Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

As a high-quality moxibustion material, Artemisia stolonifera has high economic value and research prospects. However, due to difficulties in seed germination, its wild germplasm resources are sparsely distributed in China. This study used young stem segments grown in the current year to investigate the effects of explant sterilization, different combinations and concentrations of plant growth regulators on the proliferation and rooting of adventitious shoots, with the aim of constructing an in vitro rapid propagation technology system for A. stolonifera. The results showed that the lowest contamination rate of 25.83% was achieved when sterilizing the stem segments by rinsing with running water for 30 min, soaking in 75% ethanol for 30 s, followed by a 5 min treatment with 0.1% HgCl_2, 10 min with 8% NaClO, and 10 min with 0.6% phytosaniline. There was no browning of the stem segments, and surface sterilization of the A. stolonifera stem segments was successfully achieved. In the induction culture phase, when the concentration of kinetin(KT) was 0.05 mg·L~(-1) and 6-benzylaminopurine(6-BA) was 0.05 mg·L~(-1), the adventitious shoot proliferation coefficient was 2.02, effectively promoting the proliferation and growth of A. stolonifera. In the rooting culture phase, 0.1 mg·L~(-1) 1-naphthaleneacetic acid(NAA) effectively induced A. stolonifera test-tube seedlings to root within a short period, achieving a rooting rate of 100%. The addition of a small amount of activated charcoal also promoted rooting and strengthened seedling growth. The survival rate of A. stolonifera seedlings transplanted into a substrate consisting of 90% nutrient soil and 10% perlite was 100%. This study established an efficient in vitro rapid propagation system for A. stolonifera, overcoming difficulties with seed germination, shortening the breeding cycle, and reducing production and planting costs. It provides technical support for the introduction, domestication, seedling propagation, germplasm conservation, and industrial development of A. stolonifera.
Artemisia/drug effects* ; Tissue Culture Techniques/methods* ; Plant Growth Regulators/pharmacology* ; Plant Stems/drug effects* ; Plant Shoots/drug effects*

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

OBJECTIVE: To observe the therapeutic effects and underlying mechanism of baicalin against colon cancer. METHODS: The effects of baicalin on the proliferation and growth of colon cancer cells MC38 and CT26. WT were observed and predicted potential molecular targets of baicalin for colon cancer therapy were studied by network pharmacology. Furthermore, molecular docking and drug affinity responsive target stability (DARTS) analysis were performed to confirm the interaction between potential targets and baicalin. Finally, the mechanisms predicted by in silico analyses were experimentally verified in-vitro and in-vivo. RESULTS: Baicalin significantly inhibited proliferation, invasion, migration, and induced apoptosis in MC38 and CT26 cells (all P<0.01). Additionally, baicalin caused cell cycle arrest at the S phase, while the G₀/G₁ phase was detected in the tiny portion of the cells. Subsequent network pharmacology analysis identified 6 therapeutic targets associated with baicalin, which potentially affect various pathways including 39 biological processes and 99 signaling pathways. In addition, molecular docking and DARTS predicted the potential binding of baicalin with cyclin dependent kinase inhibitor 2A (CDKN2A), protein kinase B (AKT), caspase 3, and mitogen-activated protein kinase (MAPK). In vitro, the expressions of CDKN2A, MAPK, and p-AKT were suppressed by baicalin in MC38 and CT26 cells. In vivo, baicalin significantly reduced the tumor size and weight (all P<0.01) in the colon cancer mouse model via inactivating p-AKT, CDKN2A, cyclin dependent kinase 4, cyclin dependent kinase 2, interleukin-1, tumor necrosis factor α, and activating caspase 3 and mouse double minute 2 homolog signaling (all P<0.05). CONCLUSION Baicalin suppressed the CDKN2A protein level to prevent colon cancer and could be used as a therapeutic target for colon cancer.
Flavonoids/pharmacology* ; Colonic Neoplasms/prevention & control* ; Animals ; Cell Line, Tumor ; Molecular Docking Simulation ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Cyclin-Dependent Kinase Inhibitor p16/metabolism* ; Mice ; Mice, Inbred BALB C ; Cell Movement/drug effects* ; Humans ; Gene Expression Regulation, Neoplastic/drug effects* ; Cell Cycle Checkpoints/drug effects*

Objective: Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk. Methods: This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed. Latent class analysis was performed using 16 common chronic conditions to identify mortality risk among potentially different latent classes. Logistic regression was performed to determine the adjusted association of the determined latent classes with the 5-year mortality rate. Results: With adjustment for age, a three-class model was identified, with 42.7% of participants classified as “low comorbidity class (cluster 1)”, 44.2% as “cardiometabolic multimorbidity class (cluster 2)”, and 13.1% as “FRINGED class (cluster 3, characterized by FRacture, Infection, NasoGastric feeding, and bleEDing over upper gastrointestinal tract).” The incidence of 5-year mortality was 17.6% in cluster 1, 26.7% in cluster 2, and 59.6% in cluster 3. Compared with cluster 1, the odds ratio for mortality was 9.828 (95% confidence interval [CI]=6.708–14.401; p<0.001) in cluster 2 and 1.582 (95% CI=1.281–1.953; p<0.001) in cluster 3. Conclusion Among patients with dementia, the risk for 5-year mortality was highest in the subpopulation characterized by fracture, urinary and pulmonary infection, upper gastrointestinal bleeding, and nasogastric intubation, rather than cancer or cardiometabolic comorbidities. These findings may improve decision-making and advance care planning for patients with dementia.

Aim To study the effect of Morinda officinalis on serum metabolisms in ovariectomized osteoporosis rats based on metabonomics, and explore the mechanism of Morinda officinalis in the treatment of postmenopausal osteoporosis.Methods Forty-eight SD rats were randomly divided into sham operation group, model group and Morinda officinalis group.The Morinda officinalis group was given Morinda officinalis water extract by gavage.The model group and sham operation group were given normal saline by gavage.The bone mineral density(BMD)of the right femur was measured by dual energy X-ray absorptiometry; the maximum load of the tibia bending at three points and the lumbar compression was measured by universal material testing machine.The endogenous metabolites of ovariectomized osteoporosis rats were identified by serum metabonomics, and the potential differential metabolites were screened and identified..Results The BMD and maximum load of the model group decreased significantly, while the Morinda officinalis group increased significantly compared with the model group.The serum metabolic spectrum of the sham operation group was completely separated from that of the model group, and the Morinda officinalis group was close to the sham operation group, suggesting that the body had a tendency to return to normal after intervention of Morinda officinalis.28 metabolites and 5 metabolic pathways were identified to be related to ovariectomized osteoporosis.Morinda officinalis could regulate the contents of stearic acid, uracil and other metabolites, which were related to the biosynthesis of unsaturated fatty acids, the metabolism of pyrimidine and so on.Conclusion Morinda officinalis can prevent ovariectomized osteoporosis by regulating the lipid metabolism and nucleic acid metabolism.