Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective To observe the clinical efficacy and safety of vericiguat tablets combined with sacubitril valsartan sodium(Sac/Val)tablets in the treatment of patients with heart failure with reduced ejection fraction(HFrEF).Methods The HFrEF patients were divided into control group and treatment group according to the cohort method.The control group was treated with Sac/Val tablets 200 mg per time,bid,orally.On the basis of control group,the treatment group was treated with vericiguat tablets 2.5 mg per time,qd,taken with meal.Two groups were treated for 3 months.The clinical efficacy,left ventricular ejection fraction(LVEF),left ventricular end-diastolic dimension(LVEDD)and end-systolic diameter(LVESD),levels of high sensitivity C-reactive protein(hs-CRP),interleukin-6(IL-6),nitric oxide(NO),N-terminal pro-brain natriuretic peptide(NT-proBNP),blood urea nitrogen(BUN)and serum creatinine(SCr),and safety were compared between the two groups.During follow-up,the heart failure rehospitalization rates and major adverse cardiovascular events were compared between the two groups.Results Treatment group was enrolled 53 patients,control group was enrolled 53 patients.After treatment,the total effective rates of treatment and control groups were 94.34％(50 cases/53 cases)and 81.13％(43 cases/53 cases)with statistical significant difference(P＜0.05).After treatment,the LVEF of treatment and control groups were(48.02±5.20)％and(43.02±4.33)％,the LVEDDs were(52.85±6.30)and(55.63±6.88)mm,the LVESDs were(41.64±6.40)and(44.22±5.85)mm,the levels of hs-CRP were(10.22±2.63)and(14.60±2.98)mg L-1,the levels of IL-6 were(14.48±2.40)and(17.36±2.52)pg·mL-1,the levels of NO were(102.60±20.16)and(92.16±16.33)μmol·L-1,the levels of NT-proBNP were(898.74±102.20)and(1315.60±182.64)ng·L-1,the levels of BUN were(12.02±2.28)and(13.45±2.33)mmol·L-1,the levels of SCr were(82.22±5.89)and(85.64±6.03)μmol·L-1,the heart failure rehospitalization rates were 5.66％and 13.21％,respectively;the differences were statistical significant between two groups(all P＜0.05).The adverse drug reactions of treatment group were hyperkalemia,hypotension,renal dysfunction,dizziness and headache,while those in control group were renal dysfunction,hyperkalemia,and hypotension.The major adverse cardiovascular events of treatment group were angina pectoris and acute myocardial infarction,while those in control group were angina pectoris,acute myocardial infarction and atrial fibrillation.The incidences of total adverse drug reactions in treatment and control groups were 13.21％and 7.55％,the incidences of major adverse cardiovascular events were 5.66％and 13.21％,respectively,without statistically significant differences(all P＞0.05).Conclusion Vericiguat tablets combined with Sac/Val tablets have a definitive clinical efficacy in the treatment of HFrEF patients,which can improve cardiac and endothelial function,reduce inflammatory response and readmission times,without increasing the incidences of adverse drug reactions.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate the effects of laparoscopic sleeve gastrectomy(LSG)on the cardiac structure and function in obese patients with heart failure(HF)and compare the efficacy of LSG across obese patients with different HF types.Methods This study included 33 obese patients with HF who underwent LSG.The clinical indicators were compared between before operation and 12 months after operation.Repeated measures analysis of variance was employed to evaluate the changes in echocardiographic parameters before operation and 3,6,and 12 months after operation.Patients were allocated into a HF with preserved ejection fraction group(n=17),a HF with mildly reduced ejection fraction group(n=5)and a HF with reduced ejection fraction(HFrEF)group(n=11)based on left ventricular ejection fraction(LVEF)before operation for subgroup analyses of the effects of LSG on the cardiac structure and function of obese patients with HF.The paired samples t-test was conducted to assess the degree of cardiac structural and functional alterations after LSG.Results The 33 patients included 69.7% males,with an average age of(35.3±9.9)years,and a body mass index(BMI)of(51.2±9.8)kg/m².The median follow-up was 9.0(5.0,13.3)months.Compared with the preoperative values,the postoperative BMI(P=0.002),body surface area(BSA)(P=0.009),waist circumference(P=0.010),hip circumference(P=0.031),body fat content(P=0.007),and percentage of patients with cardiac function grades Ⅲ-IV(P<0.001)decreased.At the 12-month follow-up left atrial diameter(P=0.006),right