Effects of autotaxin and lysophosphatidic acid deficiencies on depression-like behaviors in mice exposed to chronic unpredictable mild stress.

The involvement of lipids in the mechanism of depression has triggered extensive discussions. Earlier studies have identified diminished levels of lysophosphatidic acid (LPA) and autotaxin (ATX) in individuals experiencing depression. However, the exact significance of this phenomenon in relation to depression remains inconclusive. This study seeks to explore the deeper implications of these observations. We assessed alterations in ATX and LPA in both the control group and the chronic unpredictable mild stress (CUMS) model group. Additionally, the impact of ATX adeno-associated virus (AAV-ATX) injection into the hippocampus was validated through behavioral tests in CUMS-exposed mice. Furthermore, we probed the effects of LPA on synapse-associated proteins both in HT22 cells and within the mouse hippocampus. The mechanisms underpinning the LPA-triggered shifts in protein expression were further scrutinized. Hippocampal tissues were augmented with ATX to assess its potential to alleviate depression-like behavior by modulating synaptic-related proteins. Our findings suggest that the decrement in ATX and LPA levels alters the expression of proteins associated with synaptic plasticity in vitro and in vivo, such as synapsin-I (SYN), synaptophysin (SYP), and brain-derived neurotrophic factor (BDNF). Moreover, we discerned a role for the ERK/CREB signaling pathway in mediating the effects of ATX and LPA. Importantly, strategic supplementation of ATX effectively mitigated depression-like behaviors. This study indicates that the ATX-LPA pathway may influence depression-like behaviors by modulating synaptic plasticity in the brains of CUMS-exposed mice. These insights augment our understanding of depression's potential pathogenic mechanism in the context of lipid metabolism and propose promising therapeutic strategies for ameliorating the disease.

Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging.

BACKGROUND: Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. PURPOSE: To develop an interpretable DL model capable of grading and localizing LDH. STUDY TYPE: Retrospective. SUBJECTS: 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. FIELD STRENGTH/SEQUENCE: 1.5T MRI for axial T2-weighted sequences (spin echo). ASSESSMENT: The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. STATISTICAL TESTS: Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. RESULTS: The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). DATA CONCLUSION: The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. TECHNICAL EFFICACY: Stage 2.

Oligodendrocyte-derived exosomes-containing SIRT2 ameliorates depressive-like behaviors and restores hippocampal neurogenesis and synaptic plasticity via the AKT/GSK-3ß pathway in depressed mice.

AIMS: To investigate the antidepressant role of oligodendrocyte-derived exosomes (ODEXs)-containing sirtuin 2 (SIRT2) and the underlying mechanism both in vivo and in vitro. METHODS: Oligodendrocyte-derived exosomes isolated from mouse serum were administered to mice with chronic unpredictable mild stress (CUMS)-induced depression via the tail vein. The antidepressant effects of ODEXs were assessed through behavioral tests and quantification of alterations in hippocampal neuroplasticity. The role of SIRT2 was confirmed using the selective inhibitor AK-7. Neural stem/progenitor cells (NSPCs) were used to further validate the impact of overexpressed SIRT2 and ODEXs on neurogenesis and synapse formation in vitro. RESULTS: Oligodendrocyte-derived exosome treatment alleviated depressive-like behaviors and restored neurogenesis and synaptic plasticity in CUMS mice. SIRT2 was enriched in ODEXs, and blocking SIRT2 with AK-7 reversed the antidepressant effects of ODEXs. SIRT2 overexpression was sufficient to enhance neurogenesis and synaptic protein expression. Mechanistically, ODEXs mediated transcellular delivery of SIRT2, targeting AKT deacetylation and AKT/GSK-3ß signaling to regulate neuroplasticity. CONCLUSION: This study establishes how ODEXs improve depressive-like behaviors and hippocampal neuroplasticity and might provide a promising therapeutic approach for depression.

The Neuroprotective Effects of BMSC-Derived Exosomes against Glutamate-Induced HT22 Cell Cytotoxicity.

