ObjectiveTo address the limitations of the current quality standard for Polygalae Radix（PR）， which relies on a single component for quality assessment and struggles to holistically control its intrinsic quality， by constructing a comprehensive quality evaluation system integrating "macro-characterization of chemical profile， synchronous quantification of multiple index components， and quantitative analysis of multi-components by single marker（QAMS） for key component groups". This study aims to facilitate the scientific revision of the quality standard for PR. MethodsHigh performance liquid chromatography（HPLC） characteristic chromatograms were established for 11 batches of PR medicinal materials（YZ）， 10 batches of PR decoction pieces（YP）， and 10 batches of licorice-processed PR decoction pieces（ZYZ）， followed by similarity evaluation and identification of common peaks. HPLC-QAMS was developed for xanthones（sibiricaxanthone B， polygalaxanthone Ⅺ， polygalaxanthone Ⅲ） in the characteristic chromatograms. Simultaneously， the external standard method（ESM） was used to determine the contents of the corresponding xanthones and 3，6'-disinapoyl sucrose in YZ， YP， and ZYZ， followed by multivariate statistical analysis and Spearman correlation analysis. ResultsThe similarity between the characteristic chromatograms of 31 batches of PR samples and the reference chromatogram was>0.9. A total of 13 common peaks were identified， and 10 of these peaks were characterized through reference standard comparison. The successfully constructed QAMS method showed that the relative correction factors（RCFs） of sibiricaxanthone B and polygalaxanthone Ⅺ to polygalaxanthone Ⅲ were 0.76 and 0.88， and their relative retention times（RRTs） were 0.85 and 0.97， respectively. The results calculated by the QAMS method showed no significant difference from those obtained by the ESM. According to the limit standard for polygalaxanthone Ⅲ in the 2020 edition of the Pharmacopoeia of the People's Republic of China（hereinafter referred to as the Chinese Pharmacopoeia）， the pass rate of 31 batches of samples was only 19.35%. Multivariate statistical analysis indicated certain compositional differences between different batches of YZ and YP， as well as between YP and ZYZ， with 3，6'-disinapoyl sucrose identified as the main differentiating component. Furthermore， correlation analysis revealed that the content of polygalaxanthone Ⅲ was positively correlated with the contents of sibiricaxanthone B and polygalaxanthone Ⅺ， but showed no association with the content of 3，6'-disinapoyl sucrose. ConclusionIt is recommended that the content limit for polygalaxanthone Ⅲ in YZ，YP and ZYZ be revised to not less than 0.07%， or the total content of polygalaxanthone Ⅲ， sibiricaxanthone B and polygalaxanthone Ⅺ be not less than 0.18%. The newly established triple quality control model of "holistic control via characteristic chromatograms， precise quantification of oligosaccharide esters， and efficient detection of xanthones by QAMS" provides a systematic and precise solution for quality evaluation of PR and similar Chinese herbal medicines.

Depressive disorder is a prevalent mental illness characterized by pronounced and enduring symptoms of depression and cognitive impairment. The escalating pressures of modern society have led to a corresponding rise in the number of depressive disorder patients, particularly those exposed to adverse social, economic, political, and environmental factors which exacerbate the risk of this disorder. The pathogenesis of depressive disorder is multifaceted, encompassing oxidative stress, neuroplasticity alterations, neuroinflammation, neurotransmitter system imbalances, and intestinal microecological disruptions, among others. Clinically, conventional antidepressants are primarily predicated on the monoamine neurotransmitter hypothesis. This theory posits that depressive disorder can be ameliorated by regulating the levels of neurotransmitters within the body through a singular mechanism. However, the complex and multifaceted pathogenesis of depressive disorder results in limited selectivity for these drugs. Mitogen-activated protein kinase (MAPK) is a conserved serine/threonine kinase that plays a crucial role in various cellular physiological and pathological processes, including cell growth, differentiation, stress adaptation, and inflammatory response. It is instrumental in maintaining cellular homeostasis and regulating cellular responses. Numerous studies indicate that MAPK is involved in the pathogenesis and progression of depressive disorder through various pathogenesis. However, what deserves attention is that the interaction between the pathogenesis and dynamics of regulatory process remains unclear. Modulating MAPK has been shown to influence the onset and progression of depressive disorder, though the precise mechanism remains elusive. Within the MAPK family, aberrant activity of extracellular signal-regulated kinase (ERK) can damage hippocampal neurons and overactivate microglia, precipitating depressive disorder. Excessive activation of c-Jun N-terminal kinase (JNK) results in heightened neuronal apoptosis in the hippocampus and prefrontal cortex, and suppresses the expression of neurotrophic factors. p38, a key regulator in inflammatory reactions, can induce neuroinflammation when overactive, leading to depressive disorder. ERK, JNK, and p38 sub-pathways do not function in isolation but rather interact synergistically and/or antagonistically through shared activators and common target molecules. Consequently, these sub-pathways form a complementary and coordinated regulatory network. In addition, MAPK family members can jointly influence the process of depressive disorder by sharing upstream factors and regulating common downstream targets, and there is a lack of identification of their markers and screening for subgroups. The collective abnormal activities of these MAPK family members illuminate the underlying mechanisms of depressive disorder, suggesting that MAPK could serve as a potential therapeutic target for this disorder. As for the study of ERK, different models of depressive disorder have contradictory effects on its activity. The primary cause of these differences can be attributed to the distinct pathological environments utilized in the creation of depressive disorder models. In the future, it is suggested that we use the inducement of depressive disorder as a modeling standard to accurately simulate the onset of depressive disorder to carry out accurate treatment according to the causes of depressive disorder. Research shows that classic clinical drugs, novel MAPK inhibitors and certain traditional Chinese medicines can prevent and treat depressive disorder by regulating the MAPK signaling pathway. Research on MAPK remains limited, particularly concerning the permeability and cellular specificity across the blood-brain barrier and the identification of objective predictive markers. Although inhibitors face challenges, they also possess significant advantages and developmental potential. This paper systematically summarizes the current status of MAPK in the treatment of depressive disorder, in order to provide insights for researching the pathogenesis of depressive disorder and developing new antidepressant drugs.

