Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

SCAR: Single-cell and Spatially-resolved Cancer Resources.

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

SCAN: Spatiotemporal Cloud Atlas for Neural cells.

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.

It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.

Enhanced quality of hESC-derived melanocytes through modified concentration of endothelin-1.

The study investigated the effectiveness of EDN1 and EDN3 cytokines in the differentiation of melanocytes from hESCs. The findings showed that 100 nM EDN1 was more effective in promoting hESC to CD117+/TYR+ melanoblasts compared to 100 nM EDN3. Additionally, maintaining melanoblasts is beneficial for preserving the ability to proliferate. The study found that 10 nM EDN1 helped maintain the proliferation of melanoblasts without over maturing them into melanocytes in the late stage of differentiation. Thus, using 100 nM EDN1 in the initial stage and 10 nM EDN1 in the late stage proved to be an efficient and cost-effective method for obtaining hESC-derived melanocytes. The preliminary results suggest that EDN1 promotes melanoblast formation during the initial differentiation stage through its binding to both the EDNRB receptor and EDNRA receptor. This study provides a valuable tool for studying the development of human melanocytes and modelling the biology of disease.

Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica.

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems' neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia's grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia's grasper, has not been characterized. Because of the importance of the musculature's properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior of Aplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle's peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex's kinematics in vivo.

Boosting the sonodynamic performance of CoBiMn-layered double hydroxide nanoparticles via tumor microenvironment regulation for ultrasound imaging-guided sonodynamic therapy.

Sonodynamic therapy (SDT), a promising strategy for cancer treatment with the ability for deep tissue penetration, has received widespread attention in recent years. Sonosensitizers with intrinsic characteristics for tumor-specific curative effects, tumor microenvironment (TME) regulation and tumor diagnosis are in high demand. Herein, amorphous CoBiMn-layered double hydroxide (a-CoBiMn-LDH) nanoparticles are presented as multifunctional sonosensitizers to trigger reactive oxygen species (ROS) generation for ultrasound (US) imaging-guided SDT. Hydrothermal-synthesized CoBiMn-LDH nanoparticles are etched via a simple acid treatment to obtain a-CoBiMn-LDH nanoparticles with abundant defects. The a-CoBiMn-LDH nanoparticles give greater ROS generation upon US irradiation, reaching levels ~ 3.3 times and ~ 8.2 times those of the crystalline CoBiMn-LDH nanoparticles and commercial TiO2 sonosensitizer, respectively. This excellent US-triggered ROS generation performance can be attributed to the defect-induced narrow band gap and promoted electrons and holes (e-/h+) separation. More importantly, the presence of Mn4+ enables the a-CoBiMn-LDH nanoparticles to regulate the TME by decomposing H2O2 into O2 for hypoxia relief and US imaging, and consuming glutathione (GSH) for protection against ROS clearance. Biological mechanism analysis shows that a-CoBiMn-LDH nanoparticles modified with polyethylene glycol can serve as a multifunctional sonosensitizer to effectively kill cancer cells in vitro and eliminate tumors in vivo under US irradiation by activating p53, apoptosis, and oxidative phosphorylation-related signaling pathways.

Ametoctradin resistance risk and its resistance-related point mutation in PsCytb of Phytophthora sojae confirmed using ectopic overexpression.

Ametoctradin is mainly used to treat plant oomycetes diseases, but the mechanism and resistance risk of ametoctradin in Phytophthora sojae remain unknown. This study determined the ametoctradin sensitivity of 106 P. sojae isolates and found that the frequency distribution of the median effective concentration (EC50) of ametoctradin was unimodal with a mean value of 0.1743 ± 0.0901 µg/mL. Furthermore, ametoctradin-resistant mutants had a substantially lower fitness index compared with that of wild-type isolates. Although ametoctradin did not show cross-resistance to other fungicides, negative cross-resistance to amisulbrom was found. In comparison to sensitive isolates, the control efficacy of ametoctradin to resistant mutants was lower, implying a low to moderate ametoctradin resistance risk in P. sojae. All ametoctradin-resistant mutants contained a S33L point mutation in PsCytb. A system with overexpression of PsCytb in the nucleus was established. When we ectopically overexpressed S33L-harboring PsCytb, P. sojae developed ametoctradin resistance. We hypothesized that the observed negative resistance between ametoctradin and amisulbrom could be attributed to conformational changes in the binding cavity of PsCytb at residues 33 and 220.

Post-Pandemic: Investigation of the Degradation of Various Commercial Masks in the Marine Environment.

