Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

Spatial proteomics of immune microenvironment in nonalcoholic steatohepatitis-associated hepatocellular carcinoma.

BACKGROUND AND AIMS: NASH-HCC is inherently resistant to immune checkpoint blockade, but its tumor immune microenvironment is largely unknown. APPROACH AND RESULTS: We applied the imaging mass cytometry to construct a spatially resolved single-cell atlas from the formalin-fixed and paraffin-embedded tissue sections from patients with NASH-HCC, virus-HCC (HBV-HCC and HCV-HCC), and healthy donors. Based on 35 biomarkers, over 750,000 individual cells were categorized into 13 distinct cell types, together with the expression of key immune functional markers. Higher infiltration of T cells, myeloid-derived suppressor cell (MDSCs), and tumor-associated macrophages (TAMs) in HCC compared to controls. The distribution of immune cells in NASH-HCC is spatially heterogeneous, enriched at adjacent normal tissues and declined toward tumors. Cell-cell connections analysis revealed the interplay of MDSCs and TAMs with CD8 + T cells in NASH-HCC. In particular, exhausted programmed cell death 1 (PD-1 + )CD8 + T cells connected with programmed cell death-ligand 1 (PD-L1 + )/inducible T cell costimulator (ICOS + ) MDSCs and TAMs in NASH-HCC, but not in viral HCC. In contrast, CD4 + /CD8 + T cells with granzyme B positivity were reduced in NASH-HCC. Tumor cells expressed low PD-L1 and showed few connections with immune cells. CONCLUSIONS: Our work provides the first detailed spatial map of single-cell phenotypes and multicellular connections in NASH-HCC. We demonstrate that interactions between MDSCs and TAMs with effector T cells underlie immunosuppression in NASH-HCC and are an actionable target.

Visualization of Biomolecular Radiation Damage at the Single-Particle Level Using Lanthanide-Sensitized DNA Origami.

Precise monitoring of biomolecular radiation damage is crucial for understanding X-ray-induced cell injury and improving the accuracy of clinical radiotherapy. We present the design and performance of lanthanide-DNA-origami nanodosimeters for directly visualizing radiation damage at the single-particle level. Lanthanide ions (Tb3+ or Eu3+) coordinated with DNA origami nanosensors enhance the sensitivity of X-ray irradiation. Atomic force microscopy (AFM) revealed morphological changes in Eu3+-sensitized DNA origami upon X-ray irradiation, indicating damage caused by ionization-generated electrons and free radicals. We further demonstrated the practical applicability of Eu3+-DNA-origami integrated chips in precisely monitoring radiation-mediated cancer radiotherapy. Quantitative results showed consistent trends with flow cytometry and histological examination under comparable X-ray irradiation doses, providing an affordable and user-friendly visualization tool for preclinical applications. These findings provide new insights into the impact of heavy metals on radiation-induced biomolecular damage and pave the way for future research in developing nanoscale radiation sensors for precise clinical radiography.

An epigenetic circuit controls neurogenic programs during neocortex development.

The production and expansion of intermediate progenitors (IPs) are essential for neocortical neurogenesis during development and over evolution. Here, we have characterized an epigenetic circuit that precisely controls neurogenic programs, particularly properties of IPs, during neocortical development. The circuit comprises a long non-coding RNA (LncBAR) and the BAF (SWI/SNF) chromatin-remodeling complex, which transcriptionally maintains the expression of Zbtb20. LncBAR knockout neocortex contains more deep-layer but fewer upper-layer projection neurons. Intriguingly, loss of LncBAR promotes IP production, but paradoxically prolongs the duration of the cell cycle of IPs during mid-later neocortical neurogenesis. Moreover, in LncBAR knockout mice, depletion of the neural progenitor pool at embryonic stage results in fewer adult neural progenitor cells in the subventricular zone of lateral ventricles, leading to a failure in adult neurogenesis to replenish the olfactory bulb. LncBAR binds to BRG1, the core enzymatic component of the BAF chromatin-remodeling complex. LncBAR depletion enhances association of BRG1 with the genomic locus of, and suppresses the expression of, Zbtb20, a transcription factor gene known to regulate both embryonic and adult neurogenesis. ZBTB20 overexpression in LncBAR-knockout neural precursors reverses compromised cell cycle progressions of IPs.

Rational Design of Conjugated Acetylenic Polymers Enables a Two-Electron Water Oxidation Pathway for Enhanced Photosynthetic Hydrogen Peroxide Generation.

