The Golgi apparatus (GA) is a key membranous organelle in eukaryotic cells, acting as a central component of the endomembrane system. It plays an irreplaceable role in the processing, sorting, trafficking, and modification of proteins and lipids. Under normal conditions, the GA cooperates with other organelles, including the endoplasmic reticulum (ER), lysosomes, mitochondria, and others, to achieve the precise processing and targeted transport of nearly one-third of intracellular proteins, thereby ensuring normal cellular physiological functions and adaptability to environmental changes. This function relies on Golgi protein quality control (PQC) mechanisms, which recognize and handle misfolded or aberrantly modified proteins by retrograde transport to the ER, proteasomal degradation, or lysosomal clearance, thus preventing the accumulation of toxic proteins. In addition, Golgi-specific autophagy (Golgiphagy), as a selective autophagy mechanism, is also crucial for removing damaged or excess Golgi components and maintaining its structural and functional homeostasis. Under pathological conditions such as oxidative stress and infection, the Golgi apparatus suffers damage and stress, and its homeostatic regulatory network may be disrupted, leading to the accumulation of misfolded proteins, membrane disorganization, and trafficking dysfunction. When the capacity and function of the Golgi fail to meet cellular demands, cells activate a series of adaptive signaling pathways to alleviate Golgi stress and enhance Golgi function. This process reflects the dynamic regulation of Golgi capacity to meet physiological needs. To date, 7 signaling pathways related to the Golgi stress response have been identified in mammalian cells. Although these pathways have different mechanisms, they all help restore Golgi homeostasis and function and are vital for maintaining overall cellular homeostasis. It is noteworthy that the regulation of Golgi homeostasis is closely related to multiple programmed cell death pathways, including apoptosis, ferroptosis, and pyroptosis. Once Golgi function is disrupted, these signaling pathways may induce cell death, ultimately participating in the occurrence and progression of diseases. Studies have shown that Golgi homeostatic imbalance plays an important pathological role in various major diseases. For example, in Alzheimer’s disease (AD) and Parkinson’s disease (PD), Golgi fragmentation and dysfunction aggravate the abnormal processing of amyloid β-protein (Aβ) and Tau protein, promoting neuronal loss and advancing neurodegenerative processes. In cancer, Golgi homeostatic imbalance is closely associated with increased genomic instability, enhanced tumor cell proliferation, migration, invasion, and increased resistance to cell death, which are important factors in tumor initiation and progression. In infectious diseases, pathogens such as viruses and bacteria hijack the Golgi trafficking system to promote their replication while inducing host defensive cell death responses. This process is also a key mechanism in host-pathogen interactions. This review focuses on the role of the Golgi apparatus in cell death and major diseases, systematically summarizing the Golgi stress response, regulatory mechanisms, and the role of Golgi-specific autophagy in maintaining homeostasis. It emphasizes the signaling regulatory role of the Golgi apparatus in apoptosis, ferroptosis, and pyroptosis. By integrating the latest research progress, it further clarifies the pathological significance of Golgi homeostatic disruption in neurodegenerative diseases, cancer, and infectious diseases, and reveals its potential mechanisms in cellular signal regulation.

Sepsis is a condition characterized by organ dysfunction resulting from the systemic inflammatory response triggered by an infection. Excessive inflammation and immunosuppression are intertwined, and severe cases may even develop into multiple organ failure. Studies have shown that indoleamine 2,3-dioxygenase 1-mediated tryptophan metabolism is involved in the occurrence and development of sepsis, and elevated plasma kynurenine levels and Kyn/Trp ratios are early indicators of sepsis development. In this paper, we provide a comprehensive summary of the role of IDO1 in the acute inflammatory phase of sepsis, late immunosuppression, and organ damage. This includes its regulation of inflammatory state, immune cell function, blood pressure, and other aspects. Additionally, we analyze preclinical studies on targeted IDO1 drugs. An in-depth understanding and study of IDO may help to understand the pathogenesis and clinical significance of sepsis and multiple organ damage from a new perspective and provide new research ideas for exploring its prevention and treatment methods.

