Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals.

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.

Risk prediction for preeclampsia in CKD patients: development of a model in a retrospective cohort.

BACKGROUND: Chronic kidney disease (CKD) may affect women of childbearing age and may lead to substantial maternal and foetal morbidity and mortality in pregnancy. There is a lack of prediction models for  preeclampsia and adverse pregnancy outcomes in pregnant women with CKD. This study aimed to create a prediction nomogram for these issues. METHODS: This retrospective cohort study included clinical data from 627 women with CKD and their 627 pregnancies at Peking University First Hospital between January 1, 2009, and December 31, 2022. Multivariate logistic regression analysis was conducted to identify independent prognostic factors and develop a nomogram for predicting the occurrence of preeclampsia. The identified risk factors were utilised to construct the nomogram, which was subsequently internally validated using receiver operating characteristic (ROC) analysis and calibration curve assessment. RESULTS: According to our multivariate analysis, age, blood urea nitrogen (BUN), serum creatinine (Scr), mean arterial pressure (MAP), 24-h proteinuria, and CKD stage were identified as predictors of preeclampsia. Additionally, Scr, MAP, BUN, and 24-h proteinuria were found to be predictors of adverse pregnancy outcomes. The nomogram for predicting preeclampsia had an area under the ROC curve of 0.910, while the nomogram for predicting adverse pregnancy outcomes had an area under the ROC curve of 0.906. Both models demonstrated excellent discriminatory ability. CONCLUSIONS: A nomogram based on 24-h proteinuria, serum creatinine, serum urea and age, and MAP allows predicting the occurrence of preeclampsia and other adverse pregnancy-related outcomes in CKD patients.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling.

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today's complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

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Objective: Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes. Methods: The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy. Results: Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant. Conclusions: This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

Deubiquitinating Enzyme USP19 Regulates Ferroptosis and Mitochondrial Damage in SH-SY5Y Cells by Targeting the NOX4 Protein.

Background: Ferroptosis is extremely relevant to the progression of neurodegenerative pathologies such as Alzheimer's disease (AD). Ubiquitin-specific proteases (USP) can affect the NADPH oxidase family. Objective: Our study aimed to elucidate the potential role and molecular basis of a certain USP19 in reducing ferroptosis and mitochondrial injury in AD cells by targeting NOX4 stability. Methods: The deubiquitinase USP family gene USP19, which affects the stability of NOX4 protein, was first screened. The cell model of AD was constructed after interfering with SH-SY5Y cells by Aß1-40, and then SH-SY5Y cells were infected with lentiviral vectors to knock down USP19 and overexpress NOX4, respectively. Finally, the groups were tested for cell viability, changes in cellular mitochondrial membrane potential, lipid reactive oxygen species, intracellular iron metabolism, and NOX4, Mf1, Mf2, and Drp1 protein expression. Results: 5 µmol/L Aß1-40 intervened in SH-SY5Y cells for 24 h to construct a cell model of AD. Knockdown of USP19 decreased the expression of NOX4 protein, promoted the expression of mitochondrial fusion proteins Mnf1 and Mnf2, and inhibited the expression of the splitting protein Drp1. Furthermore, USP19 knockdown decreased mitochondrial membrane potential, SOD, MDA, intracellular iron content and increased GSH/GSSG ratio in SH-SY5Y cells. Our study revealed that NOX4 protein interacts with USP19 and knockdown of USP19 enhanced ubiquitination to maintain NOX4 protein stability. Conclusions: USP19 attenuates mitochondrial damage in SH-SY5Y cells by targeting NOX4 protein with Aß1-40.

Reconfigurable spintronic logic gate utilizing precessional magnetization switching.

In traditional von Neumann computing architecture, the efficiency of the system is often hindered by the data transmission bottleneck between the processor and memory. A prevalent approach to mitigate this limitation is the use of non-volatile memory for in-memory computing, with spin-orbit torque (SOT) magnetic random-access memory (MRAM) being a leading area of research. In this study, we numerically demonstrate that a precise combination of damping-like and field-like spin-orbit torques can facilitate precessional magnetization switching. This mechanism enables the binary memristivity of magnetic tunnel junctions (MTJs) through the modulation of the amplitude and width of input current pulses. Building on this foundation, we have developed a scheme for a reconfigurable spintronic logic gate capable of directly implementing Boolean functions such as AND, OR, and XOR. This work is anticipated to leverage the sub-nanosecond dynamics of SOT-MRAM cells, potentially catalyzing further experimental developments in spintronic devices for in-memory computing.

