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Abstract Hematological malignancies are a group of cancers that affect the blood, bone marrow, and lymphatic system. Cancer stem cells (CSCs) are believed to be responsible for the initiation, progression, and relapse of hematological malignancies. However, identifying and targeting CSCs presents many challenges. We aimed to develop a stemness index (HSCsi) to identify and guide the therapy targeting CSCs in hematological malignancies. We developed a novel one-class logistic regression (OCLR) algorithm to identify transcriptomic feature sets related to stemness in hematologic malignancies. We used the HSCsi to measure the stemness degree of leukemia stem cells (LSCs) and correlate it with clinical outcomes.We analyze the correlation of HSCsi with genes and pathways involved in drug resistance and immune microenvironment of acute myeloid leukemia (AML). HSCsi revealed stemness-related biological mechanisms in hematologic malignancies and effectively identify LSCs. The index also predicted survival and relapse rates of various hematologic malignancies. We also identified potential drugs and interventions targeting cancer stem cells (CSCs) of hematologic malignancies by the index. Moreover, we found a correlation between stemness and bone marrow immune microenvironment in AML. Our study provides a novel method and tool to assess the stemness degree of hematologic malignancies and its implications for clinical outcomes and therapeutic strategies. Our HSC stemness index can facilitate the precise stratification of hematologic malignancies, suggest possible targeted and immunotherapy options, and guide the selection of patients.

Abstract Background The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H. pylori infection, and then examined the potential benefit of this model to endoscopists in their diagnosis of H. pylori infection. Materials and methods A CNN neural network system for endoscopic diagnosis of H.pylori infection was established by collecting 7377 endoscopic images from 639 patients. The accuracy, sensitivity, and specificity were determined. Then, a randomized controlled study was used to compare the accuracy of diagnosis of H. pylori infection by endoscopists who were assisted or unassisted by this CNN model. Results The deep CNN model for diagnosis of H. pylori infection had an accuracy of 89.6%, a sensitivity of 90.9%, and a specificity of 88.9%. Relative to the group of endoscopists unassisted by AI, the AI-assisted group had better accuracy (92.8% [194/209; 95%CI: 89.3%, 96.4%] vs. 75.6% [158/209; 95%CI: 69.7%, 81.5%]), sensitivity (91.8% [67/73; 95%CI: 85.3%, 98.2%] vs. 78.6% [44/56; 95%CI: 67.5%, 89.7%]), and specificity (93.4% [127/136; 95%CI: 89.2%, 97.6%] vs. 74.5% [114/153; 95%CI: 67.5%, 81.5%]). All of these differences were statistically significant (P

Abstract Background Sheep and goats have undergone domestication and improvement to produce similar phenotypes, which have been greatly impacted by structural variants (SVs). Here, we report a high-quality chromosome-level reference genome of Asiatic mouflon, and implement a comprehensive analysis of SVs in 897 genomes of worldwide wild and domestic populations of sheep and goats to reveal genetic signatures underlying convergent evolution. Results We characterize the SV landscapes in terms of genetic diversity, chromosomal distribution and their links with genes, QTLs and transposable elements, and examine their impacts on regulatory elements. We identify several novel SVs and annotate corresponding genes (e.g., BMPR1B, BMPR2, RALYL, COL21A1, and LRP1B) associated with important production traits such as fertility, meat and milk production, and wool/hair fineness. We detect signatures of selection involving the parallel evolution of orthologous SV-associated genes during domestication, local environmental adaptation, and improvement. In particular, we find that fecundity traits experienced convergent selection targeting the gene BMPR1B, with the DEL00067921 deletion explaining ~10.4% of the phenotypic variation observed in goats. Conclusions Our results provide new insights into the convergent evolution of SVs and serve as a rich resource for the future improvement of sheep, goats, and related livestock.

