Factors affecting suboptimal maturation of autogenous arteriovenous fistula in elderly patients with diabetes:A narrative review.

Autogenous arteriovenous fistula (AVF) is considered the preferred vascular access choice for individuals undergoing maintenance hemodialysis (MHD) and is widely utilized in China, as reported by the Dialysis Outcomes and Practice Patterns Study. Despite its popularity, the significant incidence of poor AVF maturation often leads to the need for central venous catheter insertion, increasing the risk of complications like superior vena cava stenosis and catheter-related infections, which in turn raises the overall mortality risk. With the prevalence of diabetes rising globally among the elderly and diabetic kidney disease being a leading cause of end-stage renal disease necessitating renal replacement therapy, our retrospective review aims to explore the various factors affecting AVF maturation in this specific patient population. While there have been numerous studies examining AVF complications in MHD patients, including issues like failure, patency loss, stenosis, thrombosis, poor maturation, and other influencing factors, there remains a gap in large-scale clinical studies focusing on the incidence and risk factors for immature AVF specifically in elderly diabetic patients. This paper delves into the pathophysiological mechanisms, diagnostic criteria, and unique considerations surrounding AVF maturation in elderly diabetic patients, distinguishing them from the general population. Our literature review reveals that elderly diabetic patients exhibit a higher risk of AVF immaturity compared to the general population. Additionally, there exists a continuing discourse regarding several aspects related to this group, including the choice of dialysis access, timing of AVF surgery, and surgical site selection. Furthermore, we delve into the management strategies for vascular access within this specific group with the goal of providing evidence-based guidance for the establishment and maintenance of functional vascular access in elderly diabetic patients.

The impact of dynamic caudal type homeobox 2 expression on the differentiation of human trophoblast lineage during implantation.

The trophoblast lineage differentiation represents a rate-limiting step in successful embryo implantation. Adhesion, invasion and migration processes within the trophoblast are governed by several transcription factors. Among them, CDX2 is a critical regulator shaping the destiny of the trophoblast. While its altered expression is a linchpin initiating embryo implantation in mice, the precise influence of CDX2 on the functionality and lineage differentiation of early human trophoblast remains unclear. In this study, we employed well-established human trophoblast stem cell (hTSC) lines with CDX2 overexpression coupled with a 3D in vitro culture system for early human embryos. We revealed that the downregulation of CDX2 is a prerequisite for syncytialization during human embryo implantation based on immunofluorescence, transcriptome analysis, CUT-tag sequencing and the construction of 3D human trophoblast organoids. While CDX2 overexpression inhibited syncytialization, it propelled hTSC proliferation and invasive migration. CDX2 exerted its influence by interacting with CGA, PTGS2, GCM1, LEF1 and CDH2, thereby hindering premature differentiation of the syncytiotrophoblast. CDX2 overexpression enhanced the epithelial-mesenchymal transition of human trophoblast organoids. In summary, our study provides insights into the molecular characteristics of trophoblast differentiation and development in humans, laying a theoretical foundation for advancing research in embryo implantation.

Standard: human intestine-on-a-chip.

Organs-on-chips are microphysiological systems that allow to replicate the key functions of human organs and accelerate the innovation in life sciences including disease modeling, drug development, and precision medicine. However, due to the lack of standards in their definition, structural design, cell source, model construction, and functional validation, a wide range of translational application of organs-on-chips remains a challenging. "Organs-on-chips: Intestine" is the first group standard on human intestine-on-a-chip in China, jointly agreed and released by the experts from the Chinese Society of Biotechnology on 29th April 2024. This standard specifies the scope, terminology, definitions, technical requirements, detection methods, and quality control in building the human intestinal model on a chip. The publication of this group standard will guide the institutional establishment, acceptance and execution of proper practical protocols and accelerate the international standardization of intestine-on-a-chip for translational applications.

