Identification of functional heterogeneity of immune cells and tubular-immune cellular interplay action in diabetic kidney disease.

Background: Renal inflammation plays key roles in the pathogenesis of diabetic kidney disease (DKD). Immune cell infiltration is the main pathological feature in the progression of DKD. Sodium glucose cotransporter 2 inhibitor (SGLT2i) were reported to have antiinflammatory effects on DKD. While the heterogeneity and molecular basis of the pathogenesis and treatment with SGLT2i in DKD remains poorly understood. Methods: To address this question, we performed a single-cell transcriptomics data analysis and cell cross-talk analysis based on the database (GSE181382). The single-cell transcriptome analysis findings were validated using multiplex immunostaining. Results: A total of 58760 cells are categorized into 25 distinct cell types. A subset of macrophages with anti-inflammatory potential was identified. We found that Ccl3+ (S100a8/a9 high) macrophages with anti-inflammatory and antimicrobial in the pathogenesis of DKD decreased and reversed the dapagliflozin treatment. Besides, dapagliflozin treatment enhanced the accumulation of Pck1+ macrophage, characterized by gluconeogenesis signaling pathway. Cell-cross talk analysis showed the GRN/SORT1 pair and CD74 related signaling pathways were enriched in the interactions between tubular epithelial cells and immune cells. Conclusions: Our study depicts the heterogeneity of macrophages and clarifies a new possible explanation of dapagliflozin treatment, showing the metabolism shifts toward gluconeogenesis in macrophages, fueling the anti-inflammatory function of M2 macrophages, highlighting the new molecular features and signaling pathways and potential therapeutic targets, which has provided an important reference for the study of immune-related mechanisms in the progression of the disease.

Cost-effectiveness of population-wide genomic screening for Lynch Syndrome and Polygenic Risk Scores to inform Colorectal Cancer screening.

INTRODUCTION: Genomic screening to identify individuals with Lynch Syndrome (LS) and those with a high polygenic risk score (PRS) promises to personalize Colorectal Cancer (CRC) screening. Understanding its clinical and economic impact is needed to inform screening guidelines and reimbursement policies. METHODS: We developed a Markov model to simulate individuals over a lifetime. We compared LS+PRS genomic screening to standard of care (SOC) for a cohort of US adults at age 30. The Markov model included health states of "no CRC", CRC stages (A-D) and death. We estimated incidence, mortality, and discounted economic outcomes of the population under different interventions. RESULTS: Screening 1000 individuals for LS+PRS resulted in 1.36 fewer CRC cases and 0.65 fewer deaths compared to SOC. The incremental cost-effectiveness ratio (ICER) was $124,415 per quality-adjusted life-year (QALY); screening had a 69% probability of being cost-effective using a willingness to pay threshold of $150,000/QALY. Setting the PRS threshold at the 90th percentile of the LS+PRS screening program to define individuals at high risk was most likely to be cost-effective compared to 95th, 85th, and 80th percentiles. CONCLUSION: Population-level LS+PRS screening is marginally cost-effective and a threshold of 90th percentile is more likely to be cost-effective than other thresholds.

Severe stress cardiomyopathy following spinal corrective surgery for scoliosis complicated with pectus excavatum: a case report.

BACKGROUND: Stress cardiomyopathy (SCM) is an acute heart failure syndrome characterized by transient, usually reversible left ventricular systolic dysfunction with normal or enhanced basal compensatory wall motion abnormalities involving the left ventricular anterior septum and apex, resulting in a "ballooning" appearance. However, it has rarely been reported in patients undergoing spinal surgery. CASE PRESENTATION: We report a case of severe stress cardiomyopathy in a scoliosis patient with pectus excavatum who underwent spinal corrective surgery. During the wake-up period, circulatory collapse occurred. After multidisciplinary consultation, the patient was diagnosed with stress cardiomyopathy. At last, she had a good prognosis after a series of treatments including ECMO. CONCLUSION: Stress cardiomyopathy is a reversible but uncommon condition. It can cause death if it is not diagnosed in time. Consequently, this report should improve the awareness of orthopedists and anesthesiologists for timely identification and management. For patients with potential risk factors, timely preoperative intervention should be performed to reduce the occurrence of stress cardiomyopathy.

A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs.

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher-student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet's performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.

DNase II Can Efficiently Digest RNA and Needs to Be Redefined as a Nuclease.

