Effects of square spatial periodic forcing on oscillatory hexagon patterns in coupled reaction-diffusion systems.

One of the central issues in pattern formation is understanding the response of pattern-forming systems to an external stimulus. While significant progress has been made in systems with only one instability, much less is known about the response of complex patterns arising from the interaction of two or more instabilities. In this paper, we consider the effects of square spatial periodic forcing on oscillatory hexagon patterns in a two-layer coupled reaction diffusion system which undergoes both Turing and Hopf instabilities. Two different types of additive forcings, namely direct and indirect forcing, have been applied. It is shown that the coupled system exhibits different responses towards the spatial forcing under different forcing types. In the indirect case, the oscillatory hexagon pattern transitions into other oscillatory Turing patterns or resonant Turing patterns, depending on the forcing wavenumber and strength. In the direct forcing case, only non-resonant Turing patterns can be obtained. Our results may provide new insight into the modification and control of spatio-temporal patterns in multilayered systems, especially in biological and ecological systems.

Pyrolysis Kinetics of Lignin-Based Flame Retardants Containing MOFs Structure for Epoxy Resins.

This study describes the preparation of a lignin-based expandable flame retardant (Lignin-N-DOPO) using grafting melamine and covering 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) using the Mannich reaction. Then, through in situ growth, a metal-organic framework (MOF) HKUST-1 (e.g., Cu3(BTC)2, BTC = benzene-1,3,5-tricarboxylate)/lignin-based expandable flame retardant (F-lignin@HKUST-1) was created. Before that, lignin epoxy resin containing phosphorus (P) and nitrogen (N) components had been created by combining epoxy resin (EP) with F-lignin@HKUST-1. Thermogravimetric analysis was used to examine the thermal characteristics of epoxy resin (EP) composite. The findings indicate that the thermal stability of EP is significantly affected by the presence of F-lignin@HKUST-1. Last but not least, the activation energy (E) of EP/15% F-lignin@HKUST-1 was examined using four different techniques, including the Kissinger-SY iteration method, the Ozawa-SY iteration method, the Lee-Beck approximation-iteration method, and the Gorbatchev approximation-iteration method. It was discovered that the activation energy was significantly higher than that of lignin. Higher activation energy suggests that F-lignin@HKUST-1 pyrolysis requires more energy from the environment, which will be significant about the application of lignin-based flame retardants.

Deconvolution of Cytochrome P450 Induction Mechanisms in HepaRG Nuclear Hormone Receptor Knockout Cells.

Pregnane X receptor (PXR), constitutive androstane receptor (CAR), and PXR/CAR knockout (KO) HepaRG cells, as well as a PXR reporter gene assay, were used to investigate the mechanism of CYP3A4 and CYP2B6 induction by prototypical substrates and a group of compounds from the Merck KGaA oncology drug discovery pipeline. The basal and inducible gene expression of CYP3A4 and CYP2B6 of nuclear hormone receptor (NHR) KO HepaRG relative to control HepaRG was characterized. The basal expression of CYP3A4 was markedly higher in the PXR (10-fold) and CAR (11-fold) KO cell lines compared with control HepaRG, whereas inducibility was substantially lower. Inversely, basal expression of CYP3A4 in PXR/CAR double KO (dKO) was low (10-fold reduction). Basal CYP2B6 expression was high in PXR KO (9-fold) cells which showed low inducibility, whereas the basal expression remained unchanged in CAR and dKO cell lines compared with control cells. Most of the test compounds induced CYP3A4 and CYP2B6 via PXR and, to a lesser extent, via CAR. Furthermore, other non-NHR-driven induction mechanisms were implicated, either alone or in addition to NHRs. Notably, 5 of the 16 compounds (31%) that were PXR inducers in HepaRG did not activate PXR in the reporter gene assay, illustrating the limitations of this system. This study indicates that HepaRG is a highly sensitive system fit for early screening of cytochrome P450 (P450) induction in drug discovery. Furthermore, it shows the applicability of HepaRG NHR KO cells as tools to deconvolute mechanisms of P450 induction using novel compounds representative for oncology drug discovery. SIGNIFICANCE STATEMENT: This work describes the identification of induction mechanisms of CYP3A4 and CYP2B6 for an assembly of oncology drug candidates using HepaRG nuclear hormone receptor knockout and displays its advantages compared to a pregnane X receptor reporter gene assay. With this study, risk assessment of drug candidates in early drug development can be improved.

Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases.

BACKGROUND: Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. RESULTS: In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR CONCLUSIONS: The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network.

Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attracts wide spread interest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports.

BACKGROUND: Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports. METHODS: In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs. RESULTS: We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs. CONCLUSIONS: The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time. AVAILABILITY: The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.

Predicting drug-disease associations by using similarity constrained matrix factorization.

BACKGROUND: Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task. RESULTS: In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing. CONCLUSION: We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.

KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs.

Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in-depth information fusion, enabling distinct encoding of treatment-covariate and outcome-covariate relationships. KG-TREAT also incorporates two pre-training tasks to ensure a thorough grounding and contextualization of patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's superiority over existing methods, with an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence Function-based Precision of Estimating Heterogeneous Effects (IF-PEHE). The effectiveness of our estimated treatment effects is further affirmed by alignment with established randomized clinical trial findings.

CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation.

