BACKGROUND: Inflammation plays a critical role in severe cerebral venous thrombosis (CVT) pathogenesis, but the benefits of anti-inflammatory therapies remain unclear. This study aimed to investigate the association between steroid therapy combined with anticoagulation and the prognosis of acute/subacute severe CVT patients. METHODS: A prospective cohort study enrolled patients with acute/subacute severe CVT at Xuanwu Hospital (July 2020-January 2024). Patients were allocated into steroid and non-steroid groups based on the treatment they received. Functional outcomes (modified Rankin scale [mRS]) were evaluated at admission, discharge, and 6 months after discharge. Serum high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), cerebrospinal fluid (CSF) IL-6, and intracranial pressure were measured at admission and discharge in the steroid group. Fundoscopic Frisén grades were assessed at admission and 6 months after discharge. Univariate and multivariate logistic regression were used to evaluat associations between steroid use and favorable outcomes (mRS ≤2) at the 6-month follow-up. Paired tests assessed changes in hs-CRP and other variables before and after treatment, and Spearman's correlations were used to analyze relationships between these changes and functional improvements. RESULTS: A total of 107 and 58 patients in the steroid and non-steroid groups, respectively, were included in the analysis. Compared with the non-steroid group, the steroid group had a higher likelihood of achieving an mRS score of 0-2 (93.5% vs . 82.5%, odds ratio [OR] = 2.98, P  = 0.037) at the 6-month follow-up. After adjusting for confounding factors, the result remained consistent. Pulsed steroid therapy did not increase mortality during hospitalization or follow-up, nor did it lead to severe steroid-related complications (all P  >0.05). Patients in the steroid group showed a significant reduction in serum hs-CRP, IL-6, CSF IL-6, and intracranial pressure at discharge compared to at admission, as well as a significant reduction in the fundoscopic Frisén grade at the 6-month follow-up compare to at admission (all P  <0.001). A reduction in serum inflammatory marker levels during hospitalization positively correlated with improvements in functional outcomes ( P  <0.05). CONCLUSION: Short-term steroid use may be an effective and safe adjuvant therapy for acute/subacute severe CVT when used alongside standard anticoagulant treatments, which are likely due to suppression of the inflammatory response. However, these findings require further validation in randomized controlled trials. TRAIL REGISTRATION ClinicalTrials.gov , NCT05990894.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Anticoagulants/therapeutic use* ; C-Reactive Protein/metabolism* ; Interleukin-6/metabolism* ; Intracranial Thrombosis/drug therapy* ; Prospective Studies ; Steroids/therapeutic use* ; Venous Thrombosis/drug therapy*

Septic shock, a prevalent critical condition in intensive care units (ICU) and a major cause of patient mortality, is fundamentally attributed to microcirculatory dysfunction. Traditional macrocirculatory parameters are often insufficiently sensitive to reflect microcirculatory status. Consequently monitoring peripheral microcirculatory function holds crucial significance for assessing disease progression and evaluating therapeutic efficacy in septic shock. The peripheral perfusion index (PPI), obtained from a standard pulse oximeter, is based on photoplethysmography (PPG). It calculates the differential absorption of red and infrared light emitted by the sensor between pulsatile arterial blood and non-pulsatile tissue, enabling real-time reflection of peripheral perfusion and thus providing non-invasive, continuous monitoring of microcirculatory function. Although often overlooked compared to other ICU monitoring parameters, PPI has demonstrated notable clinical advances in septic shock management. Specifically, in early identification, PPI combined with sequential organ failure assessment (SOFA) predicts disease progression, with its dynamic changes further aiding prognosis assessment. During fluid resuscitation, it guides fluid responsiveness evaluation and serves as a therapeutic target to optimize strategies. In circulatory support, it assists in determining vasoactive drug initiation timing and dosage titration. Additionally, PPI aids mechanical ventilation weaning and organ dysfunction evaluation. This article reviews the principles, influencing factors, and clinical application advances of PPI in septic shock, aiming to provide clinicians with a basis for individualized intervention, improved patient outcomes, and the advancement of precision medicine in septic shock management.
Humans ; Shock, Septic/therapy* ; Microcirculation ; Perfusion Index ; Prognosis ; Photoplethysmography

