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This study explores the feasibility, safety, and efficacy of percutaneous transluminal angioplasty (PTA) for reinserting tunneled cuffed catheters (TCC) with a Dacron sheath in the right internal jugular vein (RIJV) in hemodialysis patients with a history of prior RIJV catheterization and subsequent stenosis or occlusion of the RIJV, right innominate vein, and superior vena cava. Clinical data from 21 hemodialysis patients with dysfunctional vascular access who underwent PTA for reinsertion of TCC in the RIJVs from July 2020 to July 2023 at the First and Second Affiliated Hospitals of Bengbu Medical College were retrospectively analyzed. Clinical efficacy during hospitalization, postoperative TCC blood flow, and related complications during follow-up were observed. The procedure was successful in all 21 patients, with postoperative TCC blood flow meeting daily hemodialysis requirements. Only one case experienced acute bleeding with contrast agent extravasation at the intersection of the left and right innominate veins during sharp recanalization. No severe complications, such as arrhythmias, vascular rupture, pneumothorax, mediastinal hematoma, or pericardial tamponade, occurred during the procedures. Upon discharge, all patients exhibited satisfactory TCC blood flow (247.14 ± 11.46 ml/min). Postoperatively, TCC blood flow ranged between 200 and 260 ml/min, meeting the demands of regular hemodialysis. For patients with a history of repeated TCC or non-tunneled catheter (NTC) placement in the RIJV, reinserting TCC in the RIJVs through PTA is a safe and reliable technique. It effectively utilizes vascular resources and prevents vascular resource depletion associated with changing the venous catheter placement location.

Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results. The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion. The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD), and its early diagnosis remains a major global challenge because conventional biomarkers lack sensitivity. The East Asian population is characterized by distinct genetic, environmental, and lifestyle factors that may influence the development and progression of DKD, highlighting the importance of population-specific research. The primary objective of this study was to apply a multi-omics strategy, including Mendelian randomization (MR) analysis, within an East Asian cohort to investigate potential causal relationships among microbiota, metabolites, and DKD, with the aim of identifying candidate biomarkers relevant to this population. Secondary objectives included the analysis of clinical samples from East Asian participants to characterize microbiota composition, metabolomic profiles, and tongue image features (TIFs), as well as the development of machine learning (ML) models to distinguish patients with type 2 diabetes mellitus (T2DM) from those with DKD. MR analysis was performed to investigate potential causal associations between more than 190 microbiota taxa and 404 differential metabolites in relation to DKD within the East Asian cohort. Clinical samples (n = 535) were collected from East Asian individuals and analyzed for microbiota composition, metabolomic profiling, and TIFs. Subsequently, ML models were constructed to differentiate patients with T2DM from those with DKD in this cohort. MR analysis identified significant associations between specific microbiota taxa (e.g., Haemophilus-A, TM7x, Lachnoanaerobaculum, and Bacteroides) and metabolites (e.g., tyrosine and glutamine) in relation to DKD within the East Asian cohort. However, the causal nature of these associations requires further experimental or longitudinal validation. Clinical analyses revealed microbial dysbiosis in patients with DKD, including a 2.5-fold increase in Klebsiella and a 60% reduction in Faecalibaculum and Dubosiella. Metabolomic profiling demonstrated alterations in branched-chain amino acids (BCAAs) and fatty acids. Integrated multi-omics analysis suggested complex interactions among microbiota and metabolites that may contribute to DKD progression. The ML models achieved an accuracy exceeding 90% in distinguishing T2DM from DKD in the East Asian cohort. Multi-omics integration combined with ML may provide candidate biomarkers for the early detection of DKD in the East Asian population. These approaches could improve the accuracy of non-invasive diagnosis and support the development of personalized management strategies. Nevertheless, further studies are required to validate the identified associations and confirm their clinical applicability in real-world East Asian settings.

Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 ( P  < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. Conclusions The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

Background: Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnosis criteria, it is often neglected in clinical diagnosis, especially in the early disease stage. Many clinical practices and research have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptoms, indexes, and tongue & pulse data is of great significance for timely and effective clinical treatment. Methods: In this study, 2632 physical examination populations were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out the core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue populations. Pajek software was used to construct the core symptoms/indexes network and core symptoms-indexes combined network. Simultaneously, the canonical correlation analysis method was used to analyze the objective tongue & pulse data between the two groups of fatigue population and analyze the distribution of tongue & pulse data. Results: Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue & pulse data in the disease fatigue group was 0.42 (P < 0.05). On the contrast, correlation analysis of tongue & pulse in the sub-health fatigue group showed no statistical significance. Conclusions: The complex network technology was suitable for the correlation analysis of symptoms and indexes in the fatigue population, and the tongue & pulse data had a certain diagnostic contribution to the classification of the fatigue population. Name of the registry: Chinese Clinical Trial Registry Trial registration number: ChiCTR-IOR-15006502; ChiCTR1900026008 Date of registration: Jun. 04th, 2015 URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119; http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

The far-reaching impacts of central Pacific El Niño events on global climate differ appreciably from those associated with eastern Pacific El Niño events. Central Pacific El Niño events may become more frequent in coming decades as atmospheric greenhouse gas concentrations rise, but the instrumental record of central Pacific sea-surface temperatures is too short to detect potential trends. Here we present an annually resolved reconstruction of NIÑO4 sea-surface temperature, located in the central equatorial Pacific, based on oxygen isotopic time series from Taiwan tree cellulose that span from 1190 AD to 2007 AD. Our reconstruction indicates that relatively warm Niño4 sea-surface temperature values over the late twentieth century are accompanied by higher levels of interannual variability than observed in other intervals of the 818-year-long reconstruction. Our results imply that anthropogenic greenhouse forcing may be driving an increase in central Pacific El Niño-Southern Oscillation variability and/or its hydrological impacts, consistent with recent modelling studies. El Niño events in the Central Pacific may be changing due to climate change, but long records to support this are lacking. Here, the authors present sea surface temperature reconstructions from tree cellulose for the last 800 years which suggest the variability of Central Pacific El Niño events has increased.

Background Ultrasonograpic retrobulbar optic nerve sheath diameter (ONSD) measurement is considered to be an alternative noninvasive method to estimate intracranial pressure,but the further validation is urgently needed. The aim of the current study was to investigate the association of the ultrasonographic ONSD and intracranial pressure (ICP) in patients. Methods One hundred and ten patients whose intracranial pressure measured via lumbar puncture were enrolled in the study. Their retrobulbar ONSD with B-scan ultrasound was determined just before lumber puncture. The correlation between the ICP and the body mass index (BMI), ONSD or age was established respectively with the Pearson correlation coefficient analysis. The discriminant analysis was used to obtain a discriminant formula for predicting ICP with the ONSD、BMI、gender and age. Another 20 patients were recruited for further validation the efficiency of this discriminant equation. Results The mean ICP was 215.3 ± 81.2 mmH 2 O. ONSD was 5.70 ± 0.80 mm in the right eye and 5.80 ± 0.77 mm in the left eye. A significant correlation was found between ICP and BMI ( r  = 0.554, p  < 0.001), the mean ONSD ( r  = 0.61, P  < 0.001), but not with age ( r  = −0.131, p  = 0.174) and gender ( r  = 0.03, p  = 0.753). Using receiver operating characteristic (ROC) curve analysis, the critical value for the risk mean-ONSD was 5.6 mm from the ROC curve, with the sensitivity of 86.2% and specificity of 73.1%. With 200 mmH 2 O as the cutoff point for a high or low ICP, stepwise discriminant was applied, the sensitivity and specificity of ONSD predicting ICP was 84.5%-85.7% and 86.5%-92.3%. Conclusions Ophthalmic ultrasound measurement of ONSD may be a good surrogate of invasive ICP measurement. This non-invasive method may be an alternative approach to predict the ICP value of patients whose ICP measurement via lumbar puncture are in high risk. The discriminant formula, which incorporated the factor of BMI, had similar sensitivity and higher specificity than the ROC curve.