Many central nervous system diseases are closely related to nerve damage caused by dysregulation of the endogenous neurotransmitter glutamate. Exosomes derived from bone marrow mesenchymal stem cells (BMSC-Exos) play an important role in improving injury and regeneration functions. However, its mechanism remains unknown. Therefore, the aim of this study is to investigate whether and how BMSC-Exos improve neurotoxicity caused by glutamate and to fill the gap in the literature. In this study, glutamate-treated HT22 cells were first exposed to mouse-derived BMSC-Exos at different concentrations to observe their effects on HT22 apoptosis. Next, we treated glutamate-treated HT22 cells with mouse-derived BMSC-Exos. We then inhibited the PI3K/Akt/mTOR signaling pathways using the PI3K/Akt inhibitor and the mTOR inhibitor, respectively, and observed the protective effect of mouse-derived BMSC-Exos on HT22 cells treated with glutamate. Our results show that BMSC-Exos reduced apoptosis triggered by glutamate stimulation, increased cell vitality, and decreased the levels of proapoptotic proteins while increasing the levels of anti-apoptotic proteins. The protective effect of BMSC-Exos was weakened when PI3K/Akt inhibitor and mTOR inhibitor were added. To sum up, we draw the following conclusions: BMSC-Exos can reduce neuronal apoptosis and apoptosis-related protein expression after glutamate stimulation by regulating the PI3K/Akt/mTOR signaling pathway.

Study on Vertical Bearing Capacity of Pile Foundation with Distributed Geopolymer Post-Grouting on Pile Side.

To study the applicability of the new geopolymer grouting material for super-long and large-diameter post-grouting bored piles in silty fine sand geology, this paper compares the bearing capacity of two grouting materials, geopolymer and normal Portland cement, and different grouting volume pile side-distributed grouting piles in silty fine sand based on field model tests are analyzed through the diffusion forms of the two materials in silty fine sand through the morphology of the grouted body after excavation. The results show that the ultimate bearing capacities of P0 (ungrouted pile), P1 (8 kg cement grouted pile), P2 (6 kg geopolymer-grouted pile), P3 (8 kg geopolymer-grouted pile) and P4 (10 kg geopolymer-grouted pile) are 5400 N, 8820 N, 9450 N, 11,700 N and 12,600 N, respectively, and that the ultimate bearing capacity of the grouted pile is improved compared with that of the ungrouted pile since, under the same grouting amount, the maximum bearing capacity of the pile using geopolymer grouting is increased by 133% compared with that of the pile with cement grouting. This further verifies the applicability of the geopolymer grouting material for the post-grouting of the pile foundation in silty fine sand. Under the action of the ultimate load, the pile side friction resistance of P1, P2, P3 and P4 is increased by 200%, 218%, 284% and 319% compared with that of P0. In addition, the excavation results show that the geopolymer post-grouting pile forms the ellipsoidal consolidation body at the pile side grouting location, which mainly comprises extrusion diffusion with a small amount of infiltration diffusion, and the cement grouting pile forms a sheet-like consolidation body at the lower grouting location, which primarily comprises split diffusion. This study can provide a reference basis for the theoretical and engineering application of post-grouting piles using geopolymers.

Prediction of anxious depression using multimodal neuroimaging and machine learning.

Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.

Elevated triglycerides and low triiodothyronine: Key risk factors for coronary artery calcification in patients with schizophrenia.

OBJECTIVE: Coronary artery calcification (CAC) is a well-established independent predictor of coronary heart disease, and patients with schizophrenia have significantly higher rates compared to the general population. We performed this study to examine the population-specific risk factors associated with CAC in patients with schizophrenia. METHODS: In this cross-sectional study, patients with schizophrenia who underwent low-dose chest CT scans between January 2020 and December 2021 were analyzed. Ordinary CAC scores and results of routine blood tests were obtained. Logistic regression was used to calculate the odds ratio (OR) for potential risk factors in patients with and without CAC, while the negative binomial additive model was used to explore the dose-response relationship between risk factors and CAC score. RESULTS: Of the 916 patients, 233 (25.4 %) had CAC, while 683 (74.6 %) did not. After adjusting for confounding factors, higher triglyceride levels (OR = 1.20, 95 % confidence interval (CI): 1.04 to 1.38, p = 0.013) and low triiodothyronine levels (OR = 0.50, 95 % CI: 0.29 to 0.84; p = 0.010) were identified as risk factors for CAC. Both triglycerides (p = 0.021) and triiodothyronine (p = 0.010) were also found to have significant dose-response relationships with CAC scores according to the negative binomial additive model in the exploratory analysis. CONCLUSIONS: This study highlights elevated serum triglycerides and decreased triiodothyronine levels as population-specific risk factors for CAC in patients with schizophrenia, suggest the need for close monitoring of CAC in patients with schizophrenia and further prospective trials to provide additional evidence on this topic.

