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.

OBJECTIVES: To evaluate the early risk factors for death in neonates with persistent pulmonary hypertension of the newborn (PPHN) treated with inhaled nitric oxide (iNO). METHODS: A retrospective analysis was performed on 105 infants with PPHN (gestational age ≥34 weeks and age <7 days on admission) who received iNO treatment in the Department of Neonatology, Children's Hospital of Nanjing Medical University, from July 2017 to March 2021. Related general information and clinical data were collected. According to the clinical outcome at discharge, the infants were divided into a survival group with 79 infants and a death group with 26 infants. Univariate and multivariate Cox regression analyses were used to evaluate the risk factors for death in infants with PPHN treated with iNO. The receiver operating characteristic (ROC) curve was used to calculate the cut-off values of the factors in predicting the death risk. RESULTS: A total of 105 infants with PPHN treated with iNO were included, among whom 26 died (26/105, 24.8%). The multivariate Cox regression analysis showed that no early response to iNO (HR=8.500, 95%CI: 3.024-23.887, P<0.001), 1-minute Apgar score ≤3 points (HR=10.094, 95%CI: 2.577-39.534, P=0.001), a low value of minimum PaO₂/FiO₂ within 12 hours after admission (HR=0.067, 95%CI: 0.009-0.481, P=0.007), and a low value of minimum pH within 12 hours after admission (HR=0.049, 95%CI: 0.004-0.545, P=0.014) were independent risk factors for death. The ROC curve analysis showed that the lowest PaO₂/FiO₂ value within 12 hours after admission had an area under the ROC curve of 0.783 in predicting death risk, with a sensitivity of 84.6% and a specificity of 73.4% at the cut-off value of 50, and the lowest pH value within 12 hours after admission had an area under the ROC curve of 0.746, with a sensitivity of 76.9% and a specificity of 65.8% at the cut-off value of 7.2. CONCLUSIONS Infants with PPHN requiring iNO treatment tend to have a high mortality rate. No early response to iNO, 1-minute Apgar score ≤3 points, the lowest PaO₂/FiO₂ value <50 within 12 hours after admission, and the lowest pH value <7.2 within 12 hours after admission are the early risk factors for death in such infants. Monitoring and evaluation of the above indicators will help to identify high-risk infants in the early stage.
Administration, Inhalation ; Child ; Humans ; Hypertension, Pulmonary/drug therapy* ; Infant ; Infant, Newborn ; Nitric Oxide ; Persistent Fetal Circulation Syndrome/drug therapy* ; Retrospective Studies ; Risk Factors