Public sentiment analysis and topic modeling regarding ChatGPT in mental health on Reddit: Negative sentiments increase over time

In order to uncover users' attitudes towards ChatGPT in mental health, this study examines public opinions about ChatGPT in mental health discussions on Reddit. Researchers used the bert-base-multilingual-uncased-sentiment techniques for sentiment analysis and the BERTopic model for topic modeling. It was found that overall, negative sentiments prevail, followed by positive ones, with neutral sentiments being the least common. The prevalence of negative emotions has increased over time. Negative emotions encompass discussions on ChatGPT providing bad mental health advice, debates on machine vs. human value, the fear of AI, and concerns about Universal Basic Income (UBI). In contrast, positive emotions highlight ChatGPT's effectiveness in counseling, with mentions of keywords like "time" and "wallet." Neutral discussions center around private data concerns. These findings shed light on public attitudes toward ChatGPT in mental health, potentially contributing to the development of trustworthy AI in mental health from the public perspective.Comment: 11 pages.8 figures, 2 table

3D carbon allotropes: Topological quantum materials with obstructed atomic insulating phases, multiple bulk-boundary correspondences, and real topology

The study of topological phases with unconventional bulk-boundary correspondences and nontrivial real Chern number has garnered significant attention in the topological states of matter. Using the first-principle calculations and theoretical analysis, we perform a high-throughput material screening of the 3D obstructed atomic insulators (OAIs) and 3D real Chern insulators (RCIs) based on the Samara Carbon Allotrope Database (SACADA). Results show that 422 out of 703 3D carbon allotropes are 3D OAIs with multiple bulk-boundary correspondences, including 2D obstructed surface states (OSSs) and 1D hinge states, which are in one dimension and two dimensions lower than the 3D bulk, respectively. The 2D OSSs in these OAIs can be modified when subjected to appropriate boundaries, which benefits the investigation of surface engineering and the development of efficient topological catalysts. These 422 OAIs, which have 2D and 1D boundary states, are excellent platforms for multi-dimensional topological boundaries research. Remarkably, 138 of 422 OAIs are also 3D RCIs, which show a nontrivial real topology in the protection of spacetime inversion symmetry. Our work not only provides a comprehensive list of 3D carbon-based OAIs and RCIs, but also guides their application in various aspects based on multiple bulk-boundary correspondences and real topological phases

Autologous Skin Fibroblast-Based PLGA Nanoparticles for Treating Multiorgan Fibrosis

Fibrotic diseases remain a substantial health burden with few therapeutic approaches. A hallmark of fibrosis is the aberrant activation and accumulation of myofibroblasts, which is caused by excessive profibrotic cytokines. Conventional anticytokine therapies fail to undergo clinical trials, as simply blocking a single or several antifibrotic cytokines cannot abrogate the profibrotic microenvironment. Here, biomimetic nanoparticles based on autologous skin fibroblasts are customized as decoys to neutralize multiple fibroblast-targeted cytokines. By fusing the skin fibroblast membrane onto poly(lactic-co-glycolic) acid cores, these nanoparticles, termed fibroblast membrane-camouflaged nanoparticles (FNPs), are shown to effectively scavenge various profibrotic cytokines, including transforming growth factor-beta, interleukin (IL)-11, IL-13, and IL-17, thereby modulating the profibrotic microenvironment. FNPs are sequentially prepared into multiple formulations for different administration routines. As a proof-of-concept, in three independent animal models with various organ fibrosis (lung fibrosis, liver fibrosis, and heart fibrosis), FNPs effectively reduce the accumulation of myofibroblasts, and the formation of fibrotic tissue, concomitantly restoring organ function and indicating that FNPs are a potential broad-spectrum therapy for fibrosis management.Peer reviewe

A brain-targeting lipidated peptide for neutralizing RNA-mediated toxicity in Polyglutamine Diseases

Abstract Polyglutamine (PolyQ) diseases are progressive neurodegenerative disorders caused by both protein- and RNA-mediated toxicities. We previously showed that a peptidyl inhibitor, P3, which binds directly to expanded CAG RNA can inhibit RNA-induced nucleolar stress and suppress RNA-induced neurotoxicity. Here we report a N-acetylated and C-amidated derivative of P3, P3V8, that showed a more than 20-fold increase in its affinity for expanded CAG RNA. The P3V8 peptide also more potently alleviated expanded RNA-induced cytotoxicity in vitro, and suppressed polyQ neurodegeneration in Drosophila with no observed toxic effects. Further N-palmitoylation of P3V8 (L1P3V8) not only significantly improved its cellular uptake and stability, but also facilitated its systemic exposure and brain uptake in rats via intranasal administration. Our findings demonstrate that concomitant N-acetylation, C-amidation and palmitoylation of P3 significantly improve both its bioactivity and pharmacological profile. L1P3V8 possesses drug/lead-like properties that can be further developed into a lead inhibitor for the treatment of polyQ diseases

Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1α upregulation and autophagy.

