AIM: To investigate the correlation of the expression of stromal cell-derived factor-1(SDF-1)and angiopoietin like protein 4(ANGPTL4)in serum with the severity of disease in patients with diabetic macular edema(DME).METHODS: From April 2020 to August 2023, 193 patients with diabetic retinopathy who were admitted to our hospital were prospectively separated into DME group(128 cases)(56 cases in mild group, 44 cases in moderate group, 28 cases in severe group)and non DME group(65 cases)according to whether the patients had macular edema and the severity of disease. Enzyme-linked immunosorbent assay(ELISA)was applied to determine the levels of ANGPTL4 and SDF-1 in serum. Multivariate Logistic regression was applied to analyze the factors that affected the severity of DME; receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of ANGPTL4 and SDF-1 levels in serum of DME patients for the severity of DME.RESULTS: The levels of ANGPTL4 and SDF-1 in serum of the DME group were obviously higher than those of the non DME group(P<0.01); the expression levels of ANGPTL4 and SDF-1 in serum of the mild, moderate, and severe groups increased obviously in sequence(P<0.05); multivariate Logistic regression analysis showed that the levels of ANGPTL4 and SDF-1 in serum were risk factors affecting the severity of DME(P<0.01); The area under the curve(AUC)of serum SDF-1 in the diagnosis of DME severity was 0.772(95%CI: 0.690-0.842), and the AUC of ANGPTL4 in the diagnosis of DME severity was 0.801(95%CI: 0.722-0.867). The AUC of ANGPTL4 combined with SDF-1 in the diagnosis of DME was 0.884(95%CI: 0.816-0.934), the sensitivity was 87.50%, and the specificity was 85.71%, which were significantly higher than ANGPTL4 or SDF-1 alone(Z=2.658, 2.469, all P<0.05).CONCLUSION: The levels of ANGPTL4 and SDF-1 in serum of DME patients are significantly increased, and their levels increase with the severity of the disease. They can be used as auxiliary indicators for diagnosing the severity of DME disease, and the combined diagnosis has a better effect.

AIM: To investigate the correlation of the expression of stromal cell-derived factor-1(SDF-1)and angiopoietin like protein 4(ANGPTL4)in serum with the severity of disease in patients with diabetic macular edema(DME).METHODS: From April 2020 to August 2023, 193 patients with diabetic retinopathy who were admitted to our hospital were prospectively separated into DME group(128 cases)(56 cases in mild group, 44 cases in moderate group, 28 cases in severe group)and non DME group(65 cases)according to whether the patients had macular edema and the severity of disease. Enzyme-linked immunosorbent assay(ELISA)was applied to determine the levels of ANGPTL4 and SDF-1 in serum. Multivariate Logistic regression was applied to analyze the factors that affected the severity of DME; receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of ANGPTL4 and SDF-1 levels in serum of DME patients for the severity of DME.RESULTS: The levels of ANGPTL4 and SDF-1 in serum of the DME group were obviously higher than those of the non DME group(P<0.01); the expression levels of ANGPTL4 and SDF-1 in serum of the mild, moderate, and severe groups increased obviously in sequence(P<0.05); multivariate Logistic regression analysis showed that the levels of ANGPTL4 and SDF-1 in serum were risk factors affecting the severity of DME(P<0.01); The area under the curve(AUC)of serum SDF-1 in the diagnosis of DME severity was 0.772(95%CI: 0.690-0.842), and the AUC of ANGPTL4 in the diagnosis of DME severity was 0.801(95%CI: 0.722-0.867). The AUC of ANGPTL4 combined with SDF-1 in the diagnosis of DME was 0.884(95%CI: 0.816-0.934), the sensitivity was 87.50%, and the specificity was 85.71%, which were significantly higher than ANGPTL4 or SDF-1 alone(Z=2.658, 2.469, all P<0.05).CONCLUSION: The levels of ANGPTL4 and SDF-1 in serum of DME patients are significantly increased, and their levels increase with the severity of the disease. They can be used as auxiliary indicators for diagnosing the severity of DME disease, and the combined diagnosis has a better effect.