Herein, the design of conjugated acetylenic polymers (CAPs) featuring diverse spatial arrangements and intramolecular spacers of diacetylene moieties (─C≡C─C≡C─) for photocatalytic hydrogen peroxide (H2 O2 ) production from water and O2 , without the need for sacrificial agents, is presented. It is shown that the linear configuration of diacetylene moieties within conjugated acetylenic polymers (CAPs) induces a pronounced polarization of electron distribution, which imparts enhanced charge-carrier mobility when compared to CAPs' networks featuring cross-linked arrangements. Moreover, optimizing the intramolecular spacer between diacetylene moieties within the linear structure leads to the exceptional modulation of the band structures, specifically resulting in a downshifted valence band (VB) and rendering the two-electron water oxidation pathway thermodynamically feasible for H2 O2 production. Consequently, the optimized CAPs with a linear configuration (LCAP-2), featuring spatially separated reduction centers (benzene rings) and oxidation centers (diacetylene moieties), exhibit a remarkable H2 O2 yield rate of 920.1 µmol g-1 h-1 , superior than that of the linear LCAP-1 (593.2 µmol g-1 h-1 ) and the cross-linked CCAP (433.4 µmol g-1 h-1 ). The apparent quantum efficiency (AQE) and solar-to-chemical energy conversion (SCC) efficiency of LCAP-2 are calculated to be 9.1% (λ = 420 nm) and 0.59%, respectively, surpassing the performance of most previously reported conjugated polymers.

ICAT-Mediated Crosstalk Between Cervical Cancer Cells and Macrophages Promotes M2-Like Macrophage Polarization to Reinforce Tumor Malignant Behaviors.

Inhibitor of ß-catenin and T-cell factor (ICAT) is a classical inhibitor of the Wnt signaling pathway. Nonetheless, our previous work found that ICAT is overexpressed in cervical cancer (CC), resulting in the augmentation of migration and invasion capabilities of CC cells. It remains unclear what molecular mechanism underlies this phenomenon. The interaction between cancer cells and the tumor microenvironment (TME) promotes the outgrowth and metastasis of tumors. Tumor-associated macrophages (TAMs) are a major constituent of the TME and have a significant impact on the advancement of CC. Consequently, our inquiry pertains to the potential of ICAT to facilitate tumor development through its modulation of the cervical TME. In this study, we first verified that ICAT regulated the secretion of cytokines interleukin-10 (IL-10) and transforming growth factor-ß (TGF-ß) in CC cells, leading to M2-like macrophage polarization and enhancement of the migration and invasion of CC cells. Furthermore, the system of co-culturing human umbilical vein endothelial cells (HUVECs) with macrophages revealed that depending on the CC cells' overexpression or inhibition of ICAT, the vascular tube formation by HUVECs can be either increased or decreased. Overall, our study indicates that ICAT stimulates M2-like polarization of TAMs via upregulating IL-10 and TGF-ß, which results in increased neovascularization, tumor metastasis, and immunosuppression in CC. In upcoming times, inhibiting crosstalk between CC cells and TAMs may be a possible strategy for CC therapy.

Bio-inspired chemical space exploration of terpenoids.

Many computational methods are devoted to rapidly generating pseudo-natural products to expand the open-ended border of chemical spaces for natural products. However, the accessibility and chemical interpretation were often ignored or underestimated in conventional library/fragment-based or rule-based strategies, thus hampering experimental synthesis. Herein, a bio-inspired strategy (named TeroGen) is developed to mimic the two key biosynthetic stages (cyclization and decoration) of terpenoid natural products, by utilizing physically based simulations and deep learning models, respectively. The precision and efficiency are validated for different categories of terpenoids, and in practice, more than 30 000 sesterterpenoids (10 times as many as the known sesterterpenoids) are predicted to be linked in a reaction network, and their synthetic accessibility and chemical interpretation are estimated by thermodynamics and kinetics. Since it could not only greatly expand the chemical space of terpenoids but also numerate plausible biosynthetic routes, TeroGen is promising for accelerating heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids and derivatives.

Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.

Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical explanation on the model determination and prediction. To satisfy such demands, we developed Vec2image, an explainable convolutional neural network framework for characterizing the feature engineering, feature selection and classifier training that is mainly based on the collaboration of principal component coordinate conversion, deep residual neural networks and embedded k-nearest neighbor representation on pseudo images of high-dimensional biological data, where the pseudo images represent feature measurements and feature associations simultaneously. Vec2image has achieved better performance compared with other popular methods and illustrated its efficiency on feature selection in cell marker identification from tissue-specific single-cell datasets. In particular, in a case study on type 2 diabetes (T2D) by multiple human islet scRNA-seq datasets, Vec2image first displayed robust performance on T2D classification model building across different datasets, then a specific Vec2image model was trained to accurately recognize the cell state and efficiently rank feature genes relevant to T2D which uncovered potential T2D cellular pathogenesis; and next the cell activity changes, cell composition imbalances and cell-cell communication dysfunctions were associated to our finding T2D feature genes from both population-shared and individual-specific perspectives. Collectively, Vec2image is a new and efficient explainable artificial intelligence methodology that can be widely applied in human-readable classification and prediction on the basis of pseudo image representation of biological deep sequencing data.

Deep latent space fusion for adaptive representation of heterogeneous multi-omics data.

The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.

Type 1 diabetes, its complications, and non-ischemic cardiomyopathy: a mendelian randomization study of European ancestry.

BACKGROUND: Type 1 diabetes (T1D) is a significant risk factor for a range of cardiovascular diseases. Nonetheless, the causal relationship between T1D and non-ischemic cardiomyopathy (NICM) remains to be elucidated. Furthermore, the mechanisms responsible for the progression from T1D to NICM have not been definitively characterized. OBJECTIVE: The aim of this study was to conduct a Mendelian randomization (MR) study to investigate the causal effects of T1D and its complications on the development of NICM. Additionally, this study aimed to conduct a mediation analysis to identify potential mediators within this correlation. METHODS: Genetic variants were used as instrumental variables for T1D. The summary data for T1D were obtained from two genome-wide association study datasets. The summary data for T1D with complications and NICM were obtained from the Finnish database. Two-sample MR, multivariable MR and mediation MR were conducted in this study. RESULTS: The study revealed a causal association between T1D, T1D with complications, and NICM (with odds ratios of 1.02, 95% CI 1.01-1.04, p = 1.17e-04 and 1.03, 95% CI 1.01-1.05, p = 3.15e-3). Even after adjusting for confounding factors such as body mass index and hypertension, T1D remained statistically significant (with odds ratio of 1.02, 95% CI 1.01-1.04, p = 1.35e-4). Mediation analysis indicated that monokine induced by gamma interferon may play a mediating role in the pathogenesis of T1D-NICM (mediation effect indicated by odds ratio of 1.005, 95% CI 1.001-1.01, p = 4.9e-2). CONCLUSION: The study demonstrates a causal relationship between T1D, its complications, and NICM. Additionally, monokine induced by gamma interferon may act as a potential mediator in the pathogenesis of T1D-NICM.

Discovery of metabolite biomarkers for odontogenic keratocysts.

INTRODUCTION: Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate the serum metabolomic profile of OKCs and discover potential biomarkers. METHODS: Metabolomic analysis was performed on 42 serum samples from 22 OKC patients and 20 healthy controls (HCs) using gas chromatography‒mass spectrometry to identify dysregulated metabolites in the OKC samples. LASSO regression and receiver operating characteristic (ROC) curve analyses were used to select and validate metabolic biomarkers and develop diagnostic models. RESULTS: A total of 73 metabolites were identified in the serum samples, and 24 metabolites were dysregulated in the OKC samples, of which 4 were upregulated. Finally, a diagnostic panel of 10 metabolites was constructed that accurately diagnosed OKCs (sensitivity of 100%, specificity of 100%, area under the curve of 1.00). CONCLUSION: This study is the first to investigate the metabolic characteristics and potential metabolic biomarkers in the serum of OKC patients using GC‒MS. Our study provides further evidence to explore the pathogenesis of OKC.

KDM4A promotes malignant progression of breast cancer by down-regulating BMP9 inducing consequent enhancement of glutamine metabolism.