Resource regulation and green innovation: Evidence from China's water rights trading policy.

Green innovation is the pivotal part connected with achieving urban sustainable development. Resource regulation, represented by water rights trading policy (WRTP), is playing an increasingly important role in supporting urban green innovation (UGI). Therefore, this paper uses the WRTP conducted in 2014 in China as a quasi-natural experiment to evaluate the net effect of resource regulation on green innovation by the identification methods of difference-in-differences (DID) model. The results show that: (1) WRTP promotes UGI significantly, and this finding holds after a series of robustness tests. (2)The influencing mechanisms are mainly by optimizing industrial structure and enhancing the efficiency of resource allocation and information exchange efficiency and the promoting function is moderated by urban competitiveness and population agglomeration. (3) The promotion effects are greater in cities with higher level of government intervention, environmental pollution, water using quantity and local economy.(4) WRTP has the spatial spillover effect on UGI. These findings provide insights into sustainable development of water resources, management of water trading market, urban green innovation and sustainable economic development.

An improved genome assembly of Chrysanthemum nankingense reveals expansion and functional diversification of terpene synthase gene family.

BACKGROUND: Terpenes are important components of plant aromas, and terpene synthases (TPSs) are the key enzymes driving terpene diversification. In this study, we characterized the volatile terpenes in five different Chrysanthemum nankingense tissues. In addition, genome-wide identification and expression analysis of TPS genes was conducted utilizing an improved chromosome-scale genome assembly and tissue-specific transcriptomes. The biochemical functions of three representative TPSs were also investigated. RESULTS: We identified tissue-specific volatile organic compound (VOC) and volatile terpene profiles. The improved Chrysanthemum nankingense genome assembly was high-quality, including a larger assembled size (3.26 Gb) and a better contig N50 length (3.18 Mb) compared to the old version. A total of 140 CnTPS genes were identified, with the majority representing the TPS-a and TPS-b subfamilies. The chromosomal distribution of these TPS genes was uneven, and 26 genes were included in biosynthetic gene clusters. Closely-related Chrysanthemum taxa were also found to contain diverse TPS genes, and the expression profiles of most CnTPSs were tissue-specific. The three investigated CnTPS enzymes exhibited versatile activities, suggesting multifunctionality. CONCLUSIONS: We systematically characterized the structure and diversity of TPS genes across the Chrysanthemum nankingense genome, as well as the potential biochemical functions of representative genes. Our results provide a basis for future studies of terpene biosynthesis in chrysanthemums, as well as for the breeding of improved chrysanthemum varieties.

Identifying modifiable factors and their joint effect on brain health: an exposome-wide association study.

Considerable uncertainty remains regarding the associations of multiple factors with brain health. We aimed to conduct an exposome-wide association study on neurodegenerative disease and neuropsychiatry disorders using data of participants from the UK Biobank. Multivariable Cox regression models with the least absolute shrinkage and selection operator technique as well as principal component analyses were used to evaluate the exposures in relation to common disorders of central nervous system (CNS). Restricted cubic splines were conducted to explore potential nonlinear correlations. Then, weighted standardized scores were generated based on the coefficients to calculate the joint effects of risk factors. We also estimated the potential impact of eliminating the unfavorable profiles of risk domains on CNS disorders using population attributable fraction (PAF). Finally, sensitivity analyses were performed to reduce the risk of reverse causality. The current study discovered the significantly associated exposures fell into six primary exposome categories. The joint effects of identified risk factors demonstrated higher risks for common disorders of CNS (HR = 1.278 ~ 3.743, p < 2e-16). The PAF varied by exposome categories, with lifestyle and medical history contributing to majority of disease cases. In total, we estimated that up to 3.7 ~ 64.1% of disease cases could be prevented.This study yielded modifiable variables of different categories and assessed their joint effects on common disorders of CNS. Targeting the identified exposures might help formulate effective strategies for maintaining brain health.

Insights into dynamic evolution of glucuronofucogalactoglucan from water extract of Agrocybe cylindracea during maturation.