OBJECTIVE: To investigate the application value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) in etiological diagnosis of children with hematological malignancies complicated with pneumonia. METHODS: We retrospectively analyzed the clinical data of children with hematological malignancies who underwent BALF mNGS pathogenic detection due to pneumonia. All patients underwent mNGS detection of bronchoalveolar lavage fluid as well as traditional methods(including sputum culture, bronchoalveolar lavage fluid culture, blood culture, serological detection of pathogens, etc.). By analyzing the results of mNGS and traditional methods, we compared key indicators such as the positive rate, etiological distribution. RESULTS: A total of 26 children with hematological malignancies enrolled in the study, including 12 males and 14 females, with a median age of 4.9 (1.8-14.9) years, underwent bronchoalveolar lavage (BAL) 35 times. A total of 17 pathogenic microorganisms were detected in BALF mNGS, including 9 cases of bacterial infection, 10 cases of viral infection, 3 cases of fungal infection, 2 cases of mycoplasma infection and 8 cases of mixed infection, and the most commonly detected bacteria, viruses and fungi were streptococcus pneumoniae, cytomegalovirus and pneumocystis jirovecii, respectively. The positive rate of mNGS detection (91.43%) was significantly higher than that of traditional methods detection (20%, P <0.001). A total of 25 cases were adjusted according to BALF mNGS results. CONCLUSION The application of BALF mNGS technology can improve the detection rate of the pathogens in children with hematological malignancies complicated with pneumonia, initially revealed the pathogen spectrum of pulmonary infection in this group, and effectively guide clinical medication, improve treatment outcomes.
Humans ; Bronchoalveolar Lavage Fluid/microbiology* ; Hematologic Neoplasms/complications* ; Child ; Child, Preschool ; Infant ; Retrospective Studies ; Male ; Female ; Adolescent ; High-Throughput Nucleotide Sequencing ; Pneumonia/microbiology*

OBJECTIVE: To retrospectively analyze the clinical characteristics, prognosis and prognostic factors of patients with light-chain (AL) amyloidosis, so as to provide reference for the diagnosis and treatment of AL amyloidosis. METHODS: Clinical data of 52 patients diagnosed with AL amyloidosis at two hospitals from January 2017 to November 2022 were collected. The clinical characteristics, differences in clinical indexes between the deceased group and the survival group were analyzed. Kaplan-Meier curves were used for overall survival (OS) analysis, and Cox regression models were used to analyze the factors affecting the prognosis. RESULTS: The median age of the 52 patients at diagnosis was 61(41-81) years old, and 63.5% of the patients were male. Heart (69.2%) and kidney (67.3%) were the most involved organs, and 67.3% of the patients had two or more organs involved. Most patients (71.2%) received chemotherapy regimens containing bortezomib, including 5 patients (9.6%) who received treatment with daratumumab in combination with bortezomib. The proportion of male patients (81.0%), the proportion of patients with cardiac involvement (95.2%), and the proportion of patients with Mayo 2012 stage ≥III (95.2%), as well as the levels of hs-cTnI and NT-proBNP in the deceased group were significantly higher than those in the survival group ( P < 0.05). The median OS time of the enrolled patients was 33.4(2.6-60.2) months, with 1-year, 2-year, 3-year and 5-year OS rates of 83.7%, 79.3%, 58.9% and 32.7%, respectively. The Kaplan-Meier survival curve analysis revealed that patients with male gender (P =0.040), NT-proBNP ≥3 600 ng/L ( P < 0.001), Mayo 2012 stage ≥III ( P < 0.001), and cardiac involvement (P =0.008) had poor prognosis and shorter overall survival (OS) time. The multivariate regression analysis showed that Mayo 2012 stage ≥III was an independent risk factor for prognosis. CONCLUSION In recent years, the survival rate of patients with AL amyloidosis has improved significantly, but the 5-year survival rate is still relatively low. Cardiac biomarkers (NT-proBNP and hs-cTnI) and Mayo 2012 stage at diagnosis continue to provide important prognostic information. Bortezomib-based regimens were used as the primary treatment in most patients, and the addition of daratumumab is becoming increasingly common.
Humans ; Retrospective Studies ; Prognosis ; Middle Aged ; Male ; Female ; Aged ; Immunoglobulin Light-chain Amyloidosis/diagnosis* ; Adult ; Aged, 80 and over ; Kaplan-Meier Estimate