Altered in vivo early neurogenesis traits in patients with depression: Evidence from neuron-derived extracellular vesicles and electroconvulsive therapy.

BACKGROUND: The neurogenesis hypothesis is a promising candidate etiologic hypothesis for depression, and it is associated with electroconvulsive therapy (ECT). However, human in vivo molecular-level evidence is lacking. OBJECTIVE: We used neuron-derived extracellular vesicles (NDEVs) as a "window to the neurons" to explore the in vivo neurogenesis status associated with ECT in patients with treatment-resistant depression (TRD). METHODS: In this study, we enrolled 40 patients with TRD and 35 healthy controls (HCs). We isolated NDEVs from the plasma of each participant to test the levels of doublecortin (DCX), a marker of neurogenesis, and cluster of differentiation (CD) 81, a marker of EVs. We also assessed the plasma levels of brain-derived neurotrophic factor (BDNF), a protein that is known to be associated with ECT and neuroplastic processes. RESULTS: Our findings indicated that both the levels of DCX in NDEVs and BDNF in plasma were significantly lower in TRD patients compared to HCs at baseline, but increased following ECTs. Conversely, levels of CD81 in NDEVs were found higher in TRD patients at baseline, but did not change after the ECT treatments. Exploratory analyses revealed that lower levels of BDNF in plasma and DCX in NDEVs, along with higher CD81 levels in NDEVs, were associated with more severe depressive symptoms and reduced cognitive function at baseline. Furthermore, higher baseline CD81 concentrations in NDEVs were correlated with greater decreases in depression symptoms. CONCLUSIONS: We first present human in vivo evidence of early neurogenesis using DCX through NDEVs: decreased in TRD patients, increased after ECTs.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers. METHODS: Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve. RESULTS: In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics). CONCLUSIONS: The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Real-time 4K computer-generated hologram based on encoding conventional neural network with learned layered phase.

Learning-based computer-generated hologram (CGH) demonstrates great potential for real-time high-quality holographic displays. However, real-time 4K CGH generation for 3D scenes remains a challenge due to the computational burden. Here, a variant conventional neural network (CNN) is presented for CGH encoding with learned layered initial phases for layered CGH generation. Specifically, the CNN predicts the CGH based on the input complex amplitude on the CGH plane, and the learned initial phases act as a universal phase for any target images at the target depth layer. These phases are generated during the training process of the coding CNN to further optimize the quality. The CNN is trained to learn encoding 3D CGH by randomly selecting the depth layer in the training process, and contains only 938 parameters. The generation time for a 2D 4K CGH is 18 ms, and is increased by 12 ms for each layer in a layered 3D scene. The average Peak Signal to Noise Ratio (PSNR) of each layer is above 30dB in the depth range from 160 to 210 mm. Experiments verify that our method can achieve real-time layered 4K CGH generation.

Interpretable Machine Learning Model to Predict Bone Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fracture Based on SHapley Additive exPlanations.