This study explored the physicochemical properties and structural characteristics of Agrocybe cylindracea polysaccharides at four developmental stages, as well as their dynamic evolution during maturation. Results showed that the polysaccharides from A. cylindracea water extract exhibited similar structural characteristics across all four maturity stages, despite a significant reduction in yields. Four water-soluble heteroglycans, including one high molecular weight (ACPM-Et50-I) and three low molecular weight (ACPM-Et50-II, ACPM-Et60, ACPM-Et80), were isolated from A. cylindracea at each maturity stage. ACPM-Et50-I was identified as branched heterogalactans, while ACPM-Et60 and ACPM-Et80 were branched heteroglucans. However, ACPM-Et50-II was characterized as a branched glucuronofucogalactoglucan at the tide-turning stage but a glucuronofucoglucogalactan at the pileus expansion stage due to the increase of its α-(1 â†’ 6)-D-Galp. In general, although the structural skeletons of most A. cylindracea heteroglycans were similar during maturation as shown by their highly consistent glycosyl linkages, there were still differences in the distribution of some heteroglucans. This work has for the first time reported a glucuronofucogalactoglucan in A. cylindracea and its dynamic evolution during maturation, which may facilitate the potential application of A. cylindracea in food and biomedicine industries.

Enhanced Reendothelialization and Thrombosis Prevention with a New Drug-Eluting Stent.

PURPOSE: The objective of the study is to test the efficacy of cyclopentenyl cytosine (CPEC)-coated stents on blocking artery stenosis, promoting reendothelialization, and reducing thrombosis. METHODS: Scanning electron microscopy was employed to observe the morphological characteristics of stents coated with a mixture of CPEC and poly(lactic-co-glycolic acid) (PLGA) copolymer. PLGA has been used in various Food and Drug Administration (FDA)-approved therapeutic devices. In vitro release of CPEC was tested to measure the dynamic drug elution. Comparison between CPEC- and everolimus-coated stents on neointimal formation and thrombosis formation was conducted after being implanted into the human internal mammary artery and grafted to the mouse aorta. RESULTS: Optimization in stent coating resulted in uniform and consistent coating with minimal variation. In vitro drug release tests demonstrated a gradual and progressive discharge of CPEC. CPEC- or everolimus-coated stents caused much less stenosis than bare-metal stents. However, CPEC stent-implanted arteries exhibited enhanced reendothelialization compared to everolimus stents. Mechanistically, CPEC-coated stents reduced the proliferation of vascular smooth muscle cells while simultaneously promoting reendothelialization. More significantly, unlike everolimus-coated stents, CPEC-coated stents showed a significant reduction in thrombosis formation even in the absence of ongoing anticoagulant treatment. CONCLUSIONS: The study establishes CPEC-coated stent as a promising new device for cardiovascular interventions. By enhancing reendothelialization and preventing thrombosis, CPEC offers advantages over conventional approaches, including the elimination of the need for anti-clogging drugs, which pave the way for improved therapeutic outcomes and management of atherosclerosis-related medical procedures.

Transforming crystal structures of cobalt molybdate to generate electron-rich sites for electrochemical detection of Pb(II).

BACKGROUND: Most of the investigations on distinct crystal structures of catalysts are individually focused on the difference of surface functional groups or adsorption properties, but rarely explore the changes of active sites to affect the electrocatalytic performance. Catalysts with diverse crystal structures had been applied to modified electrodes in different electrocatalytic reactions. However, there is currently a lack of an essential understanding for the role of real active sites in catalysts with crystalline structures in electroanalysis, which is crucial for designing highly sensitive sensing interfaces. RESULTS: Herein, cobalt molybdate with divergent crystal structures (α-CoMoO4 and ß-CoMoO4) were synthesized by adjusting the calcination temperature, indicating that α-CoMoO4 (800 °C) (60.00 µA µM-1) had the highest catalytic ability than ß-CoMoO4 (700 °C) (38.68 µA µM-1) and α-CoMoO4 (900 °C) (29.55 µA µM-1) for the catalysis of Pb(II). It was proved that the proportion of Co(II) and Mo(IV) as electron-rich sites in α-CoMoO4 (800 °C) were higher than ß-CoMoO4 (700 °C) and α-CoMoO4 (900 °C), possessing more electrons to participate in the valence cycles of Co(II)/Co(III) and Mo(IV)/Mo(VI) to boost the catalytic reduction of Pb(II). Specifically, Co(II) transferred a part of electrons to Mo(VI), promoting the formation of Mo(IV). Co(II) and Mo(IV), as the electron-rich sites, providing electrons to Pb(II), further accelerating the conversion of Pb(II) into Pb(0). SIGNIFICANCE: In the process of detecting Pb(II), the CoMoO4 structures under different temperatures have distinct content of electron-rich sites Co(II) and Mo(IV). α-CoMoO4 (800 °C), with the highest content are benefited to detect Pb(II). This work is conducive to understanding the effect of the changes of active sites resulting from crystal transformation on the electrocatalytic performance, and provides a way to construct sensitive electrochemical interfaces of distinct active sites.