Hygroscopic Mg-Al LDHs composite microspheres for highly efficient hyperspectral camouflage in the VIS and NIR wavebands.

In order to enhance the hyperspectral camouflage efficacy of stealth coatings against a natural vegetative backdrop, LiCl, known for its significant hygroscopic properties, was incorporated into green Mg-Al layered double hydroxide (Mg-Al LDHs) material. Micron-sized composite microspheres were subsequently synthesized via the spray-drying granulation technique. The structure, morphology, and chemical composition of these microspheres were thoroughly characterized by X-ray diffraction, scanning electron microscopy, laser particle size analysis, nitrogen adsorption-desorption isotherms, and Fourier-transform infrared spectroscopy. The effect of LiCl content on the moisture absorption capacity and near-infrared reflectance spectra of the microspheres was systematically evaluated. We found that incorporating an optimal amount of LiCl into the internal pores of the Mg-Al LDHs microspheres did not compromise their smooth surface morphology and uniform particulate distribution. Notably, when the LiCl content was 10%, the maximum saturation moisture uptake ratio of the coating increased to 0.75 g/g. This hygroscopicity significantly enhanced the absorption and scattering of near-infrared radiation by the coating while concurrently improving its ability to modulate the shape and reflectance of both the visible and near-infrared spectral curves. Spectral congruence between the synthetic coating and natural green foliage was quantified at 97.41%. Moreover, this performance was maintained over 10 cycles of programmed drying and re-humidification, and the coating consistently demonstrated stable hygroscopic properties and sustained over 95% spectral congruence. These optimized artificial coatings were found to effectively confuse hyperspectral classification algorithms, thus blending seamlessly into a natural foliage backdrop. This study provides a new method for regulating VIS and NIR spectral (visible-near infrared spectrum) features, which will be critical for applications in advanced hyperspectral camouflage materials.

ANCA-associated systemic vasculitis initially mimicking peripheral neuropathy in an elderly woman.

Anti-neutrophil cytoplasmic antibodies (ANCA)-associated systemic vasculitis (AASV) is a rare systemic immunological condition that predominantly impacts small arteries, veins, and capillaries, often leading to kidney damage and pulmonary injury. It is important to note that individuals primarily presenting with peripheral neuropathy (PN) are uncommon in AASV, which can result in significant misdiagnosis or undiagnosed cases. The severity and location of PN can vary among patients. In this article, we present a case of an AASV patient initially showing signs of PN. This case highlights the significance of considering AASV as a potential cause of unexplained neurological symptoms. Timely identification and proper treatment are essential for improving the survival rate and functional prognosis of AASV patients.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data.

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

Mea6/cTAGE5 cooperates with TRAPPC12 to regulate PTN secretion and white matter development.

Mutations of TRAPPC12 are associated with progressive childhood encephalopathy including abnormal white matter. However, the underlying pathogenesis is still unclear. Here, we found that Trappc12 deficiency in CG4 and oligodendrocyte progenitor cells (OPCs) affects their differentiation and maturation. In addition, TRAPPC12 interacts with Mea6/cTAGE5, and Mea6/cTAGE5 ablation in OPCs affects their proliferation and differentiation, leading to marked hypomyelination, compromised synaptic functionality, and aberrant behaviors in mice. We reveal that TRAPPC12 is associated with COPII components at ER exit site, and Mea6/cTAGE5 cKO disrupts the trafficking pathway by affecting the distribution and/or expression of TRAPPC12, SEC13, SEC31A, and SAR1. Moreover, we observed marked disturbances in the secretion of pleiotrophin (PTN) in Mea6-deficient OPCs. Notably, exogenous PTN supplementation ameliorated the differentiation deficits of these OPCs. Collectively, our findings indicate that the association between TRAPPC12 and MEA6 is important for cargo trafficking and white matter development.

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning.

BACKGROUND: Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare. METHODS: In this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities. RESULTS: Here we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched. CONCLUSIONS: KnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.