STUDY DESIGN: Retrospective study. OBJECTIVES: Our objective is to create comprehensible machine learning (ML) models that can forecast bone cement leakage in percutaneous vertebral augmentation (PVA) for individuals with osteoporotic vertebral compression fracture (OVCF) while also identifying the associated risk factors. METHODS: We incorporated data from patients (n = 425) which underwent PVA. To predict cement leakage, we devised six models based on a variety of parameters. Evaluate and juxtapose the predictive performances relied on measures of discrimination, calibration, and clinical utility. SHapley Additive exPlanations (SHAP) methodology was used to interpret model and evaluate the risk factors associated with cement leakage. RESULTS: The occurrence rate of cement leakage was established at 50.4%. A binary logistic regression analysis identified cortical disruption (OR 6.880, 95% CI 4.209-11.246), the basivertebral foramen sign (OR 2.142, 95% CI 1.303-3.521), the fracture type (OR 1.683, 95% CI 1.083-2.617), and the volume of bone cement (OR 1.198, 95% CI 1.070-1.341) as independent predictors of cement leakage. The XGBoost model outperformed all others in predicting cement leakage in the testing set, with AUC of .8819, accuracy of .8025, recall score of .7872, F1 score of .8315, and a precision score of .881. Several important factors related to cement leakage were drawn based on the analysis of SHAP values and their clinical significance. CONCLUSION: The ML based predictive model demonstrated significant accuracy in forecasting bone cement leakage for patients with OVCF undergoing PVA. When combined with SHAP, ML facilitated a personalized prediction and offered a visual interpretation of feature importance.

The Mediation Role of Sleep Disturbances between Vitamin D and Depressive Symptoms: A Cross-Sectional Study.

Depression and sleep disturbances are highly prevalent health problems that have been suggested to be associated with vitamin D deficiency. This study investigated whether sleep disturbances affect the association between vitamin D and depressive symptoms. A total of 425 patients with depression were included in this study. Spearman correlation coefficients were chosen to assess the relation between vitamin D concentrations and depressive symptomatology (according to the PHQ-9 and HAMD-17 scores). The GLM Mediation Model in the Medmod module for data analysis in Jamovi 2.2.5 was used to analyze the mediation models for sleep disturbances. Vitamin D concentrations were significantly correlated with PHQ-9 and HAMD-17 scale scores. In addition, item 3 was suggested to have a mediating effect between vitamin D and depressive symptoms in the mediating model of PHQ-9, and item 4 was suggested to have a mediating effect between vitamin D and depressive symptoms in the mediating model of HAMD-17. Sleep disturbances (especially difficulty falling asleep) are mediators between vitamin D and depressive symptoms, suggesting that increasing vitamin D levels at the right time to regulate sleep disturbances may improve depression symptoms, yet further research is necessary.

The needle in the haystack: Identifying and validating common genes of depression, insomnia, and inflammation.

BACKGROUND: Insomnia, inflammation, and depression are often co-occurring conditions. The mechanisms underlying these conditions remain unclear. MATERIALS AND METHODS: We collected microarray datasets of depression and insomnia from GEO and analyzed them for differentially expressed genes (DEGs). We then overlapped the DEGs with a list of inflammatory response-related genes to identify genes associated with all three conditions. We next performed analyses of enrichment analyses, KEGG mapping, and protein-protein interaction to identify hub genes. Furthermore, we established a depression rat model with inflammation and insomnia to validate the potential genes. At last, a two-sample Mendelian randomization (MR) study was conducted to confirm the association of identified target genes with depression outcomes. RESULTS: We obtained 32 common DEGs associated with the depression, insomnia and inflammatory, and found that the PI3K-AKT signaling pathway might be involved in the inflammatory response in insomnia and depression. CREB1, CYBB, FYN, and CCR5 were identified as targets for the next validation. In model rats, the CCR5 and PI3K-AKT pathways were significantly up-regulated, while the model group exhibited significantly lower hippocampal p-CREB protein expression. The MR study suggested a potential causal relationship between CREB1 and the risk of depression (OR = 1.11, p = 0.013). LIMITATIONS: The identified potential genes and pathways require further laboratory and clinical evidence verification. CONCLUSION: We identified four potential inflammatory related-genes (CREB1, CYBB, FYN, and CCR5). CREB1 may be a potential inflammatory response-related biomarker and drug target for depression and insomnia, as validated by the followed rat model and MR study.

Human in vivo evidence of reduced astrocyte activation and neuroinflammation in patients with treatment-resistant depression following electroconvulsive therapy.