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Objective To explore the changes and clinical significance of D-dimer(D-D),prothrombin fragment 1+2(F1+2)and P-selectin in patients with acute deep venous thrombosis of lower extremities(DVT)before and after catheterization and thrombolysis.Methods A total of 186 patients with acute DVT in the Third People's Hospital of Yunnan Province from March 2020 to March 2022 were selected as the study objects.And all of them underwent catheterization and hemolysis and were followed up in the outpatient form 12 months after the surgery.4 cases were lost to follow-up,and a total of 182 cases completed postoperative follow-up.Postthrombotic syndrome(PTS)was divided into PTS group(n = 27)and non-PTS group(n = 155)according to whether post-thrombotic syndrome(PTS)occurred 12 months after the surgery.The general data of the two groups and the expression of D-D,F1+2,P-selectin in plasma before and after thrombolytic therapy were compared,and the influencing factors of PTS were analyzed by Logistic analysis.Receiver operating characteristic curve(ROC)and area under curve(AUC)were plotted to analyze the value of plasma D-D,F1+2,P-selectin in predicting the occurrence of PTS,and relative risk(RR)was used to analyze the influence of different plasma D-D,F1+2,P-selectin expression on PTS.Results Age,BMI,venous patency score,and plasma D-D,F1+2,P-selectin expression 1 week and 1 month after thrombolysis in PTS group were higher than those in non-PTS group(P<0.05).Logistic showed that BMI and plasma D-D,F1+2 and P-selectin 1 week and 1 month after thrombolysis were the influential factors for PTS in acute DVT patients(P<0.05).ROC curve showed that the combined efficacy of D-D,F1+2 and P-selectin 1 month after thrombolysis was significantly better than that of D-D,F1+2 and P-selectin 1 week after thrombolysis in predicting PTS.One month after thrombolysis,the risk of PTS in patients with high plasma D-D,F1+2,P-selectin expression was 4.211,2.550 and 3.189 times higher than that in patients with low plasma D-D,F1+2,P-selectin expression.Conclusion The expression of D-D,F1+2 and P-selectin in plasma increases after thrombolysis in acute DVT patients,and the combination of D-D,F1+2 and P-selectin can predict the occurrence of PTS.

Fluorescence anisotropy(FA)analysis has many advantages such as no requirement of separation,high throughput and real-time detection,and thus has been widely used in many fields,including biochemical analysis,food safety detection,environmental monitoring,etc.However,due to the small volume or mass of the target,its combination with the fluorescence probe cannot produce significant signal change.To solve this issue,researchers often use nanomaterials to enhance the mass or volume of fluorophore to improve the sensitivity.Nevertheless,this FA amplification strategy also has some disadvantages.Firstly,nanomaterials are easy to quench fluorescence.As a result,the FA value is easily influenced by light scattering,which reduces the detection accuracy.Secondly,fluorescent probes in most methods require complex modification steps.Therefore,it is necessary to develop new FA probes that do not require the amplification of volume and mass or modification.As a new kind of nanomaterials,luminescent metal-organic framework(MOF)has a large volume(or mass)and strong fluorescence emission.It does not require additional signal amplification materials.As a consequence,it can be used as a potential FA probe.This study successfully synthesized a lanthanide metal organic framework(Ce-TCPP MOF)using cerium ion(Ce3+)as the central ion and 5,10,15,20-tetra(4-carboxylphenyl)porphyrin(H2TCPP)as the ligand through microwave assisted method,and used it as a novel unmodified FA probe to detect phosphate ions(Pi).In the absence of Pi,Ce-TCPP MOF had a significant FA value(r).After addition of Pi,Pi reacted with Ce3+in MOF and destroyed the structure of MOF into the small pieces,resulting in a decrease in r.The experimental results indicated that with the increase of Pi concentration,the change of the r of Ce-TCPP MOF(Δr)gradually increased.The Δr and Pi concentration showed a good linear relationship within the range of 0.5-3.5 μmol/L(0.016-0.108 mg/L).The limit of detection(LOD,3σ/k)was 0.41 μmol/L.The concentration of Pi in the Jialing River water detected by this method was about 0.078 mg/L,and the Pi value detected by ammonium molybdate spectrophotometry was about 0.080 mg/L.The two detection results were consistent with each other,and the detection results also meet the ClassⅡwater quality standard,proving that this method could be used for the detection of Pi in complex water bodies.

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.