Association between the Maternal Gut Microbiome and Macrosomia.

Fetal macrosomia is defined as a birthweight ≥4000 g and causes harm to pregnant women and fetuses. Studies reported that the maternal intestinal microbiome plays a key role in the establishment, growth, and development of the fetal intestinal microbiome. However, whether there is a relationship between maternal gut microbiota and macrosomia remains unclear. Our study aimed to identify gut microbiota that may be related to the occurrence of macrosomia, explore the possible mechanisms by which it causes macrosomia, and establish a prediction model to determine the feasibility of predicting macrosomia by early maternal gut microbiota. We conducted a nested case-control study based on an early pregnancy cohort (ChiCTR1900020652) in the Maternity and Child Health Hospital of Hunan Province on fecal samples of 93 women (31 delivered macrosomia as the case group and 62 delivered normal birth weight newborns as the control group) collected and included in this study. We performed metagenomic analysis to compare the composition and function of the gut microbiome between cases and controls. Correlation analysis was used to explore the association of differential species and differential functional pathways. A random forest model was used to construct an early pregnancy prediction model for macrosomia. At the species level, there were more Bacteroides salyersiae, Bacteroides plebeius, Ruminococcus lactaris, and Bacteroides ovatus in the intestinal microbiome of macrosomias' mothers compared with mothers bearing fetuses that had normal birth weight. Functional pathways of the gut microbiome including gondoate biosynthesis, L-histidine degradation III, cis-vaccenate biosynthesis, L-arginine biosynthesis III, tRNA processing, and mannitol cycle, which were more abundant in the macrosomia group. Significant correlations were found between species and functional pathways. Bacteroides plebeius was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.28, p = 0.005) and gondoate biosynthesis (r = 0.28, p < 0.001) and Bacteroides ovatus was positively associated with the pathway of cis-vaccenate biosynthesis (r = 0.29, p = 0.005) and gondoate biosynthesis (r = 0.32, p = 0.002). Bacteroides salyersiae was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.24, p = 0.018), gondoate biosynthesis (r = 0.31, p = 0.003), and L-histidine degradation III (r = 0.22, p = 0.291). Finally, four differential species and four clinical indicators were included in the random forest model for predicting macrosomia. The areas under the working characteristic curves of the training and validation sets were 0.935 (95% CI: 0.851~0.979) and 0.909 (95% CI: 0.679~0.992), respectively. Maternal gut microbiota in early pregnancy may play an important role in the development of macrosomia and can be used as potential predictors to prevent macrosomia.

Recent research on the physicochemical properties and biological activities of quinones and their practical applications: a comprehensive review.

Quinones represent a class of crude organic compounds ubiquitously distributed in nature. Their distinctive quinone-type structure confers upon them unique properties and applications. Quinones demonstrate significant biological activities, including antioxidant, antimicrobial, and antitumor properties. Additionally, they demonstrate noteworthy physicochemical characteristics, including excellent dyeing properties and stability. Given their diverse qualities, quinones hold significant promise for applications in industrial manufacturing, healthcare, and food production, thus garnering considerable attention in recent years. While there is a growing body of research on quinones, the existing literature falls short of providing a comprehensive review encompassing recent advancements in this field along with established knowledge. This paper offers a comprehensive review of research progress for quinones, encompassing structural classification, source synthesis, extraction methods, properties, functions, and specific applications. It serves as a reference and theoretical foundation for the further development and utilization of quinones.

Explainable machine learning for predicting 30-day readmission in acute heart failure patients.

We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703-0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.

Inhibition of polyphenols on Maillard reaction products and their induction of related diseases: A comprehensive review.

