Incidence of metabolic syndrome in patients with unilateral or bilateral staghorn renal stones and its impact on percutaneous nephrolithotomy outcomes.

BACKGROUND: To evaluate the incidence of metabolic syndrome (MetS) in patients with unilateral and bilateral staghorn calculi (SC) and evaluate the impact on the outcome of percutaneous nephrolithotomy (PCNL). METHODS: The clinical data of patients who underwent PCNL for the treatment of SC between 2019 and 2022 were retrospectively reviewed. SC was divided into unilateral and bilateral. The incidence of MetS was compared between the patients with unilateral SC and the patients with bilateral SC, and the impact on the outcome of PCNL was assessed. RESULTS: A total of 1778 patients underwent PCNL between 2019 and 2022. After screening computed tomography, 379 patients were confirmed to have SC, finally, leaving 310 patients with follow-up and complete data to be included in the study. Eighty-four had bilateral SC and 226 had unilateral SC. The patients with bilateral SC had a significantly higher body mass index and higher rates of complete staghorn stones and metabolic syndrome. Higher body mass index, hypertension, diabetes mellitus, hyperlipidaemia, and MetS were present in 62.58%, 44.84%, 21.94%, 60.65% and 27.42% of all patients, respectively. The number of MetS components remained significantly associated with bilateral SC. Specifically, when the number of MetS components increases from 0 to 3-4, the likelihood of developing bilateral staghorn calculi increases by 21.967 times. Eighty-five patients with MetS( +) had a higher rate of overall complications (number (N)(%), 29 (34.12) vs.33 (14.46), P < 0.001) and a comparable stone-free rate to 225 MetS(-) patients. Multivariable analysis confirmed that hyperlipidaemia (P = 0.044, odds ratio [OR] = 1.991, 95% confidence interval [CI] 1.020-3.888) and MetS (P = 0.005, OR = 2.427, 95% CI 1.316-4.477) were independent risk factors for overall complications. CONCLUSIONS: MetS is correlated with the formation of bilateral SC and is the main predictor for complications of PCNL especially for low-grade complications (I-II).

LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.

BACKGROUND: Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes. RESULTS: We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance. SCIENTIFIC CONTRIBUTION: We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.

Acetylcytidine modification of Amotl1 by N-acetyltransferase 10 contributes to cardiac fibrotic expansion in mice after myocardial infarction.

Cardiac fibrosis is a pathological scarring process that impairs cardiac function. N-acetyltransferase 10 (Nat10) is recently identified as the key enzyme for the N4-acetylcytidine (ac4C) modification of mRNAs. In this study, we investigated the role of Nat10 in cardiac fibrosis following myocardial infarction (MI) and the related mechanisms. MI was induced in mice by ligation of the left anterior descending coronary artery; cardiac function was assessed with echocardiography. We showed that both the mRNA and protein expression levels of Nat10 were significantly increased in the infarct zone and border zone 4 weeks post-MI, and the expression of Nat10 in cardiac fibroblasts was significantly higher compared with that in cardiomyocytes after MI. Fibroblast-specific overexpression of Nat10 promoted collagen deposition and induced cardiac systolic dysfunction post-MI in mice. Conversely, fibroblast-specific knockout of Nat10 markedly relieved cardiac function impairment and extracellular matrix remodeling following MI. We then conducted ac4C-RNA binding protein immunoprecipitation-sequencing (RIP-seq) in cardiac fibroblasts transfected with Nat10 siRNA, and revealed that angiomotin-like 1 (Amotl1), an upstream regulator of the Hippo signaling pathway, was the target gene of Nat10. We demonstrated that Nat10-mediated ac4C modification of Amotl1 increased its mRNA stability and translation in neonatal cardiac fibroblasts, thereby increasing the interaction of Amotl1 with yes-associated protein 1 (Yap) and facilitating Yap translocation into the nucleus. Intriguingly, silencing of Amotl1 or Yap, as well as treatment with verteporfin, a selective and potent Yap inhibitor, attenuated the Nat10 overexpression-induced proliferation of cardiac fibroblasts and prevented their differentiation into myofibroblasts in vitro. In conclusion, this study highlights Nat10 as a crucial regulator of myocardial fibrosis following MI injury through ac4C modification of upstream activators within the Hippo/Yap signaling pathway.

A transition from arbuscular to ectomycorrhizal forests halts soil carbon sequestration during subtropical forest rewilding.