DNase II, identified in 1947 and named in 1953, is an acidic DNA endonuclease prevalent across organisms and crucial for normal growth. Despite its expression in nearly all human tissues, as well as its biological significance, DNase II's detailed functions and corresponding mechanisms remain unclear. Although many groups are trying to figure this out, progress is very limited. It is very hard to connect its indispensability with its DNA cleavage activity. In this study, we find that DNase II secreted to saliva can digest RNA in mildly acidic conditions, prompting us to hypothesize that salivary DNase II might digest RNA in the stomach. This finding is consistent with the interesting discovery reported in 1964 that RNA could inhibit DNase II's activity, which has been largely overlooked. This RNA digestion activity is further confirmed by using purified DNase II, showing activity to digest both DNA and RNA effectively. Here, we suggest redesignating DNase II as DNase II (RNase). The biological functions of DNase II are suggested to recycle intracellular RNA or digest external nucleic acids (both RNA and DNA) as nutrients. This discovery may untangle the mystery of DNase II and its significant biofunctions.

Biomedical event causal relation extraction with deep knowledge fusion and Roberta-based data augmentation.

Biomedical event causal relation extraction (BECRE), as a subtask of biomedical information extraction, aims to extract event causal relation facts from unstructured biomedical texts and plays an essential role in many downstream tasks. The existing works have two main problems: i) Only shallow features are limited in helping the model establish potential relationships between biomedical events. ii) Using the traditional oversampling method to solve the data imbalance problem of the BECRE tasks ignores the requirements for data diversifying. This paper proposes a novel biomedical event causal relation extraction method to solve the above problems using deep knowledge fusion and Roberta-based data augmentation. To address the first problem, we fuse deep knowledge, including structural event representation and entity relation path, for establishing potential semantic connections between biomedical events. We use the Graph Convolutional Neural network (GCN) and the predicated tensor model to acquire structural event representation, and entity relation paths are encoded based on the external knowledge bases (GTD, CDR, CHR, GDA and UMLS). We introduce the triplet attention mechanism to fuse structural event representation and entity relation path information. Besides, this paper proposes the Roberta-based data augmentation method to address the second problem, some words of biomedical text, except biomedical events, are masked proportionally and randomly, and then pre-trained Roberta generates data instances for the imbalance BECRE dataset. Extensive experimental results on Hahn-Powell's and BioCause datasets confirm that the proposed method achieves state-of-the-art performance compared to current advances.

Regulation of glycolysis-derived L-lactate production in astrocytes rescues the memory deficits and Aß burden in early Alzheimer's disease models.

Aberrant energy metabolism in the brain is a common pathological feature in the preclinical Alzheimer's Disease (AD). Recent studies have reported the early elevations of glycolysis-involved enzymes in AD brain and cerebrospinal fluid according to a large-scale proteomic analysis. It's well-known that astrocytes exhibit strong glycolytic metabolic ability and play a key role in the regulation of brain homeostasis. However, its relationship with glycolytic changes and cognitive deficits in early AD patients is unclear. Here, we investigated the mechanisms by which astrocyte glycolysis is involved in early AD and its potential as a therapeutic target. Our results suggest that Aß-activated microglia can induce glycolytic-enhanced astrocytes in vitro, and that these processes are dependent on the activation of the AKT-mTOR-HIF-1α pathway. In early AD models, the increase in L-lactate produced by enhanced glycolysis of astrocytes leads to spatial cognitive impairment by disrupting synaptic plasticity and accelerating Aß aggregation. Furthermore, we find rapamycin, the mTOR inhibitor, can rescue the impaired spatial memory and Aß burden by inhibiting the glycolysis-derived L-lactate in the early AD models. In conclusion, we highlight that astrocytic glycolysis plays a critical role in the early onset of AD and that the modulation of glycolysis-derived L-lactate by rapamycin provides a new strategy for the treatment of AD.

Multimodal imaging diagnosis and analysis of prognostic factors in patients with adult-onset Coats disease.

