Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses.

BACKGROUND: The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses. METHODS AND FINDINGS: We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian-Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices. CONCLUSIONS: In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

Tensor-structured decomposition improves systems serology analysis.

Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.

Phase modulation and structural effects in a D-shaped all-solid photonic crystal fiber surface plasmon resonance sensor.

We numerically investigate a D-shaped fiber surface plasmon resonance sensor based on all-solid photonic crystal fiber (PCF) with finite element method. In the side-polished PCF sensor, field leakage is guided to penetrate through the gap between the rods, causing a pronounced phase modulation in the deep polishing case. Taking advantage of these amplified phase shifts, a high-performance fiber sensor design is proposed. The significant enhancements arising from this new sensor design should lift the performance of the fiber SPR sensor into the range capable of detecting a wide range of biochemical interactions, which makes it especially attractive for many in vivo and in situ bioanalysis applications. Several parameters which influence the field leakage, such as the polishing position, the pitch of the PCF, and the rod diameter, are inspected to evaluate their impacts. Furthermore, we develop a mathematical model to describe the effects of varying the structural parameters of a D-shaped PCF sensor on the evanescent field and the sensor performance.

The structure is the message: Preserving experimental context through tensor decomposition.

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.

Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.

Tensor modeling of MRSA bacteremia cytokine and transcriptional patterns reveals coordinated, outcome-associated immunological programs.

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common and life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy determined by minimum inhibitory concentration breakpoints, antibiotics often fail to resolve these infections in vivo, resulting in persistent MRSA bacteremia. Recently, several genetic, epigenetic, and proteomic correlates of persistent outcomes have been identified. However, the extent to which single variables or their composite patterns operate as independent predictors of outcome or reflect shared underlying mechanisms of persistence is unknown. To explore this question, we employed a tensor-based integration of host transcriptional and cytokine datasets across a well-characterized cohort of patients with persistent or resolving MRSA bacteremia outcomes. This method yielded high correlative accuracy with outcomes and immunologic signatures united by transcriptomic and cytokine datasets. Results reveal that patients with persistent MRSA bacteremia (PB) exhibit signals of granulocyte dysfunction, suppressed antigen presentation, and deviated lymphocyte polarization. In contrast, patients with resolving bacteremia (RB) heterogeneously exhibit correlates of robust antigen-presenting cell trafficking and enhanced neutrophil maturation corresponding to appropriate T lymphocyte polarization and B lymphocyte response. These results suggest that transcriptional and cytokine correlates of PB vs. RB outcomes are complex and may not be disclosed by conventional modeling. In this respect, a tensor-based integration approach may help to reveal consensus molecular and cellular mechanisms and their biological interpretation.

The NF-κB multidimer system model: A knowledge base to explore diverse biological contexts.

The nuclear factor κB (NF-κB) system is critical for various biological functions in numerous cell types, including the inflammatory response, cell proliferation, survival, differentiation, and pathogenic responses. Each cell type is characterized by a subset of 15 NF-κB dimers whose activity is regulated in a stimulus-responsive manner. Numerous studies have produced different mathematical models that account for cell type-specific NF-κB activities. However, whereas the concentrations or abundances of NF-κB subunits may differ between cell types, the biochemical interactions that constitute the NF-κB signaling system do not. Here, we synthesized a consensus mathematical model of the NF-κB multidimer system, which could account for the cell type-specific repertoires of NF-κB dimers and their cell type-specific activation and cross-talk. Our review demonstrates that these distinct cell type-specific properties of NF-κB signaling can be explained largely as emergent effects of the cell type-specific expression of NF-κB monomers. The consensus systems model represents a knowledge base that may be used to gain insights into the control and function of NF-κB in diverse physiological and pathological scenarios and that describes a path for generating similar regulatory knowledge bases for other pleiotropic signaling systems.

Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates.

