associated with psychological symptoms were estimated by ordered and non-conditional logistic regression analysis. Of 4754 participants, 25.0%, 29.7%, 3.4%, 15.3%, 17.1%, and 2.9% reported anxiety, depression, PTSD symptoms, one, any two, and all three, respectively. In cases with anxiety or depression symptoms, there was a 9.11% comorbidity with PTSD. Factors associated with fears of being infected, social, family, and economic changes increased the risk of psychological symptoms in college students caused by COVID-19. Female college students, identified with anxiety or depression symptoms, were at a lower risk of developing PTSD symptoms (OR, 0.61, 95% CI: 0.43-0.86). Non-medical majors at university, rural residence, higher educational background, fear of taking public transport, and deterioration of family relationships increased the risk for PTSD symptoms among male respondents with anxiety or depression symptoms due to COVID-19. Factors correlated with psychological symptoms had expanded from the fear of being infected to extensive social, family, and economic changes caused by COVID-19. Therefore, screening and interventions for psychological symptoms should be consistently strengthened and more targeted to college students in the post-COVID-19 era.
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Background: Findings on the associations between psychological symptoms and driving behaviors in private car drivers are inadequate. Method: The study consisted of 3,115 private car drivers in Yulin, China. The measurements included socio-demographic data, traffic violations, accidents, and Symptom Checklist-90 (SCL-90). In addition, an ordered logistic regression model was employed to examine the association between each psychological symptom and risky driving behaviors. Results: The overall prevalence rate of any self-reported psychological symptom was 10.24%, with 9.22% for males and 11.49% for females. Among them, obsessive-compulsive, interpersonal sensitivity, additional items, hostility, and depression were the five most common psychological symptoms, with prevalence rates of 7.90, 6.29, 6.00, 5.91, and 5.62%, respectively. Any psychological symptom factor was associated with a higher risk of traffic violations and accidents. However, the intensity of the correlations varied, with obsessive-compulsive symptoms the strongest in general traffic violations and anxiety symptoms in traffic accidents. All psychological symptoms except phobic anxiety and paranoid ideation contributed to a higher risk of failing the driver's license test. Conclusions: The prevalence rate of psychological symptoms was high in private car drivers. This study calls for an urgent need to establish a pilot tertiary prevention strategy to reduce risky driving behaviors through psychological symptom screening and interventions among private car drivers.
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Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70-0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65-0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.
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Background: Previous studies have showed that individuals infected with COVID-19 were more likely to report psychological symptoms. However, little is known about the changes from testing positive to negative to positive again. Methods: This survey was conducted through the questionnaires including the 7-item Generalized Anxiety Disorder (GAD-7), the 9-item Patient Health Questionnaire (PHQ-9), as well as the Self-Rating Scale of Sleep (SRSS) to explore the psychological status of COVID-19 and re-positive cases.â³re-positiveâ³ is defined as a positive RT-PCR test at any time during the recovery period after testing negative. Results: A total of 94 COVID-19 patients presented the prevalence rates of anxiety, depression, insomnia, and any of the three psychological symptoms being 26.6, 8.6, 12.8, and 31.9%, respectively. Among these, 32 cases were re-tested positive during the recovery period, with the prevalence rates of anxiety, depression, insomnia, and any of the three psychological symptoms being 21.9, 18.7, 31.2, and 37.5%, respectively. The psychological status after re-positive showed a significant decrease in anxiety (P = 0.023), an increase in depression, and a significant rise in insomnia (P = 0.035). For those with no psychological symptoms during initial-positive, after re-positive, 5.88% reported anxiety, 5.88% reported depression, and 11.76% reported insomnia. For those who experienced only anxiety symptoms during initial-positive, after re-positive, 33.3% reported depression, and 33.3% reported insomnia. Conclusions: Our findings encompassed the urgent concern for anxiety in initial-positive COVID-19 patients, depression in re-positive COVID-19 patients, and insomnia in both initial and re-positive patients, hence enabling targeted interventions for appeasing the psychological burden of COVID-19 patients.
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Ferulic acid (FA), a natural phenolic phytochemical abundantly present in whole grains, displays promising therapeutic effects on hypercholesterolemia while its underlying mechanism not fully elucidated. This study aimed to investigate the cholesterol-lowering effect of FA in high-fat diet (HFD)-fed mice and its potential molecular mechanism. FA supplementation alleviated HFD-induced hypercholesterolemia (-13.2%, p < 0.05), along with increased excretion of bile acids (BAs) in feces (37.0%, p < 0.05). Mechanism studies showed that FA activated the expression of cholesterol 7α hydroxylase (CYP7A1), a rate-limiting enzyme in BA biosynthesis in the liver, which increased the BAs biosynthesis from cholesterol. Surprisingly, increased excretion of BAs in feces is a consequence, not a cause, of CYP7A1 activation. Furthermore, enterohepatic farnesoid X receptor (FXR) signaling is not involved in the activation of hepatic CYP7A1 by FA. In conclusion, FA activates CYP7A1 through non-FXR signaling, which on the one hand effectively prevents hypercholesterolemia, and on the other hand leads to secondary BAs elevation in plasma. The latter may be the key to the anti-obesity and hypoglycemic effects of FA.
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BACKGROUND: CBT and its modifications are effective for depression, but its efficacy for preventing depressive relapse/recurrence remains unclear. METHODS: A systematic review and meta-analysis was conducted scrutinizing the efficacy of CBT and its modifications for preventing depressive relapse/recurrence in randomized controlled trials. The retrieval database included PubMed, Web of Science, MEDLINE, and three major Chinese databases (CNKI, VIP, Wanfang), and the retrieval period was from January 1, 2009, to July 1, 2022. Data were analyzed by RevMan 5.4 and Stata 16. RESULTS: A total of 28 randomized controlled trials with 3938 participants were included. The relapse/recurrence rates in CBT and its modifications and the control group were 31.6 % and 41.3 %, respectively; CBT and its modifications significantly reduced the risk of developing a new depressive episode (RR, 0.73, 95%CI, 0.64-0.83). Stratified by follow-up time, both CBT and its modifications significantly reduced the relapse/recurrence of depression at ≤12 months (RR, 0.61, 95%CI, 0.47-0.79) and > 12 months (RR, 0.74, 95%CI, 0.59-0.93) compared with other active treatments. The beneficial effects of CBT and its modifications alone in reducing depressive relapse/recurrence were equivalent to those of antidepressant medication alone (RR, 1.01, 95 % CI, 0.79-1.28). Meta-regression results showed that the intervention method (P = 0.005) was one of the sources of heterogeneity. LIMITATIONS: Further subgroup analysis is limited by the literature. CONCLUSION: CBT and its modifications are reliable interventions for preventing relapse/recurrence in patients with depression. Further research is needed to determine the specific conditions for CBT and its applications in clinical practice.
Subject(s)
Cognitive Behavioral Therapy , Psychotherapy , Humans , Psychotherapy/methods , Cognitive Behavioral Therapy/methods , Depression , Randomized Controlled Trials as Topic , RecurrenceABSTRACT
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.