AIM: To describe the multimodal imaging features, treatment, and outcomes of patients diagnosed with adult-onset Coats disease. METHODS: This retrospective study included patients first diagnosed with Coats disease at ≥18 years of age between September 2017 and September 2021. Some patients received anti-vascular endothelial growth factor (VEGF) therapy (conbercept, 0.5 mg) as the initial treatment, which was combined with laser photocoagulation as needed. All the patients underwent best corrected visual acuity (BCVA) and intraocular pressure examinations, fundus color photography, spontaneous fluorescence tests, fundus fluorescein angiography, optical coherence tomography (OCT), OCT angiography, and other examinations. BCVA alterations and multimodal image findings in the affected eyes following treatment were compared and the prognostic factors were analyzed. RESULTS: The study included 15 patients who were aged 24-72 (57.33±12.61)y at presentation. Systemic hypertension was the most common associated systemic condition, occurring in 13 (86.7%) patients. Baseline BCVA ranged from 2.0 to 5.0 (4.0±1.1), which showed improvement following treatment (4.2±1.0). Multimodal imaging revealed retinal telangiectasis in 13 patients (86.7%), patchy hemorrhage in 5 patients (33.3%), and stage 2B disease (Shield's staging criteria) in 11 patients (73.3%). OCT revealed that the baseline central macular thickness (CMT) ranged from 129 to 964 µm (473.0±230.1 µm), with 13 patients (86.7%) exhibiting a baseline CMT exceeding 250 µm. Furthermore, 8 patients (53.3%) presented with an epiretinal membrane at baseline or during follow-up. Hyper-reflective scars were observed on OCT in five patients (33.3%) with poor visual prognosis. Vision deteriorated in one patient who did not receive treatment. Final vision was stable in three patients who received laser treatment, whereas improvement was observed in one of two patients who received anti-VEGF therapy alone. In addition, 8 of 9 patients (88.9%) who received laser treatment and conbercept exhibited stable or improved BCVA. CONCLUSION: Multimodal imaging can help diagnose adult-onset Coats disease. Anti-VEGF treatment combined with laser therapy can be an option for improving or maintaining BCVA and resolving macular edema. The final visual outcome depends on macular involvement and the disease stage.

A review of Legionella transmission risk in built environments: sources, regulations, sampling, and detection.

The risk of Legionella transmission in built environments remains a significant concern. Legionella can spread within buildings through aerosol transmission, prompting the exploration of airborne transmission pathways and proposing corresponding prevention and control measures based on building characteristics. To this end, a comprehensive literature review on the transmission risk of Legionella in built environments was performed. Four electronic databases (PubMed, Web of Science, Google Scholar, and CNKI) were searched from inception to March 2024 for publications reporting the risk of Legionella transmission in built environments. Relevant articles and gray literature reports were hand-searched, and 96 studies were finally included. Legionella pollution comes from various sources, mainly originates in a variety of built environments in which human beings remain for extended periods. The sources, outbreaks, national standards, regulations, and monitoring techniques for Legionella in buildings are reviewed, in addition to increases in Legionella transmission risk due to poor maintenance of water systems and long-distance transmission events caused by aerosol characteristics. Air and water sampling using various analytical methods helps identify Legionella in the environment, recognize sources in the built environments, and control outbreaks. By comparing the standard regulations of national organizations globally, the authors further highlight gaps and deficiencies in Legionella surveillance in China. Such advancements offer essential insights and references for understanding and addressing Legionella transmission risk in the built environment, with the potential to contribute to safeguarding public health and building environment safety.

[Etiology, Clinical Characteristics and Prognosis of Secondary Hemophagocytic Syndrome].

OBJECTIVE: To understand the etiology, clinical characteristics and prognosis of secondary hemophagocytic syndrome (HLH), so as to improve the understanding of HLH and reduce the rates of misdiagnosis and missed diagnosis of HLH. METHODS: A retrospective study was conducted to analyze the cause, clinical characteristics, laboratory findings, therapy and outcomes of 75 adult patients with secondary HLH admitted to our hospital from January 2015 to December 2021. Follow-up continued until the last discharge time. RESULTS: Among 75 patients, infection-related HLH was the most common (45.33%), followed by lymphoma-related HLH (17.33%). Fever was the most common clinical manifestation (97.67%). Laboratory indicators such as NK cell activity (98.31% low or absent), sCD25 (93.22% increased), and serum ferritin (94.44% elevated) had higher sensitivity in diagnosis. By comparing the clinical manifestations and laboratory indicators of HLH patients with different causes, sex, lymph node enlargement and bone marrow morphology were more valuable for the diagnosis of primary disease (all P <0.05). By comparing the treatment and clinical outcomes of HLH patients with different causes, the highest clinical remission rate (83.3%) was achieved in patients with autoimmune disease-related HLH treated with hormone+cyclosporine (P <0.05). The overall 12-month survival rate of all patients was 26.7%, in which the infection-related HLH was the lowest (14.7%) while autoimmune disease-related HLH was the highest (63.6%). CONCLUSION: The causes and clinical characteristics of adult secondary HLH are varied, with poor prognosis and heterogeneity in disease severity. It is important to identify HLH cause early for diagnosis and needed to further understand HLH.