Three 2,3-diketoquinoxaline alkaloids were isolated from Heterosmilax yunnanensis Gagnep. Their structures were determined through 1D and 2D NMR, HR-ESI-MS, UV, and IR as 1-[5′-(3″-hydroxy-3″-methyl) glutaryl] ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (1), 1-[2′-(3″-hydroxy-3″-methyl) glutaryl]ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (2), and 1-ribityl-2,3-diketo-1,2,3,4-tetrahydro-6,7-dimethylquinoxaline (3). Compounds 1 and 2 are novel compounds, and 3 was isolated from H. yunnanensis for the first time. The hepatoprotective activity of these three compounds was evaluated, with compound 3 showing promising hepatoprotective activity.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objective:To explore brain network properties and their relationship with cognitive function in children with spastic cerebral palsy (SCP) using diffusion tensor imaging (DTI) based graph theory analysis.Methods:The study was a cross-sectional study. Clinical and imaging data of 21 children with SCP (SCP group) and 32 healthy children (control group) who underwent cranial MRI at the Affiliated Hospital of Zunyi Medical University from August 2020 to April 2022 were analyzed retrospectively. 3D-T 1WI, DTI and Wechsler Intelligence Scale were assessed for all subjects. The Wechsler Intelligence Scale included the verbal comprehension index (VCI), the processing speed index (PSI), the work memory index (WMI), and the perceptual reasoning index (PRI), etc., and ultimately the full scale intelligence quotient (FSIQ) scores were obtained based on the indices of each subscale. Independent samples t-test was used to analyze the differences in the small world attributes [small-world index (σ), normalized shortest path length (λ), normalized clustering coefficients (γ)], global attributes [global efficiency (Eglob), local efficiency (Eloc), characteristic path length (Lp), clustering efficiency (Cp)] and node attributes [degree centrality(DC), nodal efficiency (Ne), betweeness centrality (Bc), nodal shortest path length (NLp), nodal clustering efficiency, nodal local efficiency] between two groups of children′s brain networks. Brain network indicators with statistically significant differences between the 2 groups were correlated with Wechsler Intelligence Scale scores using Spearman. Results:The FSIQ scores on the Wechsler Intelligence Scale and the VCI, WMI, PSI, and PRI were lower in the SCP group than in the control group, and the differences were all statistically significant (all P<0.05). Both groups of children′s brain networks had small world properties. Compared with the control group, Eglob decreased, Lp and λ increased in the SCP group (all P<0.05). Compared with the control group, DC and Ne in multiple brain regions decreased, NLp increased in the SCP group (all P<0.05, FDR corrected). Correlation analysis showed that DC in the right parsopercularis was positively correlated with FSIQ, VCI, WMI and PRI( r=0.53, 0.47, 0.47, 0.60, P=0.019, 0.045, 0.044, 0.020, respectively); NLp in the right parsopercularis was negatively correlated with PRI( r=-0.56, P=0.030); Ne in left paracentral, the right parsopercularis, right precentral, right postcentra were positively correlated with PRI( r=0.62, 0.56, 0.53, 0.54, P=0.015, 0.031, 0.044, 0.039, respectively); Ne in the right precentral was positively correlated with WMI ( r=0.48, P=0.039) in the SCP group. Conclusions:There are changes in the topological attributes of global and multiple regional brain networks in SCP. The changes in the attributes of nodes in the right parsopercularis, right precentral, right postcentral, and left paracentral could reflect cognitive dysfunction in children with SCP.