BACKGROUND: Food products undergo a pronounced Maillard reaction (MR) during the cooking process, leading to the generation of substantial quantities of Maillard reaction products (MRPs). Within this category, advanced glycation end products (AGEs), acrylamide (AA), and heterocyclic amines (HAs) have been implicated as potential risk factors associated with the development of diseases. PURPOSE: To explore the effects of polyphenols, a class of bioactive compounds found in plants, on the inhibition of MRPs and related diseases. Previous research has mainly focused on their interactions with proteins and their effects on the gastrointestinal tract and other diseases, while fewer studies have examined their inhibitory effects on MRPs. The aim is to offer a scientific reference for future research investigating the inhibitory role of polyphenols in the MR. METHODS: The databases PubMed, Embase, Web of Science and The Cochrane Library were searched for appropriate research. RESULTS: Polyphenols have the potential to inhibit the formation of harmful MRPs and prevent related diseases. The inhibition of MRPs by polyphenols primarily occurs through the following mechanisms: trapping α-dicarbonyl compounds, scavenging free radicals, chelating metal ions, and preserving protein structure. Simultaneously, polyphenols exhibit the ability to impede the onset and progression of related diseases such as diabetes, atherosclerosis, cancer, and Alzheimer's disease through diverse pathways. CONCLUSION: This review presents that inhibition of polyphenols on Maillard reaction products and their induction of related diseases. Further research is imperative to enhance our comprehension of additional pathways affected by polyphenols and to fully uncover their potential application value in inhibiting MRPs.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Investigating causal associations among gut microbiota, gut microbiota-derived metabolites, and gestational diabetes mellitus: a bidirectional Mendelian randomization study.

BACKGROUND: Previous studies have shown that gut microbiota (GM) and gut microbiota-derived metabolites are associated with gestational diabetes mellitus (GDM). However, the causal associations need to be treated with caution due to confounding factors and reverse causation. METHODS: This study obtained genetic variants from genome-wide association study including GM (N = 18,340), GM-derived metabolites (N = 7,824), and GDM (5,687 cases and 117,89 controls). To examine the causal association, several methods were utilized, including inverse variance weighted, maximum likelihood, weighted median, MR-Egger, and MR.RAPS. Additionally, reverse Mendelian Randomization (MR) analysis and multivariable MR were conducted to confirm the causal direction and account for potential confounders, respectively. Furthermore, sensitivity analyses were performed to identify any potential heterogeneity and horizontal pleiotropy. RESULTS: Greater abundance of Collinsella was detected to increase the risk of GDM. Our study also found suggestive associations among Coprobacter, Olsenella, Lachnoclostridium, Prevotella9, Ruminococcus2, Oscillibacte, and Methanobrevibacter with GDM. Besides, eight GM-derived metabolites were found to be causally associated with GDM. For the phenylalanine metabolism pathway, phenylacetic acid was found to be related to the risk of GDM. CONCLUSIONS: The study first used the MR approach to explore the causal associations among GM, GM-derived metabolites, and GDM. Our findings may contribute to the prevention and treatment strategies for GDM by targeting GM and metabolites, and offer novel insights into the underlying mechanism of the disease.

Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis.

BACKGROUND: High-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. Therefore, close monitoring of delayed MTX elimination in ALL patients is essential. OBJECTIVE: This study aimed to identify the risk factors associated with delayed MTX elimination and to develop a predictive tool for its occurrence. METHODS: Patients who received MTX chemotherapy during hospitalization were selected for inclusion in our study. Univariate and least absolute shrinkage and selection operator (LASSO) methods were used to screen for relevant features. Then four machine learning (ML) algorithms were used to construct prediction model in different sampling method. Furthermore, the performance of the model was evaluated using several indicators. Finally, the optimal model was deployed on a web page to create a visual prediction tool. RESULTS: The study included 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination who met the inclusion criteria. Univariate and LASSO regression analysis identified eleven predictors, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole. The XGBoost algorithm with SMOTE exhibited AUROC of 0.897, AUPR of 0.729, sensitivity of 0.808, specificity of 0.847, outperforming the other models. And had AUROC of 0.788 in external validation. CONCLUSION: The XGBoost algorithm provides superior performance in predicting the delayed elimination of MTX. We have created a prediction tool to assist medical professionals in predicting MTX metabolic delay.

Development of a prediction model for the risk of 30-day unplanned readmission in older patients with heart failure: A multicenter retrospective study.

BACKGROUND AND AIM: Heart failure (HF) imposes significant global health costs due to its high incidence, readmission, and mortality rate. Accurate assessment of readmission risk and precise interventions have become important measures to improve health for patients with HF. Therefore, this study aimed to develop a machine learning (ML) model to predict 30-day unplanned readmissions in older patients with HF. METHODS AND RESULTS: This study collected data on hospitalized older patients with HF from the medical data platform of Chongqing Medical University from January 1, 2012, to December 31, 2021. A total of 5 candidate algorithms were selected from 15 ML algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC) and accuracy. Then, the 5 candidate algorithms were hyperparameter tuned by 5-fold cross-validation grid search, and performance was evaluated by AUC, accuracy, sensitivity, specificity, and recall. Finally, an optimal ML model was constructed, and the predictive results were explained using the SHapley Additive exPlanations (SHAP) framework. A total of 14,843 older patients with HF were consecutively enrolled. CatBoost model was selected as the best prediction model, and AUC was 0.732, with 0.712 accuracy, 0.619 sensitivity, and 0.722 specificity. NT.proBNP, length of stay (LOS), triglycerides, blood phosphorus, blood potassium, and lactate dehydrogenase had the greatest effect on 30-day unplanned readmission in older patients with HF, according to SHAP results. CONCLUSIONS: The study developed a CatBoost model to predict the risk of unplanned 30-day special-cause readmission in older patients with HF, which showed more significant performance compared with the traditional logistic regression model.

Shear behavior and construction method of steel shear keyed joints in precast segmental beams.

The joints between segments represent weak points and introduce discontinuity into structures, therefore they are particularly significant in precast concrete segmental bridges. In this study, a new steel shear key was designed, and 6 full-scale tests were conducted. Various shear keys and joint types were taken as experimental parameters to study crack propagation, failure mode, shear slip, ultimate bearing capacity, and the residual bearing capacity of various joints under direct shear loading. The results show that the stiffness and shear capacity of steel shear keyed joints were higher than concrete key joints, and the structural system was more stable than concrete keyed joints at the moment of cracking. Both the concrete key and steel key epoxied joints suffered direct shear failure. However, different to the concrete epoxied joints which experienced brittle failure, steel key epoxied joints demonstrated a large residual capacity. Based on traditional segmental bridges construction, construction methods involving steel shear keyed joints included short-line match, long-line match, and modular methods are introduced. Finally, the feasibility of steel shear keyed joints construction was verified via engineering tests.

Creatinine-based equations to estimate glomerular filtration rate should be used with caution in patients with neurogenic bladder.

BACKGROUND: Patients with neurogenic bladder (NGB) are at an increased risk of developing chronic kidney disease (CKD). However, data related to the real performance of the serum creatinine (Cr)-based estimated glomerular filtration rate (eGFR) equation in patients with NGB are limited. This study is to evaluate the performance of new Cr-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race and the GFR estimation equation for Chinese CKD patients for the estimation of GFR in Chinese patients with NGB. MATERIALS AND METHODS: GFR was determined simultaneously by three methods: a) GFR measured by renal dynamic imaging with 99mTc-DTPA (G-GFR), which was used as the reference GFR; b) GFR estimated by the new Cr-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race (EPI-GFR); and c) GFR estimated by the equation for Chinese CKD patients (C-GFR). Pearson correlation and linear regression were used to compare eGFR and G-GFR. Differences, absolute differences, precision, and accuracy were compared to identify which equation showed better performance in evaluating GFR in patients with NGB. RESULTS: A total of 171 patients with NGB, including 121 men and 50 women from 20 provinces, 4 autonomous regions, and 3 municipalities in China, were enrolled in the final analysis, and the average age was 31.3 ± 11.9 years. Both C-GFR and EPI-GFR were moderately correlated with G-GFR and overestimated G-GFR. The difference between EPI-GFR and G-GFR was similar to that between C-GFR and G-GFR (median of 9.97 vs. 9.95 mL/min/1.73m2 for difference, Wilcoxon signed ranks test, Z = -1.704, p = 0.088), but the absolute difference between EPI-GFR and G-GFR was significantly lower than that between C-GFR and G-GFR (median of 22.3 vs. 25.1 mL/min/1.73m2 for absolute difference, Wilcoxon signed ranks test, Z = -4.806, p < 0.001). Both EPI-GFR and C-GFR displayed similar results of 15, 30, and 50% accuracies (χ2-test, p > 0.05), and there were no significant differences between EPI-GFR and C-GFR in misclassification percentages at different G-GFR levels (χ2-test, p > 0.05). CONCLUSION: Our study indicated that for patients with NGB in China, Cr-based eGFR equations, which include the new CKD-EPI equation without race and the Chinese GFR estimation equation, showed suboptimal performance, and limited their application in GFR estimation. Further studies are needed to investigate whether incorporating additional biomarkers, such as cystatin C, could improve their performance of GFR estimating equations in patients with NGB.