atrial long-axis inner diameter(RAD1)(P<0.001),right atrial short-axis inner diameter(RAD2)(P<0.001),right ventricular inner diameter(P=0.002),interventricular septal thickness at end-diastolic(P=0.002),and left ventricular end-diastolic volumes(P=0.004)and left ventricular end-systolic volumes(P=0.003) all significantly reduced compared with preoperative values.Additionally,left ventricular fractional shortening and LVEF improved(both P<0.001).Subgroup analyses revealed that cardiac structural parameters significantly decreased in the HF with preserved ejection fraction,HF with mildly reduced ejection fraction,and HFrEF subgroups compared with preoperative values.Notably,the HFrEF group demonstrated the best performance in terms of left atrial diameter(P=0.003),left ventricular inner diameter at end-diastole(P=0.008),RAD1(P<0.001),RAD2(P=0.004),right ventricular inner diameter(P=0.019),left ventricular end-diastolic volume(P=0.004)and left ventricular end-systolic volume(P=0.001),cardiac output(P=0.006),tricuspid regurgitation velocity(P=0.002),and pulmonary artery systolic pressure(P=0.001) compared to preoperatively.Postoperative left ventricular fractional shortening(P<0.001,P=0.003,P<0.001)and LVEF(P<0.001,P=0.011,P=0.001)became higher in all the three subgroups than the preoperative values.Conclusions LSG decreased the body weight,BMI,and BSA,improved the cardiac function grade,reversed the enlargement of the left atrium and left ventricle,reduced the right atrium and right ventricle,and enhanced the left ventricular systolic function.It was effective across obese patients with different HF types.Particularly,LSG demonstrates the best performance in improving the structures of both atria and ventricles in obese patients with HFrEF.
Humans ; Male ; Female ; Gastrectomy/methods* ; Heart Failure/complications* ; Adult ; Obesity/physiopathology* ; Laparoscopy ; Middle Aged ; Heart/physiopathology* ; Stroke Volume

Objective To observe the clinical efficacy and safety of vericiguat tablets combined with sacubitril valsartan sodium(Sac/Val)tablets in the treatment of patients with heart failure with reduced ejection fraction(HFrEF).Methods The HFrEF patients were divided into control group and treatment group according to the cohort method.The control group was treated with Sac/Val tablets 200 mg per time,bid,orally.On the basis of control group,the treatment group was treated with vericiguat tablets 2.5 mg per time,qd,taken with meal.Two groups were treated for 3 months.The clinical efficacy,left ventricular ejection fraction(LVEF),left ventricular end-diastolic dimension(LVEDD)and end-systolic diameter(LVESD),levels of high sensitivity C-reactive protein(hs-CRP),interleukin-6(IL-6),nitric oxide(NO),N-terminal pro-brain natriuretic peptide(NT-proBNP),blood urea nitrogen(BUN)and serum creatinine(SCr),and safety were compared between the two groups.During follow-up,the heart failure rehospitalization rates and major adverse cardiovascular events were compared between the two groups.Results Treatment group was enrolled 53 patients,control group was enrolled 53 patients.After treatment,the total effective rates of treatment and control groups were 94.34％(50 cases/53 cases)and 81.13％(43 cases/53 cases)with statistical significant difference(P＜0.05).After treatment,the LVEF of treatment and control groups were(48.02±5.20)％and(43.02±4.33)％,the LVEDDs were(52.85±6.30)and(55.63±6.88)mm,the LVESDs were(41.64±6.40)and(44.22±5.85)mm,the levels of hs-CRP were(10.22±2.63)and(14.60±2.98)mg L-1,the levels of IL-6 were(14.48±2.40)and(17.36±2.52)pg·mL-1,the levels of NO were(102.60±20.16)and(92.16±16.33)μmol·L-1,the levels of NT-proBNP were(898.74±102.20)and(1315.60±182.64)ng·L-1,the levels of BUN were(12.02±2.28)and(13.45±2.33)mmol·L-1,the levels of SCr were(82.22±5.89)and(85.64±6.03)μmol·L-1,the heart failure rehospitalization rates were 5.66％and 13.21％,respectively;the differences were statistical significant between two groups(all P＜0.05).The adverse drug reactions of treatment group were hyperkalemia,hypotension,renal dysfunction,dizziness and headache,while those in control group were renal dysfunction,hyperkalemia,and hypotension.The major adverse cardiovascular events of treatment group were angina pectoris and acute myocardial infarction,while those in control group were angina pectoris,acute myocardial infarction and atrial fibrillation.The incidences of total adverse drug reactions in treatment and control groups were 13.21％and 7.55％,the incidences of major adverse cardiovascular events were 5.66％and 13.21％,respectively,without statistically significant differences(all P＞0.05).Conclusion Vericiguat tablets combined with Sac/Val tablets have a definitive clinical efficacy in the treatment of HFrEF patients,which can improve cardiac and endothelial function,reduce inflammatory response and readmission times,without increasing the incidences of adverse drug reactions.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To investigate the clinical efficacy and safety of ixazomib-containing regimens in the treatment of patients with multiple myeloma(MM).Methods:A retrospective analysis was performed on the clinical efficacy and adverse reactions of 32 MM patients treated with a combined regimen containing ixazomib in the Hematology Department of the First People's Hospital of Lianyungang from January 2020 to February 2022.Among the 32 patients,15 patients were relapsed and refractory multiple myeloma(R/RMM)(R/RMM group),17 patients who responded to bortezomib induction therapy but converted to ixazomib-containing regimen due to adverse events(AE)or other reasons(conversion treatment group).The treatment included IPD regimen(ixazomib+pomalidomide+dexamethasone),IRD regimen(ixazomib+lenalidomide+dexamethasone),ICD regimen(ixazomib+cyclophosphamide+dexamethasone),ID regimen(ixazomib+dexamethasone).Results:Of 15 R/RMM patients,overall response rate(ORR)was 53.3％(8/15),among them,1 achieved complete response(CR),2 achieved very good partial response(VGPR)and 5 achieved partial response(PR).The ORR of the IPD,IRD,ICD and ID regimen group were 100％(3/3),42.9％(3/7),33.3％(1/3),50％(1/2),respectively,there was no statistically significant difference in ORR between four groups(x2=3.375,P=0.452).The ORR of patients was 50％after first-line therapy,42.9％after second line therapy,60％after third line therapy or more,with no statistically significant difference among them(x2=2.164,P=0.730).In conversion treatment group,ORR was 88.2％(15/17),among them,6 patients achieved CR,5 patients achieved VGPR and 4 patients achieved PR.There was no statistically significant difference in ORR between the IPD(100％,3/3),IRD(100％,6/6),ICD(100％,3/3)and ID(60％,3/5)regimen groups(x2=3.737,P=0.184).The median progression-free survival(PFS)time of R/RMM patients was 9 months(95％CI:6.6-11.4 months),the median overall survival(OS)time was 18 months(95％CI:11.8-24.4 months).The median PFS time of conversion treatment group was 15 months(95％CI:7.3-22.7 months),the median OS time not reached.A total of 10 patients suffered grade 3-4 adverse event(AE).The common hematological toxicities were leukocytopenia,anemia,thrombocytopenia.The common non-hematological toxicities were gastrointestinal symptoms(diarrhea,nausea and vomit),peripheral neuropathy,fatigue and infections.Grade 1-2 peripheral neurotoxicity occurred in 7 patients.Conclusion:The ixazomib-based chemotherapy regimens are safe and effective in R/RMM therapy,particularly for conversion patients who are effective for bortezomib therapy.The AE was manageable and safe.

This study was designed to evaluate the protective effect of CPD1, a novel phosphodiesterase 5 inhibitor, on renal interstitial fibrosis after unilateral renal ischemia-reperfusion injury (UIRI). Male BALB/c mice were subjected to UIRI, and treated with CPD1 once daily (i.g, 5 mg/kg). Contralateral nephrectomy was performed on day 10 after UIRI, and the UIRI kidneys were harvested on day 11. Hematoxylin-eosin (HE), Masson trichrome and Sirius Red staining methods were used to observe the renal tissue structural lesions and fibrosis. Immunohistochemical staining and Western blot were used to detect the expression of proteins related to fibrosis. HE, Sirius Red and Masson trichrome staining showed that CPD1-treated UIRI mice had lower extent of tubular epithelial cell injury and deposition of extracellular matrix (ECM) in renal interstitium compared with those in the fibrotic mouse kidneys. The results from immunohistochemistry and Western blot assay indicated significantly decreased protein expressions of type I collagen, fibronectin, plasminogen activator inhibitor-1 (PAI-1) and α-smooth muscle actin (α-SMA) after CPD1 treatment. In addition, CPD1 dose-dependently inhibited the expression of ECM-related proteins induced by transforming growth factor β1 (TGF-β1) in normal rat kidney interstitial fibroblasts (NRK-49F) and human renal tubular epithelial cell line (HK-2). In summary, the novel PDE inhibitor, CPD1, displays strong protective effects against UIRI and fibrosis by suppressing TGF-β signaling pathway and regulating the balance between ECM synthesis and degradation through PAI-1.
Animals ; Humans ; Male ; Mice ; Rats ; Extracellular Matrix Proteins ; Fibrosis ; Kidney ; Kidney Diseases ; Phosphodiesterase 5 Inhibitors ; Plasminogen Activator Inhibitor 1

OBJECTIVE: To analyze the effects of mangiferin combined with bortezomib on the proliferation, invasion, apoptosis and autophagy of human Burkitt lymphoma Raji cells, as well as the expression of CXC chemokine receptors (CXCRs) family, and explore the molecular mechanism between them to provide scientific basis for basic research and clinical work of Burkitt lymphoma. METHODS: Raji cells were intervened with different concentrations of mangiferin and bortezomib alone or in combination, then cell proliferation was detected by CCK-8 assay, cell invasion ability was detected by Transwell chamber method, cell apoptosis was detected by Annexin V/PI double-staining flow cytometry, apoptosis, autophagy and Akt/mTOR pathway protein expression were detected by Western blot, and the expression changes of CXCR family was detected by real-time quantitative PCR (RT-qPCR). RESULTS: Different concentrations of mangiferin intervened Raji cells for different time could inhibit cell viability in a concentration- and time-dependent manner (r =-0.682, r =-0.836). When Raji cells were intervened by combination of mangiferin and bortezomib, compared with single drug group, the proliferation and invasion abilities were significantly decreased, while the apoptosis level was significantly increased (P <0.01). Mangiferin combined with bortezomib could significantly up-regulate the expression of pro-apoptotic protein Bax and down-regulate the expression of anti-apoptotic protein Bcl-2 after intervention in Raji cells. Caspase-3 was also hydrolyzed and activated, and then induced the apoptosis of Raji cells. Mangiferin combined with bortezomib could up-regulate the expression of LC3Ⅱ protein in Raji cells, and the ratio of LC3Ⅱ/LC3Ⅰ in cells was significantly up-regulated compared with single drug or control group (P <0.01). Mangiferin combined with bortezomib could significantly inhibit the phosphorylation levels of Akt and mTOR, inhibit the proliferation and invasion of Raji cells by inhibiting Akt/mTOR pathway, and induce cell autophagy and apoptosis. Mangiferin and bortezomib could down-regulate the expressions of CXCR4 and CXCR7 mRNA after single-agent intervention in Raji cells, and the down-regulations of CXCR4 and CXCR7 mRNA expression were more significant when the two drugs were combined (P <0.01). Mangiferin alone or combined with bortezomib had no significant effect on CXCR5 mRNA expression in Raji cells (P >0.05), while the combination of the two drugs could down-regulate the expression of CXCR3 (P <0.05). CONCLUSION Mangiferin combined with bortezomib can synergistically inhibit the proliferation and invasion of Raji cells, and induce autophagy and apoptosis. The mechanism may be related to the inhibition of Akt/mTOR signaling pathway, down-regulation of anti-apoptotic protein Bcl-2 and up-regulation of pro-apoptotic protein Bax, and the inhibition of the expression of CXCR family.
Humans ; Antineoplastic Agents/therapeutic use* ; Apoptosis/drug effects* ; Apoptosis Regulatory Proteins/immunology* ; Autophagy/immunology* ; bcl-2-Associated X Protein/immunology* ; Bortezomib/therapeutic use* ; Burkitt Lymphoma/immunology* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Drug Therapy, Combination ; Proto-Oncogene Proteins c-akt ; Proto-Oncogene Proteins c-bcl-2 ; Receptors, CXCR/immunology* ; RNA, Messenger ; TOR Serine-Threonine Kinases ; Xanthones/therapeutic use*

Background: The association of serum retinol-binding protein (RBP) levels with nonalcoholic fatty liver disease (NAFLD) remains controversial. Furthermore, few studies have investigated their relationship in type 2 diabetes mellitus (T2DM) patients. Therefore, the aim of the present study was to explore the association between serum RBP levels and NAFLD in Chinese inpatients with T2DM. Methods: This cross-sectional, real-world study included 2,263 Chinese T2DM inpatients. NAFLD was diagnosed by abdominal ultrasonography. The subjects were divided into four groups based on RBP quartiles, and clinical characteristics were compared among the four groups. The associations of both RBP levels and quartiles with the presence of NAFLD were also analyzed. Results: After adjustment for sex, age, and diabetes duration, there was a significant increase in the prevalence of NAFLD from the lowest to the highest RBP quartiles (30.4%, 40.0%, 42.4%, and 44.7% for the first, second, third, and fourth quartiles, respectively, P<0.001 for trend). Fully adjusted multiple logistic regression analysis revealed that both increased RBP levels (odds ratio, 1.155; 95% confidence interval, 1.012 to 1.318; P=0.033) and quartiles (P=0.014 for trend) were independently associated with the presence of NAFLD in T2DM patients. Conclusion Increased serum RBP levels were independently associated with the presence of NAFLD in Chinese T2DM inpatients. Serum RBP levels may be used as one of the indicators to assess the risk of NAFLD in T2DM patients.