Harnessing the plant microbiome for environmental sustainability: From ecological foundations to novel applications.

In plant environments, there exist heterogeneous microbial communities, referred to as the plant microbiota, which are recruited by plants and play crucial roles in promoting plant growth, aiding in resistance against pathogens and environmental stresses, thereby maintaining plant health. These microorganisms, along with their genomes, collectively form the plant microbiome. Research on the plant microbiome can help unravel the intricate interactions between plants and microbes, providing a theoretical foundation to reduce pesticide use, enhance agricultural productivity, and promote environmental sustainability. Despite significant progress in the field of research, unresolved challenges persist due to ongoing technological limitations and the complexities inherent in studying microorganisms at small scales. Recently, synthetic community (SynCom) has emerged as a novel technique for microbiome research, showing promising prospects for applications in the plant microbiome field. This article systematically introduces the origin and distribution of plant microbiota, the processes of their recruitment and colonization, and the mechanisms underlying their beneficial functions for plants, from the aspects of composition, assembly, and function. Furthermore, we discuss the principles, applications, challenges, and prospects of SynCom for promoting plant health.

Language-aware multiple datasets detection pretraining for DETRs.

Pretraining on large-scale datasets can boost the performance of object detectors while the annotated datasets for object detection are hard to scale up due to the high labor cost. What we possess are numerous isolated filed-specific datasets, thus, it is appealing to jointly pretrain models across aggregation of datasets to enhance data volume and diversity. In this paper, we propose a strong framework for utilizing Multiple datasets to pretrain DETR-like detectors, termed METR, without the need for manual label spaces integration. It converts the typical multi-classification in object detection into binary classification by introducing a pre-trained language model. Specifically, we design a category extraction module for extracting potential categories involved in an image and assign these categories into different queries by language embeddings. Each query is only responsible for predicting a class-specific object. Besides, to adapt our novel detection paradigm, we propose a Class-wise Bipartite Matching strategy that limits the ground truths to match queries assigned to the same category. Extensive experiments demonstrate that METR achieves extraordinary results on either multi-task joint training or the pretrain & finetune paradigm. Notably, our pre-trained models have high flexible transferability and increase the performance upon various DETR-like detectors on COCO val2017 benchmark. Our code is publicly available at: https://github.com/isbrycee/METR.

Evaluating China's primary healthcare services' efficiency and spatial correlation: a three-stage DEA-Malmquist model.

Introduction: Enhancing the efficiency of primary healthcare services is essential for a populous and developing nation like China. This study offers a systematic analysis of the efficiency and spatial distribution of primary healthcare services in China. It elucidates the fundamental landscape and regional variances in efficiency, thereby furnishing a scientific foundation for enhancing service efficiency and fostering coordinated regional development. Methods: Employs a three-stage DEA-Malmquist model to assess the efficiency of primary healthcare services across 31 provincial units in mainland China from 2012 to 2020. Additionally, it examines the spatial correlation of efficiency distribution using the Moran Index. Results: The efficiency of primary healthcare services in China is generally suboptimal with a noticeable declining trend, highlighting significant potential for improvement in both pure technical efficiency and scale efficiency. There is a pronounced efficiency gap among provinces, yet a positive spatial correlation is evident. Regionally, efficiency ranks in the order of East > Central > West. Factors such as GDP per capita and population density positively influence efficiency enhancements, while urbanization levels and government health expenditures appear to have a detrimental impact. Discussion: The application of the three-stage DEA-Malmquist model and the Moran Index not only expands the methodological framework for researching primary healthcare service efficiency but also provides scientifically valuable insights for enhancing the efficiency of primary healthcare services in China and other developing nations.

Growth Properties and Metabolomic Analysis Provide Insight into Drought Tolerance in Barley (Hordeum vulgare L.).