Distinct metabolic programs, either energy-consuming anabolism or energy-generating catabolism, were required for different biological functions. Macrophages can adopt different immune phenotypes in response to various cues and exhibit anti- or pro-inflammatory properties relying on catabolic pathways associated with oxidative phosphorylation (OXPHOS) or glycolysis. Spermidine, a natural polyamine, has been reported to regulate inflammation through inducing anti-inflammatory (M2) macrophages. However, the underlying mechanisms remain elusive. We show here that the M2-polarization induced by spermidine is mediated by mitochondrial reactive oxygen species (mtROS). The levels of mitochondrial superoxide and H2O2 were markedly elevated by spermidine. Mechanistically, mtROS were found to activate AMP-activated protein kinase (AMPK), which in turn enhanced mitochondrial function. Furthermore, hypoxia-inducible factor-1α (Hif-1α) was upregulated by the AMPK activation and mtROS and was required for the expression of anti-inflammatory genes and induction of autophagy. Consistent with previous report that autophagy is required for the M2 polarization, we found that the M2 polarization induced by spermidine was also mediated by increased autophagy. The macrophages treated with spermidine in vitro were found to ameliorate Dextran Sulfate Sodium (DSS)-induced inflammatory bowel disease (IBD) in mice. Thus, spermidine can elicit an anti-inflammatory program driven by mtROS-dependent AMPK activation, Hif-1α stabilization and autophagy induction in macrophages. Our studies revealed a critical role of mtROS in shaping macrophages into M2-like phenotype and provided novel information for management of inflammatory disease by spermidine

MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series

Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning

Electrochemical Cytosensor Based on a Gold Nanostar-Decorated Graphene Oxide Platform for Gastric Cancer Cell Detection

Effectively capturing and sensitively detecting cancer cells are critical to clinical diagnosis and cancer therapy. In this work, we prepared gold nanostar-decorated graphene oxide (GO-AuNSs) nanocomposites using a ultraviolet (UV)-induced strategy, and then modified them with a layer of bio-complex rBSA-FA (coupled reduced bovine serum albumin with folic acid) to generate GO-AuNSs@rBSA-FA nanocomposites. Herein, the application of GO and AuNSs not only strengthened the conductivity of the sensing platform but also guaranteed nanocomposites with biocompatible performance. Moreover, the adopted rBSA-FA layer could effectively enhance the stability and specificity towards gastric cancer cells (MGC-803). According to a systemic construction procedure, a novel electrochemical cytosensor based on GO-AuNSs@rBSA-FA was fabricated for MGC-803 cell detection. With the assistance of cyclic voltammetry (CV) and differential pulse voltammetry (DPV), the cytosensor reached a detection limit of 100 cell/mL in a wide linear range of 3 × 102~7 × 106 cell/mL towards MGC-803 cells. The good electrochemical characteristics for the cancer cell analysis indicate a promising prospect of this electrochemical cytosensor in clinical cancer diagnosis

M6A-mediated upregulation of HOXC10 promotes human hepatocellular carcinoma development through PTEN/AKT/mTOR signaling pathway

Abstract Human Hox genes (Homeobox) play a crucial role in embryonic development and cancer. The HOXC10 gene, a member of the HOX family, has been reported abnormally expressed in several cancers. However, the association between HOXC10 and hepatocellular carcinoma (HCC) remains to be elucidated. In the present study, tissue microarray cohort data showed that high levels of HOXC10 expression predicted a poor survival in HCC patients. Meanwhile, HOXC10 was significantly upregulated in the Huh7 cell line compared with the well differentiated cell line HepG2 and human normal liver cells. Functionally, silencing HOXC10 in Huh7 cells inhibited cell proliferation, increased apoptosis, and inhibited invasion and migration of HCC cells. HOXC10 overexpression in HepG2 cells increased cell proliferation, decreased apoptosis, and increased invasion and migration of HCC cells. In the HepG2 xenograft models, HOXC10 increased the tumor volume and weight compared with control. Mechanistically, the m6A modification of HOXC10 by METTL3 enhanced its expression by enhancing its mRNA stability. Both the in vitro and in vivo results showed that overexpressed HOXC10 activated the PTEN/AKT/mTOR pathway. In summary, the findings highlight the importance of HOXC10 in the regulation of HCC progression. HOXC10 is potentially a future therapeutic target for HCC treatment

Effects of Muddy Water Infiltration on the Hydraulic Conductivity of Soils

Despite the high sand content of Yellow River water in arid Northwest China, locals in the region opt to use muddy water to meet the demand for agricultural irrigation. Muddy water irrigation is a complex process and is still poorly understood. In this study, six sets of saturated soil column infiltration tests were designed, considering soil texture (silt loam, sandy loam, and sand) and muddy water sand content (3%, 6%, 9%, and 12%) as the influencing factors, with two sets of validation tests. Change in hydraulic conductivity (Kh), the average change rate of hydraulic conductivity (ΔK), and cumulative infiltration volume (I) were experimentally studied in the context of muddy water infiltration to respectively establish the separate functional models and developed to fit their relationship with time. The study results indicated that the hydraulic conductivity (Kh) decreased with increasing muddy water infiltration time. For silt loam and sandy loam, Kh stabilized at 0.0030 and 0.0109 cm/min, respectively, after 70 min of infiltration. In contrast, Kh in the saturated sandy soil column significantly declined throughout the muddy water infiltration, showing a 90.84% reduction after 90 min compared to the saturated hydraulic conductivity of the sandy soil. As the sand content of the muddy water increased from 3% to 12%, Kh decreased by 83.99%, 90.90%, 91.92%, and 92.21% for 3%, 6%, 9%, and 12% sand content, respectively, in the saturated sandy soil columns at the end of the infiltration period. The I values were 21.20, 9.29, 7.90, and 6.25 cm for 3%, 6%, 9%, and 12% sand content, respectively. The ΔK values were 0.0037, 0.0041, 0.0043, and 0.0044 cm/min2 for the respective sand contents, at an infiltration time of 80 min. The validation test demonstrated that the segmented function model accurately emulated the changes in hydraulic conductivity of sandy soil textures throughout the infiltration period. Results from this study provide a significant basis for understanding the mechanisms to hinder muddy water infiltration and to efficiently utilize muddy water for irrigation

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

Abstract Background Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. Purpose To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. Methods Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. Results The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson’s and Spearman’s correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. Conclusion CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV