Objective To investigate the clinical features of dyskinesia and related risk factors in female patients with Parkinson disease （PD）. Methods A cross-sectional study was conducted among the female patients who met the diagnostic criteria for PD at the outpatient service of PD in Aerospace Center Hospital， and demographic data and clinical data were collected and compared between groups， including levodopa equivalent daily dose （LEDD）， Unified Parkinson’s Disease Rating Scale-Ⅲ（UPDRS-Ⅲ）， UPDRS-Ⅳ， scores of non-motor symptoms （cognition and depression）， presence or absence of dyskinesia， and single levodopa dose （LD） during the onset of dyskinesia. A binary logistic regression analysis was used to investigate the influencing factors for dyskinesia in female patients with PD. Results A total of 146 female PD patients were enrolled， among whom 30 patients had dyskinesia， with an incidence rate of 20.5%. Compared with the non-dyskinesia group in terms of clinical features， the dyskinesia group had a significantly younger age of onset [（54.3±12.5） years vs （62.7±10.0） years， P<0.001]， a significantly longer disease duration [（9.9±3.7） years vs （4.5±3.7） years， P<0.001]， a significantly higher severity of disease [H-Y stage： （2.65±0.58） vs （2.35±0.83）， P=0.03]， a significantly longer duration of LD administration [（7.5±3.2） years vs （3.2±2.6） years， P<0.001]， a significantly higher LEDD [（703.2±203.9） mg vs （442.1±226.3） mg， P<0.001]， and significantly lower body weight [（54.1±8.2） kg vs （60.0±8.7） kg， P=0.001] and BMI [（20.9±3.1） kg/m2 vs （23.4±3.1） kg/m2， P<0.001]. The multivariate logistic regression analysis showed that high BMI （OR=0.770， P=0.005） was a protective factor against dyskinesia in female PD patients， while long disease duration （OR=1.304， P=0.001） and high LEDD （OR=1.003， P=0.012） were risk factors for dyskinesia. Conclusion There is a relatively high incidence rate of dyskinesia in female PD patients， which should be taken seriously in clinical practice， and high BMI is a protective factor， while long disease duration and high LEDD are risk factors for dyskinesia in female PD patients.
Parkinson Disease ; Dyskinesias ; Levodopa

To analyze the distribution of serotypes and antimicrobial resistance of clinical Salmonella isolates from multiple centers across China.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

Palmitoylation, an essential covalent attachment of a fatty acid (usually C16 palmitate) to cysteine residues within proteins, is crucial for regulating protein functionality and enzymatic activities. This lipid modification facilitates the anchoring of proteins to cellular membranes, dictating their subcellular distribution and influencing protein transport dynamics and intracellular positioning. Additionally, it plays a role in regulating protein degradation through the ubiquitin-proteasome system. Palmitoylation is implicated in the pathogenesis and progression of cardiovascular diseases by modulating substrates and prompting additional post-translational modifications, as well as by interacting with other molecular alterations. Moreover, an intervention strategy focusing on palmitoylation processes is anticipated to offer novel therapeutic avenues for cardiovascular pathologies and address extant challenges in clinical settings. This review consolidates current research on the role and importance of palmitoylation in cardiovascular diseases by exploring its regulatory functions, the catalyzing enzymes, and the involved substrates. It highlights recent discoveries connecting palmitoylation-targeted therapies to cardiovascular health and examines potential approaches and future challenges in cardiovascular treatment.

This paper briefly introduces the definition, classification and significance of effect modification in epidemiological studies, summarizes the difference between effect modifier and confounders, and analyze the influence as well as the role of effect modification in epidemiological studies and Meta-analysis. In this paper, the possible scenarios of effect modification and related analysis strategy in Meta-analysis are indicated by graphics, aiming to arouse researchers' attention to effect modification. This paper also demonstrates how to identify and deal with effect modification in Meta-analysis through a study case of "Efficacy of sodium-glucose cotransporter 2 inhibitors in patients with type 2 diabetes", and shows the analysis process and interpretation of results of subgroup analysis and Meta-regression methods respectively. The advantages and disadvantages of these two methods are summarized to provide reference for the method selection of future research.

This paper briefly introduces the characteristics, research significance, and global reporting status of effect modification in network Meta-analysis, demonstrates the heterogeneity caused by effect modification in network Meta-analysis, and emphasizes the importance of exploring effect modification in network Meta-analysis. This paper also summarizes the normalized description and analysis strategies of effect modification in network Meta-analysis. Finally, by the case of "comparison of efficacy of three new hypoglycemic drugs in reducing body weight in type 2 diabetes patients", this paper demonstrates the realization of subgroup analysis and network Meta-regression in exploring effect modification, summarizes the advantages and disadvantages of the two methods, to provide references for future researchers.

Objective:Differences between randomized controlled trial (RCT) results and real world study (RWS) results may not represent a true efficacy-effectiveness gap because efficacy-effectiveness gap estimates may be biased when RWS and RCT differ significantly in study design or when there is bias in RWS result estimation. Secondly, when there is an efficacy- effectiveness gap, it should not treat every patient the same way but assess the real-world factors influencing the intervention's effectiveness and identify the subgroup likely to achieve the desired effect.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:Ten articles were included to discuss how to use the RCT research protocol as a template to develop the corresponding RWS research protocol. Moreover, based on correctly estimating the efficacy-effectiveness gap, evaluate the intervention effect in the patient subgroup to confirm the subgroup that can achieve the expected benefit-risk ratio to bridge the efficacy-effectiveness gap.Conclusion:Using real-world data to simulate key features of randomized controlled clinical trial study design can improve the authenticity and effectiveness of study results and bridge the efficacy-effectiveness gap.

This paper briefly introduces the unique advantages, overall analysis ideas and existing analysis methods of individual patient data Meta-analysis in terms of effect modification. In addition to Meta-regression and subgroup analysis, this paper also introduces the analysis methods based on part of individual patient data integrated with aggregated data and summarizes the current reporting of the above mentioned methods. In addition, the application and results interpretation of the above mentioned methods in individual patient data Meta-analysis are presented in this paper by taking "Effects of sodium-glucose cotransporter 2 inhibitors on SBP in patients with type 2 diabetes" as an example and by introducing their advantages and limitations.

Objective:Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics.Conclusions:Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.