A 42-year-old male with chest tightness and dyspnea was admitted to the hospital. Chest CT indicated diffuse interstitial lung infiltration. Despite receiving anti-infective therapy, glucocorticoid therapy, and immunosuppressive agents, the patient developed refractory hypoxaemia. Endotracheal intubation and invasive mechanical ventilation failed to improve oxygenation. Therefore the patient was diagnosed with rapidly progressive interstitial lung disease (RP-ILD) accompanied by type Ⅰ respiratory failure. Veno-venous (VV) extracorporeal membrane oxygenation (ECMO) was initiated, and oxygenation improved in this patient. The patient subsequently underwent bilateral lung transplantation with veno-arterio-venous (VAV) ECMO support. ECMO machine was withdrawn on day 1, and extubation was achieved on day 9 after surgery. Histopathology revealed fibrotic nonspecific interstitial pneumonia (NSIP) with hyaline membrane formation. The patient developed ICU-acquired myasthenia and received early rehabilitation, with gradual recovery of muscle strength. During follow-up, graft lung function remained stable. This case demonstrates that ECMO can serve as a bridge to lung transplantation in RP-ILD patients.

Objective To identify immune cell-related biomarkers in lung adenocarcinoma (LUAD) using weighted gene co-expression network analysis (WGCNA). Methods Based on data from The Cancer Genome Atlas (TCGA) database, a gene co-expression network was constructed for the TCGA-LUAD dataset using the "WGCNA" R package, and genes were clustered into different modules. Concurrently, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was applied to the tumor samples in the TCGA-LUAD dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to evaluate the biological functions of genes within the most significantly correlated module. Candidate hub genes from the key module were intersected with a protein-protein interaction (PPI) network to identify the final hub genes. The prognostic performance of these hub genes and their correlation with immune cell infiltration were validated using Kaplan-Meier curves and the Tumor IMmune Estimation Resource (TIMER) algorithm. Finally, a multivariate Cox regression analysis was conducted on the identified hub genes to construct a prognostic risk model. Results In the co-expression network, the brown module was found to be highly correlated with the ImmuneScore, StromalScore, and ESTIMATE Score. Five immune-related hub genes were identified: CD53, PLEK, SPI1, IL10RA, and C3AR1. Enrichment analysis of the brown module revealed that its genes were primarily enriched in GO terms such as "regulation of innate immune response" and KEGG pathways like the "NF-kappa B signaling pathway". Furthermore, the expression levels of these five hub genes were significantly and positively correlated with the infiltration abundance of various immune cells. The immune relevance of the model was validated by the Immunophenoscore (IPS) and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Moreover, the established RiskScore demonstrated significant potential in predicting the response to immunotherapy. Conclusion These five immune-related key genes may serve as novel and effective potential therapeutic targets for LUAD immunotherapy, facilitating the development of personalized diagnosis and treatment strategies for patients with LUAD.

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.

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.

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.