Challenges of Predicting Arteriovenous Access Survival Prior to Conversion from Catheter.

OBJECTIVE: The decision to convert from catheter to arteriovenous access is difficult yet very important. The ability to accurately predict fistula survival prior to surgery would significantly improve the decision making process. Many previously investigated demographic and clinical features have been associated with fistula failure. However, it is not conclusively understood how reliable predictions based on these parameters are on an individual level. The aim of this study was to investigate the probability of arteriovenous fistula maturation and survival after conversion using machine learning workflows. METHODS: A retrospective cohort study on multicentre data from a large North American dialysis organisation was conducted. The study population comprised 73 031 chronic in centre haemodialysis patients. The dataset included 49 variables including demographic and clinical features. Two distinct feature selection/prediction pipelines were used: LASSO regression and Boruta followed by a random forest classifier. Predictions were facilitated for re-conversion to catheter within one year. Additionally, all cause mortality predictions were conducted to serve as a comparator. RESULTS: In total, 38 151 (52.2%) had complete data and made up the main cohort. Sensitivity analyses were conducted in 67 421 patients (92.3%) after eliminating variables with a high proportion of missing data points. Selected features diverged between datasets and workflows. A previously failed arteriovenous access appears to be the most stable predictor for subsequent failure. Prediction of re-conversion based on the demographic and clinical information resulted in an area under the receiver operating characteristic curve (ROCAUC) between 0.541 and 0.571, whereas models predicting all cause mortality performed considerably better (ROCAUC 0.662 - 0.683). CONCLUSION: While group level depiction of major adverse outcomes after catheter to arteriovenous fistula/graft conversion is possible using the included variables, patient level predictions are associated with limited performance. Factors during and after fistula creation as well as biomolecular and genetic biomarkers might be more relevant predictors of fistula survival than baseline clinical conditions.

Effects of individualized anemia therapy on hemoglobin stability: a randomized controlled pilot trial in hemodialysis patients.

BACKGROUND: Anemia is common among hemodialysis patients. Maintaining stable hemoglobin levels within predefined target levels can be challenging, particularly in patients with frequent hemoglobin fluctuations both above and below the desired targets. We conducted a multi-center, randomized, controlled trial comparing our anemia therapy assistance software against a standard population-based anemia treatment protocol. We hypothesized that personalized dosing of erythropoiesis stimulating agents (ESA) improves hemoglobin target attainment. METHODS: Ninety-six patients undergoing hemodialysis and receiving methoxy polyethylene glycol-epoetin beta were randomized 1:1 to the intervention group (personalized ESA dose recommendations computed by the software) or the standard of care group for twenty-six weeks. The therapy assistance software combined a physiology-based mathematical model and a model predictive controller designed to stabilize hemoglobin levels within a tight target range (10 to 11 g/dl). The primary outcome measure was the percentage of hemoglobin measurements within the target. Secondary outcome measures included measures of hemoglobin variability and ESA utilization. RESULTS: The intervention group showed an improved median percentage of hemoglobin measurements within target at 47% (IQR 39 to 58), with a 10 percentage points median difference between the two groups (95% CI: 3 to 16; P=0.008). The odds ratio of being within the hemoglobin target in the standard of care group compared to the group receiving the personalized ESA recommendations was 0.68 (95% CI: 0.51 to 0.92). The variability of hemoglobin levels decreased in the intervention group, with the percentage of patients experiencing fluctuating hemoglobin levels being 45% vs 82% in the standard of care group. ESA usage was reduced by about 25% in the intervention group. CONCLUSIONS: Our results demonstrated an improved hemoglobin target attainment and variability by employing personalized ESA recommendations using the physiology-based anemia therapy assistance software.

The upregulation and transcriptional regulatory mechanisms of Extra spindle pole bodies like 1 in bladder cancer: An immunohistochemistry and high-throughput screening Evaluation.