Depressive disorder is a prevalent mental illness characterized by pronounced and enduring symptoms of depression and cognitive impairment. The escalating pressures of modern society have led to a corresponding rise in the number of depressive disorder patients, particularly those exposed to adverse social, economic, political, and environmental factors which exacerbate the risk of this disorder. The pathogenesis of depressive disorder is multifaceted, encompassing oxidative stress, neuroplasticity alterations, neuroinflammation, neurotransmitter system imbalances, and intestinal microecological disruptions, among others. Clinically, conventional antidepressants are primarily predicated on the monoamine neurotransmitter hypothesis. This theory posits that depressive disorder can be ameliorated by regulating the levels of neurotransmitters within the body through a singular mechanism. However, the complex and multifaceted pathogenesis of depressive disorder results in limited selectivity for these drugs. Mitogen-activated protein kinase (MAPK) is a conserved serine/threonine kinase that plays a crucial role in various cellular physiological and pathological processes, including cell growth, differentiation, stress adaptation, and inflammatory response. It is instrumental in maintaining cellular homeostasis and regulating cellular responses. Numerous studies indicate that MAPK is involved in the pathogenesis and progression of depressive disorder through various pathogenesis. However, what deserves attention is that the interaction between the pathogenesis and dynamics of regulatory process remains unclear. Modulating MAPK has been shown to influence the onset and progression of depressive disorder, though the precise mechanism remains elusive. Within the MAPK family, aberrant activity of extracellular signal-regulated kinase (ERK) can damage hippocampal neurons and overactivate microglia, precipitating depressive disorder. Excessive activation of c-Jun N-terminal kinase (JNK) results in heightened neuronal apoptosis in the hippocampus and prefrontal cortex, and suppresses the expression of neurotrophic factors. p38, a key regulator in inflammatory reactions, can induce neuroinflammation when overactive, leading to depressive disorder. ERK, JNK, and p38 sub-pathways do not function in isolation but rather interact synergistically and/or antagonistically through shared activators and common target molecules. Consequently, these sub-pathways form a complementary and coordinated regulatory network. In addition, MAPK family members can jointly influence the process of depressive disorder by sharing upstream factors and regulating common downstream targets, and there is a lack of identification of their markers and screening for subgroups. The collective abnormal activities of these MAPK family members illuminate the underlying mechanisms of depressive disorder, suggesting that MAPK could serve as a potential therapeutic target for this disorder. As for the study of ERK, different models of depressive disorder have contradictory effects on its activity. The primary cause of these differences can be attributed to the distinct pathological environments utilized in the creation of depressive disorder models. In the future, it is suggested that we use the inducement of depressive disorder as a modeling standard to accurately simulate the onset of depressive disorder to carry out accurate treatment according to the causes of depressive disorder. Research shows that classic clinical drugs, novel MAPK inhibitors and certain traditional Chinese medicines can prevent and treat depressive disorder by regulating the MAPK signaling pathway. Research on MAPK remains limited, particularly concerning the permeability and cellular specificity across the blood-brain barrier and the identification of objective predictive markers. Although inhibitors face challenges, they also possess significant advantages and developmental potential. This paper systematically summarizes the current status of MAPK in the treatment of depressive disorder, in order to provide insights for researching the pathogenesis of depressive disorder and developing new antidepressant drugs.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

N-acetyl-p-aminophenol （APAP） is an antipyretic analgesic commonly used in clinical practice， and APAP overdose can cause severe liver injury and even death. In recent years， the incidence rate of APAP-induced liver injury （AILI） tends to increase， and it has become the second most common cause of liver transplantation worldwide. Autophagy is a highly conserved catabolic process that removes unwanted cytosolic proteins and organelles through lysosomal degradation to achieve the metabolic needs of cells themselves and the renewal of organelles. A large number of studies have shown that autophagy plays a key role in the pathophysiology of AILI， involving the mechanisms such as APAP protein conjugates， oxidative stress， JNK activation， mitochondrial dysfunction， inflammatory response and apoptosis. This article elaborates on the biological mechanism of autophagy in AILI， in order to provide a theoretical basis for the treatment of AILI and the development of autophagy regulators.

With the rapid development of sequencing technologies, the detection of alternative polyadenylation (APA) in mammals has become more precise. APA precisely regulates gene expression by altering the length and position of the poly(A) tail, and is involved in various biological processes such as disease occurrence and embryonic development. The research on APA in mammals mainly focuses on the following aspects:(1) identifying APA based on transcriptome data and elucidating their characteristics; (2) investigating the relationship between APA and gene expression regulation to reveal its important role in life regulation;(3) exploring the intrinsic connections between APA and disease occurrence, embryonic development, differentiation, and other life processes to provide new perspectives and methods for disease diagnosis and treatment, as well as uncovering embryonic development regulatory mechanisms. In this review, the classification, mechanisms and functions of APA were elaborated in detail and the methods for APA identifying and APA data resources based on various transcriptome data were systematically summarized. Moreover, we epitomized and provided an outlook on research on APA, emphasizing the role of sequencing technologies in driving studies on APA in mammals. In the future, with the further development of sequencing technology, the regulatory mechanisms of APA in mammals will become clearer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.