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Background The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) serves as a novel composite lipid indicator for atherosclerosis. However, the association between NHHR and mortality in patients with diabetes or prediabetes remains unclear. Consequently, the objective of this study was to investigate the relationship between NHHR and both all-cause and cardiovascular mortality in US adults with diabetes or prediabetes. Methods This study included 12,578 adult participants with diabetes or prediabetes from the US National Health and Nutrition Examination Survey (1999–2018). Mortality outcomes were ascertained by linking to the National Death Index (NDI) record up to December 31, 2019. We employed a weighted multivariate Cox proportional hazards model and restricted cubic splines to assess the associations between NHHR and all-cause and cardiovascular mortality. A segmented Cox proportional hazards model was used for evaluating threshold effects. Furthermore, a competing risks analysis was performed to explore the relationship between NHHR and cardiovascular mortality. Results During a median follow-up period of 8.08 years, 2403 participants encountered all-cause mortality, with 662 of them specifically succumbing to cardiovascular mortality. The restricted cubic splines revealed a U-shaped association between NHHR and all-cause mortality, while an L-shaped association was observed for cardiovascular mortality. The analysis of threshold effects revealed that the inflection points for NHHR and all-cause and cardiovascular mortality were 2.72 and 2.83, respectively. Specifically, when the baseline NHHR was below the inflection points, a negative correlation was observed between NHHR and both all-cause mortality (HR: 0.76, 95% CI: 0.68–0.85) and cardiovascular mortality (HR: 0.70, 95% CI: 0.57–0.85). Conversely, when the baseline NHHR exceeded the inflection points, a positive correlation was observed between NHHR and both all-cause mortality (HR: 1.11, 95% CI: 1.06–1.16) and cardiovascular mortality (HR: 1.08, 95% CI: 1.00–1.16). Conclusions Among US adults with diabetes or prediabetes, a U-shaped association was observed between NHHR and all-cause mortality, whereas an L-shaped association was identified with cardiovascular mortality. The inflection points for all-cause and cardiovascular mortality were 2.72 and 2.83, respectively.

Synthetic auxotrophy in which cell viability depends on the presence of an unnatural amino acid (unAA) provides a powerful strategy to restrict unwanted propagation of genetically modified organisms (GMOs) in open environments and potentially prevent industrial espionage. Here, we describe a generic approach for robust biocontainment of budding yeast dependent on unAA. By understanding escape mechanisms, we specifically optimize our strategies by introducing designed “immunity” to the generation of amber-suppressor tRNAs and developing the transcriptional- and translational-based biocontainment switch. We further develop a fitness-oriented screening method to easily obtain multiplex safeguard strains that exhibit robust growth and undetectable escape frequency (<~10 −9 ) on solid media for 14 days. Finally, we show that employing our multiplex safeguard system could restrict the proliferation of strains of interest in a real fermentation scenario, highlighting the great potential of our yeast biocontainment strategy to protect the industrial proprietary strains. Synthetic auxotrophy in which cell viability depends on the presence of an unnatural amino acid provides a powerful strategy to restrict unwanted propagation of genetically modified organisms in open environments and potentially prevent industrial espionage. Here the authors establish a general framework for the creation and optimization of synthetic auxotrophs in yeast.

Cell death mechanisms are integral to the pathogenesis of breast cancer (BC), with ATP-induced cell death (AICD) attracting increasing attention due to its distinctive specificity and potential therapeutic applications. This study employed genomic methodologies to investigate the correlation between drug sensitivity and types of AICD in BC. Initially, data from TCGA were utilized to construct a prognostic model and classification system for AICD. Subsequently, a series of bioinformatics analyses assessed the prognostic and clinical significance of this model within the context of BC. Analysis revealed a cohort of 18 genes associated with AICD, exhibiting prognostic relevance. Survival analyses indicated that overall survival rates were significantly lower in high-risk populations compared to their low-risk counterparts. Furthermore, prognostic indicators linked to AICD demonstrated high accuracy in predicting survival outcomes in BC. Immunological assessments indicated heightened expression of anti-tumor infiltrating immune cells and immune checkpoint molecules in low-risk populations, correlating with various anti-tumor immune functions. Ultimately, a comprehensive prognostic model related to AICD was developed through univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. As Adenosine triphosphate (ATP) concentration increased, the viability of BC cells exhibited a general decline at each time point. Notably, ATP diminished the mitochondrial membrane potential in BC cells while enhancing it in normal breast epithelial cells. Additionally, ATP inhibited the migration of BC cells and promoted their apoptosis. ATP also stimulated reactive oxygen species (ROS) production in MCF-10A cells, with implications for the immune response in BC cells. Compared to the control group, expression levels of , , and were significantly reduced in the ATP intervention group, whereas expression was elevated. , , and share genetic variants with BC, while does not exhibit genetic causal variation with the disease. A valuable prognostic model associated with AICD has been established, capable of accurately predicting BC prognosis. The induction of cell death by ATP appears to play a protective role in BC progression. These findings carry significant implications for the implementation of personalized and tailored treatment strategies for BC patients.

Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD. Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects. 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD. Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

Background The red blood cell distribution width to albumin ratio (RAR) is a novel comprehensive biomarker of inflammation and nutrition, which has emerged as a reliable prognostic indicator for adverse outcomes and mortality in patients with various diseases. However, the association between RAR and low cognitive performance in older adults remains unclear. This study aims to investigate the relationship between RAR and low cognitive performance among older adults in the United States. Methods This study, a retrospective analysis, included 2,765 participants aged 60 years and older from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2014. Low cognitive performance was assessed using word learning subset from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT). Low cognitive performance was defined as scores below the lowest quartile in each cognitive test. The association between RAR and low cognitive performance was evaluated using weighted multivariable logistic regression, restricted cubic splines (RCS), and subgroup analyses. Results After adjusting for all potential confounders, RAR was independently and linearly positively associated with both low DSST performance and low AFT performance. Specifically, compared to participants in the first quartile of RAR, those in the fourth quartile had adjusted ORs (95% CIs) of 1.81 (1.03, 3.20) for low DSST performance and 1.68 (1.05, 2.67) for low AFT performance. Subgroup analysis did not reveal significant interactions between stratification variables. Conclusion RAR is significantly linearly positively associated with low cognitive performance. Maintaining a lower RAR may be a crucial strategy for mitigating the risk of cognitive decline in the elderly population.

Pharmacogenomics (PGx) is a powerful tool for clinical optimization of drug efficacy and safety. However, due to many factors affecting drugs in the real world, PGx still accounts for a small proportion of actual clinical application scenarios. Therefore, based on the information software, pharmacists use their professional advantages to integrate PGx into all aspects of pharmaceutical care, which is conducive to promoting the development of personalized medicine. In this paper, the establishment of an information software platform is summarized for the optimization of a personalized medication program based on PGx. Taking colorectal cancers (CRC) as an example, this paper also discusses the role of PGx in different working modes and participation in drug management of CRC patients by pharmacists with the help of information systems. Finally, we summarized the recommendations of different PGx guidelines to provide reference for the follow-up personalized pharmaceutical care.

Background Blinding drugs through simulation techniques is an important means to control the subjective bias of investigators and subjects. However, clinical trials face significant challenges in the placebo production of drugs, and many trials cannot be double-blinded. Objective This study was conducted to ascertain the consistency between non-blind and blind evaluation results in clinical trials and to pioneer strategies to control information bias, particularly in trials where double-blinding is not feasible. Methods In this investigation, a randomized controlled trial (RCT) studying diabetic foot infections (DFIs) was utilized as a representative case. In this trial, the grading of DFIs, as per guidelines by the Infectious Disease Society of America (IDSA) and International Working Group on Diabetic Foot (IWGDF), was used as the primary efficacy indicator. A sample of sixteen patients was randomly chosen from the RCT, and DFI grading was assessed jointly by both non-blinded investigators and blinded center-reading investigators. A consistency test was then deployed to compare the evaluation results, forming the basis for our proposed strategies for effective blinded evaluation. In addition, other perspectives were collected at the end of this study, including with those involved in designing and conducting the recent blinded evaluation trial. Results Five subjects were excluded due to the quality of photos or the lack of post-treatment visits. The post-treatment IDSA/IWGDF grading results were compared in 11 subjects (experimental group=6, control group=5), and the consistency test showed inconsistent results between the non-blinded and center reading blinded evaluations (Kappa=0.248, p =0.384). In the experimental group, three cases were judged as grade 1 in the non-blinded evaluation and grade 2 in the central reading blinded evaluation; in the control group, three cases were judged as grade 2 in the non-blinded evaluation and grade 1 in the central reading blinded evaluation. The sum of these two cases in 22 post-treatment determinations was 27% (6/22). Furthermore, researchers propose several strategies for implementing blinded evaluations in clinical trials after this trial, which encompass aspects such as staff allocation, training, participant management, trial drug administration, efficacy indicator collection, and safety event management. Conclusions The study highlighted that evaluations from non-blinded site investigators may potentially exaggerate the efficacy of the experimental group and that deep wounds can present challenges for observation via center-reading photos. These findings underline the vital necessity for objective assessment in open clinical trials, especially those where wound observation serves as the primary efficacy indicator. The study suggests the adoption of independent blinded investigators at each site, complemented by a comprehensive set of standard operating procedures for blinding evaluation. These measures could serve as an effective counterbalance to subjective bias, thereby augmenting the credibility and consistency of results in open clinical trials. The implications of these findings and recommendations could be of great significance for the design and execution of future open clinical trials, potentially bolstering the quality of clinical research in this area. Trial registration ChiCTR2000041443. Registered on December 2020