Understanding how drugs interact is crucial for safe healthcare and the development of new medicines. We developed a computational tool that can analyze the data about medicines within large medical databases and predict the impact of being treated by multiple drugs at the same time on the person taking the drugs. Our tool, named KnowDDI, can predict which drugs interact with each other and also provide an explanation for why the interaction is likely to take place. We demonstrated that our tool can identify known drug interactions. It could potentially be used in the future to identify previously unknown or unanticipated interactions that could have negative consequences to people being treated with unusual combinations of medicines.

PCDHA9 as a candidate gene for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease. To identify additional genetic factors, we analyzed exome sequences in a large cohort of Chinese ALS patients and found a homozygous variant (p.L700P) in PCDHA9 in three unrelated patients. We generated Pcdhα9 mutant mice harboring either orthologous point mutation or deletion mutation. These mice develop progressive spinal motor loss, muscle atrophy, and structural/functional abnormalities of the neuromuscular junction, leading to paralysis and early lethality. TDP-43 pathology is detected in the spinal motor neurons of aged mutant mice. Mechanistically, we demonstrate that Pcdha9 mutation causes aberrant activation of FAK and PYK2 in aging spinal cord, and dramatically reduced NKA-α1 expression in motor neurons. Our single nucleus multi-omics analysis reveals disturbed signaling involved in cell adhesion, ion transport, synapse organization, and neuronal survival in aged mutant mice. Together, our results present PCDHA9 as a potential ALS gene and provide insights into its pathogenesis.

The Causal Associations between Adipokines and Alzheimer's Disease: A Two-Sample Mendelian Randomization Study.

Background: Observational studies have indicated the association of alteration of adipokines with Alzheimer's disease (AD). However, it remains unclear whether the associations are causal. Objective: To determine the causal associations between adipokines and AD. Methods: A Mendelian randomization (MR) method was applied to investigate the causal relationships of adipokines, including adiponectin and resistin, with risk of AD. Genetic proxies from genome-wide association studies (GWAS) of adiponectin and resistin were selected as instrumental variables. GWAS summary statistics for AD were extracted as outcome. Results: In this study, we found evidence of the causal effects of adiponectin on AD (OR: 0.850, 95% CI: 0.731-0.990, p = 0.037). However, no relationship between resistin and AD (OR: 0.936, 95% CI: 0.851-1.029, p = 0.171) was detected. In the reverse causation analysis, null associations of AD were found for adiponectin and resistin (all p > 0.05). Conclusions: This study provides evidence of causality between adiponectin and risk of AD. However, no genetic susceptibility of resistin was discovered for AD.

Property-Aware Relation Networks for Few-Shot Molecular Property Prediction.

Molecular property prediction plays a fundamental role in AI-aided drug discovery to identify candidate molecules, which is also essentially a few-shot problem due to lack of labeled data. In this paper, we propose Property-Aware Relation networks (PAR) to handle this problem. We first introduce a property-aware molecular encoder to transform the generic molecular embeddings to property-aware ones. Then, we design a query-dependent relation graph learning module to estimate molecular relation graph and refine molecular embeddings w.r.t. the target property. Thus, the facts that both property-related information and relationships among molecules change across different properties are utilized to better learn and propagate molecular embeddings. Generally, PAR can be regarded as a combination of metric-based and optimization-based few-shot learning method. We further extend PAR to Transferable PAR (T-PAR) to handle the distribution shift, which is common in drug discovery. The keys are joint sampling and relation graph learning schemes, which simultaneously learn molecular embeddings from both source and target domains. Extensive results on benchmark datasets show that PAR and T-PAR consistently outperform existing methods on few-shot and transferable few-shot molecular property prediction tasks, respectively. Besides, ablation and case studies are conducted to validate the rationality of our designs in PAR and T-PAR.

A Novel Tensor Learning Model for Joint Relational Triplet Extraction.