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus.

Background: Aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) has been reported affect the risk of type 2 diabetes (T2DM), but it is uncertain if it has relationship with gestational diabetes mellitus (GDM). Objectives: Our study aimed to investigate the association between AST/ALT ratio in the first trimester and the risk of subsequent development of GDM. Method: This prospective cohort study enrolling 870 pregnant women, 204 pregnant women with missing data or liver diseases were excluded, 666 pregnant women were included in this study containing 94 GDM women. Blood samples were collected in the first trimester. Univariate analysis and multivariate logistic regression were used to evaluate the association between AST/ALT and GDM. Nomogram was established based on the results of multivariate logistic analysis. Receiver Operating Characteristic (ROC) curves and calibration curves were used to evaluate the predictive ability of this nomogram model for GDM. Decision curve analysis (DCA) was used to examine the clinical net benefit of predictive model. Results: AST/ALT ratio (RR:0.228; 95% CI:0.107-0.488) was associated with lower risk of GDM after adjusting for confounding factors. Indicators used in nomogram including AST/ALT, maternal age, preBMI, waist circumference, glucose, triglycerides, high density lipoprotein cholesterol and parity. The area under the ROC curve (AUC) value of this predictive model was 0.778, 95% CI (0.724, 0.832). Calibration curves for GDM probabilities showed acceptable agreement between nomogram predictions and observations. The DCA curve demonstrated a good positive net benefit in the predictive model. Conclusions: The early AST/ALT level of pregnant women negatively correlated with the risk of GDM. The nomogram including AST/ALT at early pregnancy shows good predictive ability for the occurrence of GDM.

Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study.

Background: Short-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH. Methods: This study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP). Results: A total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77-0.86), high accuracy for 0.74 (95% CI: 0.72-0.76), sensitivity 0.78 (95% CI: 0.69-0.87), and specificity 0.74 (95% CI: 0.72-0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results. Conclusions: This study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH.

Low-Loss Dual-Band Transparency Metamaterial with Toroidal Dipole.

In this paper, a low-loss toroidal dipole metamaterial composed of four metal split ring resonators is proposed and verified at microwave range. Dual-band Fano resonances could be excited by normal incident electromagnetic waves at 6 GHz and 7.23 GHz. Analysis of the current distribution at the resonance frequency and the scattered power of multipoles shows that both Fano resonances derive from the predominant novel toroidal dipole. The simulation results exhibit that the sensitivity to refractive index of the analyte is 1.56 GHz/RIU and 1.8 GHz/RIU. Meanwhile, the group delay at two Fano peaks can reach to 11.38 ns and 12.85 ns, which means the presented toroidal metamaterial has significant slow light effects. The proposed dual-band toroidal dipole metamaterial may offer a new path for designing ultra-sensitive sensors, filters, modulators, slow light devices, and so on.

An Ensemble Model for Tumor Type Identification and Cancer Origins Classification.