BACKGROUND: Recent studies have found that histone-modified genes play an increasingly important role in tumor progression. Lysine(K) specific demethylase 4A (KDM4A) is a histone lysine-specific demethylase highly expressed in a variety of malignant tumors, data showed that KDM4A was negatively correlated with the Bone Morphogenetic Protein 9 (BMP9) in breast cancer. And previous experiments have demonstrated that exogenous BMP9 significantly inhibits breast cancer development. MATERIALS AND METHODS: We detected the expression of KDM4A in breast cancer and the relationship between KDM4A and BMP9 using real-time quantitative PCR (RT-qPCR) and Western blot, and verified the interaction between KDM4A and BMP9 by ChIP experiments. At the same time, we also detected whether KDM4A had effects on the RNA and protein stability of BMP9 using actinomycin D and cycloheximide. Measurement of alpha-ketoglutarate (α-KG) level by ELISA to observe the effect of BMP9 on glutamine metabolism in breast cancer cells. Nucleoplasmic distribution of KDM4A after exogenous BMP9 treatment in breast cancer cells were observed by immunofluorescence staining and Western blot. A subcutaneous xenograft tumor model in nude mice was used to study the therapeutic effects of exogenous BMP9 and KDM4A inhibitor (JIB-04) in breast cancer. CCK-8, conoly formation, Transwell, wound healing, and immunohistochemistry were used to monitor the growth of tumor and cell function. RESULTS: We found that KDM4A was abnormally highly expressed in breast cancer, and silenced BMP9 expression by removing histone methyl groups from the BMP9 gene region. Meanwhile, KDM4A could also reduce the stability of BMP9 protein. BMP9 inhibit glutamine metabolism in breast cancer, resulting in a decrease in its product α-KG, is confirmed by ELISA. Altered nucleoplasmic distribution of KDM4A due to decreased α-KG was confirmed by immunofluorescence staining and Western blot. Animal experiments confirm that the combination of exogenous BMP9 and JIB-04 shows significantly better results in breast cancer. CONCLUSIONS: KDM4A silences BMP9 expression by removing histone methyl groups from the BMP9 gene region, leading to further enhancement of glutamine metabolism, which contributes to malignant tumor progression. In addition, using JIB-04 in combination with exogenous BMP9 could inhibit the malignant progression of breast cancer cells and the growth of tumors more significantly.

Adiabat-to-Diabat Angle in Seam Space: Renner-Teller-Type and Pseudo-Jahn-Teller-Type Problems.

In this study, we examine the adiabat-to-diabat (ATD) angles for trajectories in 2-dimensional vibrational subspace of the seam space of two degenerate states. In circulating around the tangential touching degeneracy center, the ATD angle is changed by 2 π ${2\pi }$ or 0, similar to the Renner-Teller problem and the pseudo-Jahn-Teller problem, respectively. These ATD angle profiles may be indistinguishable from those of circulating multiple conical intersections or a pseudo-Jahn-Teller center. Methods to discern those seemingly indistinguishable cases are proposed. A sharp zigzag variation of the ATD angle is seen as a feature for trajectories that graze a pseudo-Jahn-Teller-type tangential touching center, in contrast to the monotonic steep variation for grazing a conical intersection or a Renner-Teller-type tangential touching center.

Evaporation Dynamics of Macro- and Nanodroplets on Heated Hydrophilic Rough Substrates: The Effect of Roughness and Scale.

Droplet evaporation on rough substrates plays an essential role in cooling and micro/nanoparticle assembly. Currently, there are numerous macroscopic experiments and theoretical models to investigate the droplet evaporation behavior on rough substrates. However, due to the complexity of this phenomenon, understanding its mechanisms solely through macroscale studies is difficult. To this end, molecular dynamics simulations of the models with distinct roughness factors are performed, and the obtained results are compared with those of relevant experiments of droplet evaporation on three hydrophilic substrates with different roughness average of 0.1, 0.15, and 0.2 µm, respectively. In this way, we assess the evaporation on these rough systems and the effect of scale on macro- and nanodroplets, which allows us to explore deeper the mechanism of droplet evaporation on rough hydrophilic substrates. In particular, we find that in the case of macroscale droplets, the evaporation mode remains the same with increasing roughness, pointing to a combined mixed and constant-contact-radius (CCR) mode. In the case of nanoscale droplets, the evaporation model is the constant-contact-angle mode when the roughness factor r = 1, while the mixed and CCR modes are found for r = 1.5 and 2, respectively. The scale effect has significant influence on the evaporation pattern of droplets on rough hydrophilic substrates. Moreover, it is also found that increasing the roughness of substrates expands the substrate-droplet contact area on both the macro- and nanoscale, which in turn enhances the heat transfer from the substrate toward the droplet. We anticipate that this first systematic analysis of scale effects provides further insights into the evaporation dynamics of droplets on rough hydrophilic substrates and has significant implications for the advancement of nanotechnology.