Background: This study aimed to explore the expression level and transcriptional regulation mechanism of Extra Spindle Pole Bodies Like 1 (ESPL1) in bladder cancer (BC). Methods: A multicentre database of samples (n = 1391) was assayed for ESPL1 mRNA expression in BC and validated at the protein level by immunohistochemical (IHC) staining of in-house samples (n = 202). Single-cell sequencing (scRNA-seq) analysis and enrichment analysis explored ESPL1 distribution and their accompanying molecular mechanisms. ATAC-seq, ChIP-seq and Hi-C data from multiple platforms were used to investigate ESPL1 upstream transcription factors (TFs) and potential epigenetic regulatory mechanisms. Immune-related analysis, drug sensitivity and molecular docking of ESPL1 were also calculated. Furthermore, upstream microRNAs and the binding sites of ESPL1 were predicted. The expression level and early screening efficacy of miR-299-5p in blood (n = 6625) and tissues (n = 537) were examined. Results: ESPL1 was significantly overexpressed at the mRNA level (p < 0.05, SMD = 0.75; 95 % CI = 0.09, 1.40), and IHC staining of in-house samples verified this finding (p < 0.0001). ESPL1 was predominantly distributed in BC epithelial cells. Coexpressed genes of ESPL1 were enriched in cell cycle-related signalling pathways, and ESPL1 might be involved in the communication between epithelial and residual cells in the Hippo, ErbB, PI3K-Akt and Ras signalling pathways. Three TFs (H2AZ, IRF5 and HIF1A) were detected upstream of ESPL1 and presence of promoter-super enhancer and promoter-typical enhancer loops. ESPL1 expression was correlated with various immune cell infiltration levels. ESPL1 expression might promote BC growth and affect the sensitivity and therapeutic efficacy of paclitaxel and gemcitabine in BC patients. As an upstream regulator of ESPL1, miR-299-5p expression was downregulated in both the blood and tissues, possessing great potential for early screening. Conclusions: ESPL1 expression was upregulated in BC and was mainly distributed in epithelial cells. Elevated ESPL1 expression was associated with TFs at the upstream transcription start site (TSS) and distant chromatin loops of regulatory elements. ESPL1 might be an immune-related predictive and diagnostic marker for BC, and the overexpression of ESPL1 played a cancer-promoting role and affected BC patients' sensitivity to drug therapy. miR-299-5p was downregulated in BC blood and tissues and was also expected to be a novel marker for early screening.

Enzyme-Empowered "Two Birds with One Stone" Strategy for Amplifying Tumor Apoptosis and Metabolic Clearance.

Nanomedicine has reshaped the landscape of cancer treatment. However, its efficacy is still hampered by innate tumor defense systems that rely on adenosine triphosphate (ATP) for fuel, including damage repair, apoptosis resistance, and immune evasion. Inspired by the naturally enzymatic reaction of glucose oxidase (GOx) with glucose, here a novel "two birds with one stone" technique for amplifying enzyme-mediated tumor apoptosis and enzyme-promoted metabolic clearance is proposed and achieved using GOx-functionalized rhenium nanoclusters-doped polypyrrole (Re@ReP-G). Re@ReP-G reduces ATP production while increasing H2O2 concentrations in the tumor microenvironment through GOx-induced enzymatic oxidation, which in turn results in the downregulation of defense (HSP70 and HSP90) and anti-apoptotic Bcl-2 proteins, the upregulation of pro-apoptotic Bax, and the release of cytochrome c. These processes are further facilitated by laser-induced hyperthermia effect, ultimately leading to severe tumor apoptosis. As an enzymatic byproduct, H2O2 catalyzes the conversion of rhenium nanoclusters in Re@ReP-G nanostructures into rhenate from the outside in, which accelerates their metabolic clearance in vivo. This Re@ReP-G-based "two birds with one stone" therapeutic strategy provides an effective tool for amplifying tumor apoptosis and safe metabolic mechanisms.

Network analysis of spread of SARS-CoV-2 within dialysis clinics: A multi-center network analysis.