Ecological succession and restoration rapidly promote multiple dimensions of ecosystem functions and mitigate global climate change. However, the factors governing the limited capacity to sequester soil organic carbon (SOC) in old forests are poorly understood. Ecological theory predicts that plants and microorganisms jointly evolve into a more mutualistic relationship to accelerate detritus decomposition and nutrient regeneration in old than young forests, likely explaining the changes in C sinks across forest succession or rewilding. To test this hypothesis, we conducted a field experiment of root-mycorrhizal exclusion in successional subtropical forests to investigate plant-decomposer interactions and their effects on SOC sequestration. Our results showed that SOC accrual rate at the 0-10 cm soil layer was 1.26 mg g-1 yr-1 in early-successional arbuscular mycorrhizal (AM) forests, which was higher than that in the late-successional ectomycorrhizal (EcM) forests with non-significant change. A transition from early-successional AM to late-successional EcM forests increase fungal diversity, especially EcM fungi. In the late-successional forests, the presence of ectomycorrhizal hyphae promotes SOC decomposition and nutrient cycle by increasing soil nitrogen and phosphorus degrading enzyme activity as well as saprotrophic microbial richness. Across early- to late-successional forests, mycorrhizal priming effects on SOC decomposition explain a slow-down in the capacity of older forests to sequester soil C. Our findings suggest that a transition from AM to EcM forests supporting greater C decomposition can halt the capacity of forests to provide nature-based global climate change solutions.

Chemical Composition and Phytotoxic Activity of Mentha vagans Boriss. Essential Oil.

This study explored the composition of essential oil (EO) and the first phytotoxic screening of EO obtained from the stems and leaves of Mentha vagans Boriss (MVEO) via hydro-distillation technique. The EO ingredients were detected through Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS analysis revealed that MVEO contained 49 constituents, constituting 93.95 % of the total oil. Among MVEO constituents, dihydrocarvone was observed as the dominant constituent (24.14%), followed by D-carvone (16.28%) and piperitone (18.14%). The phytotoxic effects of MVEO and its dominant compounds were examined against Amaranthus retroflexus, Lolium perenne, and Poa annua. Significant inhibition was observed by MVEO in comparison with the major constituents and their mixture, suppressing the seedling growth of tested species at the lowest dosage (0.01 mg/mL); in general, seedling growth of all tested species was markedly inhibited when applied concentration of the EO and its constituents reached 0.05 mg/mL. Our results also indicated that constituents other than the dominant compounds of MVEO possessed considerable phytotoxic effects because the EO's activity was stronger than its major constituents and their mixture. Thus, additional studies are required to investigate MVEO and its constituents and commercialize them as environment-friendly bio-herbicides.

Nobiletin enhances the antifungal activity of eugenol nanoemulsion against Penicillium italicum in both in vitro and in vivo settings.

The study prepared and used eugenol nanoemulsion loaded with nobiletin as fungistat to study its antifungal activity and potential mechanism of Penicillium italicum (P. italicum). The results showed that the minimum inhibitory concentration (MIC) of eugenol nanoemulsion loaded with nobiletin (EGN) was lower than that of pure eugenol nanoemulsion (EG), which were 160 µg/mL and 320 µg/mL, respectively. At the same time, the mycelial growth inhibition rate of EGN nanoemulsion (54.68 %) was also higher than that of EG nanoemulsion (9.92 %). This indicates that EGN nanoemulsion is more effective than EG nanoemulsion. Compared with EG nanoemulsion, the treatment of EGN nanoemulsion caused more serious damage to the cell structure of P. italicum. At the same time, in vitro inoculation experiments found that EGN nanoemulsion has better control and delay the growth and reproduction of P. italicum in citrus fruits. And the results reflected that EGN nanoemulsion may be considered as potential resouces of natural antiseptic to inhibit blue mold disease of citrus fruits, because it has good antifungal activity.

Mycorrhizal associations relate to stable convergence in plant-microbial competition for nitrogen absorption under high nitrogen conditions.