During COVID-19, personal protective equipment such as face masks was in urgent demand in the daily life. As the pandemic may have withdrawn from public attention, the disposal of face masks is a significant issue, especially plastic pollution. To address the degradation of the polymers in the marine environment, seven commercial masks were investigated via artificial weathering procedures in substitute ocean water. A suite of structural and chemical characterization techniques was employed, including scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, X-ray diffraction (XRD), and contact angle goniometry, to probe the treatment impact on commercially available N95, surgical, polyurethane, polyester, nylon, silk, and cotton masks. This work provides insights into the comprehensive analysis of material degradation in nature and raises public awareness of environmental issues post-pandemic.

Smartphone-based hand-held polarized light microscope for on-site pharmaceutical crystallinity characterization.

Polarized light microscopy (PLM) is a common but critical method for pharmaceutical crystallinity characterization, which has been widely introduced for research purposes or drug testing and is recommended by many pharmacopeias around the world. To date, crystallinity characterization of pharmaceutical solids is restricted to laboratories due to the relatively bulky design of the conventional PLM system, while little attention has been paid to on-site, portable, and low-cost applications. Herein, we developed a smartphone-based polarized microscope with an ultra-miniaturization design ("hand-held" scale) for these purposes. The compact system consists of an optical lens, two polarizers, and a tailor-made platform to hold the smartphone. Analytical performance parameters including resolution, imaging quality of interference color, and imaging reproducibility were measured. In a first approach, we illustrated the suitability of the device for pharmaceutical crystallinity characterization and obtained high-quality birefringence images comparable to a conventional PLM system, and we also showed the great promise of the device for on-site characterization with high flexibility. In a second approach, we employed the device as a proof of concept for a wider application ranging from liquid crystal to environmental pollutants or tissues from plants. As such, this smartphone-based hand-held polarized light microscope shows great potential in helping pharmacists both for research purposes and on-site drug testing, not to mention its broad application prospects in many other fields.

Association of serum cystatin C level with coronary atherosclerotic plaque burden: a comprehensive analysis of observational studies and genetic study.

BACKGROUND AND AIMS: Epidemiological studies show that high circulating cystatin C is associated with risk of cardiovascular disease (CVD), independent of creatinine-based renal function measurements. However, the relationship between serum cystatin C level and coronary atherosclerotic plaque burden is limited. We aimed to evaluate the relationship between circulating cystatin C and coronary atherosclerotic plaque burden. METHODS: This study was a cross-sectional study based on China community population. Measurements of plaque burden were based on the segment-involvement score (SIS) and segment stenosis score (SSS), which derived from the Coronary Artery Tree Model Depicting Coronary Artery Plaque Scores. Logistic regression model was used to demonstrate the association between cystatin C level and coronary artery plaque burden. Mendelian randomization (MR) analyses were conducted to assess the causal effect of cystatin C level on coronary atherosclerosis risk. RESULTS: A total of 3,043 objects were included in the present study. The odds risks (OR) of severe plaque burden in the highest serum cystatin C levels (OR: 2.50; Cl:1.59-3.91; P < 0.001) and medium-level cystatin C levels (OR: 1.86; 95% Cl: 1.21-2.88; P = 0.005) were significantly higher after fulled adjusted confounders compared with the lowest levels of serum cystatin C by SSS. The MR analysis showed that genetic predicted cystatin C levels was associated with an increased risk of coronary atherosclerosis (OR, 1.004; 95% CI, 1.002-1.006, P < 0.001) . CONCLUSION: Elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. Cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level. WHAT IS ALREADY KNOWN ON THIS TOPIC?: Coronary artery disease is currently the most common cardiovascular disease and the leading global cause of mortality. Previous studies reported that higher serum cystatin C levels were associated with an increased risk for future cardiovascular events, independent of the normal creatinine levels or estimated glomerular filtration rate (eGFR) values. The presence of high-risk coronary atherosclerotic plaque burden is associated with increased risk of cardiovascular events. However, the association between serum cystatin C and coronary atherosclerotic plaque burden is not very clear. WHAT THIS STUDY ADDS?: Our study demonstrated that the elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. In addition, we found that serum cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY?: Current research finds that serum cystatin C levels were associated with coronary atherosclerosis. The metabolic pathway of cystatin C could be a target for new therapies against CAD.

Epidemiological study of calcified aortic valve stenosis in a Chinese community population.