AIM: The current study aimed to investigate the neuroinflammatory hypothesis of depression and the potential anti-inflammatory effect of electroconvulsive therapy (ECT) in vivo, utilizing astrocyte-derived extracellular vesicles (ADEVs) isolated from plasma. METHODS: A total of 40 patients with treatment-resistant depression (TRD) and 35 matched healthy controls were recruited at baseline, and 34 patients with TRD completed the post-ECT visits. Blood samples were collected at baseline and post-ECT. Plasma ADEVs were isolated and confirmed, and the concentrations of two astrocyte markers (glial fibrillary acidic protein [GFAP] and S100ß), an extracellular vesicle marker cluster of differentiation 81 (CD81), and nine inflammatory markers in ADEVs were measured as main analyses. In addition, correlation analysis was conducted between clinical features and ADEV protein levels as exploratory analysis. RESULTS: At baseline, the TRD group exhibited significantly higher levels of two astrocyte markers GFAP and S100ß, as well as CD81 compared with the healthy controls. Inflammatory markers interferon γ (IFN-γ), interleukin (IL) 1ß, IL-4, IL-6, tumor necrosis factor α, IL-10, and IL-17A were also significantly higher in the TRD group. After ECT, there was a significant reduction in the levels of GFAP, S100ß, and CD81, along with a significant decrease in the levels of IFN-γ and IL-4. Furthermore, higher levels of GFAP, S100ß, CD81, and inflammatory cytokines were associated with more severe depressive symptoms and poorer cognitive function. CONCLUSION: This study provides direct insight supporting the astrocyte activation and neuroinflammatory hypothesis of depression using ADEVs. ECT may exert an anti-inflammatory effect through inhibition of such activation of astrocytes.

Portrait stylized rendering for 3D light-field display based on radiation field and example guide.

With the development of three-dimensional (3D) light-field display technology, 3D scenes with correct location information and depth information can be perceived without wearing any external device. Only 2D stylized portrait images can be generated with traditional portrait stylization methods and it is difficult to produce high-quality stylized portrait content for 3D light-field displays. 3D light-field displays require the generation of content with accurate depth and spatial information, which is not achievable with 2D images alone. New and innovative portrait stylization techniques methods should be presented to meet the requirements of 3D light-field displays. A portrait stylization method for 3D light-field displays is proposed, which maintain the consistency of dense views in light-field display when the 3D stylized portrait is generated. Example-based portrait stylization method is used to migrate the designated style image to the portrait image, which can prevent the loss of contour information in 3D light-field portraits. To minimize the diversity in color information and further constrain the contour details of portraits, the Laplacian loss function is introduced in the pre-trained deep learning model. The three-dimensional representation of the stylized portrait scene is reconstructed, and the stylized 3D light field image of the portrait is generated the mask guide based light-field coding method. Experimental results demonstrate the effectiveness of the proposed method, which can use the real portrait photos to generate high quality 3D light-field portrait content.

A Cross-Sectional Study: Structural and Related Functional Connectivity Changes in the Brain: Stigmata of Adverse Parenting in Patients with Major Depressive Disorder?

Background: There is a high correlation between the risk of major depressive disorder (MDD) and adverse childhood experiences (ACEs) such as adverse parenting (AP). While there appears to be an association between ACEs and changes in brain structure and function, there have yet to be multimodal neuroimaging studies of associations between parenting style and brain developmental changes in MDD patients. To explore the effect of AP on brain structure and function. Methods: In this cross-sectional study, 125 MDD outpatients were included in the study and divided into the AP group and the optimal parenting (OP) group. Participants completed self-rating scales to assess depressive severity, symptoms, and their parents' styles. They also completed magnetic resonance imaging within one week of filling out the instruments. The differences between groups of gender, educational level, and medications were analyzed using the chi-squared test and those of age, duration of illness, and scores on scales using the independent samples t-test. Differences in gray matter volume (GMV) and resting-state functional connectivity (RS-FC) were assessed between groups. Results: AP was associated with a significant increase in GMV in the right superior parietal lobule (SPL) and FC between the right SPL and the bilateral medial superior frontal cortex in MDD patients. Limitations: The cross-cultural characteristics of AP will result in the lack of generalizability of the findings. Conclusions: The results support the hypothesis that AP during childhood may imprint the brain and affect depressive symptoms in adulthood. Parents should pay attention to the parenting style and avoid a style that lacks warmth.

Young Adults with Major Depression Show Altered Microbiome.