Nitrogen (N) immobilization (Nim, including microbial N assimilation) and plant N uptake (PNU) are the two most important pathways of N retention in soils. The ratio of Nim to PNU (hereafter Nim:PNU ratio) generally reflects the degree of N limitation for plant growth in terrestrial ecosystems. However, the key factors driving the pattern of Nim:PNU ratio across global ecosystems remain unclear. Here, using a global data set of 1018 observations from 184 studies, we examined the relative importance of mycorrhizal associations, climate, plant, and soil properties on the Nim:PNU ratio across terrestrial ecosystems. Our results show that mycorrhizal fungi type (arbuscular mycorrhizal (AM) or ectomycorrhizal (EM) fungi) in combination with soil inorganic N mainly explain the global variation in the Nim:PNU ratio in terrestrial ecosystems. In AM fungi-associated ecosystems, the relationship between Nim and PNU displays a weaker negative correlation (r = -.06, p < .001), whereas there is a stronger positive correlation (r = .25, p < .001) in EM fungi-associated ecosystems. Our meta-analysis thus suggests that the AM-associated plants display a weak interaction with soil microorganisms for N absorption, while EM-associated plants cooperate with soil microorganisms. Furthermore, we find that the Nim:PNU ratio for both AM- and EM-associated ecosystems gradually converge around a stable value (13.8 ± 0.5 for AM- and 12.1 ± 1.2 for EM-associated ecosystems) under high soil inorganic N conditions. Our findings highlight the dependence of plant-microbial interaction for N absorption on both plant mycorrhizal association and soil inorganic N, with the stable convergence of the Nim:PNU ratio under high soil N conditions.

The synergistic effect of typical chiral organic acids and solution chemistry conditions on the transport of 2-arylpropionic acid chiral derivatives in porous media.

The hazards of man-made chiral compounds are of great public concern, with reports of worrying stereoselective compounds and an urgent need to assess their transport. This study evaluated the transport of 2-arylpropionic acid derivatives enantiomers (2-APA) in porous media under a variety of solution chemistry conditions via column packing assays. The results revealed the introduction of Malic acid (MA) enantiomers enhanced the mobility of 2-APA enantiomers, but the enhancement effect was different for different 2-APA enantiomers. Batch sorption experiments confirmed that the MA enantiomers occupied the sorption site of the quartz sand, thus reducing the deposition of the 2-APA enantiomer. Homo- or heterochirality between 2-APA and MA dominates the transport of 2-APA enantiomers, with homochirality between them triggering stronger retention and vice versa. Further evaluating the effect of solution chemistry conditions on the transport of 2-APA enantiomers, increased ionic strength attenuated the mobility of 2-APA enantiomers, whereas introduced coexisting cations enhanced the retention of 2-APA enantiomers in the column. The redundancy analyses corroborated these solution chemistry conditions were negatively correlated with the transport of 2-APA enantiomers. The coupling of pH and these conditions reveals electrostatic forces dominate the transport behavior and stereoselective interactions of 2-APA enantiomers. Distinguishing the transport of enantiomeric pair helps to understand the difference in stereoselectivity of enantiomers and promises to remove the more hazardous one.

LRNAS: Differentiable Searching for Adversarially Robust Lightweight Neural Architecture.

The adversarial robustness is critical to deep neural networks (DNNs) in deployment. However, the improvement of adversarial robustness often requires compromising with the network size. Existing approaches to addressing this problem mainly focus on the combination of model compression and adversarial training. However, their performance heavily relies on neural architectures, which are typically manual designs with extensive expertise. In this article, we propose a lightweight and robust neural architecture search (LRNAS) method to automatically search for adversarially robust lightweight neural architectures. Specifically, we propose a novel search strategy to quantify contributions of the components in the search space, based on which the beneficial components can be determined. In addition, we further propose an architecture selection method based on a greedy strategy, which can keep the model size while deriving sufficient beneficial components. Owing to these designs in LRNAS, the lightness, the natural accuracy, and the adversarial robustness can be collectively guaranteed to the searched architectures. We conduct extensive experiments on various benchmark datasets against the state of the arts. The experimental results demonstrate that the proposed LRNAS method is superior at finding lightweight neural architectures that are both accurate and adversarially robust under popular adversarial attacks. Moreover, ablation studies are also performed, which reveals the validity of the individual components designed in LRNAS and the component effects in positively deciding the overall performance.

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.

MOTIVATION: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. RESULTS: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

N-Acetyltransferase 10 represses Uqcr11 and Uqcrb independently of ac4C modification to promote heart regeneration.