The relational triplet is a format to represent relational facts in the real world, which consists of two entities and a semantic relation between these two entities. Since the relational triplet is the essential component in a knowledge graph (KG), extracting relational triplets from unstructured texts is vital for KG construction and has attached increasing research interest in recent years. In this work, we find that relation correlation is common in real life and could be beneficial for the relational triplet extraction task. However, existing relational triplet extraction works neglect to explore the relation correlation that bottlenecks the model performance. Therefore, to better explore and take advantage of the correlation among semantic relations, we innovatively utilize a three-dimension word relation tensor to describe relations between words in a sentence. Then, we treat the relation extraction task as a tensor learning problem and propose an end-to-end tensor learning model based on Tucker decomposition. Compared with directly capturing correlation among relations in a sentence, learning the correlation of elements in a three-dimension word relation tensor is more feasible and could be addressed through tensor learning methods. To verify the effectiveness of the proposed model, extensive experiments are also conducted on two widely used benchmark datasets, that is, NYT and WebNLG. Results show that our model outperforms the state-of-the-art by a large margin of F1 scores, such as the developed model has an improvement of 3.2% on the NYT dataset compared to the state-of-the-art. Source codes and data can be found at https://github.com/Sirius11311/TLRel.git.

Advances in Microfluidic Technologies in Organoid Research.

Organoids have emerged as major technological breakthroughs and novel organ models that have revolutionized biomedical research by recapitulating the key structural and functional complexities of their in vivo counterparts. The combination of organoid systems and microfluidic technologies has opened new frontiers in organoid engineering and offers great opportunities to address the current challenges of existing organoid systems and broaden their biomedical applications. In this review, the key features of the existing organoids, including their origins, development, design principles, and limitations, are described. Then the recent progress in integrating organoids into microfluidic systems is highlighted, involving microarrays for high-throughput organoid manipulation, microreactors for organoid hydrogel scaffold fabrication, and microfluidic chips for functional organoid culture. The opportunities in the nascent combination of organoids and microfluidics that lie ahead to accelerate research in organ development, disease studies, drug screening, and regenerative medicine are also discussed. Finally, the challenges and future perspectives in the development of advanced microfluidic platforms and modified technologies for building organoids with higher fidelity and standardization are envisioned.

Induced formation of primordial germ cells from zebrafish blastomeres by germplasm factors.

The combination of genome editing and primordial germ cell (PGC) transplantation has enormous significance in the study of developmental biology and genetic breeding, despite its low efficiency due to limited number of donor PGCs. Here, we employ a combination of germplasm factors to convert blastoderm cells into induced PGCs (iPGCs) in zebrafish and obtain functional gametes either through iPGC transplantation or via the single blastomere overexpression of germplasm factors. Zebrafish-derived germplasm factors convert blastula cells of Gobiocypris rarus into iPGCs, and Gobiocypris rarus spermatozoa can be produced by iPGC-transplanted zebrafish. Moreover, the combination of genome knock-in and iPGC transplantation perfectly resolves the contradiction between high knock-in efficiency and early lethality during embryonic stages and greatly improves the efficiency of genome knock-in. Together, we present an efficient method for generating PGCs in a teleost, a technique that will have a strong impact in basic research and aquaculture.

The effect of a methylxanthine vasodilator: pentoxifylline on the treatment of diabetic nephropathy-a meta-analysis.