Stability and reactivity of alkylidene dihydropyridines.

Alkylidene dihydropyridines (ADHPs) are electron-rich nucleophilic intermediates that can be readily prepared by dearomatization of 4-alkylpyridines using chloroformate reagents and mild base. Their stability and reactivity can be tuned with the chloroformate reagent used as evidenced by NMR chemical shifts and oxidation potentials. ADHPs prepared with ethyl, allyl and trichloroethyl chloroformate undergo decomposition under an oxygen atmosphere at different rates (ethyl > allyl > trichloroethyl), predominantly to the corresponding 4-acylpyridine. The ADHPs derived from benzyl chloroformate are stable towards oxidation, and those derived from phenyl chloroformate hydrolyze readily.

The causal relationship between blood cell indices and 28-day mortality in sepsis: a retrospective study and bidirectional Mendelian randomization analysis.

BACKGROUND: Despite emerging evidence linking blood cell indices (BCIs) to sepsis mortality, the inconsistency of observational studies obscures the clarity of these associations. This study aims to clarify the causal influence of BCIs on 28-day mortality rates in sepsis patients. METHODS: Utilizing univariable and multivariable Mendelian randomization (MR) analyses, we examined the impact of BCIs on sepsis mortality by analyzing data from extensive genome-wide association studies. The inverse-variance weighted (IVW) method was our primary analytic tool, complemented by several robustness checks to mitigate pleiotropy, including weighted median, mode-based estimates, MR-Egger regression, and MR-PRESSO. Subsequently, we conducted a retrospective study to further explore the correlation between platelet indices and 28-day mortality of sepsis using real-world data. RESULTS: Our findings highlight a significant causal relationship between platelet distribution width (PDW) and 28-day mortality in sepsis, with the univariable Mendelian randomization approach yielding an odds ratio of 1.12 (95% CI, 1.06-1.26; P < 0.05). Multivariable analysis further substantiated PDW's robust association with mortality risk (OR 1.23; 95% CI, 1.03-1.48; P < 0.05). Conversely, our analysis did not uncover significant correlations between the genetic predispositions to other BCIs-including red blood cell count, erythrocyte distribution width, platelet count, mean platelet volume, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, and lymphocyte percentage-and 28-day mortality in sepsis. Additionally, an inverse MR analysis did not establish a causal impact of 28-day mortality in sepsis on PDW (OR 1.00; 95% CI, 1.00-1.07; P = 0.29). Moreover, a similar result was observed in the retrospective study. CONCLUSIONS: The study underscores the independent causal role of PDW in predicting 28-day mortality in sepsis, suggesting its potential utility in early patient assessment, risk stratification, and tailoring of therapeutic interventions.

The impact of preoperative biliary drainage on postoperative healthcare-associated infections and clinical outcomes following pancreaticoduodenectomy: a ten-year retrospective analysis.

BACKGROUND: Pancreaticoduodenectomy (PD) is a complex procedure and easily accompanied by healthcare-associated infections (HAIs). This study aimed to assess the impact of PBD on postoperative infections and clinical outcomes in PD patients. METHODS: The retrospective cohort study were conducted in a tertiary hospital from January 2013 to December 2022. Clinical and epidemiological data were collected from HAIs surveillance system and analyzed. RESULTS: Among 2842 patients who underwent PD, 247 (8.7%) were diagnosed with HAIs, with surgical site infection being the most frequent type (n = 177, 71.7%). A total of 369 pathogenic strains were detected, with Klebsiella pneumoniae having the highest proportion, followed by Enterococcu and Escherichia coli. Although no significant association were observed generally between PBD and postoperative HAIs, subgroup analysis revealed that PBD was associated with postoperative HAIs in patients undergoing robotic PD (aRR = 2.174; 95% CI:1.011-4.674; P = 0.047). Prolonging the interval between PBD and PD could reduce postoperative HAIs in patients with cholangiocarcinoma (≥4 week: aRR = 0.292, 95% CI 0.100-0.853; P = 0.024) and robotic PD (≤2 week: aRR = 3.058, 95% CI 1.178-7.940; P = 0.022). PBD was also found to increase transfer of patients to ICU (aRR = 1.351; 95% CI 1.119-1.632; P = 0.002), extended length of stay (P < 0.001) and postoperative length of stay (P = 0.004). CONCLUSION: PBD does not exhibit a significant association with postoperative HAIs or other outcomes. However, the implementation of robotic PD, along with a suitable extension of the interval between PBD and PD, appear to confer advantages concerning patients' physiological recuperation. These observations suggest potential strategies that may contribute to enhanced patient outcomes.