Translational control is crucial for protein production in various biological contexts. Here, we use Ribo-seq and RNA-seq to show that genes related to oxidative phosphorylation are translationally downregulated during heart regeneration. We find that Nat10 regulates the expression of Uqcr11 and Uqcrb mRNAs in mouse and human cardiomyocytes. In mice, overexpression of Nat10 in cardiomyocytes promotes cardiac regeneration and improves cardiac function after injury. Conversely, treating neonatal mice with Remodelin-a Nat10 pharmacological inhibitor-or genetically removing Nat10 from their cardiomyocytes both inhibit heart regeneration. Mechanistically, Nat10 suppresses the expression of Uqcr11 and Uqcrb independently of its ac4C enzyme activity. This suppression weakens mitochondrial respiration and enhances the glycolytic capacity of the cardiomyocytes, leading to metabolic reprogramming. We also observe that the expression of Nat10 is downregulated in the cardiomyocytes of P7 male pig hearts compared to P1 controls. The levels of Nat10 are also lower in female human failing hearts than non-failing hearts. We further identify the specific binding regions of Nat10, and validate the pro-proliferative effects of Nat10 in cardiomyocytes derived from human embryonic stem cells. Our findings indicate that Nat10 is an epigenetic regulator during heart regeneration and could potentially become a clinical target.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Mycorrhizal mediation of soil carbon in permafrost regions depends on soil nutrient stoichiometry and physical protection.

Mycorrhizal associations are considered as one of the key drivers for soil carbon (C) accumulation and stability. However, how mycorrhizal associations influence soil organic C (SOC) and its fractions (i.e., particulate organic C [POC] and mineral-associated organic C [MAOC]) remain unclear. In this study, we examined effects of plant mycorrhizal associations with arbuscular mycorrhiza (AM), ectomycorrhiza (ECM), and their mixture (Mixed) on SOC and its fractions as well as soil stoichiometric ratios across 800-km transect in permafrost regions. Our results showed that soil with only ECM-associated trees had significantly higher SOC and POC compared to only AM-associated tree species, while soil in Mixed plots with both AM- and ECM- associated trees tend to be somewhat in the middle. Using structural equation models, we found that mycorrhizal association significantly influenced SOC and its fraction (i.e., POC, MAOC) indirectly through soil stoichiometric ratios (C:N, C:P, and N:P). These results suggest that selecting ECM tree species, characterized by a "slow cycling" nutrient uptake strategy, can effectively enhance accumulation of SOC and its fractions in permafrost forest ecosystems. Our findings provide novel insights for quantitatively assessing the influence of mycorrhiza-associated tree species on the management of soil C pool and biogeochemical cycling.

Soil nitrogen availability drives the response of soil microbial biomass to warming.

Although soil microbial biomass responses to experimental warming have been extensively studied, the mechanisms through which elevated temperatures influence soil microbial biomass remain unclear. In this study, we performed a global meta-analysis to quantify the global pattern of soil microbial biomass in response to warming. Our findings suggest that global warming effect is not apparent when all the data are pooled together, while warming does increase microbial biomass under specific conditions (Δ°C ≥ 2 °C). This constructive influence is particularly accentuated under certain circumstances, including high precipitation levels (>800 mm), short treatment durations (<1 year), and within agricultural ecosystems. More importantly, our findings suggest that the impact of global warming on soil microbial biomass is largely mediated by changes in soil nitrogen availability. These findings underscore the pivotal role of nitrogen availability in modulating the response of soil microbial biomass to warming, while also emphasizing the intricate influence between multiple factors such as temperature, duration, and precipitation in shaping the patterns of warming effects.

Cannabidiol represses miR-143 to promote cardiomyocyte proliferation and heart regeneration after myocardial infarction.

Mammalian heart is capable to regenerate almost completely early after birth through endogenous cardiomyocyte proliferation. However, this regenerative capacity diminishes gradually with growth and is nearly lost in adulthood. Cannabidiol (CBD) is a major component of cannabis and has various biological activities to regulate oxidative stress, fibrosis, inflammation, and cell death. The present study was conducted to investigate the pharmacological effects of CBD on heart regeneration in post-MI mice. MI models in adult mice were constructed via coronary artery ligation, which were administrated with or without CBD. Our results demonstrate that systemic administration (10 mg/kg) of CBD markedly increased cardiac regenerative ability, reduced infarct size, and restored cardiac function in MI mice. Consistently, in vitro study also showed that CBD was able to promote the proliferation of neonatal cardiomyocytes. Mechanistically, the expression of miR-143-3p related to cardiomyocyte proliferation was significantly down-regulated in CBD-treated cardiomyocytes, while the overexpression of miR-143-3p inhibited cardiomyocyte mitosis and eliminated CBD-induced cardiomyocyte proliferation. Moreover, CBD enhanced the expression of Yap and Ctnnd1, which were demonstrated as the target genes of miR-143-3p. Silencing of Yap and Ctnnd1 hindered the proliferative effects of CBD. We further revealed that inhibition of the cannabinoid receptor 2 impeded the regulatory effect of CBD on miR-143-3p and its downstream target Yap/Ctnnd1, which ultimately eliminated the pro-proliferative effect of CBD on neonatal and adult cardiomyocytes. Taken together, CBD promotes cardiomyocyte proliferation and heart regeneration after MI via miR-143-3p/Yap/Ctnnd1 signaling pathway, which provides a new strategy for cardiac repair in adult myocardium.