Introduction Acute hypoxic respiratory failure (AHRF) is one of the most common causes of admission to paediatric intensive care units (PICUs) around the world, posing a serious health concern for the global community. Non-invasive respiratory support (NRS) is considered effective in reducing mortality and intubation rates in adults. However, it is not yet clear whether NRS is beneficial for children and which NRS modalities are most effective. This network meta-analysis aims to summarise existing evidence and compare the efficacy and safety of different NRS modalities in paediatric patients with acute hypoxaemic respiratory failure.Methods and analysis To identify randomised controlled trials, we will perform a systematic search of key databases (Embase, PubMed, CENTRAL, CINAHL Complete and Web of Science) and registered clinical trials (ClinicalTrials.gov, WHO ICTRP and ISRCTN). To ensure the inclusion of the latest literature, an initial pilot search was conducted on 8 July 2024, and an updated search will be conducted after the main research work of this study. AHRF in children treated with NRS will be included. Hospital mortality, intubation rate, treatment failure rate and serious adverse events are critical outcomes closely related to patient-centredness and importance. Two authors will independently select the studies and extract the data. The risk of bias will be assessed using the Cochrane risk of bias tool V.2.0. In order to compare the effects of different NRS modalities, pairwise meta-analysis and network meta-analysis will be conducted using R software. Several subgroup analyses will be conducted, including analyses of different causes of AHRF. We will conduct sensitivity analyses by excluding studies with a high risk of bias and those involving neonates. Using the Grading of Recommendations Assessment, Development and Evaluation methodology, we will assess the certainty of the evidence for the effect estimates of all the outcomes.Ethics and dissemination Since this research is a network meta-analysis based on published literature, no formal ethics approval is required. The results will be disseminated through a peer-reviewed journal for publication.PROSPERO registration number CRD42024529804.

Transplanting microglia derived from human stem cells into mice reveals new possibilities for treating neurodegenerative eye diseases.

Abstract The restacking and oxidizable nature of vanadium‐based carbon/nitride (V2C‐MXene) poses a significant challenge. Herein, tellurium (Te)‐doped V2C/V2O3 electrocatalyst is constructed via mild H2O2 oxidation and calcination treatments. Especially, this work rationally exploits the inherent easy oxidation characteristic associated with MXene to alter the interfacial information, thereby obtaining stable self‐generated vanadium‐based heterointerfaces. Meanwhile, the microetching effect of H2O2 creates numerous pores to address the restacking issues. Besides, Te element doping settles the issue of awkward levels of absorption/desorption ability of intermediates. The electrocatalyst obtains an unparalleled hydrogen evolution reaction and oxygen evolution reaction with the overpotential of 83.5 and 279.8 mV at −10 and 10 mA cm−2, respectively. In addition, the overall water‐splitting device demonstrates a low cell voltage of 1.41 V to obtain 10 mA cm−2. Overall, the inherent drawbacks of MXene can be turned into benefits based on the planning strategy to create these electrocatalysts with desirable reaction kinetics.

Abstract Background The global dementia prevalence is surging, necessitating research into contributing factors. We aimed to investigate the association between metabolic syndrome (MetS), its components, serum uric acid (SUA) levels, and dementia risk. Methods Our prospective study comprised 466,788 participants without pre-existing MetS from the UK Biobank. We confirmed dementia diagnoses based on the ICD-10 criteria (F00-03). To evaluate the dementia risk concerning MetS, its components, and SUA levels, we applied Cox proportional hazards models, while adjusting for demographic factors. Results Over a median follow-up of 12.7 years, we identified 6845 dementia cases. Individuals with MetS had a 25% higher risk of all-cause dementia (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.19–1.31). The risk increased with the number of MetS components including central obesity, dyslipidemia for high-density lipoprotein (HDL) cholesterol, hypertension, hyperglycemia, and dyslipidemia for triglycerides. Particularly for those with all five components (HR = 1.76, 95% CI = 1.51–2.04). Dyslipidemia for HDL cholesterol, hypertension, hyperglycemia, and dyslipidemia for triglycerides were independently associated with elevated dementia risk (p < 0.01). MetS was further linked to an increased risk of all-cause dementia (11%) and vascular dementia (VD, 50%) among individuals with SUA levels exceeding 400 μmol/L (all-cause dementia: HR = 1.11, 95% CI = 1.02–1.21; VD: HR = 1.50, 95% CI = 1.28–1.77). Conclusions Our study provides robust evidence supporting the association between MetS, its components, and dementia risk. These findings emphasize the importance of considering MetS and SUA levels in assessing dementia risk, offering valuable insights for prevention and management strategies.