BACKGROUND: In-center hemodialysis entails repeated interactions between patients and clinic staff, potentially facilitating the spread of COVID-19. We examined if in-center hemodialysis is associated with the spread of SARS-CoV-2 between patients. METHODS: Our retrospective analysis comprised all patients receiving hemodialysis in four New York City clinics between March 12th, 2020, and August 31st, 2022. Treatment-level clinic ID, dialysis shift, dialysis machine station, and date of COVID-19 diagnosis by RT-PCR were documented. To estimate the donor-to-potential recipient exposure ("donor" being the COVID-19 positive patient denoted as "COV-Pos"; "potential recipient" being other susceptible patients in the same shift), we obtained the spatial coordinates of each dialysis station, calculated the Euclidean distances between stations and weighted the exposure by proximity between them. For each donor, we estimated the donor-to-potential recipient exposure of all potential recipients dialyzed in the same shift and accumulated the exposure over time within the 'COV-Pos infectious period' as cumulative exposures. The 'COV-Pos infectious period' started 5 days before COVID-19 diagnosis date. We deployed network analysis to assess these interactions and summarized the donor-to-potential recipient exposure in 193 network diagrams. We fitted mixed effects logistic regression models to test whether more donor-to-potential recipient exposure conferred a higher risk of SARS-CoV-2 infection. RESULTS: Out of 978 patients, 193 (19.7%) tested positive for COVID-19 and had contact with other patients during the COV-Pos infectious period. Network diagrams showed no evidence that more exposed patients would have had a higher chance of infection. This finding was corroborated by logistic mixed effect regression (donor-to-potential recipient exposure OR: 0.63; 95% CI 0.32 to 1.17, p = 0.163). Separate analyses according to vaccination led to materially identical results. CONCLUSIONS: Transmission of SARS-CoV-2 between in-center hemodialysis patients is unlikely. This finding supports the effectiveness of non-pharmaceutical interventions, such as universal masking and other procedures to control spread of COVID-19.

OX40L-expressing M1-like macrophage exosomes for cancer immunotherapy.

With only limited clinical patient benefit, focusing on new immune checkpoint pathways could be an important complement to current immune checkpoint drugs. In addition, not only does T cell-mediated adaptive immunity play an important role, but also macrophage-mediated innate immunity, due to its abundant presence in solid tumors. Here, we developed an engineered M1-like macrophage exosome, OX40L M1-exos. OX40L M1-exos can activate the adaptive immunity by activating the OX40/OX40L pathway and can reprogram M2-like tumor-associated macrophages into M1-like macrophages, thereby restoring and enhancing macrophage-mediated innate immunity. Our OX40L M1-exos achieved an effective synergistic effect of innate and adaptive immunity and achieved a potent therapeutic effect in a mouse breast cancer model, effectively inhibiting tumor growth and metastasis. These results suggest that OX40L M1-exos are an attractive therapeutic strategy and may be an important complement to current cancer immunotherapies.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Assembly and co-occurrence patterns of rhizosphere bacterial communities are closely linked to soil fertility during continuous cropping of cut chrysanthemum (Chrysanthemum morifolium Ramat).

AIMS: Continuous cropping is known to have profound effects on the soil microbial community in different planting systems. However, we lack an understanding of how different years of continuous cropping affects rhizosphere soil bacterial community co-occurrence pattern and assembly processes in the cut chrysanthemum (Chrysanthemum morifolium Ramat.) field. METHODS AND RESULTS: We collected the soils from cut chrysanthemum rhizospheres with planting for 1 year (PY1) and continuous cropping for 6 years (CY6) and 12 years (CY12). Real-time quantitative PCR and flow cytometry (FCM) techniques were used to test the 16S rRNA gene copy number and bacterial cell count, respectively. The bacterial community structure was analysed by using high-throughput sequencing technology. The CY12 had a significantly decreased soil fertility index and rhizosphere bacterial living cell counts and gene copy numbers compared to CY6 and PY1 (P < 0.05). The rhizosphere bacterial community dissimilarity increased as the continuous cropping years increased. Three main ecological clusters (modules #1, #2, and #3) were observed in the bacterial co-occurrence network across all samples, and only the relative abundance of module #1 (enriched in the CY12) was significantly correlated with soil fertility (P < 0.05). Moreover, the rhizosphere bacterial community assembly was primarily governed by the deterministic process under 12 years of continuous cropping. CONCLUSIONS: Soil fertility decline correlates with ecological network modularization and the deterministic assembly process of the rhizosphere bacterial community of cut chrysanthemum during continuous cropping.