Local Peaks Search Method for Solving Lamb Waves' Dispersion Equation of Laminated Structures and the Application.

To study the acoustic characteristics of sound scattered from laminated structures such as elastic plates and shells, it is usually required to solve the Lamb waves' dispersion equations. Many traditional root-finding methods such as bisection, the Newton-Raphson method, and the Muller method are not able to tackle the problem completely. A simple but powerful method named local peaks search (LPS) is proposed to overcome their drawbacks. Firstly, the non-zero part of the dispersion equation is defined as the dispersion function, and its reciprocal is used to transform the zeros (i.e., roots) into local peaks. Secondly, the chosen complex domain is discretized, and the coarse local domains where the local peaks exist are determined by the direct search method globally. Thirdly, the Muller method is applied to obtain the refined locations of local peaks. Lastly, in order to refine the results, a hierarchical scheme is designed and the iteration of the above procedures is implemented; the error is set to be 10-16 as the stop criteria. The accuracy of the LPS method is validated by comparing it with the bisection method for the problem of elastic plates in the vacuum. The acoustic echo structures are analyzed experimentally. By computation of Lamb waves' phase velocity, the critical angles are derived numerically and compared with the results acquired by an experiment using monostatic sound transducers. In this way, it is validated that the elastic scattered wave components are the highlights shown in the time-angle figure. Furthermore, the work can be applied for non-destructive testing, especially underwater structural health monitoring.

Depletion of L-Methionine in Foods with an Engineered Thermophilic Methionine γ-lyase Efficiently Inhibits Tumor Growth.

Dietary restriction of l-methionine, an essential amino acid, exerts potent antitumor effects on l-methionine-dependent cancers. However, dietary restriction of l-methionine has not been practical for human therapy because of the problem with the administration of l-methionine concentration in foods. Here, a thermophilic methionine γ-lyase (MGL), that catalyzes the cleavage of the C-S bond in l-methionine to produce α-ketobutyric acid, methanethiol, and ammonia was engineered from human cystathionine γ-lyase and almost completely depleted l-methionine at 65 °C, a temperature that accelerates the volatilization of methanethiol and its oxidation products. The high efficiency of l-methionine lysis may be attributed to the cooperative fluctuation and moderate the structural rigidity of 4 monomers in the thermophilic MGL, which facilitates l-methionine access to the entrance of the active site. Experimental diets treated with thermophilic MGL markedly inhibited prostate tumor growth in mice, and in parallel, the in vivo concentrations of l-methionine, its transformation product l-cysteine, and the oxidative stress indicator malondialdehyde significantly decreased. These findings provide a technology for the depletion of l-methionine in foods with an engineered thermophilic MGL, which efficiently inhibits tumor growth in mice.

The relationship between central obesity and risk of breast cancer: a dose-response meta-analysis of 7,989,315 women.