Tissue biopsy can be wildly used in cancer diagnosis. However, manually classifying the cancerous status of biopsies and tissue origin of tumors for cancerous ones requires skilled specialists and sophisticated equipment. As a result, a data-based model is urgently needed. In this paper, we propose a data-based ensemble model for tumor type identification and cancer origins classification. Our model is an ensemble model that combines different models based on mRNA groups which serve distinct functions. The experiment on the TCGA dataset exhibits a promising result on both tasks - 98% on tumor type identification and 96.1% on cancer origin classification. We also test our model on external validation datasets, which prove the robustness of our model.

Risk Stratification Model for Predicting the Overall Survival of Elderly Triple-Negative Breast Cancer Patients: A Population-Based Study.

Background: The objective of this study was to evaluate the prognostic value of clinical characteristics in elderly patients with triple-negative breast cancer (TNBC). Methods: The cohort was selected from the Surveillance, Epidemiology, and End Results (SEER) program dating from 2010 to 2015. Univariate and multivariate analyses were performed using a Cox proportional risk regression model, and a nomogram was constructed to predict the 1-, 3-, and 5-year prognoses of elderly patients with TNBC. A concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to verify the nomogram. Results: The results of the study identified a total of 5,677 patients who were randomly divided 6:4 into a training set (n = 3,422) and a validation set (n = 2,255). The multivariate analysis showed that age, race, grade, TN stage, chemotherapy status, radiotherapy status, and tumor size at diagnosis were independent factors affecting the prognosis of elderly patients with TNBC. Together, the 1 -, 3 -, and 5-year nomograms were made up of 8 variables. For the verification of these results, the C-index of the training set and validation set were 0.757 (95% CI 0.743-0.772) and 0.750 (95% CI 0.742-0.768), respectively. The calibration curve also showed that the actual observation of overall survival (OS) was in good agreement with the prediction of the nomograms. Additionally, the DCA showed that the nomogram had good clinical application value. According to the score of each patient, the risk stratification system of elderly patients with TNBC was further established by perfectly dividing these patients into three groups, namely, low risk, medium risk, and high risk, in all queues. In addition, the results showed that radiotherapy could improve prognosis in the low-risk group (P = 0.00056), but had no significant effect in the medium-risk (P < 0.4) and high-risk groups (P < 0.71). An online web app was built based on the proposed nomogram for convenient clinical use. Conclusion: This study was the first to construct a nomogram and risk stratification system for elderly patients with TNBC. The well-established nomogram and the important findings from our study could guide follow-up management strategies for elderly patients with TNBC and help clinicians improve individual treatment.

[Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions].

Objective: To screen the influencing factors of hypertensive heart disease (HHD), establish the predictive model of HHD, and provide early warning for the occurrence of HHD. Methods: Select the patients diagnosed as hypertensive heart disease or hypertensionfrom January 1, 2016 to December 31, 2019, in the medical data science academy of a medical school. Influencing factors were screened through single factor and multi-factor analysis, and R software was used to construct the logistics model, random forest (RF) model and extreme gradient boosting (XGBoost) model. Results: Univariate analysis screened 60 difference indicators, and multifactor analysis screened 18 difference indicators (P<0.05). The area under the curve (AUC) of Logistics model, RF model and XGBoost model are 0.979, 0.983 and 0.990, respectively. Conclusion: The results of the three HHD prediction models established in this paper are stable, and the XGBoost prediction model has a good diagnostic effect on the occurrence of HHD.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma.

BACKGROUND: Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved. METHODS: Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism. RESULTS: This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients. CONCLUSIONS: In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

Effects of intrastriatal injection of the dopamine receptor agonist SKF38393 and quinpirole on locomotor behavior in hemiparkinsonism rats.