Objectives: This study aimed to develop and validate a biparametric MRI (bpMRI)-based radiomics model for the noninvasive prediction of tumor–stroma ratio (TSR) in prostate cancer (PCa). Additionally, we sought to explore lesion distribution patterns in the peripheral zone (PZ) and transition zone (TZ) to provide deeper insights into the biological behavior of PCa. Methods: This multicenter retrospective study included 223 pathologically confirmed PCa patients, with 146 for training and 39 for internal validation at Center 1, and 38 for external testing at Center 2. All patients underwent preoperative bpMRI (T2WI, DWI acquired with a b-value of 1400 s/mm2, and ADC maps), with TSR histopathologically quantified. Regions of interest (ROIs) were manually segmented on bpMRI images using ITK-SNAP software (version 4.0.1), followed by high-throughput radiomic feature extraction. Redundant features were eliminated via Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression. Five machine learning (ML) classifiers—Logistic Regression (LR), Support Vector Machine (SVM), BernoulliNBBayes, Ridge, and Stochastic Gradient Descent (SGD)—were trained and optimized. Model performance was rigorously evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The Ridge demonstrated superior diagnostic performance, achieving AUCs of 0.846, 0.789, and 0.745 in the training, validation, and test cohorts, respectively. Lesion distribution analysis revealed no significant differences between High-TSR and Low-TSR groups (p = 0.867), suggesting that TSR may not be strongly associated with zonal localization. Conclusions: This exploratory study suggests that a bpMRI-based radiomic model holds promise for noninvasive TSR estimation in prostate cancer and may provide complementary insights into tumor aggressiveness beyond conventional pathology.

To investigate the associations between retinal parameters and cognitive impairment and identify retinal biomarkers for detecting cognitive dysfunction in patients with cerebral small vessel disease (CSVD). This cross-sectional study enrolled 125 patients with CSVD-related white matter hyperintensities. Participants were independent in daily activities and free of significant ophthalmic diseases. Cognition was assessed using the Mini-Mental State Examination (MMSE). Multimodal retinal imaging was employed to evaluate retinal structural and vascular features, including peripapillary retinal nerve fibre layer (pRNFL) and ganglion cell complex thicknesses and vessel densities (VDs) of the radial peripapillary capillary (RPC) network, macular superficial retinal capillary plexus (SRCP), and deep RCP (DRCP). The central retina vascular diameter, mean angle, angle tortuosity, arc length tortuosity, and fractal dimension of the retinal arteries and veins were quantified using a fully automated vessel segmentation and parameter calculation method. Linear and logistic regression models were applied to assess the associations between retinal parameters with general cognitive domains (general cognition, memory, visuospatial space, attention and numeracy, language, and orientation). Receiver operating characteristic (ROC) curves and areas under the ROC curve (AUCs) were used to evaluate the predictive performance of the retinal biomarkers. A smaller central retinal arteriolar equivalent/central retinal venular equivalent ratio [odds ratio (OR) = 0.013,  = 0.035] and lower VDs in whole macular SRCP (OR = 0.864,  = 0.029), perifoveal SRCP (OR = 0.862,  = 0.027), perifoveal superior SRCP (OR = 0.855,  = 0.014), perifoveal nasal SRCP (OR = 0.833,  = 0.008), and inside-disc capillary RPC network (OR = 0.901,  = 0.028) were significantly associated with lower MMSE scores. Attention and orientation scores were significantly correlated with the VDs of the SRCP (  = 0.734,  = 0.027 and β = -9.460,  = 0.037, respectively) and DRCP (  = -0.553,  = 0.004 and  = 0.044, respectively), whereas visuospatial and language function scores were associated with the VD of the inside-disc RPC network (  = 0.018,  = 0.026 and β = 0.068,  = 0.012, respectively). The VDs of the whole macular, perifoveal, perifoveal superior, and nasal SRCPs achieved AUCs of 0.71-0.74 for screening cognitive impairment. Specific retina parameters are associated with cognitive decline in patients with CSVD. These findings suggest that multimodal retinal evaluation might provide an objective, imaging-based adjunct to conventional subjective cognitive tests for screening cognitive impairment in patients with CSVD. https://www.chictr.org.cn/, identifier ChiCTR 2100043346.