Theory and Applications of Bayesian Lag Joint Model (BLJM) for Right-censored Time-to-event Data

Fluctuation often implies unknown risks among all walks of life, especially in stock market, global temperature and medicine. I propose to utilize a new measurement, defined by arc length in mathematical literature, to capture fluctuations or variations of longitudinal risk factors. Specifically, its advantages can be fused into our proposed Bayesian Lag Joint Model (BLJM) to address the research problem of interest in survival analysis. Most statistical methods built upon the Cox proportional hazards model assume the hazard rate at a specific time is impacted by either the baseline or the instantaneous value (time-dependent Cox model) of covariate. But this assumption is often questionable when covariates demonstrate cumulative effects. That is, both the past and the current status of the covariate impact the hazard. Meanwhile, the lag models in survival analysis by Gasparrini et al. (2010, 2014, 2017) and Bender et al. (2018, 2019) utilized a general functional form to capture cumulative effects and added this function to the hazard modeling. However, their specific requirements, such as the fine partition of follow-up times, can become problematic. A parallel extensive literature on joint modeling of longitudinal and survival data provides nice model structures for comprehensive applications. Their advantages rely on the unique association structure (current value or the area under the curve) between the event and the longitudinal trajectory. The established structures, nevertheless, fail to capture fluctuations in one way or another. In BLJM, the three parallel components (Survival Joint Model, Distributed Lag Nonlinear Model and Arc Length) are fused into one united framework. The main purpose of arc length is to capture and reflect the cumulative effect of variations of covariates. The larger value of arc length often suggests the greater risk. Our proposed model can capture more useful information from these longitudinal covariates to improve the estimation of hazard rates. The cumulative effect of variation assessed by arc length can be calculated as an indicator to identify high-risk populations. In summary, BLJM consists of two submodels within the context of joint models: one for survival data and the other for longitudinal data. The two submodels are linked by cumulative effects through arc length. Its major goal is to achieve the high-accuracy in dynamic prediction of hazards. When variations of a longitudinal factor pose a significant impact on the hazard, BLJM outperforms joint models with AUC. It can be used to identify possible biomarkers for future cancer treatments. It can be performed in various applications and disciplines. We also illustrate its usage in both simulation and clinical studies

Multiparameter Cell Affinity Chromatography: Separation and Analysis in a Single Microfluidic Channel

The ability to sort and capture more than one cell type from a complex sample will enable a wide variety of studies of cell proliferation and death and the analysis of disease states. In this work, we integrated a pneumatic actuated control layer to an affinity separation layer to create different antibody-coating regions on the same fluidic channel. The comparison of different antibody capture capabilities to the same cell line was demonstrated by flowing Ramos cells through anti-CD19- and anti-CD71-coated regions in the same channel. It was determined that the cell capture density on the anti-CD19 region was 2.44 ± 0.13 times higher than that on the anti-CD71-coated region. This approach can be used to test different affinity molecules for selectivity and capture efficiency using a single cell line in one separation. Selective capture of Ramos and HuT 78 cells from a mixture was also demonstrated using two antibody regions in the same channel. Greater than 90% purity was obtained on both capture areas in both continuous flow and stop flow separation modes. A four-region antibody-coated device was then fabricated to study the simultaneous, serial capture of three different cell lines. In this case the device showed effective capture of cells in a single separation channel, opening up the possibility of multiple cell sorting. Multiparameter sequential blood sample analysis was also demonstrated with high capture specificity (>97% for both CD19+ and CD4+ leukocytes). The chip can also be used to selectively treat cells after affinity separation

Table_1_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.xlsx

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

Data_Sheet_1_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.zip

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

Fluorination and Conjugation Engineering Synergistically Enhance the Optoelectronic Properties of Two-Dimensional Hybrid Organic–Inorganic Perovskites

Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) are expected to be a viable alternative to three-dimensional (3D) analogs in solar cells (SCs) and optoelectronic devices due to their high stability, diverse composition, and physical properties. However, unsuitable band alignment and large bandgaps limit the power conversion efficiency (PCE) improvement of SCs based on 2D HOIPs. Here, we report a molecular design strategy that combines fluorination and conjugation engineering to tune the electronic structure and optimize the PCE of 2D HOIPs. Our results show that type IIa band alignment and tunable bandgaps can be achieved in 2D Dion–Jacobson (DJ) HOIPs by H/F substitution of organic cations with different degrees of conjugation. In general, the bandgap of 2D DJ-HOIPs decreases monotonously with the increase of the number of F atoms, which is due to the gradual decrease of the lowest unoccupied molecular orbitals (LUMO) of organic cations. In addition, the enhanced interlayer charge transfer and higher dielectric constant suggest that the fluorination-induced dielectric limitations are weakened. The estimated PCE of 2D DJ-HOIPs is exponentially increased and positively correlated with the degree of conjugation and fluorination of organic cations, with a PCE approaching 29% under their synergistic effect. Our results not only provide promising candidates for photovoltaic device applications but also provide an effective method for PCE optimization

Data_Sheet_2_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.zip

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

The prognostic value of C-reactive protein to albumin ratio in patients with sepsis: a systematic review and meta-analysis

