Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopamine (DA) neurons in the substantia nigra pars compacta (SNpc), primarily manifesting as motor dysfunctions such as resting tremor, muscle rigidity, and bradykinesia. According to the classical model of basal ganglia motor control, approximately half of the medium spiny neurons (MSNs) in the striatum are D1-MSNs, which constitute the direct pathway. These neurons express D₁-dopamine receptor (D₁R) and substance P, and they mainly participate in the selection, initiation, and execution of movements. The other half are D2-MSNs, which constitute the indirect pathway. These neurons express D₂-dopamine receptor (D₂R) and adenosine 2A receptors and are involved in inhibiting unnecessary movements or terminating ongoing movements, thereby adjusting movement sequences to perform more precise motor behaviors. The direct pathway in the striatum modulates the activity of motor cortex neurons by exciting D1-MSNs through neurotransmitters such as glutamate (Glu), allowing the motor cortex to send signals more freely to the motor system, thus facilitating the generation and execution of specific motor behaviors. Studies using D1-Cre and D2-Cre mice with neurons labeled for D₁R and D₂R have shown that both types of neurons are involved in the execution of movements, with D1-MSNs participating in movement initiation and D2-MSNs in inhibiting actions unrelated to the target movement. These findings suggest that the structural and functional plasticity of D1-MSNs and D2-MSNs in the basal ganglia circuitry enables motor learning and behavioral regulation. Additionally, when SNpc DA neurons begin to degenerate, D1-MSNs are initially affected but do not immediately cause motor impairments. In contrast, when D2-MSNs undergo pathological changes, they are first activated by upstream projecting neurons, leading to the inhibition of most motor behaviors and resulting in motor dysfunction. Therefore, it is hypothesized that motor impairments such as bradykinesia and initiation difficulties are more closely related to the functional activity of D2-MSNs. The extracellular signal-regulated kinase (Erk)/mitogen-activated protein kinase (MAPK) signaling pathway has been identified as a critical modulator in the pathophysiology of PD. Recent findings indicate that Erk/MAPK signaling pathway can mediate DA and Glu signaling in the central nervous system, maintaining normal functional activity of striatal MSNs and influencing the transmission of motor control signals. Within this complex regulatory network, the Erk/MAPK signaling pathway plays a key role in transmitting motor information to downstream neurons, regulating normal movements, avoiding unnecessary movements, and finely tuning motor behaviors. Our laboratory’s previous research found that 4 weeks of aerobic exercise intervention improved motor dysfunction in PD mice by inhibiting the Erk1/2 signaling upstream of striatal MSNs, primarily involving the Erk1/2 signaling in D2-MSNs rather than D1-MSNs. This review summarizes the neurobiological mechanisms of Erk/MAPK signaling pathway in D2-MSNs for the prevention and treatment of motor dysfunction in PD. By exploring the role of this signaling pathway in regulating motor abnormalities and preventing motor dysfunction in the central nervous system of PD, this review provides new theoretical perspectives for related mechanistic research and therapeutic strategies.