Objective: To screen and identify the key active molecules, signaling pathways, and therapeutic targets of Shuxuening (SXN) injection for treating liver cirrhosis (LC) and to evaluate its therapeutic potential using a mouse model. Methods: Target genes of SXN and LC were retrieved from public databases, and enrichment analysis was performed. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and hub genes were identified using Molecular Complex Detection (MCODE). LC was induced in rats and mice via intraperitoneal injections of diethylnitrosamine and carbon tetrachloride (CCl4) for 12 weeks. Starting at week 7, SXN was administered intraperitoneally to the mice in the treatment group. Serum and liver tissues of the mice were collected for the detection of indicators, pathological staining, and expression analysis of hub targets using quantitative real-time polymerase chain reaction (qRT-PCR). Results: We identified 368 overlapping genes (OLGs) between SXN and LC targets. These OLGs were subsequently used to build a PPI network and to screen for hub genes. Enrichment analysis showed that these genes were associated with cancer-related pathways, including phosphoinositide-3-kinase/Akt and mitogen-activated protein kinase signaling and various cellular processes, such as responses to chemicals and metabolic regulation. In vivo experiments demonstrated that SXN treatment significantly improved liver function and pathology in CCl4-induced LC mice by reducing inflammation and collagen deposition. Furthermore, qRT-PCR demonstrated that SXN regulated the expression of MAPK8, AR and CASP3 in the livers of LC mice. Conclusion This study highlighted the therapeutic effects of SXN in alleviating LC using both bioinformatics and experimental methods. The observed effect was associated with modulation of hub gene expression, particularly MAPK8, and CASP3.

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Objective: To investigate the association between screen time (ST) during leisure time and anxiety-depression symptoms among middle school students, so as to provide a basis for formulating relevant intervention measures. Methods: From November to December 2023, a stratified cluster random sampling method was used to select 2 542 students from junior and senior high school in Taiyuan City. A self designed questionnaire, the Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire (PHQ-9) were used to investigate ST and anxiety/depression symptoms among middle school students. The Logistic regression model was used to explore the association of ST with symptoms of anxiety and depression, as well as with anxiety and depression comorbiditles (CAD). Results: The detection rates of anxiety symptoms, depression symptoms, and CAD were 13.69%, 15.77%, and 10.11%, respectively. The median ST was 2.00 h/d [interquartile range ( IQR =2.38) for weekly averages], with 0.33 h/d ( IQR =1.67) on work days and 5.00 h/d ( IQR=5.50) on rest days. Logistic regression analysis indicated that the total ST of mobile phones during rest days ( OR =1.07, 1.10, 1.11) and the averages ST of mobile phones over a week ( OR = 1.20 , 1.22, 1.29), as well as the total ST of all screen types during rest days ( OR =1.04, 1.04, 1.05) and the averages ST of all screen types over a week ( OR =1.08, 1.09, 1.21) were positively correlated with anxiety symptoms, depression symptoms, and CAD (all P <0.01). Conclusions Among middle school students in Taiyuan City, screen time is positively correlated with symptoms of anxiety or depression and the comorbidity of anxiety and depression, especially smartphone screen time and weekend screen use. Therefore, measures should be implemented to reduce unnecessary screen time among middle school students, especially the use of mobile phones, in order to mitigate the occurrence of anxiety and depression.

