Nonparametric and Parametric Survival Analysis of Censored Data with Possible Violation of Method Assumptions

Estimating survival functions has interested statisticians for numerous years. A survival function gives information on the probability of a time-to-event of interest. Research in the area of survival analysis has increased greatly over the last several decades because of its large usage in areas related to biostatistics and the pharmaceutical industry. Among the methods which estimate the survival function, several are widely used and available in popular statistical software programs. One purpose of this research is to compare the efficiency between competing estimators of the survival function. Results are given for simulations which use nonparametric and parametric estimation methods on censored data. The simulated data sets have right-, left-, or interval-censored time points. Comparisons are done on various types of data to see which survival function estimation methods are more suitable. We consider scenarios where distributional assumptions or censoring type assumptions are violated. Another goal of this research is to examine the effects of these incorrect assumptions

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09\%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains

Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration

Microfluidic mass production of stabilized and stealthy liquid metal nanoparticles

Functional nanoparticles comprised of liquid metals, such as eutectic gallium indium (EGaIn) and Galinstan, present exciting opportunities in the fields of flexible electronics, sensors, catalysts, and drug delivery systems. Methods used currently for producing liquid metal nanoparticles have significant disadvantages as they rely on both bulky and expensive high-power sonication probe systems, and also generally require the use of small molecules bearing thiol groups to stabilize the nanoparticles. Herein, we describe an innovative microfluidics-enabled platform as an inexpensive, easily accessible method for the on-chip mass production of EGaIn nanoparticles with tunable size distributions in an aqueous medium. We also report a novel nanoparticle-stabilization approach using brushed polyethylene glycol chains with trithiocarbonate end-groups negating the requirements for thiol additives whilst imparting a ‘stealth’ surface layer. Furthermore, we demonstrate a surface modification of the nanoparticles using galvanic replacement, and conjugation with antibodies. We envision that the demonstrated microfluidic technique can be used as an economic and versatile platform for the rapid production of liquid metal-based nanoparticles for a range of biomedical applications.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints

BackgroundThe role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model.MethodsIn the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework’s validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities.ResultsIn our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels.ConclusionThis research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer

Effects of dietary energy level on antioxidant capability, immune function and rectal microbiota in late gestation donkeys

IntroductionThis study investigated the effects of dietary energy level on the antioxidant capability, immune function, and rectal microbiota in donkey jennets during the last 60 days of gestation.MethodsFifteen pregnant DeZhou donkeys with age of 6.0 ± 0.1 years, body weight of 292 ± 33 kg, parity of 2.7 ± 0.1 parities and similar expected date of confinement (74 ± 4 days) were randomly allocated to three groups and feed three diets: high energy (10.92 MJ/kg, H), medium energy (10.49 MJ/kg, M), and low energy (9.94 MJ/kg, L).Results and DiscussionThe serum activity of catalase (CAT), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), and total antioxidant capacity (T-AOC) in group M was significantly higher, whereas the concentrations of malondialdehyde (MDA), interleukin 1 (IL-1), IL-2, and IL-6 were lower than those recorded for groups H and L (p ≤ 0.05). The dietary energy level significantly affected rectal microbial community structure in the jennet donkeys 35 days and 7 days before the parturition (p ≤ 0.05). The abundances of norank_f_norank_o_Coriobacteriales genus was significantly higher (p ≤ 0.05) in group H, and the abundances of norank_f_norank_o_Mollicutes_RF39 and the Candidatus_Saccharimonas were higher in group L (p ≤ 0.05). The abundance of Fibrobacter in group M was significantly increased (p ≤ 0.05). The abundance of norank_f_norank_o_Coriobacteriales was positively correlated with average daily gain (ADG) and tumor necrosis factor-α (TNF-α) concentrations (p ≤ 0.05). The abundance of norank_f_norank_o_Mollicutes_RF39 was positively correlated with IL-2 and IL-6 concentrations. The abundance of Candidatus_Saccharimonas was positively correlated with CAT, T-SOD and GSH-Px activities (p ≤ 0.05). The abundance of Fibrobacter was positively correlated with CAT and T-SOD activities (p ≤ 0.05), but negatively correlated with IL-2 concentration (p ≤ 0.05). In conclusion, an appropriate dietary with an energy content of 10.49 MJ/kg for jennet donkeys during late gestation increased the prenatal antioxidant capacity, reduced inflammatory cytokines, and promoted fetal growth, and these changes were related to diet-induced changes in rectal microbiota compositions

Enhanced biphasic reactions in amphiphilic silica mesopores

In this study, we investigated the effect of the pore volume and mesopore size of surface-active catalytic organosilicas on the genesis of particle-stabilized (Pickering) emulsions for the dodecanal/ethylene glycol system and their reactivity for the acid-catalyzed biphasic acetalization reaction. To this aim, we functionalized a series of fumed silica superparticles (size 100–300 nm) displaying an average mesopore size in the range of 11–14 nm and variable mesopore volume, with a similar surface density of octyl and propylsulfonic acid groups. The modified silica superparticles were characterized in detail using different techniques, including acid–base titration, thermogravimetric analysis, TEM, and dynamic light scattering. The pore volume of the particles impacts their self-assembly and coverage at the dodecanal/ethylene glycol (DA/EG) interface. This affects the stability and the average droplet size of emulsions and conditions of the available interfacial surface area for reaction. The maximum DA-EG productivity is observed for A200 super-SiNPs with a pore volume of 0.39 cm3·g–1 with an interfacial coverage by particles lower than 1 (i.e., submonolayer). Using dissipative particle dynamics and all-atom grand canonical Monte Carlo simulations, we unveil a stabilizing role of the pore volume of porous silica superparticles for generating emulsions and local micromixing of immiscible dodecanal and ethylene glycol, allowing fast and efficient solvent-free acetalization in the presence of Pickering emulsions. The micromixing level is interrelated to the adsorption energy of self-assembled particles at the DA/EG interface