Immunoglobulin (Ig)G antibodies coordinate immune effector responses by selectively binding to target antigens and then interacting with various effector cells via the Fcγ receptors. The Fc domain of IgG can promote or inhibit distinct effector responses across several different immune cell types through variation based on subclass and Fc domain glycosylation. Extensive characterization of these interactions has revealed how the inclusion of certain Fc subclasses or glycans results in distinct immune responses. During an immune response, however, IgG is produced with mixtures of Fc domain properties, so antigen-IgG immune complexes are likely to almost always be comprised of a combination of Fc forms. Whether and how this mixed composition influences immune effector responses has not been examined. Here, we measured Fcγ receptor binding to immune complexes of mixed Fc domain composition. We found that the binding properties of the mixed-composition immune complexes fell along a continuum between those of the corresponding pure cases. Binding quantitatively matched a mechanistic binding model, except for several low-affinity interactions mostly involving IgG2. We found that the affinities of these interactions are different than previously reported, and that the binding model could be used to provide refined estimates of these affinities. Finally, we demonstrated that the binding model can predict effector-cell elicited platelet depletion in humanized mice, with the model inferring the relevant effector cell populations. Contrary to the previous view in which IgG2 poorly engages with effector populations, we observe appreciable binding through avidity, but insufficient amounts to observe immune effector responses. Overall, this work demonstrates a quantitative framework for reasoning about effector response regulation arising from IgG of mixed Fc composition. Summary points: The binding behavior of mixed Fc immune complexes is a blend of the binding properties for each constituent IgG species.An equilibrium, multivalent binding model can be generalized to incorporate immune complexes of mixed Fc composition.Particularly for low-affinity IgG-Fcγ receptor interactions, immune complexes provide better estimates of affinities.The FcγR binding model predicts effector-elicited cell clearance in humanized mice.

Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates.

Immunoglobulin G (IgG) antibodies coordinate immune effector responses by interacting with effector cells via fragment crystallizable γ (Fcγ) receptors. The IgG Fc domain directs effector responses through subclass and glycosylation variation. Although each Fc variant has been extensively characterized in isolation, during immune responses, IgG is almost always produced in Fc mixtures. How this influences effector responses has not been examined. Here, we measure Fcγ receptor binding to mixed Fc immune complexes. Binding of these mixtures falls along a continuum between pure cases and quantitatively matches a mechanistic model, except for several low-affinity interactions mostly involving IgG2. We find that the binding model provides refined estimates of their affinities. Finally, we demonstrate that the model predicts effector cell-elicited platelet depletion in humanized mice. Contrary to previous views, IgG2 exhibits appreciable binding through avidity, though it is insufficient to induce effector responses. Overall, this work demonstrates a quantitative framework for modeling mixed IgG Fc-effector cell regulation.

Measurement of the [Formula: see text]Ta([Formula: see text]) cross sections up to stellar s-process temperatures at the CSNS Back-n.

The neutron capture cross section of [Formula: see text]Ta is relevant to s-process of nuclear astrophysics, extraterrestrial samples analysis in planetary geology and new generation nuclear energy system design. The [Formula: see text]Ta([Formula: see text]) cross section had been measured between 1 eV and 800 keV at the back-streaming white neutron facility (Back-n) of China spallation neutron source(CSNS) using the time-of-flight (TOF) technique and [Formula: see text] liquid scintillator detectors. The experimental results are compared with the data of several evaluated libraries and previous experiments in the resolved and unresolved resonance region. Resonance parameters are extracted using the R-Matrix code SAMMY in the 1-700 eV region. The astrophysical Maxwell average cross section(MACS) from kT = 5 to 100 keV is calculated over a sufficiently wide range of neutron energies. For the characteristic thermal energy of an astrophysical site, at kT = 30keV the MACS value of [Formula: see text]Ta is 834 ± 75 mb, which shows an obvious discrepancy with the Karlsruhe Astrophysical Database of Nucleosynthesis in Stars (KADoNiS) recommended value 766 ± 15 mb. The new measurements strongly constrain the MACS of [Formula: see text]Ta([Formula: see text]) reaction in the stellar s-process temperatures.

A general model of multivalent binding with ligands of heterotypic subunits and multiple surface receptors.

