With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

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.

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: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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.

Acute lung injury (ALI) is a significant complication of sepsis, characterized by high morbidity, mortality, and poor prognosis. Neutrophils, as critical intrinsic immune cells in the lung, play a fundamental role in the development and progression of ALI. During ALI, neutrophils generate neutrophil extracellular traps (NETs), and excessive NETs can intensify inflammatory injury. Research indicates that Taohe Chengqi decoction (THCQD) can ameliorate sepsis-induced lung inflammation and modulate immune function. This study aimed to investigate the mechanisms by which THCQD improves ALI and its relationship with NETs in sepsis patients, seeking to provide novel perspectives and interventions for clinical treatment. The findings demonstrate that THCQD enhanced survival rates and reduced lung injury in the cecum ligation and puncture (CLP)-induced ALI mouse model. Furthermore, THCQD diminished neutrophil and macrophage infiltration, inflammatory responses, and the production of pro-inflammatory cytokines, including interleukin-1β (IL-1β), IL-6, and tumor necrosis factor α (TNF-α). Notably, subsequent experiments confirmed that THCQD inhibits NET formation both in vivo and in vitro. Moreover, THCQD significantly decreased the expression of peptidyl arginine deiminase 4 (PAD4) protein, and molecular docking predicted that certain active compounds in THCQD could bind tightly to PAD4. PAD4 overexpression partially reversed THCQD's inhibitory effects on PAD4. These findings strongly indicate that THCQD mitigates CLP-induced ALI by inhibiting PAD4-mediated NETs.
Extracellular Traps/immunology* ; Acute Lung Injury/immunology* ; Animals ; Sepsis/immunology* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Neutrophils/immunology* ; Male ; Protein-Arginine Deiminase Type 4/genetics* ; Mice, Inbred C57BL ; Humans ; Disease Models, Animal ; Cytokines/metabolism*