Purpose: Central obesity may contribute to breast cancer (BC); however, there is no dose-response relationship. This meta-analysis examined the effects of central obesity on BC and their potential dose-response relationship. Methods: In the present study, PubMed, Medline, Embase, and Web of Science were searched on 1 August 2022 for published articles. We included the prospective cohort and case-control studies that reported the relationship between central obesity and BC. Summary effect size estimates were expressed as risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals (95% CI) and were evaluated using random-effect models. The inconsistency index (I2) was used to quantify the heterogeneity magnitude derived from the random-effects Mantel-Haenszel model. Results: This meta-analysis included 57 studies (26 case-control and 31 prospective cohort) as of August 2022. Case-control studies indicated that waist circumference (WC) (adjusted OR = 1.18; 95% CI: 1.00-1.38; P = 0.051) and waist-to-hip ratio (WHR) (adjusted OR = 1.28; 95% CI: 1.07-1.53; P = 0.008) were significantly positively related to BC. Subgroup analysis showed that central obesity measured by WC increased the premenopausal (adjusted OR = 1.15; 95% CI: 0.99-1.34; P = 0.063) and postmenopausal (adjusted OR = 1.18; 95% CI: 1.03-1.36; P = 0.018) BC risk and the same relationship appeared in WHR between premenopausal (adjusted OR = 1.38; 95% CI: 1.19-1.59; P < 0.001) and postmenopausal (adjusted OR = 1.41; 95% CI: 1.22-1.64; P < 0.001). The same relationship was observed in hormone receptor-positive (HR+) (adjusted ORWC = 1.26; 95% CI: 1.02-1.57; P = 0.035, adjusted ORWHR = 1.41; 95% CI: 1.00-1.98; P = 0.051) and hormone receptor-negative (HR-) (adjusted ORWC = 1.44; 95% CI: 1.13-1.83; P = 0.003, adjusted ORWHR = 1.42; 95% CI: 0.95-2.13; P = 0.087) BCs. Prospective cohort studies indicated that high WC (adjusted RR = 1.12; 95% CI: 1.08-1.16; P < 0.001) and WHR (adjusted RR = 1.05; 95% CI: 1.018-1.09; P = 0.017) may increase BC risk. Subgroup analysis demonstrated a significant correlation during premenopausal (adjusted RR = 1.08; 95% CI: 1.02-1.14; P = 0.007) and postmenopausal (adjusted RR = 1.14; 95% CI: 1.10-1.19; P < 0.001) between BC and central obesity measured by WC, and WHR was significantly positively related to BC both premenopausal (adjusted RRpre = 1.04; 95% CI: 0.98-1.11; P = 0.169) and postmenopausal (adjusted RRpost = 1.04; 95% CI: 1.02-1.07; P = 0.002). Regarding molecular subtype, central obesity was significantly associated with HR+ (adjusted ORWC = 1.13; 95% CI: 1.07-1.19; P < 0.001, adjusted ORWHR = 1.03; 95% CI: 0.98-1.07; P = 0.244) and HR- BCs (adjusted ORWC =1.11; 95% CI: 0.99-1.24; P = 0.086, adjusted ORWHR =1.01; 95% CI: 0.91-1.13; P = 0.808). Our dose-response analysis revealed a J-shaped trend in the relationship between central obesity and BC (measured by WC and WHR) in case-control studies and an inverted J-shaped trend between BMI (during premenopausal) and BC in the prospective cohort. Conclusion: Central obesity is a risk factor for premenopausal and postmenopausal BC, and WC and WHR may predict it. Regarding the BC subtype, central obesity is proven to be a risk of ER+ and ER- BCs. The dose-response analysis revealed that when BMI (during premenopausal) exceeded 23.40 kg/m2, the risk of BC began to decrease, and WC higher than 83.80 cm or WHR exceeded 0.78 could efficiently increase the BC risk. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022365788.

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity.

Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the "off-the-shelf" GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.

No Difference Between Tacrolimus and Cyclosporine A on Depression Among Kidney Transplantation Recipients.

BACKGROUND: The 2 main types of calcineurin inhibitors (CNI) are tacrolimus (TAC) and cyclosporine A (CsA); both are needed by patients who receive kidney transplants. A common adverse reaction of TAC is depression, which is listed in its instructions. However, depression occurred rarely, according to the instructions manual for CsA. METHODS: Scales measuring depression were sent to recipients who had taken TAC or CsA to observe whether there was a difference in depression between patients who consumed the 2 drugs. From September 23rd-December 8th 2022, a questionnaire was sent to kidney transplant recipients online to investigate depression by PHQ-9 score. Then, the questionnaires returned were divided into 2 groups: TAC group and CsA group. The difference of basic characteristics was made to equal by means of propensity score matching (PSM). The scores, degrees of depression, and prevalence of major depression between the 2 groups were compared. RESULTS: Of 259 questionnaires returned, 220 questionnaires were valid. Among them, 170 recipients used TAC and 50 recipients used CsA. There were no significant differences in baseline characteristics after PSM. After PSM, there was no statistically significant difference in PHQ-9 (0.8) score, degree of depression (P = .7), or rate of major depression between the 2 groups. CONCLUSIONS: There was no significant difference between kidney transplant recipients taking TAC or CsA in PHQ-9 score, degree of depression, or prevalence of major depression.