Interactions between intradialytic central venous oxygen saturation, relative blood volume, and all-cause mortality in maintenance hemodialysis patients.

INTRODUCTION: In maintenance hemodialysis (HD) patients, low central venous oxygen saturation (ScvO2 ) and small decline in relative blood volume (RBV) have been associated with adverse outcomes. Here we explore the joint association between ScvO2 and RBV change in relation to all-cause mortality. METHODS: We conducted a retrospective study in maintenance HD patients with central venous catheters as vascular access. During a 6-month baseline period, Crit-Line (Fresenius Medical Care, Waltham, MA) was used to measure continuously intradialytic ScvO2 and hematocrit-based RBV. We defined four groups per median change of RBV and median ScvO2 . Patients with ScvO2 above median and RBV change below median were defined as reference. Follow-up period was 3 years. We constructed Cox proportional hazards model with adjustment for age, diabetes, and dialysis vintage to assess the association between ScvO2 and RBV and all-cause mortality during follow-up. FINDINGS: Baseline comprised 5231 dialysis sessions in 216 patients. The median RBV change was -5.5% and median ScvO2 was 58.8%. During follow-up, 44 patients (20.4%) died. In the adjusted model, all-cause mortality was highest in patients with ScvO2 below median and RBV change above median (HR 6.32; 95% confidence interval [CI] 1.37-29.06), followed by patients with ScvO2 below median and RBV change below median (HR 5.04; 95% CI 1.14-22.35), and ScvO2 above median and RBV change above median (HR 4.52; 95% CI 0.95-21.36). DISCUSSION: Concurrent combined monitoring of intradialytic ScvO2 and RBV change may provide additional insights into a patient's circulatory status. Patients with low ScvO2 and small changes in RBV may represent a specifically vulnerable group of patients at particularly high risk for adverse outcomes, possibly related to poor cardiac reserve and fluid overload.

Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.

BACKGROUND: In maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates. METHODS: We developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance. IDH was defined as systolic blood pressure (SBP) <90 mmHg. Demographic, clinical, treatment-related and laboratory data were retrieved from electronic health records and merged with intradialytic machine data that were sent in real-time to the cloud. For model development, dialysis sessions were randomly split into training (80%) and testing (20%) sets. The area under the receiver operating characteristic curve (AUROC) was used as a measure of the model's predictive performance. RESULTS: We utilized data from 693 patients who contributed 42 656 hemodialysis sessions and 355 693 intradialytic SBP measurements. IDH occurred in 16.2% of hemodialysis treatments. Our model predicted IDH 15-75 min in advance with an AUROC of 0.89. Top IDH predictors were the most recent intradialytic SBP and IDH rate, as well as mean nadir SBP of the previous 10 dialysis sessions. CONCLUSIONS: Real-time prediction of IDH during an ongoing hemodialysis session is feasible and has a clinically actionable predictive performance. If and to what degree this predictive information facilitates the timely deployment of preventive interventions and translates into lower IDH rates and improved patient outcomes warrants prospective studies.

[Teaching reform and practice of 'Production Internship' course for biotechnology specialty from the perspective of training application-oriented talents].

Production internship is an important teaching tache for undergraduate students to carry out engineering training by using professional skills, and it is a key starting point for fostering application-oriented talents in biotechnology. The Course Group of 'production internship of biotechnology majors' of Binzhou University is investigating application-oriented transformation for local regular colleges and universities, as well as fostering high-level application-oriented talents. By taking green fluorescent protein (GFP) polyclonal antibody as an example, the reform and practice on teaching content, teaching mode, assessment method, continuous improvement of curriculum were carried out. Moreover, the characteristics of the Yellow River Delta-Binzhou Biotechnology & Pharmaceutical Industrial Cluster were taken into account to intensify academic-enterprise cooperation. On one hand, this Course Group designed and rearranged the course contents, carried out essential training through online resources and platforms such as virtual simulation, and recorded, tracked and monitored the progress of production internship through practical testing and software platforms like 'Alumni State'. On the other hand, this Course Group established a practice-and application-oriented assessment method in the process of production internship and a dual evaluation model for continuous improvement. These reform and practices have promoted the training of application-oriented talents in biotechnology, and may serve as a reference for similar courses.