This meta-analysis aimed to comprehensively evaluate the efficacy and safety of pentoxifylline (PTF) in the treatment of diabetic nephropathy (DN) and to offer fresh perspectives and evidence-based references for this condition. Meta-analysis. Relevant randomized controlled trials (RCTs) were searched from PubMed, Embase, Cochrane Library, China Knowledge Network (CNKI), Wanfang, and China Biomedical Literature Database. All trials were screened for compliance with the inclusion and exclusion criteria, and relevant data were extracted after quality evaluation. Eighteen studies with a total of 1280 patients were finally included. Compared to the control group, high sensitivity C-reactive protein (hsCRP) was improved (MD = - 0.23. 95% CI = [- 0.41, - 0.05], P = 0.01); urinary albumin excretion (UAE) rate was reduced (MD = - 16.50, 95% CI = [- 18.87, - 14.13], P<0.00001); the change of serum creatinine (Scr) from baseline was reduced (MD = - 0.05, 95%CI = [- 0.08, - 0.01], P = 0.009); fasting plasma glucose (FPG) was decreased (MD = - 5.66, 95% CI = [- 9.79, - 1.53], P = 0.007); and the improvement of glomerular filtration rate (eGFR) from baseline was increased (MD = 4.38, 95% CI = [3.28, 5.48], P<0.00001) in the treatment group. No significant difference was observed between the two groups concerning systolic blood pressure, diastolic blood pressure, total cholesterol, and triglycerides. And in terms of safety, the use of PTF was relatively safe with some self-limiting adverse events. FPG was decreased by PTF more effectively, but there was no effect of PTF on glycated hemoglobin (HbA1c). PTF could improve hsCRP, decrease UAE and Scr, and raise eGFR in the treatment of DN.

Scaling up stem cell production: harnessing the potential of microfluidic devices.

Stem cells are specialised cells characterised by their unique ability to both self-renew and transform into a wide array of specialised cell types. The widespread interest in stem cells for regenerative medicine and cultivated meat has led to a significant demand for these cells in both research and practical applications. Despite the growing need for stem cell manufacturing, the industry faces significant obstacles, including high costs for equipment and maintenance, complicated operation, and low product quality and yield. Microfluidic technology presents a promising solution to the abovementioned challenges. As an innovative approach for manipulating liquids and cells within microchannels, microfluidics offers a plethora of advantages at an industrial scale. These benefits encompass low setup costs, ease of operation and multiplexing, minimal energy consumption, and the added advantage of being labour-free. This review presents a thorough examination of the prominent microfluidic technologies employed in stem cell research and explores their promising applications in the burgeoning stem cell industry. It thoroughly examines how microfluidics can enhance cell harvesting from tissue samples, facilitate mixing and cryopreservation, streamline microcarrier production, and efficiently conduct cell separation, purification, washing, and final cell formulation post-culture.

Water flow promoted charge separation in piezoelectric Bi4Ti3O12 for the enhanced photocatalytic degradation of antibiotic.

Addressing the issue of antibiotic residues in the environment is key to improving the quality of aquatic environments and reducing human health risks. In this study, piezoelectric bismuth titanate (Bi4Ti3O12) nanosheets are synthesized and employed to conduct antibiotic degradation. The piezoelectric potential induced by the water flow shear force is utilized to facilitate charge separation and migration in the photocatalytic process and enhance the catalytic degradation of antibiotic wastewater. As a result, 85% of tetracycline hydrochloride (TC) is degraded within 90 min. The piezo-photocatalytic process exhibits a 2.4 times faster reaction rate and a 15% higher mineralization rate than photocatalysis. Different environmental factors are investigated for their effects on the catalytic activity in piezo-photocatalysis. In situ electrochemical measurement and photoluminescence (PL) spectroscopy under stress demonstrated that the piezoelectric potential shifted the energy band of Bi4Ti3O12 and promoted the charge migration and separation, which produce more active species that favor the efficient catalytic degradation. Finally, the intermediate products of the tetracycline hydrochloride degradation process are analyzed and possible degradation pathways are suggested. This study elucidates the degradation mechanism of Bi4Ti3O12 as a piezo-photocatalyst for antibiotic pollutants, and meticulously investigates the charge transfer mechanism of the catalyst material in response to micro-stress. Hence, it provides an efficient solution for organic wastewater treatment and can potentially provide theoretical support for the development and performance optimization of catalyst materials applied in natural environments.