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.

Objective:To investigate the co-occurrence patterns of psychosocial and behavioral problems among children and to identify associated influencing factors.Methods:A multicenter cross-sectional survey was conducted in 2023. A cluster random sample of 19 176 children aged 6-16 years was recruited from middle-income areas across 10 provincial capitals and municipalities in China. Psychological and behavioral problems, including anxiety, compulsive behavior, social withdrawal, depression, somatic complaints, social problems, schizoid, delinquent behaviors, hyperactivity, sexual issues, and aggression, were assessed using the Achenbach Child Behavior Checklist parent version. Co-occurrence was defined as ≥2 concurrent problems. Children were divided into 4 groups by gender and age: boys aged 6-11 years, girls aged 6-11 years, boys aged 12-16 years, and girls aged 12-16 years. Those children who had psychosocial and behavioral problems were further categorized into the single-problem group, and the co-occurrence group based on assessment results. High-frequency co-occurrence phenotypes of children′s psychosocial and behavioral problems were identified. Demographic factors, such as parental employment, education, as well as psychosocial factors like parent-child relationship, screen time and outdoor activity, were investigated. χ 2 test was used to analyze differences between groups. Multivariate Logistic regression modeling was conducted to identify potential factors. Results:Among 14 711 children (7 501 boys, 7 210 girls) who provided effective questionnaires, the detection rates of single problem in the boys aged 6-11 years, girls aged 6-11 years, boys aged 12-16 years, and girls aged 12-16 years groups were 4.9% (171/3 461), 6.2% (193/3 120), 3.9% (158/4 040), and 5.1% (208/4 090), respectively; the detection rates of co-occurrence were 7.6% (262/3 461), 7.7% (241/3 120), 4.9% (199/4 040), and 5.7% (234/4 090), respectively. The overall detection rates of co-occurrence was higher than that of single problem ( χ2=25.47, P<0.001). Among children with co-occurrence, there were varied manifestations: in the boys aged 6-11 years group, the detection rates of social withdrawal (69.8% (183/262)), schizoid-like behavior (68.3% (179/262)), and compulsive behavior (67.6% (177/262)) were relatively high; in the girls aged 6-11 years group, the detection rates of schizoid-compulsive behavior (69.3% (167/241)), delinquent behavior (65.6% (158/241)), and hyperactivity (58.9% (142/241)) were relatively high; in the boys aged 12-16 years group, the detection rates of hyperactivity (78.9% (157/199)), compulsive behavior (67.3% (134/199)), and immature behavior (57.3% (114/199)) were relatively high; in the girls aged 12-16 years group, the detection rates of schizoid-like behavior (89.7% (210/234)), immature behavior (59.0% (138/234)), and cruelty (57.7% (135/234)) were relatively high. Maternal bachelor′s degree or higher ( OR=0.78, 95% CI 0.61-0.99, P=0.038) served as co-occurrence protective factors, whereas having 1 or more siblings, increased parent-child conflict and decreased parent-child interaction time ( OR=1.24, 1.41, 1.36; 95% CI 1.02-1.52, 1.15-1.73, 1.02-1.82, all P<0.05) were co-occurrence risk factors. Conclusions:Children exhibit strong co-occurrence tendencies in psychosocial and behavioral problems. Compulsive and schizoid traits are the predominant co-occurring phenotypes for childhood and girls respectively. ?Familial environment plays a critical role, necessitating ?multidimensional clinical assessments and ?family-centered interventions.

Objective To explore the relationship between the coagulation and fibrinolysis function in the early pregnancy and the occurrence of gestational diabetes mellitus(GDM)in women with different pre pregnancy body mass indexes(BMI).Methods 290 pregnant women undergoing the prenatal check ups at the Second Affiliated Hospital of Kunming Medical University from September 2023 to February 2024 were selected.Pre pregnancy BMI,age,family genetic history,parity,parity,and early pregnancy coagulation and fibrinolysis function test results were collected.Based on whether GDM had occurred,they were divided into GDM group(n=72)and non GDM group(n=218),and further divided into low weight GDM group(n=8),low weight non GDM group(n=29),normal weight GDM group(n=39),normal weight non GDM group(n=145),overweight/obesity GDM group(n=25),overweight/obesity non GDM group(n=44)based on pre pregnancy BMI.Basic data comparison was conducted on the total population and BMI groups.Independent sample t-test or Mann Whitney U test was used for quantitative data,and chi square test or Fisher's exact probability method was used for qualitative data.Multivariate logistic regression was used to correct the influencing factors.Results After adjusting the confounding factors such as age,family history,and pre pregnancy BMI,APTT was negatively correlated with the occurrence of GDM in the overall population(P<0.05,OR=0.840),while FIB was positively correlated with GDM(P<0.01,OR=2.598).In low body weight recombination,APTT was negatively correlated with GDM(P<0.05,OR=0.483),FIB was positively correlated with GDM(P<0.05,OR=82.501),while there was no significant correlation between APTT,FIB and GDM after adjusting the age,family history,and pre pregnancy BMI;In the normal weight group,APTT was negatively correlated with GDM(P<0.01,OR=0.786)and FIB was positively correlated with GDM(P<0.05,OR=2.413).However,after adjusting the age,family history,and pre pregnancy BMI,APTT remained negatively correlated with GDM(P<0.05,OR=0.812)and FIB remained positively correlated with GDM(P<0.05,OR=2.391);In the overweight/obese group,TT was negatively correlated with GDM(P<0.05,OR=0.510),while there was no significant correlation between TT and GDM after adjusting the age,family history,and pre pregnancy BMI.Conclusion In the normal weight population,APTT is negatively correlated with the occurrence of GDM,while FIB is positively correlated with the occurrence of GDM;In the low weight and overweight/obese populations,coagulation and fibrinolysis related indicators are greatly influenced by BMI and have no significant correlation with the occurrence of GDM.

Objective To explore the advantages of combining small private online courses(SPOC)with artificial intelligence(AI)in radiology nursing teaching,in order to compensate for the shortcomings of traditional teaching models.Methods Eighty nursing students interning in the radiology department were randomly selected as research subjects and divided into an experimental group(SPOC+flipped classroom+AI-assisted teaching mode)and a control group(traditional teaching mode),with 40 students in each group.The effectiveness of the SPOC+flipped classroom+AI-assisted teaching mode was evaluated by comparing theoretical tests,nursing skills tests,self-learning ability assessments,and satisfaction with teaching modes between the two groups.Results The average scores of chapter tests,month-end assessments and graduation examinations in the experimental group were higher than those in the control group(P<0.001);The average scores of indwelling needle embedding,contrast agent injection,and contrast agent allergy treatment tests in the experimental group were higher than those in the control group(P<0.001);The online learning time,homework completion rate,and online test scores of the experimental group were higher than those in the control group(P<0.001);The overall satisfaction with the teaching mode was higher in the experimental group than in the control group,with statistically significant differences(P<0.001).Conclusion The SPOC+flipped classroom+AI-assisted teaching model possesses important advantages in the instruction of nursing the department of radiology,and provides strong support for the innovation and development of nursing education in the field.

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,of-fering 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 accu-racy 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 summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations 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.

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.

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.

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) 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