Deep Learning for Image Analysis in Kidney Care.

Analysis of medical images, such as radiological or tissue specimens, is an indispensable part of medical diagnostics. Conventionally done manually, the process may sometimes be time-consuming and prone to interobserver variability. Image classification and segmentation by deep learning strategies, predominantly convolutional neural networks, may provide a significant advance in the diagnostic process. In renal medicine, most evidence has been generated around the radiological assessment of renal abnormalities and histological analysis of renal biopsy specimens' segmentation. In this article, the basic principles of image analysis by convolutional neural networks, brief descriptions of convolutional neural networks, and their system architecture for image analysis are discussed, in combination with examples regarding their use in image analysis in nephrology.

Omics and Artificial Intelligence in Kidney Diseases.

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

TGFß signaling activation correlates with immune-inflamed tumor microenvironment across human cancers and predicts response to immunotherapy.

Considering the determining role of TGFß signaling in the tumor microenvironment (TME) on immune evasion, the inhibition of signaling is expected to enhance the therapeutic efficacy of immunotherapies, especially immune checkpoint blockade (ICB), which is confirmed in preclinical data. However, successive failures in clinical translation occur at the initial stage. To provide a better understanding of TGFß signaling within the TME and its relation to the individual immunological status, we performed a pan-cancer analysis comparing the activation of TGFß pathway among different TMEs based on multi-omics data. Compared with non-inflamed tumors, increased TGFß signaling activity appeared in four non-cancer cell types within TME in inflamed tumors. Significant correlations were revealed between TGFß signaling and reliable biomarkers for ICB therapy, as well as between TGFß signaling and HPV status. Our findings contribute to explain the inconsistency between preclinical and clinical research, and are crucial to optimizing upcoming clinical trial design and improving patient stratification for personalized prediction.

The marriage of Xenes and hydrogels: Fundamentals, applications, and outlook.

Hydrogels have blossomed as superstars in various fields, owing to their prospective applications in tissue engineering, soft electronics and sensors, flexible energy storage, and biomedicines. Two-dimensional (2D) nanomaterials, especially 2D mono-elemental nanosheets (Xenes) exhibit high aspect ratio morphology, good biocompatibility, metallic conductivity, and tunable electrochemical properties. These fascinating characteristics endow numerous tunable application-specific properties for the construction of Xene-based hydrogels. Hierarchical multifunctional hydrogels can be prepared according to the application requirements and can be effectively tuned by different stimulation to complete specific tasks in a spatiotemporal sequence. In this review, the synthesis mechanism, properties, and emerging applications of Xene hydrogels are summarized, followed by a discussion on expanding the performance and application range of both hydrogels and Xenes.

Identification of arterial oxygen intermittency in oximetry data.

In patients with kidney failure treated by hemodialysis, intradialytic arterial oxygen saturation (SaO2) time series present intermittent high-frequency high-amplitude oximetry patterns (IHHOP), which correlate with observed sleep-associated breathing disturbances. A new method for identifying such intermittent patterns is proposed. The method is based on the analysis of recurrence in the time series through the quantification of an optimal recurrence threshold ([Formula: see text]). New time series for the value of [Formula: see text] were constructed using a rolling window scheme, which allowed for real-time identification of the occurrence of IHHOPs. The results for the optimal recurrence threshold were confronted with standard metrics used in studies of obstructive sleep apnea, namely the oxygen desaturation index (ODI) and oxygen desaturation density (ODD). A high correlation between [Formula: see text] and the ODD was observed. Using the value of the ODI as a surrogate to the apnea-hypopnea index (AHI), it was shown that the value of [Formula: see text] distinguishes occurrences of sleep apnea with great accuracy. When subjected to binary classifiers, this newly proposed metric has great power for predicting the occurrences of sleep apnea-related events, as can be seen by the larger than 0.90 AUC observed in the ROC curve. Therefore, the optimal threshold [Formula: see text] from recurrence analysis can be used as a metric to quantify the occurrence of abnormal behaviors in the arterial oxygen saturation time series.

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.