Drought stress is a major meteorological threat to crop growth and yield. Barley (Hordeum vulgare L.) is a vital cereal crop with strong drought tolerance worldwide. However, the underlying growth properties and metabolomic regulatory module of drought tolerance remains less known. Here, we investigated the plant height, spike length, effective tiller, biomass, average spikelets, 1000-grain weight, number of seeds per plant, grain weight per plant, ash content, protein content, starch content, cellulose content, and metabolomic regulation mechanisms of drought stress in barley. Our results revealed that the growth properties were different between ZDM5430 and IL-12 under drought stress at different growth stages. We found that a total of 12,235 metabolites were identified in two barley genotype root samples with drought treatment. More than 50% of these metabolites showed significant differences between the ZDM5430 and IL-12 roots. The Kyoto Encyclopedia of Genes and Genomes pathway analysis identified 368 differential metabolites mainly involved in starch and sucrose metabolism, the pentose phosphate pathway, pyrimidine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis in ZDM5430 under drought stress, whereas the different metabolites of IL-12 under drought stress related to starch and sucrose metabolism, the pentose phosphate pathway, 2-oxocarboxylic acid metabolism, cutin, suberine and wax biosynthesis, carbon metabolism, fatty acid biosynthesis, and C5-branched dibasic acid metabolism. These metabolites have application in the tricarboxylic cycle, the urea cycle, the met salvage pathway, amino acid metabolism, unsaturated fatty acid biosynthesis, phenolic metabolism, and glycolysis. On the other hand, the expression patterns of 13 genes related to the abovementioned bioprocesses in different barley genotypes roots were proposed. These findings afford an overview for the understanding of barley roots' metabolic changes in the drought defense mechanism by revealing the differently accumulated compounds.

Impact of dairy supplementation on bone acquisition in children's limbs: a 12-month cluster-randomized controlled trial and meta-analysis.

The impact of milk on bone health in rural preschoolers is under-researched. This study, through a clinical trial and a meta-analysis, finds that milk supplementation enhances forearm and calcaneus bone acquisition in children, supporting the benefits of daily milk consumption. PURPOSE: This study evaluated the impact of dairy supplementation on bone acquisition in children's limbs through a cluster-randomized controlled trial and a meta-analysis. METHODS: The trial involved 315 children (4-6 year) from Northwest China, randomized to receive either 390 ml of milk daily (n = 215) or 20-30 g of bread (n = 100) over 12 months. We primarily assessed bone mineral density (BMD) and content (BMC) changes at the limbs, alongside bone-related biomarkers, measured at baseline, the 6th and 12th months. The meta-analysis aggregated BMD or BMC changes in the forearm/legs/calcaneus from published randomized trials involving children aged 3-18 years supplemented with dairy foods (vs. control group). RESULTS: Of 278 completed the trial, intention-to-treat analysis revealed significant increases in BMD (4.05% and 7.31%) and BMC (4.69% and 7.34%) in the left forearm at the 6th and 12th months in the milk group compared to controls (P < 0.001). The calcaneus showed notable improvements in BMD (2.01%) and BMC (1.87%) at 6 months but not at 12 months. Additionally, milk supplementation was associated with beneficial changes in bone resorption markers, parathyroid hormone (- 12.70%), insulin-like growth factor 1 (6.69%), and the calcium-to-phosphorus ratio (2.22%) (all P < 0.05). The meta-analysis, encompassing 894 children, indicated that dairy supplementation significantly increased BMD (SMD, 0.629; 95%CI: 0.275, 0.983) and BMC (SMD, 0.616; 95%CI: 0.380, 0.851) (P < 0.05) in the arms, but not in the legs (P > 0.05). CONCLUSION: Milk supplementation significantly improves bone health in children's forearms, underscoring its potential as a strategic dietary intervention for bone development. Trial registration NCT05074836.

Therapeutic effects of exendin-4 on spinal cord injury via restoring autophagy function and decreasing necroptosis in neuron.

AIMS: Necroptosis is one of programmed death that may aggravate spinal cord injury (SCI). We aimed to investigate the effect and mechanism of exendin-4 (EX-4) on the recovery of motor function and necroptosis after SCI. METHODS: The SD rats with left hemisection in the T10 spinal cord as SCI model were used. The behavior tests were measured within 4 weeks. The effects of EX-4 on necroptosis-associated proteins and autophagy flux were explored. In addition, the SHSY5Y cell model was introduced to explore the direct effect of EX-4 on neurons. The effect of lysosome was explored using mTOR activator and AO staining. RESULTS: EX-4 could improve motor function and limb strength, promote the recovery of autophagy flux, and accelerate the degradation of necroptosis-related protein at 3 d after injury in rats. EX-4 reduced lysosome membrane permeability, promoted the recovery of lysosome function and autophagy flux, and accelerated the degradation of necroptosis-related proteins by inhibiting the phosphorylation level of mTOR in the SHSY5Y cell model. CONCLUSION: Our results demonstrated that EX-4 may improve motor function after SCI via inhibiting mTOR phosphorylation level and accelerating the degradation of necroptosis-related proteins in neurons. Our findings may provide new therapeutic targets for clinical treatment after SCI.

AI-assisted discovery of high-temperature dielectrics for energy storage.

Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc-1 at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85-200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.