ObjectiveTo identify the main chemical constituents of Anmeidan （AMD） and to explore the mechanism of AMD in regulating the extracellular signal-regulated kinase 1/2 （ERK1/2）/mitogen-activated protein kinase （MAPK）-interacting serine/threonine-protein kinase （MNK）/eukaryotic translation initiation factor 4E （eIF4E） signaling pathway to improve circadian rhythm disturbances in insomnia rats. MethodsThe main chemical constituents of AMD were identified using ultra-high-performance liquid chromatography-linear ion trap-electrostatic orbital trap mass spectrometry （UPLC-LTQ/Orbitrap/MS） in combination with reference standards. Sixty male Sprague-Dawley （SD） rats were randomly divided into control， model， melatonin， and AMD low-， medium-， and high-dose groups， with 10 rats in each group. Except for the control group， all rats were administered p-chlorophenylalanine via intraperitoneal injection to establish an insomnia model. The activity-rest rhythm of rats was assessed using the open field test and circadian rhythm test. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe structural changes in hypothalamic neurons. Immunofluorescence， real-time quantitative polymerase chain reaction （Real-time PCR）， and Western blot analysis were employed to detect mRNA and protein expression levels of ERK1/2， MNK， and eIF4E in the hypothalamus. ResultsA total of 50 chemical components， including flavonoids， phenylpropanoids， triterpenoid saponins， alkaloids， and lignans， were identified in AMD. Compared with the control group， the model group exhibited significantly increased total distance traveled， average speed， central area residence time， and cumulative rearing time （P<0.01）， as well as prolonged cumulative activity time and total activity time in both light and dark phases （P<0.01）. Hypothalamic neurons in the model group were sparsely arranged， reduced in number， and exhibited nuclear disappearance or nucleolar rupture， with a significantly increased apoptosis index （P<0.01）. The cytoplasm appeared turbid， Nissl body staining was lighter， and the Nissl body apoptosis index was significantly increased （P<0.01）. The mRNA expression levels of ERK1/2， MNK， and eIF4E were significantly decreased （P<0.01）， along with a significant reduction in protein expression levels of ERK1/2， phosphorylated ERK1/2 （p-ERK1/2）， MNK， phosphorylated MNK （p-MNK）， eIF4E， and phosphorylated eIF4E （p-eIF4E） （P<0.01）. Compared with the model group， the total distance， average speed， central area residence time and body upright cumulative time of the AMD high-dose group were significantly reduced （P<0.01）. The total distance， average speed and body upright cumulative time of the AMD medium-dose group were significantly reduced （P<0.01）. The cumulative time of light activity and total time of activity in each dose group of AMD were significantly shortened （P<0.01）. The cumulative time of dark activity in the high-dose group of AMD was prolonged （P<0.01）. The neurons in the middle and high dose groups of AMD were closely arranged， the number of neurons increased， and the apoptosis index of hypothalamic cells decreased significantly （P<0.05， P<0.01）. The cytoplasm of the low， middle and high dose groups of AMD was clear， the color of Nissl body became darker， and the apoptosis index of Nissl body decreased significantly （P<0.01）. The expression of ERK1/2， MNK and eIF4E mRNA and protein in the hypothalamus of the middle and high dose groups of AMD increased significantly （P<0.05， P<0.01）. ConclusionAMD primarily contains 50 chemical constituents， including flavonoids， phenylpropanoids， and triterpenoid saponins. It exhibits a "synergistic enhancement" effect through multiple components and multiple pathways to improve insomnia. AMD ameliorates circadian rhythm disturbances in p-chlorophenylalanine-induced insomnia rats by upregulating ERK1/2/MNK/eIF4E signaling pathway-related proteins.

ObjectiveTo investigate the effects of different concentrations of Shaoyaotang-containing serum on lipopolysaccharide （LPS）-induced inflammation of human colorectal adenocarcinoma （Caco-2） cells by inhibiting apoptosis via activating the tumor necrosis factor （TNF） receptor superfamily member 6 （Fas）/Fas ligand （FasL） pathway. MethodsCaco-2 cells were allocated into blank， model （LPS， 10 mg·L^-1）， Shaoyaotang-containing serum （5%， 10%， 15%， 20%）， and Fas inhibitor （KR-33493， 20 mmol·L^-1） groups. Except the blank group， the other groups were stimulated with 10 mg·L^-1 LPS for 24 h for the modeling of inflammation. After successful modeling， the blank， Fas inhibitor， and model groups were treated with blank serum， and the Shaoyaotang-containing serum groups were treated with the serum samples at corresponding concentrations for 24 h. The Fas inhibitor group was subjected to KR-33493 pretreatment for 1 h. Cell proliferation and viability were examined by the cell-counting kit-8 （CCK-8） method. The levels of interleukin （IL）-6， IL-1β， and TNF-α were measured by enzyme-linked immunosorbent assay. Apoptosis was detected by flow cytometry. The protein and mRNA levels of Fas， FasL， cysteinyl aspartate-specific proteinase （Caspase）-3， Caspase-9， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsCompared with the blank group， the model group presented a decrease in cell survival rate （P<0.01）. Compared with that in the model group， the cell survival rate showed no significant change in the 5% Shaoyaotang-containing serum group but increased in the 10%， 15%， and 20% Shaoyaotang-containing serum groups （P<0.01）. Since there was no statistical difference between the 5% Shaoyaotang-containing serum group and the model group， 10%， 15%， and 20% Shaoyaotang-containing sera were selected for the follow-up study. Compared with the blank group， the model group showed risen levels of IL-6， IL-1β， and TNF-α （P<0.01）， an increased apoptosis rate （P<0.01）， up-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.01）， and down-regulated protein and mRNA levels of Bcl-2 （P<0.01）. Compared with the model group， the Fas inhibitor group and the 10%， 15%， and 20% Shaoyaotang-containing serum groups showed declined levels of IL-6， IL-1β， and TNF-α （P<0.01）， decreased apoptosis rates （P<0.01）， down-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and up-regulated protein and mRNA levels of Bcl-2 （P<0.05， P<0.01）. In addition， the 15% and 20% Shaoyaotang-containing serum groups had lower levels of IL-6， IL-1β， and TNF-α （P<0.05， P<0.01）， lower apoptosis rates （P<0.05， P<0.01）， lower protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and higher protein and mRNA levels of Bcl-2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can reduce the content of inflammatory factors in Caco-2 cells， down-regulate the protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax， and up-regulate the protein and mRNA levels of Bcl-2 under the intervention of LPS by regulating the Fas/FasL pathway and inhibiting the apoptosis of intestinal epithelial cells in ulcerative colitis.