Dopamine (DA) in the striatum is essential to influence motor behavior and may lead to movement impairment in Parkinson's disease (PD). The present study examined the different functions of the DA D1 receptor (D1R) and DA D2 receptor (D2R) by intrastriatal injection of the D1R agonist SKF38393 and the D2R agonist quinpirole in 6-hydroxydopamine (6-OHDA)-lesioned and control rats. All rats separately underwent dose-response behavior testing for SKF38393 (0, 0.5, 1.0, and 1.5 µg/site) or quinpirole (0, 1.0, 2.0, and 3.0 µg/site) to determine the effects of the optimal modulating threshold dose. Two behavior assessment indices, the time of latency to fall and the number of steps on a rotating treadmill, were used as reliable readouts of motor stimulation variables for quantifying the motor effects of the drugs. The findings indicate that at threshold doses, SKF38393 (1.0 µg/site) and quinpirole (1.0 µg/site) produce a dose-dependent increase in locomotor activity compared to vehicle injection. The ameliorated behavioral responses to either SKF38393 or quinpirole in lesioned rats were greater than those in unlesioned control rats. Moreover, the dose-dependent increase in locomotor capacity for quinpirole was greater than that for SKF38393 in lesioned rats. These results can clarify several key issues related to DA receptors directly and may provide a basis for exploring the potential of future selective dopamine therapies for PD in humans.

State-Dependent Spike and Local Field Synchronization between the Thalamic Parafascicular Nucleus and the Dorsal Striatum in a Rat Model of Parkinson's Disease.

Recent studies on the impact of Parkinson's disease (PD) on the thalamostriatal pathway have mainly focused on the structural and functional changes in the thalamus projection to the striatum. Alterations in the electrophysiological activity of the thalamostriatal circuit in PD have not been intensively studied. To further investigate this circuit, parafascicular nucleus (PF) single-unit spikes and dorsal striatum local field potential (LFP) activities were simultaneously recorded in control and 6-hydroxydopamine (6-OHDA)-lesioned rats during inattentive rest or treadmill walking states. We classified the PF neurons into two predominant subtypes (PF I and PF II). During rest state, after dopamine loss, increased PF I spike and striatal LFP coherence was observed in the beta-frequency (12-35 Hz), with changed PF I neuronal firing pattern and unchanged firing rates of the two neuron subtypes. However, in a treadmill walking state, PF II neurons displayed markedly increased coherence to striatal beta oscillations in the dopamine-depleted rats, as well as an altered PF II neuronal firing pattern and significantly decreased firing rates of the two neuron subtypes. The results indicate that in PD animals, state transition from rest to moving, such as treadmill walking, is associated with different PF neuron types and increased spike-LFP synchronization, which may provide new paradigms for understanding and treating PD.

Altered Local Field Potential Relationship Between the Parafascicular Thalamic Nucleus and Dorsal Striatum in Hemiparkinsonian Rats.

The thalamostriatal pathway is implicated in Parkinson's disease (PD); however, PD-related changes in the relationship between oscillatory activity in the centromedian-parafascicular complex (CM/Pf, or the Pf in rodents) and the dorsal striatum (DS) remain unclear. Therefore, we simultaneously recorded local field potentials (LFPs) in both the Pf and DS of hemiparkinsonian and control rats during epochs of rest or treadmill walking. The dopamine-lesioned rats showed increased LFP power in the beta band (12 Hz-35 Hz) in the Pf and DS during both epochs, but decreased LFP power in the delta (0.5 Hz-3 Hz) band in the Pf during rest epochs and in the DS during both epochs, compared to control rats. In addition, exaggerated low gamma (35 Hz-70 Hz) oscillations after dopamine loss were restricted to the Pf regardless of the behavioral state. Furthermore, enhanced synchronization of LFP oscillations was found between the Pf and DS after the dopamine lesion. Significant increases occurred in the mean coherence in both theta (3 Hz-7 Hz) and beta bands, and a significant increase was also noted in the phase coherence in the beta band between the Pf and DS during rest epochs. During the treadmill walking epochs, significant increases were found in both the alpha (7 Hz-12 Hz) and beta bands for two coherence measures. Collectively, dramatic changes in the relative LFP power and coherence in the thalamostriatal pathway may underlie the dysfunction of the basal ganglia-thalamocortical network circuits in PD, contributing to some of the motor and non-motor symptoms of the disease.