Combined impact of sleep and obesity on female infertility in the NHANES 2017-2020.

BACKGROUND: Sleep health and obesity may affect the risk of female infertility. However, few studies focused on the interaction of obesity and sleep health on the female infertility risk. This study aimed to evaluate the combined impact of trouble sleeping / sleep duration and overweight/obesity/ abdominal obesity on the risk of female infertility. METHODS: The data for this cross-sectional study was obtained from National Health and Nutritional Examination Survey, which provided information on trouble sleeping, sleep duration, overweight/obesity, abdominal obesity, and confounding factors. Adopted weighted univariate and multivariate logistic regression models to explore the relationship between trouble sleeping, sleep duration, overweight/obesity, abdominal obesity, and the risk of infertility, respectively, and the combined effect of trouble sleeping and overweight/obesity, trouble sleeping and abdominal obesity, sleep duration and overweight/obesity, sleep duration and abdominal obesity, on the female infertility risk. RESULTS: This study included a total of 1,577 women, and 191 were diagnosed with infertility. Women with infertility had a higher proportion of people with overweight/obesity, abdominal obesity, sleep duration ≤ 7 h and trouble sleeping than those with non-infertility. The result indicated that trouble sleeping [odds ratio (OR) = 2.25, 95% confidence intervals (CI): 1.49-3.39], sleep duration ≤ 7 h (OR = 1.59, 95% CI: 1.03-2.48), and the combined impact of abdominal obesity and trouble sleeping (OR = 2.18, 95% CI: 1.28-3.72), abdominal obesity and sleep duration ≤ 7 h (OR = 2.00, 95% CI: 1.17-3.40), overweight/obesity and trouble sleeping (OR = 2.29, 95% CI: 1.24-4.26), and overweight/obesity and sleep duration ≤ 7 h (OR = 1.88, 95% CI: 1.01-3.49) were associated with increased odds of infertility, respectively. CONCLUSION: There was combined effects of trouble sleeping/sleep duration ≤ 7 h and overweight/obesity/ abdominal obesity on increased odds of female infertility.

Di-PEGylated insulin: A long-acting insulin conjugate with superior safety in reducing hypoglycemic events.

Although the discovery of insulin 100 years ago revolutionized the treatment of diabetes, its therapeutic potential is compromised by its short half-life and narrow therapeutic index. Current long-acting insulin analogs, such as insulin-polymer conjugates, are mainly used to improve pharmacokinetics by reducing renal clearance. However, these conjugates are synthesized without sacrificing the bioactivity of insulin, thus retaining the narrow therapeutic index of native insulin, and exceeding the efficacious dose still leads to hypoglycemia. Here, we report a kind of di-PEGylated insulin that can simultaneously reduce renal clearance and receptor-mediated clearance. By impairing the binding affinity to the receptor and the activation of the receptor, di-PEGylated insulin not only further prolongs the half-life of insulin compared to classical mono-PEGylated insulin but most importantly, increases its maximum tolerated dose 10-fold. The target of long-term glycemic management in vivo has been achieved through improved pharmacokinetics and a high dose. This work represents an essential step towards long-acting insulin medication with superior safety in reducing hypoglycemic events.

Whole exome sequencing analyses reveal novel genes in telomere length and their biomedical implications.