Inaccurate estimation of surface reflectance characteristics is one of the substantial sources of uncertainty in satellite aerosol optical property retrieval algorithms. Bidirectional reflectance distribution function (BRDF) models are frequently employed by retrieval algorithms to obtain the reflective properties of diverse surface types. This study, aerosol optical depth (AOD), fine mode AOD (AODF), coarse mode AOD (AODC), absorbing AOD (AAOD), single scattering albedo (SSA), Ångström exponent (AE), absorbing AE (AAE), and scattering AE (SAE) are retrieved by the GRASP/Component algorithm with Ross_Li and Rahman–Pinty–Verstraete (RPV) models. Results indicate overall consistency in AOD, AODF, and AODC between the 2 models, with slightly better performance in North and Central America, Europe, and southern Africa. In northern Africa, the Middle East, and eastern Asia, there is slightly worse performance. Generally, the Ross_Li model yields slightly lower values for AOD and AODF, and the difference in AOD retrieval uncertainty seems primarily due to AODF. AODC retrieved by Ross_Li tends to be overestimated compared to RPV, with higher uncertainties. Uncertainties in AAOD and SSA retrievals are more complex, with the RPV model producing higher AAOD values in southern Europe and western North America. SSA retrievals are more scattered, with larger root mean square error, yet lower relative deviations due to generally larger values. The RPV model yields slightly lower SSA values, with good consistency in southern Europe and southern Asia. Uncertainties in AE, AAE, and SAE retrievals are significant, particularly in AAE, where the largest bias is observed. Improving the accuracy of AAOD inversion might reduce the uncertainty in AAE.

As the scale of artificial neural networks continues to expand to tackle increasingly complex tasks or improve the prediction accuracy of specific tasks, the challenges associated with computational demand, hyper-parameter tuning, model interpretability, and deployment costs intensify. Addressing these challenges requires a deeper understanding of how network structures influence network performance. Here, we analyse 882,000 motifs to reveal the functional roles of incoherent and coherent three-node motifs in shaping overall network performance. Our findings reveal that incoherent loops exhibit superior representational capacity and numerical stability, whereas coherent loops show a distinct preference for high-gradient regions within the output landscape. By avoiding such gradient pursuit, incoherent loops sustain more stable adaptation and consequently greater robustness. This mechanism is evident in 97,240 fixed-network training experiments, where coherent-loop networks consistently prioritized high-gradient regions during learning, and is further supported by noise-resilience analyses - from classical reinforcement learning tasks to biological, chemical, and medical applications - which demonstrate that incoherent-loop networks maintain stronger resistance to training noise and environmental perturbations. This work shows the functional impact of structural motif differences on the performance of artificial neural networks, offering foundational insights for designing more resilient and accurate networks.

DNA is a promising data storage medium due to its remarkable durability and space-efficient storage. Early bit-to-base transcoding schemes have primarily pursued information density, at the expense of introducing biocompatibility challenges or decoding failure. Here we propose a robust transcoding algorithm named the yin–yang codec, using two rules to encode two binary bits into one nucleotide, to generate DNA sequences that are highly compatible with synthesis and sequencing technologies. We encoded two representative file formats and stored them in vitro as 200 nt oligo pools and in vivo as a ~54 kbps DNA fragment in yeast cells. Sequencing results show that the yin–yang codec exhibits high robustness and reliability for a wide variety of data types, with an average recovery rate of 99.9% above 10 4 molecule copies and an achieved recovery rate of 87.53% at ≤10 2 copies. Additionally, the in vivo storage demonstration achieved an experimentally measured physical density close to the theoretical maximum.