Energy transfer from two luteins to chlorophylls in light-harvesting complex II study by using exciton models with phase correction.

In light-harvesting complex II of plants, the two lutein pigments (LUT1 and LUT2) are always paired and an energy transfer pathway between them is believed to exist. However, it remains unclear whether this pathway is essential for the energy transfer between carotenoids and chlorophylls. In this work, we performed hybrid quantum mechanics/molecular mechanics simulations with Frenkel exciton models to investigate this energy transfer. The results show that the energy transfer pathways between the S2 state of LUT1 and CLAs are not affected by LUT2 S2. The energy transfer between LUT and chlorophyll-a (CLA) also follows a resonance mechanism. The two LUTs have different energy transfer pathways according to their energy gaps and coupling strengths with each CLA. The present work sheds light on the energy transfer pathways involved in the two LUTs.

Variants in BSN gene associated with epilepsy with favourable outcome.

BACKGROUND: BSN gene encodes Bassoon, an essential protein to assemble the cytomatrix at the active zone of neurotransmitter release. This study aims to explore the relationship between BSN variants and epilepsy. METHODS: Whole-exome sequencing was performed in a cohort of 313 cases (trios) with epilepsies of unknown causes. Additional cases with BSN variants were collected from China Epilepsy Gene V.1.0 Matching Platform. The Clinical Validity Framework of ClinGen was used to evaluate the relationship between BSN variants and epilepsy. RESULTS: Four pairs of compound heterozygous variants and one cosegregating heterozygous missense variant in BSN were identified in five unrelated families. These variants presented statistically higher frequency in the case cohort than in controls. Additional two de novo heterozygous nonsense variants and one cosegregating heterozygous missense variant were identified in three unrelated cases from the gene matching platform, which were not present in the Genome Aggregation Database. The missense variants tended to be located in C-terminus, including the two monoallelic missense variants. Protein modelling showed that at least one missense variant in each pair of compound heterozygous variants had hydrogen bond alterations. Clinically, two cases were diagnosed as idiopathic generalised epilepsy, two as focal epilepsy and the remaining four as epilepsy with febrile seizures plus. Seven out of eight probands showed infancy or childhood-onset epilepsy. Eight out of 10 affected individuals had a history of febrile convulsions. All the cases were seizure-free. The cases with monoallelic variants achieved seizure-free without treatment or under monotherapy, while cases with biallelic missense variants mostly required combined therapy. The evidence from ClinGen Framework suggested an association between BSN variants and epilepsy. CONCLUSION: The BSN gene was potentially a novel candidate gene for epilepsy. The phenotypical severity was associated with the genotypes and the molecular subregional effects of the variants.

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and kidney stone: evidence from NHANES 2007-2018.

BACKGROUND: As an innovative lipid parameter, NHHR (the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol) can serve as a valuable tool for assessing cardiovascular disease risk. Nevertheless, the relationship between NHHR and the risk of kidney stones remains unexplored. METHODS: A cross-sectional survey utilized data from the National Health and Population Survey (NHANES) database in the United States spanning from 2007 to 2018. Distinct statistical analyses were applied, including weighted logistic regression, stratified and interaction analysis and restricted cubic spline curve (RCS) models, to examine the correlation between NHHR and the incidence of kidney stones. RESULTS: This analysis encompassed 24,664 participants, with 9.63% reporting incidents of kidney stones. Following multivariate logistic regression and comprehensive adjustments, participants in NHHR quartile 4 (OR 1.34; 95% CI 1.12, 1.60, P < 0.01) exhibited a significantly increased risk of kidney stones compared to those in NHHR quartile 1 (Q1). The RCS result further illustrated a non-linear correlation between NHHR and the incidence of kidney stones. The result of subgroup analysis manifested that participants without diabetes had a higher risk of kidney stones when measured high NHHR levels compared those with diabetes (p for interaction < 0.05). CONCLUSION: Elevated NHHR levels were found to be associated with an increased risk of kidney stones. Based on these findings, NHHR appears to be a promising predictive indicator for the occurrence of kidney stones.