AIM: To investigate the current status of retinopathy of prematurity(ROP)in premature infants in Xiamen and analyze its influencing factors, aiming to provide a scientific basis for clinical treatment and preventive strategies.METHODS: A retrospective study was conducted on the case data of 363 preterm infants with a gestational age of <32 wk who underwent fundus examination at Xiang'an Hospital of Xiamen University from February 11, 2020 to February 25, 2023. The incidence of ROP was statistically analyzed based on the screening results. All premature infants were divided into ROP group(37 cases, 64 eyes)and non-ROP group(326 cases, 652 eyes). General clinical data and perinatal-related information of the two groups were compared, and multivariate Logistic regression analysis was used to identify factors influencing the occurrence of ROP in premature infants.RESULTS: A total of 363 premature infants were included in this study. The fundus screening results showed that a total of 37 cases(64 eyes)of premature infants were detected with ROP, including 10 cases(10 eyes)monocular and 27 cases(54 eyes)binocular, with an overall incidence of 10.2%(37/363). The severity was determined according to the ROP international classification standard(ROP is divided into 5 stages, with stage I being the least severe and stage V the most severe). Among the 64 eyes, 30 eyes(46.9%)were in stage I, 20 eyes(31.3%)were in stage II, 10 eyes(15.6%)were in stage III, 4 eyes(6.3%)were in stage IV, and there were no cases in stage V. By comparing the clinical data of the two groups, no significant differences were found in gender, mode of delivery, singleton or multiple births, premature rupture of membranes, history of asphyxia, patent ductus arteriosus(PDA), or neonatal respiratory distress syndrome(NRDS)between the two groups(all P>0.05). However, premature infants in the ROP group had significantly younger gestational age and lower birth weight compared to those in the non-ROP group(all P<0.05). Additionally, the ROP group had higher proportions of longer hospital stays, bronchopulmonary dysplasia(BPD), neonatal sepsis, anemia, oxygen therapy for more than 1 wk, oxygen concentration above 40%, and blood transfusion treatment(all P<0.05). Multivariate Logistic regression analysis revealed that combined neonatal sepsis(OR=166.985, 95% CI: 35.239-791.277, P<0.001), anemia(OR=8.111, 95% CI: 2.064-31.871, P=0.003), oxygen use time >1 wk(OR=10.216, 95% CI: 2.543-41.039, P=0.001), oxygen therapy concentration >40%(OR=7.647, 95% CI: 1.913-30.566, P=0.004), and receiving blood transfusion therapy(OR=5.879, 95% CI: 1.412-24.470, P=0.015)were the main risk factors affecting the occurrence of ROP in preterm infants, and the higher birth weight of preterm infants was a protective factor for ROP(OR=0.093, 95% CI: 0.022-0.394, P=0.001).CONCLUSION: The incidence of ROP in premature infants is relatively high, and there are multiple influencing factors. Low birth weight, neonatal sepsis, anemia, oxygen therapy, and blood transfusion treatment are high-risk factors for ROP in premature infants. Clinical attention should be given to such infants, and fundus screening should be conducted in a standardized manner to provide early treatment, thereby further reducing the risk of ROP in premature infants.

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Chronic atrophic gastritis(CAG)is a chronic gastric disorder characterized by recurrent damage to the gastric mucosal epithelium,resulting in the reduction of intrinsic glands,with or without concurrent intestinal metaplasia.The"hyperactive stomach qi"theory,derived from Huang Di Nei Jing Su Wen Ji Zhu,proposes that the core pathogenesis of CAG lies in excessive stomach qi activity,grounded in the physiological principle of"strong yang qi in earth and weak yin qi in earth".This theory synthesizes the clinical manifestations and pathological progression of CAG,asserting that its development often involves intertwined pathological factors such as stagnation,dryness-heat,phlegm-dampness and stasis-toxicity.A triphasic therapeutic framework is proposed:the spleen qi deficiency phase,marked by impaired spleen transport function and dysregulated qi-fluid distribution,requiring spleen fortification and qi-fluid regulation;the hyperactive stomach qi phase,characterized by intensified stomach qi activity coupled with dryness-damp stagnation,necessitating stagnation resolution,dampness elimination and yin nourishment;the decline and disorder of middle qi phase,characterized by the deficiency of the middle qi,with phlegm,blood stasis and toxins forming the terminal stage.Treatment should focus on reinforcing the middle and restoring balance,detoxifying and dissipating accumulation.By exploring CAG pathogenesis and treatment through the lens of"hyperactive stomach qi",this study aimed to provide novel theoretical insights and therapeutic strategies for TCM in the prevention and treatment of CAG.

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.