BACKGROUND AND AIMS: Due to the ageing global population, calcified aortic valve disease is currently the most common cardiac valve disorder. This study aimed to investigate the prevalence and the risk factors for calcified aortic valve stenosis (CAVS), and develop a prediction model for predicting CAVS risk. METHODS AND RESULTS: This study was derived from the cross-sectional baseline survey of the PRECISE study (NCT03178448). The demographic, clinical and laboratory information of each participant was obtained. Univariable and multivariable logistic regression models were used to determine CAVS risk factors. A prediction model for predicting CAVS risk based on risk factors was developed and the result was performed by nomogram. The discrimination of the prediction model was assessed by receiver operating characteristic curve analysis. The degree of fitting for the prediction model was assessed by calibration curve analysis. A total of 3067 participants (1427 men and 1640 women) were included. The prevalence of CAVS among those aged below 60 years old, 60-70 years old and over 70 years old was 4.1%, 10.3% and 21.9%, respectively. Multivariable regression analysis revealed that age (OR: 1.099; 95% CI: 1.076 to 1.123, p<0.001), pulse pressure (OR: 1.020; 95% CI: 1.009 to 1.031, p<0.001), uric acid (OR: 1.003; 95% CI: 1.001 to 1.004, p<0.001), glycosylated haemoglobin (HbA1c) (OR: 1.152; 95% CI: 1.028 to 1.292, p=0.015) and lipoprotein(a) (OR: 1.002; 95% CI: 1.001 to 1.002, p<0.001) were independent risk factors for CAVS. High-density lipoprotein cholesterol (HDL-C) was a protective factor for CAVS (OR: 0.539; 95% CI: 0.349 to 0.831, p=0.005). The prediction model including the above risk factors showed a risk prediction of CAVS with good discrimination. The area under the curve value was found to be 0.743 (95% CI: 0.711 to 0.775). CONCLUSION: CAVS is currently prevalent in the elderly Chinese population. Age, pulse pressure, HbA1c, lower-level HDL-C, lipoprotein(a) and uric acid are the independent risk factors for CAVS.

Functional Study of Amorpha fruticosa WRKY20 Gene in Response to Drought Stress.

The WRKY gene family in plants regulates the plant's response to drought through regulatory networks and hormone signaling. AfWRKY20 (MT859405) was cloned from Amorpha fruticosa (A. fruticosa) seedlings using RT-PCR. The binding properties of the AfWRKY20 protein and the W-box (a DNA cis-acting element) were verified both in vivo and in vitro using EMSA and Dual-Luciferase activity assays. RT-qPCR detected that the total expression level of AfWRKY20 in leaves and roots was 22 times higher in the 30% PEG6000 simulated drought treatment compared to the untreated group. Under the simulated drought stress treatments of sorbitol and abscisic acid (ABA), the transgenic tobacco with the AfWRKY20 gene showed enhanced drought resistance at the germination stage, with significantly increased germination rate, green leaf rate, fresh weight, and root length compared to the wild-type (WT) tobacco. In addition, the superoxide dismutase (SOD) activity, chlorophyll content, and Fv/Fm ratio of AfWRKY20 transgenic tobacco were significantly higher than those of the WT tobacco under natural drought stress, while the malondialdehyde (MDA) content and 3,3'-diaminobenzidine (DAB) and nitroblue tetrazolium (NBT) staining levels were lower. The expression levels of oxidation kinase genes (NbSOD, NbPOD, and NbCAT) in transgenic tobacco under drought stress were significantly higher than those in WT tobacco. This enhancement in gene expression improved the ability of transgenic tobacco to detoxify reactive oxygen species (ROS). The survival rate of transgenic tobacco after natural drought rehydration was four times higher than that of WT tobacco. In summary, this study revealed the regulatory mechanism of AfWRKY20 in response to drought stress-induced ABA signaling, particularly in relation to ROS. This finding provides a theoretical basis for understanding the pathways of WRKY20 involved in drought stress, and offers genetic resources for molecular plant breeding aimed at enhancing drought resistance.

Antioxidant and Antitumor Activities of Newly Synthesized Hesperetin Derivatives.

Hesperetin is a class of natural products with a wide range of sources and remarkable biological activities. In this study, we described the synthesis of a series of novel hesperetin derivatives and evaluated the in vitro antioxidant and antitumor activity of these compounds. Eleven novel compounds were synthesized in moderate yields. The compounds synthesized in this work exhibited antioxidant activities against DPPH and ABTS free radicals in a dose-dependent manner. Among them, compound 3f had the best antioxidant activity, with IC50 of 1.2 µM and 24 µM for DPPH and ABTS, respectively. The antitumor activity of the compounds against human cancer cell lines, such as breast MCF-7, liver HepG2, and cervical Hela, was determined by a standard 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Three compounds had moderate IC50 values. Interestingly, compound 3f had better biological activity than hesperetin, which matches the prediction by Maestro from Schrödinger. Therefore, the new hesperidin derivative is a promising drug for the treatment of cancer due to its effective antitumor activity. The results also suggested that the antitumor activities of hesperetin derivatives may be related to their antioxidant activities.

Unraveling tumour microenvironment heterogeneity in nasopharyngeal carcinoma identifies biologically distinct immune subtypes predicting prognosis and immunotherapy responses.