Objective:To explore the clinical and electrophysiological characteristics of peripheral neuropathy in prediabetic patients.Methods:Subjects aged 20-65 years with high-risk factors of impaired glycemia enrolled in Beijing Tiantan Hospital, Capital Medical University from 2019 to 2022 were recruited to conduct oral glucose tolerance test, after excluding other causes of neuropathy or radiculopathy. Patients with impaired fasting glucose or impaired glucose tolerance were defined by American Diabetes Association criteria. These patients were divided into clinical polyneuropathy (PN) and clinical non-PN groups, according to the 2010 Toronto consensus criteria and the presence of PN symptoms and signs or not. Nerve conduction studies (NCS), F wave, sympathetic skin response (SSR), R-R interval variation (RRIV) and current perception thresholds (CPT) were performed and the abnormal rate was compared between different electrodiagnostic methods and between clinical subgroups.Results:Among the 73 prediabetic patients ultimately enrolled, only 20 (27.4%) can be diagnosed as clinical PN according to the Toronto consensus criteria. The abnormal rate of CPT (68.5%, 50/73) was significantly higher than those of F wave (2.7%, 2/73), lower limb NCS (0, 0/73), upper limb NCS changes of carpal tunnel syndrome (26.0%, 19/73), SSR (6.8%, 5/73) and RRIV (5.5%, 4/73; McNemar test, all P<0.001). With sinusoid-waveform current stimuli at frequencies of 2 000 Hz, 250 Hz and 5 Hz, the CPT device was used to measure cutaneous sensory thresholds of large myelinated, small myelinated and small unmyelinated sensory fibers respectively. CPT revealed a 21.9% (16/73) abnormal rate of unmyelinated C fiber in the hands of prediabetic patients, significantly higher than that of large myelinated Aβ fibers [8.2% (6/73), χ2=5.352, P=0.021]. Both abnormal rates of small myelinated Aδ [42.5% (31/73)] and unmyelinated C fibers [39.7% (29/73)] in the feet of prediabetic patients were significantly higher than that of large myelinated Aβ fibers [11.0% (8/73), χ2=18.508, 15.965, both P<0.001]. Compared with the clinical non-PN group, the abnormal rates of CPT [90.0% (18/20) vs 60.4% (32/53), χ2=5.904, P=0.015] and SSR [20.0% (4/20) vs 1.9% (1/53), P=0.016) were significantly higher in the clinical PN group. Conclusions:Peripheral neuropathies in prediabetic patients are usually asymptomatic or subclinical, and predispose to affect unmyelinated and small myelinated sensory fibers. Selective electrodiagnostic measurements of small fibers help to detect prediabetic neuropathies in the earliest stages of the disease.

BACKGROUND:The application of robot-assisted technology for total knee arthroplasty is one of the current research hotspots.Since the 1980s,robot-assisted technology has been introduced into total knee arthroplasty outside China to achieve accurate osteotomy and good recovery of lower limb alignment.After decades of use,the robot has continuously improved its performance with new iterations,but has been criticized for increasing perioperative time and surgical trauma. OBJECTIVE:To summarize the advantages and disadvantages of current orthopedic surgical robots in total knee arthroplasty. METHODS:PubMed database and CNKI were searched to analyze the advantages and disadvantages of robot-assisted total knee arthroplasty in surgical trauma.English search terms were"arthroplasty,replacement,knee,knee replacement arthroplasty,procedure,robotic surgical,total knee arthroplasty,arthroplasty,replacement,knee,robotic-assisted".The Chinese search terms were"robot-assisted,robotic arm,knee osteoarthritis,arthritis".After the initial screening of all articles according to the inclusion and exclusion criteria,62 articles with high quality and relevance were reviewed. RESULTS AND CONCLUSION:(1)Robot-assisted total knee arthroplasty did not increase the degree of surgical trauma in patients,and showed a lower trauma effect than conventional manual total knee arthroplasty.(2)Robot-assisted total knee arthroplasty has the advantages of accurate auxiliary osteotomy,individualized prosthesis implantation,better protection of soft tissue around the knee joint,reduction of analgesic drug use,reduction of postoperative inflammatory index changes,and shortening of hospital stay.However,there are also shortcomings such as prolonged operation time,increased complications,and increased medical costs.(3)It is concluded that preliminary clinical application studies have shown that robot-assisted total knee arthroplasty can reduce surgical trauma,but it is necessary to be alert to potential risks.Simultaneously,its exact advantages compared with conventional manual total knee arthroplasty need to be verified by large-sample randomized controlled studies and long-term follow-up.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Critically ill patients are at high risk for hospital acquired infections, which can significantly increase the mortality rate and treatment costs for these patients. Therefore, in the process of treating the primary disease, strict prevention and control of new hospital infections is an essential component of the treatment for critically ill patients. The treatment of critically ill patients involves multiple steps and requires a concerted effort from various aspects such as theory, management, education, standards, and supervision to achieve effective prevention and control of hospital infections. However, there is currently a lack of unified understanding and standards for hospital infection prevention and control. To address this, in March 2024, a group of experts in critical care medicine, infectious diseases, and hospital infection from China discussed the current situation and issues of hospital infection control in the intensive care unit together. Based on a review of the latest evidence-based medical evidence from both domestic and international sources, 2024 Expert Consensus on Hospital Acquired Infection Control Principles in the Department of Critical Care Medicine was formed, aiming to provide a basis for the development of hospital infection prevention and control strategies in the field of critical care medicine.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.