Multivalent cell surface receptor binding is a ubiquitous biological phenomenon with functional and therapeutic significance. Predicting the amount of ligand binding for a cell remains an important question in computational biology as it can provide great insight into cell-to-cell communication and rational drug design toward specific targets. In this study, we extend a mechanistic, two-step multivalent binding model. This model predicts the behavior of a mixture of different multivalent ligand complexes binding to cells expressing various types of receptors. It accounts for the combinatorially large number of interactions between multiple ligands and receptors, optionally allowing a mixture of complexes with different valencies and complexes that contain heterogeneous ligand units. We derive the macroscopic predictions and demonstrate how this model enables large-scale predictions on mixture binding and the binding space of a ligand. This model thus provides an elegant and computationally efficient framework for analyzing multivalent binding.

A quantitative view of strategies to engineer cell-selective ligand binding.

A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

Development and Validation of a HPLC-UV Method for Urea and Related Impurities.

Urea is used in biopharmaceutical manufacturing processes for the purification of therapeutic proteins, for cleaning columns, and for refolding proteins after purification. The urea used for such purposes is typically USP grade material obtained from commercial sources and further characterization is required prior to use, such as determination of purity and identity. For this purpose, a robust analytical method is needed that can characterize the known organic impurities of urea. However, the existing methods show high assay variability and are not able to resolve all known organic impurities as desired for accurate quantification. In the present manuscript we developed a new high-performance liquid chromatography method with UV detection for the separation of urea and its impurities (biuret, cyanuric acid, and triuret). The method performance characteristics evaluated for urea and biuret were specificity, linearity, accuracy, identity, precision, and robustness and the newly developed method met all predefined performance acceptance criteria.

[Effects of Se-riched soybean peptide on antioxidant function in rats of fatty liver caused by high-fat diet].

OBJECTIVE: To explore the effect of Se-riched soybean peptide (SSP) on antioxidant function in rats of fatty liver caused by high-fat diet. METHODS: Forty Wistar rats were divided into 4 groups randomly and fed with standard diet and water (NC), high-fat diet and water (HC), high-fat diet and SSP (0.1 g/d) (SeH), standard diet and SSP (0.1 g/d) (SeN) respectively. After 10 weeks, the rats were killed to investigate the pimelosis level in liver tissues by Sudan III staining and the expression of hepatic GRP78 by immunohistochemical analysis. We also analyzed the changes of liver function, blood lipid, the glutathione peroxidase (GSH-Px), superoxide dismutase (SOD) activity and malondialdehyde (MDA) content in livers and serum. RESULTS: The pimelosis level, total cholesterol (TC), triglyceride (TG), MDA contents and the expression of GRP78 in HC group were significantly higher than those in NC, SeN, SeH groups. The activities of GSH-Px and SOD in liver and serum were markedly up-regulated in SeH (P < 0.01). There was no significant difference between NC and SeN groups. CONCLUSION: SSP can improve liver cell injury and the antioxidant functions in rats with fatty liver effectively and decrease the expression of GRP78 in liver.

A Multidisciplinary Investigation to Determine the Structure and Source of Dimeric Impurities in AMG 517 Drug Substance.

In the initial scale-up batches of the experimental drug substance AMG 517, a pair of unexpected impurities was observed by HPLC. Analysis of data from initial LC-MS experiments indicated the presence of two dimer-like molecules. One impurity had an additional sulfur atom incorporated into its structure relative to the other impurity. Isolation of the impurities was performed, and further structural elucidation experiments were conducted with high-resolution LC-MS and 2D NMR. The dimeric structures were confirmed, with one of the impurities having an unexpected C-S-C linkage. Based on the synthetic route of AMG 517, it was unlikely that these impurities were generated during the last two steps of the process. Stress studies on the enriched impurities were carried out to further confirm the existence of the C-S-C linkage in the benzothiazole portion of AMG 517. Further investigation revealed that these two dimeric impurities originated from existing impurities in the AMG 517 starting material, N-acetyl benzothiazole. The characterization of these two dimeric impurities allowed for better quality control of new batches of the N-acetyl benzothiazole starting material. As a result, subsequent batches of AMG 517 contained no reportable levels of these two impurities.