OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Objective:To explore the differences in early clinical features between Kawasaki disease shock syndrome (KDSS) and septic shock (SS).Methods:A retrospective case-control study was conducted. Clinical data was collected from 64 children who were diagnosed with KDSS or SS and admitted to the Department of Critical Care Medicine of Capital Center for Children′s Health, Capital Medical University from January 2018 to February 2025. Mann-Whitney U test, χ2 test, or Fisher′s exact test were used to compare the differences in clinical features, treatment, and outcomes between children with KDSS and SS. Lasso regression was applied to screen predictive variables, and multivariable logistic regression analysis was performed to identify factors associated with KDSS. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of parameters for KDSS. Results:Among the 64 children (30 males and 34 females), the age was 3.6 (1.2, 6.5) years. There were 51 cases in the SS group and 13 cases in the KDSS group. Compared to children with SS, children with KDSS had a longer pre-shock fever duration, lower lactate levels and serum albumin levels, and higher soluble interleukin-2 receptor (sIL-2R) levels (all P<0.05). Additionally, they exhibited a higher incidence of coronary involvement, pericardial effusion, and ascites, a higher utilization rate of intravenous immunoglobulin, and a lower utilization rate of invasive mechanical ventilation (all P<0.05). There was no significant difference in in-hospital mortality between KDSS and SS ( P=0.574). Multivariate logistic regression analysis identified pre-shock fever duration and sIL-2R as independent factors associated with KDSS ( OR=1.52 and 1.54 per 1 000 U increase, 95% CI 1.12-2.05 and 1.06-2.24, respectively; both P<0.05). ROC curve analysis showed that the areas under the curve for pre-shock fever duration and sIL-2R in identifying KDSS were 0.83 (95% CI 0.73-0.94, P=0.001) and 0.70 (95% CI 0.53-0.87, P=0.042), respectively. The optimal cutoff values were 3.5 d and 3.8×10 6 U/L, with sensitivities of 0.91 and 0.82, and specificities of 0.71 and 0.62, respectively. Conclusions:Children with KDSS have higher incidences of coronary involvement, pericardial effusion, and ascites compared to those with SS. Pre-shock fever duration and sIL-2R may serve as potential early indicators for distinguishing KDSS from SS.

OBJECTIVE: To investigate the effects of low-dose irradiated autologous peripheral blood reinfusion (LDIAPBR) on a rat model of radiation-induced leukopenia. METHODS: The rats were randomly divided into four groups. In the LDIAPBR group, LDIAPBR was performed 1 day before modeling (10% of the total circulating blood volume was withdrawn, irradiated with 100 mGy ex vivo, and completely reinfused). Meanwhile, the normal group and model group only underwent blood withdrawal and reinfusion of the same proportion without blood irradiation. Except for the normal group, all groups were subjected to 1 Gy X-ray whole-body irradiation to establish a radiation-induced leukopenia rat model. The positive drug group received subcutaneous injection of rhG-CSF after modeling. It was monitored that the general condition of the rats, peripheral blood cell counts, immune organ indices, bone marrow nucleated cell counts and viability, and the pathological analysis of bone marrow sections was conducted. RESULTS: The LDIAPBR group exhibited significant improvements in overall condition compared to the model group. Notably, compared with the model group, peripheral blood leukocyte and lymphocyte counts were markedly higher in the LDIAPBR group. Furthermore, there was a significant increase in both the number and viability of nucleated cells in the bone marrow. Pathological examination of bone marrow sections revealed increased nucleated cell density and reduced cavity area in the LDIAPBR group. CONCLUSION LDIAPBR can effectively improve hematological parameters and bone marrow hematopoietic function in a rat model of radiation-induced leukopenia, providing a new approach for the prevention and treatment of radiation-related injuries.
Animals ; Leukopenia/prevention & control* ; Rats ; Blood Transfusion, Autologous ; Whole-Body Irradiation ; Radiation Injuries, Experimental/therapy*

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*

Objective:To investigate the role of platelet-derived zyxin in promoting tumor migration by platelets.Methods:The gene expression profile of platelets was analyzed from cancer patients by using the GEO database.Isolated platelets from wild-type(WT)and Zyx-/-mice were co-cultured with B16F10 cells labeled with green fluorescence to investigate the influence of zyxin deficiency on tumor cell migration,invasion,and wound healing.Optical microscopy was employed to evaluate the impact of zyxin deficiency on epithelial-mesenchymal transition(EMT)in B16F10 cells induced by platelets.Employing specific markers to label platelets,fluorescence confocal microscopy was utilized to investigate the impact of platelet-derived zyxin on the binding between tumor cells and platelets.And an aggregometer was employed to observe the influence of zyxin deficiency on tumor cell-induced platelet aggregation.Results:Compared to platelets from healthy volunteers,zyxin was upregulated in platelets from cancer patients.Zyx-/-mouse platelets exhibited a significant reduction in tumor cell invasion and migration,impaired wound healing,and delayed tumor cell EMT compared to WT mouse platelets.Additionally,zyxin deficiency attenuated the interaction between platelets and tumor cells,and diminished the capacity for tumor cell-induced platelet aggregation.Conclusion:Platelet-derived zyxin deficiency diminishes platelet-tumor cell interactions and weakens the ability of tumor cell-induced platelet aggregation,ultimately suppressing tumor cell migration.

Objective:To explore the value of paediatric age-adjusted shock index（SIPA）in early identification of septic shock in children，and to evaluate the relationship between SIPA and disease severity and prognosis.Methods:The infected children admitted to the department of critical care medicine of the Children's Hospital Affiliated to Capital Institute of Pediatrics from May 2023 to July 2024 were collected. Dynamic assessment was performed 0 to 6 hours after admission. Patients diagnosed with sepsis without septic shock were classified as the sepsis group and those diagnosed with sepsis with septic shock were classified as the septic shock group. According to whether the blood pressure of the children decreased，they were divided into two groups：compensated septic shock group and decompensated septic shock group. The difference of SIPA among the three groups was analyzed，and the predictive value of SIPA on case fatality rate，lactate level，pediatric critical illness score，ventilator utilization rate and length of hospital stay were analyzed.Results:Among 203 children with sepsis，112 were males and 91 were females. There were 146 cases in the sepsis group，37 cases in the compensated septic shock group and 20 cases in the decompensated septic shock group. There was no significant difference between the three groups in gender（ P>0.05），but there was a statistically significant difference in age（ χ 2=32.905， P<0.001）. There was no significant difference in age between the sepsis group and the compensated septic shock group（ P>0.05）. The age of sepsis group and decompensated septic shock group，compensated septic shock group and decompensated septic shock group were statistically significant（ χ 2=29.431， P<0.001； χ 2=19.764， P=0.001）. The proportion of increased SIPA was statistically different among the three groups，with both the compensated septic shock group and the decompensated septic shock group being higher than the sepsis group（ χ2=20.383， P<0.001； χ2=33.600， P<0.001）. The decompensated septic shock group was higher than the compensated septic shock group（ χ2=6.555， P=0.01）. SIPA was correlated with case fatality rate，lactate level，pediatric critical illness score，ventilator use rate and length of stay of the children，with statistically significant differences（ P<0.05）. Conclusion:The increase of SIPA can be used for the early identification of septic shock in children，and it has a certain early warning value for the prognosis assessment of sepsis and septic shock.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.