Currently, there is no strong evidence of the well-established biomarkers for immune checkpoint inhibitors (ICIs) in nasopharyngeal carcinoma (NPC). Here, we aimed to reveal the heterogeneity of tumour microenvironment (TME) through virtual microdissection of gene expression profiles. An immune-enriched subtype was identified in 38% (43/113) of patients, which was characterized by significant enrichment of immune cells or immune responses. The remaining patients were therefore classified as a non-Immune Subtype (non-IS), which exhibited highly proliferative features. Then we identified a tumour immune evasion state within the immune-enriched subtype (18/43, 42%), in which high expression of exclusion- and dysfunction-related signatures was observed. These subgroups were designated the Evaded and Active Immune Subtype (E-IS and A-IS), respectively. We further demonstrated that A-IS predicted favourable survival and improved ICI response as compared to E-IS and non-IS. In summary, this study introduces the novel immune subtypes and demonstrates their feasibility in tailoring immunotherapeutic strategies.

Estimated plasma volume status (ePVS) is a predictor for acute myocardial infarction in-hospital mortality: analysis based on MIMIC-III database.

BACKGROUND: Estimated plasma volume status (ePVS) has been reported that associated with poor prognosis in heart failure patients. However, no researchinvestigated the association of ePVS and prognosis in patients with acute myocardial infarction (AMI). Therefore, we aimed to determine the association between ePVS and in-hospital mortality in AMI patients. METHODS AND RESULTS: We extracted AMI patients data from MIMIC-III database. A generalized additive model and logistic regression model were used to demonstrate the association between ePVS levels and in-hospital mortality in AMI patients. Kaplan-Meier survival analysis was used to pooled the in-hospital mortality between the various group. ROC curve analysis were used to assessed the discrimination of ePVS for predicting in-hospital mortality. 1534 eligible subjects (1004 males and 530 females) with an average age of 67.36 ± 0.36 years old were included in our study finally. 136 patients (73 males and 63 females) died in hospital, with the prevalence of in-hospital mortality was 8.9%. The result of the Kaplan-Meier analysis showed that the high-ePVS group (ePVS ≥ 5.28 mL/g) had significant lower survival possibility in-hospital admission compared with the low-ePVS group (ePVS < 5.28 mL/g). In the unadjusted model, high-level of ePVS was associated with higher OR (1.09; 95% CI 1.06-1.12; P < 0.001) compared with low-level of ePVS. After adjusted the vital signs data, laboratory data, and treatment, high-level of ePVS were also associated with increased OR of in-hospital mortality, 1.06 (95% CI 1.03-1.09; P < 0.001), 1.05 (95% CI 1.01-1.08; P = 0.009), 1.04 (95% CI 1.01-1.07; P = 0.023), respectively. The ROC curve indicated that ePVS has acceptable discrimination for predicting in-hospital mortality. The AUC value was found to be 0.667 (95% CI 0.653-0.681). CONCLUSION: Higher ePVS values, calculated simply from Duarte's formula (based on hemoglobin/hematocrit) was associated with poor prognosis in AMI patients. EPVS is a predictor for predicting in-hospital mortality of AMI, and could help refine risk stratification.

Cerebral microbleeds are associated with blood pressure levels in individuals with hypertension.

Objective: Cerebral microbleeds (CMBs), which appear as small dot-like hypointense lesions, are strongly associated with cerebrovascular disease. Recently, numerous investigations have suggested that hypertension and age are risk factors for CMBs; however, whether blood pressure grade and age rank are related to the severity of CMBs remains unclear. The purpose of this research was to assess the association between cerebral microbleeds and blood pressure levels.Methods: In total, 460 consecutive hypertension patients (214 males and 246 females; aged 44-96 years, mean age 60.95 ± 6.82 years) from Lishui Central Hospital were enrolled and classified as CMB or non-CMB patients according to magnetic resonance imaging (MRI). Gradient echo T2*-weighted MRI was used to detect CMBs. Differences in blood pressure, CMB severity, and other patient characteristics were compared between the two groups. Multifactorial logistic regression was used to analyze the correlation between blood pressure and microbleeds.Results: In our study, CMB lesions were identified in 123 patients (26.7%), including 39 patients with CMB lesions located deep in the brain. In the hypertensive population, smoking is an independent risk factor for CMBs. Additionally, systolic blood pressure (SBP), diastolic blood pressure (DBP) and age are also independent risk factors for CMBs. Furthermore, a modest correlation was noted between the number of microbleeds and grade of hypertension.Conclusions: This study provides novel evidence that microbleed severity is associated with hypertension grade. This conclusion emphasizes the importance of antihypertensive therapy in hypertension patients to avoid an increase in CMBs.

An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study.

BACKGROUND: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.