This study aimed to determine whether the C-reactive protein-to-albumin ratio (CAR) can serve as a prognostic marker in patients with sepsis. Chinese and English databases were searched to retrieve the included literature. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) with their 95% confidence interval (CI) were calculated using the bivariate model. Moreover, the hazard ratio (HR) and 95% CI were calculated using the random effect model. Nine articles comprising 3224 patients with sepsis were included in the meta-analysis. The pooled SEN was 0.73 (95% CI 0.65–0.80), the pooled SPE was 0.78 (95% CI 0.69–0.84), the pooled PLR was 3.29 (95% CI 2.15–5.03), the pooled NLR was 0.35 (95% CI 0.24–0.49), and the pooled DOR was 9.50 (95% CI 4.38–20.59). The AUC under the SROC was 0.82 (95% CI 0.78–0.85) for the prognostic meta-analysis. The pooled HR was 1.10 (95% CI 1.02–1.18). This meta-analysis suggests that a high CAR level is associated with increased mortality and a poor prognosis.</p

Fluorination and Conjugation Engineering Synergistically Enhance the Optoelectronic Properties of Two-Dimensional Hybrid Organic–Inorganic Perovskites

Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) are expected to be a viable alternative to three-dimensional (3D) analogs in solar cells (SCs) and optoelectronic devices due to their high stability, diverse composition, and physical properties. However, unsuitable band alignment and large bandgaps limit the power conversion efficiency (PCE) improvement of SCs based on 2D HOIPs. Here, we report a molecular design strategy that combines fluorination and conjugation engineering to tune the electronic structure and optimize the PCE of 2D HOIPs. Our results show that type IIa band alignment and tunable bandgaps can be achieved in 2D Dion–Jacobson (DJ) HOIPs by H/F substitution of organic cations with different degrees of conjugation. In general, the bandgap of 2D DJ-HOIPs decreases monotonously with the increase of the number of F atoms, which is due to the gradual decrease of the lowest unoccupied molecular orbitals (LUMO) of organic cations. In addition, the enhanced interlayer charge transfer and higher dielectric constant suggest that the fluorination-induced dielectric limitations are weakened. The estimated PCE of 2D DJ-HOIPs is exponentially increased and positively correlated with the degree of conjugation and fluorination of organic cations, with a PCE approaching 29% under their synergistic effect. Our results not only provide promising candidates for photovoltaic device applications but also provide an effective method for PCE optimization

The Efficacy and Safety of Current Treatments in Diabetic Macular Edema: A Systematic Review and Network Meta-Analysis

<div><p>Purpose</p><p>To compare the efficacy and safety of current treatments in diabetic macular edema (DME).</p><p>Methods</p><p>PubMed, Embase and CENTRAL were systematically reviewed for randomized controlled trials of current treatments in DME through August 2015. Data on the mean change of best-corrected visual acuity (BCVA) and central macular thickness (CMT) were extracted, and adverse events (AEs) were collected.</p><p>Results</p><p>A total of 21 trials were included in our network meta-analysis. Intravitreal ranibizumab improved BCVA most significantly (OR: +7.01 95%CI (2.56 to 11.39)) in 6 months and intravitreal aflibercept (+8.19 (5.07 to 11.96)) in 12 months. Intravitreal triamcinolone combined with LASER decreased CMT most significantly (-111.34 (-254.61 to 37.93)) in 6 months and intravitreal aflibercept (-110.83 (-190.25 to -35.27)) in 12 months. Compared with the relatively high rate of ocular AEs in the groups with administration of steroids, systematic AEs occurred more frequently in the groups with vascular endothelial growth factor inhibitors involved.</p><p>Conclusions</p><p>Our analysis confirms that intravitreal aflibercept is most favorable with both BCVA improvement and CMT decrease than other current therapies in the management of DME within 12 months. Vascular endothelial growth factor inhibitors for DME should be used with caution due to systematic AEs. Combined intravitreal triamcinolone with LASER has a stronger efficacy in decreasing CMT than the other interventions in the early stage after injection. More high-quality randomized controlled trials will be necessary.</p></div

Variations in abundance and community composition of denitrifying bacteria during a cyanobacterial bloom in a eutrophic shallow lake in China

<p>Although cyanobacterial blooms can change microbial communities, it is still unclear what impact such harmful blooms will have on denitrifying bacteria, the drivers of the removal of excessive nitrogen from water. In order to clarify the impact, populations of denitrifying bacteria, with periodic proliferation and dominance of cyanobacteria in a eutrophic shallow lake located in southeast China, were investigated using quantitative real-time polymerase chain reaction (qPCR) and 454-pyrosequencing based on the copper-containing nitrite reductase (<i>nirK</i>) gene, cytochrome cd1-containing nitrite reductase (<i>nirS</i>) gene and nitrous oxide reductase (<i>nosZ</i>) gene. Samples were collected periodically during a three-month period when the cyanobacterial density gradually increased. In the qPCR analyses, abundances of <i>nirK</i>, <i>nirS</i> and <i>nosZ</i> were intensely positively correlated with the biomass of cyanobacteria. Moreover, 454-pyrosequencing revealed that the community composition of denitrifying bacteria shifted with the increase in cyanobacterial density. These results indicated that the shifts of the community composition of denitrifying bacteria might be related to cyanobacterial blooms, which could potentially lead to alterations of denitrification in eutrophic water.</p