ObjectiveTo investigate the effects of different concentrations of Shaoyaotang-containing serum on lipopolysaccharide （LPS）-induced inflammation of human colorectal adenocarcinoma （Caco-2） cells by inhibiting apoptosis via activating the tumor necrosis factor （TNF） receptor superfamily member 6 （Fas）/Fas ligand （FasL） pathway. MethodsCaco-2 cells were allocated into blank， model （LPS， 10 mg·L^-1）， Shaoyaotang-containing serum （5%， 10%， 15%， 20%）， and Fas inhibitor （KR-33493， 20 mmol·L^-1） groups. Except the blank group， the other groups were stimulated with 10 mg·L^-1 LPS for 24 h for the modeling of inflammation. After successful modeling， the blank， Fas inhibitor， and model groups were treated with blank serum， and the Shaoyaotang-containing serum groups were treated with the serum samples at corresponding concentrations for 24 h. The Fas inhibitor group was subjected to KR-33493 pretreatment for 1 h. Cell proliferation and viability were examined by the cell-counting kit-8 （CCK-8） method. The levels of interleukin （IL）-6， IL-1β， and TNF-α were measured by enzyme-linked immunosorbent assay. Apoptosis was detected by flow cytometry. The protein and mRNA levels of Fas， FasL， cysteinyl aspartate-specific proteinase （Caspase）-3， Caspase-9， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsCompared with the blank group， the model group presented a decrease in cell survival rate （P<0.01）. Compared with that in the model group， the cell survival rate showed no significant change in the 5% Shaoyaotang-containing serum group but increased in the 10%， 15%， and 20% Shaoyaotang-containing serum groups （P<0.01）. Since there was no statistical difference between the 5% Shaoyaotang-containing serum group and the model group， 10%， 15%， and 20% Shaoyaotang-containing sera were selected for the follow-up study. Compared with the blank group， the model group showed risen levels of IL-6， IL-1β， and TNF-α （P<0.01）， an increased apoptosis rate （P<0.01）， up-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.01）， and down-regulated protein and mRNA levels of Bcl-2 （P<0.01）. Compared with the model group， the Fas inhibitor group and the 10%， 15%， and 20% Shaoyaotang-containing serum groups showed declined levels of IL-6， IL-1β， and TNF-α （P<0.01）， decreased apoptosis rates （P<0.01）， down-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and up-regulated protein and mRNA levels of Bcl-2 （P<0.05， P<0.01）. In addition， the 15% and 20% Shaoyaotang-containing serum groups had lower levels of IL-6， IL-1β， and TNF-α （P<0.05， P<0.01）， lower apoptosis rates （P<0.05， P<0.01）， lower protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and higher protein and mRNA levels of Bcl-2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can reduce the content of inflammatory factors in Caco-2 cells， down-regulate the protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax， and up-regulate the protein and mRNA levels of Bcl-2 under the intervention of LPS by regulating the Fas/FasL pathway and inhibiting the apoptosis of intestinal epithelial cells in ulcerative colitis.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.