Telomere length is a putative biomarker of aging and is associated with multiple age-related diseases. There are limited data on the landscape of rare genetic variations in telomere length. Here, we systematically characterize the rare variant associations with leukocyte telomere length (LTL) through exome-wide association study (ExWAS) among 390,231 individuals in the UK Biobank. We identified 18 robust rare-variant genes for LTL, most of which estimated effects on LTL were significant (> 0.2 standard deviation per allele). The biological functions of the rare-variant genes were associated with telomere maintenance and capping and several genes were specifically expressed in the testis. Three novel genes (ASXL1, CFAP58, and TET2) associated with LTL were identified. Phenotypic association analyses indicated significant associations of ASXL1 and TET2 with cancers, age-related diseases, blood assays, and cardiovascular traits. Survival analyses suggested that carriers of ASXL1 or TET2 variants were at increased risk for cancers; diseases of the circulatory, respiratory, and genitourinary systems; and all-cause and cause-specific deaths. The CFAP58 carriers were at elevated risk of deaths due to cancers. Collectively, the present whole exome sequencing study provides novel insights into the genetic landscape of LTL, identifying novel genes associated with LTL and their implications on human health and facilitating a better understanding of aging, thus pinpointing the genetic relevance of LTL with clonal hematopoiesis, biomedical traits, and health-related outcomes.

Ultraviolet-induced red fluorescence in androgenetic alopecia-indicating alterations in microbial composition.

BACKGROUND: Ultraviolet (UV)-induced fluorescence technology is widely used in dermatology to identify microbial infections. Our clinical observations under an ultraviolet-induced fluorescent dermatoscope (UVFD) showed red fluorescence on the scalps of androgenetic alopecia (AGA) patients. In this study, based on the hypothesis that microbes are induced to emit red fluorescence under UV light, we aimed to explore the microbial disparities between the AGA fluorescent area (AF group) and AGA non-fluorescent area (ANF group). METHODS: Scalp swab samples were collected from 36 AGA patients, including both fluorescent and non-fluorescent areas. The bacterial communities on the scalp were analyzed by 16S rRNA gene sequencing and bioinformatics analysis, as well as through microbial culture methods. RESULTS: Significant variations were observed in microbial evenness, abundance composition, and functional predictions between fluorescent and non-fluorescent areas. Sequencing results highlighted significant differences in Cutibacterium abundance between these areas (34.06% and 21.36%, respectively; p < 0.05). Furthermore, cultured red fluorescent colonies primarily consisted of Cutibacterium spp., Cutibacterium acnes, Staphylococcus epidermidis, and Micrococcus spp. CONCLUSIONS: This is the first study to investigate scalp red fluorescence, highlighting microbial composition variability across different scalp regions. These findings may provide novel insights into the microbiological mechanisms of AGA.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. MATERIAL AND METHODS: The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. RESULTS: The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949). CONCLUSION: The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology.

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

Chronic Intermittent Hypoxia Facilitates the Development of Angiotensin II-Induced Abdominal Aortic Aneurysm in Male Mice.

Obstructive sleep apnea (OSA), characterized by episodes of intermittent hypoxia (IH), is highly prevalent in patients with abdominal aortic aneurysm (AAA). However, whether IH serves as an independent risk factor for AAA development remains to be investigated. Here, we determined the effects of chronic (6 months) IH on angiotensin (Ang II)-induced AAA development in C57BL/6J male mice, and IH underlying mechanisms in cultured vascular smooth muscle cells (SMCs). IH increased abdominal aortic diameter and the incidence of AAA in mice infused with Ang II as assessed by transabdominal ultrasound imaging. Importantly, IH with Ang II augmented aortic elastin degradation and expression of matrix metalloproteinase (MMP)s, mainly MMP8, MMP12 and a disintegrin and metalloproteinase-17 (ADAM17) as measured by histology and immunohistochemistry. Mechanistically, IH increased the activities of MMP2, MMP8, MMP9, MMP12, and ADAM17, while reducing the expression of the MMP regulator, reversion inducing cysteine rich protein with kazal motifs (RECK) in cultured SMCs. Aortic samples from human AAA were associated with decreased RECK and increased expression of ADAM17 and MMPs. These data suggest that IH promotes the development of AAA in association with an increased expression of MMPs and ADAM17, while decreased expression of RECK may be responsible for the increased protease activity. These findings support a potential causal link between OSA and AAA and provide a better understanding of the molecular mechanisms underlying the pathogenesis of AAA.

PAI-1 Regulates the Cytoskeleton and Intrinsic Stiffness of Vascular Smooth Muscle Cells.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.