There is growing basic and clinical evidence that major depressive disorder (MDD) is associated with gut microbiome alterations, but clinical studies have tended not to adjust for confounding factors. And few studies on the gut microbiome focused on young adults with MDD. Here we performed a pilot study to compare the gut microbiome of young adults with MDD with healthy controls. Shotgun metagenomic sequencing was performed on stool samples obtained from 40 young adults with MDD and 42 healthy controls. After controlling for confounding factors including sex, age, BMI, alcohol or cigarette consumption, bowel movement quality, exercise or defecation frequency, we compared microbiome diversity between groups, identified differentially abundant taxa, and further compared functional differences through gut-brain and gut-metabolic module analysis. There were no significant differences in overall gut microbiome structure and function in young adults with MDD compared with controls. Abundance of Sutterellaceae and species belonging to Clostridium, Eubacterium, and Ruminococcus were significantly different between groups. The cysteine degradation I pathway was increased in MDD. After controlling for most confounding factors, this pilot study provides new evidence on the specific, often subtle gut dysbiosis affecting young adults with depression.

Nicotinamide ameliorates mitochondria-related neuronal apoptosis and cognitive impairment via the NAD+/SIRT3 pathway.

Emerging evidence suggests that mitochondria play a central role in mental health disorders including schizophrenia. Here we investigated whether nicotinamide (NAM) normalized cognitive impairment via a mechanism involving the mitochondrial Sirtuin 3 (SIRT3) pathway. The 24 h maternal separation (MS) rat model was used to mimic schizophrenia-associate phenotypes. Schizophrenia-like behaviors and memory impairments were detected using the pre-pulse inhibition test, novel object recognition test, and Barnes maze test, and neuronal apoptosis was characterized using multiple assays. SIRT3 activity was inhibited pharmacologically or by knockdown in HT22 cells, and BV2 microglia and SIRT3-knockdown HT22 cells were co-cultured in vitro. Mitochondrial molecules were measured by western blotting, and mitochondrial damage was measured with reactive oxygen species and mitochondrial membrane potential assays. Proinflammatory cytokines were assayed by ELISA and microglial activation was detected by immunofluorescence. MS animals showed behavioral and cognitive impairment and increased neuronal apoptosis. Supplementation with NAM or administration of honokiol, a SIRT3 activator, reversed all of the changes in behavioral and neuronal phenotypes. Administration of the SIRT3 inhibitor 3-TYP in control and NAM-treated MS rats caused behavioral and neuronal phenotypes similar to MS. In vitro, inhibition of SIRT3 activity with 3-TYP or by knockdown in HT22 cells increased ROS accumulation and caused neuronal apoptosis in a single-culture system. In co-culture systems, SIRT3 knockdown in HT22 cells activated BV2 microglia and increased levels of TNF-α, IL-6, and IL-1ß. The administration of NAM blocked these alterations. Taken together, these data suggest that NAM can rescue neuronal apoptosis and microglial over-activation through the nicotinamide adenine dinucleotide (NAD+)-SIRT3-SOD2 signaling pathway, furthering our understanding of the pathogenesis of schizophrenia and providing avenues for novel treatments.

Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy.

OBJECTIVE: To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models. METHODS: Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model. RESULTS: A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%. CONCLUSION: This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.

Assessment of brain imaging and cognitive function in a modified rhesus monkey model of depression.

Depression incurs a huge personal and societal burden, impairing cognitive and social functioning and affecting millions of people worldwide. A better understanding of the biological basis of depression could facilitate the development of new and improved therapies. Rodent models have limitations and do not fully recapitulate human disease, hampering clinical translation. Primate models of depression help to bridge this translational gap and facilitate research into the pathophysiology of depression. Here we optimized a protocol for administering unpredictable chronic mild stress (UCMS) to non-human primates and evaluated the influence of UCMS on cognition using the classical Wisconsin General Test Apparatus (WGTA) method. We used resting-state functional MRI to explore changes in amplitude of low-frequency fluctuations and regional homogeneity in rhesus monkeys. Our work highlights that the UCMS paradigm effectively induces behavioral and neurophysiological (functional MRI) changes in monkeys but without significantly impacting cognition. The UCMS protocol requires further optimization in non-human primates to authentically simulate changes in cognition associated with depression.