Treatment effect estimation (TEE) aims to identify the causal effects of treatments on important outcomes. Current machine-learning-based methods, mainly trained on labeled data for specific treatments or outcomes, can be sub-optimal with limited labeled data. In this article, we propose a new pre-training and fine-tuning framework, CURE (causal treatment effect estimation), for TEE from observational data. CURE is pre-trained on large-scale unlabeled patient data to learn representative contextual patient representations and fine-tuned on labeled patient data for TEE. We present a new sequence encoding approach for longitudinal patient data embedding both structure and time. Evaluated on four downstream TEE tasks, CURE outperforms the state-of-the-art methods, marking a 7% increase in area under the precision-recall curve and an 8% rise in the influence-function-based precision of estimating heterogeneous effects. Validation with four randomized clinical trials confirms its efficacy in producing trial conclusions, highlighting CURE's capacity to supplement traditional clinical trials.

Enhanced simultaneous removal of phosphate and ammonium from swine wastewater using magnetic magnesium-loaded Chinese herbal medicine residues: Performance, mechanism, and resource utilization.

Magnetic magnesium (Mg)-loaded Chinese herbal medicine residues (MM-TCMRs) were fabricated to simultaneously remove and recover phosphate and ammonium from wastewater. The MM-TCMRs exhibited larger specific surfaces and rougher structures with massive spherical particles than those of original residues. They could be separated by adjusting the magnetic field. The phosphate and ammonium adsorption by MM-TCMRs were matched with the pseudo-second-order model, while the Langmuir model yielded the maximum adsorption capacities of 635.35 and 615.57 mg g-1, respectively. Struvite precipitation on the MM-TCMRs surface was the primary removal mechanism with electrostatic attraction, ligand exchange, intra-particle diffusion, and ion exchange also involved. The recyclability of MM-TCMRs confirmed their good structural stability. More importantly, the nutrient-loaded MM-TCMRs enhanced alfalfa growth and improved soil fertility in planting experiments. Collectively, the MM-TCMRs are promising candidates for nutrient removal and recovery from wastewater.

Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder.

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury.

Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI .

Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis.

Sepsis is a life-threatening condition with a high in-hospital mortality rate. The timing of antibiotic administration poses a critical problem for sepsis management. Existing work studying antibiotic timing either ignores the temporality of the observational data or the heterogeneity of the treatment effects. Here we propose a novel method (called T4) to estimate treatment effects for time-to-treatment antibiotic stewardship in sepsis. T4 estimates individual treatment effects by recurrently encoding temporal and static variables as potential confounders, and then decoding the outcomes under different treatment sequences. We propose mini-batch balancing matching that mimics the randomized controlled trial process to adjust the confounding. The model achieves interpretability through a global-level attention mechanism and a variable-level importance examination. Meanwhile, we equip T4 with an uncertainty quantification to help prevent overconfident recommendations. We demonstrate that T4 can identify effective treatment timing with estimated individual treatment effects for antibiotic stewardship on two real-world datasets. Moreover, comprehensive experiments on a synthetic dataset exhibit the outstanding performance of T4 compared with the state-of-the-art models on estimation of individual treatment effect.

Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury.

Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI.

An Early Adverse Drug Event Detection Approach with False Discovery Rate Control.

Adverse drug event (ADE) is a significant challenge in clinical practice. Many ADEs have not been identified timely after the approval of the corresponding drugs. Despite the use of drug similarity network demonstrates early success on improving ADE detection, false discovery rate (FDR) control remains unclear in its application. Additionally, performance of early ADE detection has not been explicitly investigated under the time-to-event framework. In this manuscript, we propose to use the drug similarity based posterior probability of null hypothesis for early ADE detection. The proposed approach is also able to control FDR for monitoring a large number of ADEs of multiple drugs. The proposed approach outperforms existing approaches on mining labeled ADEs in the US FDA's Adverse Event Reporting System (FAERS) data, especially in the first few years after the drug initial reporting time. Additionally, the proposed approach is able to identify more labeled ADEs and has significantly lower time to ADE detection. In simulation study, the proposed approach demonstrates proper FDR control, as well as has better true positive rate and an excellent true negative rate. In our exemplified FAERS analysis, the proposed approach detects new ADE signals and identifies ADE signals in a timelier fashion than existing approach. In conclusion, the proposed approach is able to both reduce the time and improve the FDR control for ADE detection.

Characterisation of a Novel Tigecycline Resistance Gene tet(X22) and its Coexistence with blaNDM-1 in a Pseudomonas caeni Isolate.

OBJECTIVES: The emergence of pathogens that are resistant to both tigecycline and carbapenem poses a threat to public health globally. Continuous emergence of novel tet(X) variants accelerates the tigecycline resistance crisis. This study aimed to characterise the novel tigecycline resistance gene tet(X22) and its coexistence with carbapenem resistance gene blaNDM-1 in Pseudomonas caeni. METHODS: This P. caeni isolate co-harbouring tet(X22) and blaNDM-1 was systematically investigated using antimicrobial susceptibility testing, conjugation assays, genome sequencing, bioinformatic analyses, cloning of tet(X22) and functional analysis, and protein structure prediction. RESULTS: The carbapenem-resistant and tigecycline-resistant P. caeni isolate CE14 was obtained from chicken faeces in 2022. CE14 carried multiple antibiotic resistance genes, including the novel tet(X22) and blaNDM-1. Tet(X22) exhibited 64.72-90.48% amino acid identity with other variants [Tet(X) to Tet(X21)]. Cloning of the gene tet(X22) and protein structure prediction revealed that Tet(X22) confers resistance to tetracyclines, including tigecycline. tet(X22) and blaNDM-1 were located in two multidrug-resistant regions of the chromosome. CONCLUSIONS: The occurrence of the novel ISCR2-flanked tet(X22) in P. caeni suggests that the tet(X) variant has adapted to new hosts and may widely spread to further expand the host range. The future global spread of such pathogens co-harbouring tet(X) and blaNDM variants needs to be continuously monitored according to the One Health approach.

Enantioselective Nucleophilic Vinylic Substitution (SNV) toward 3-Alkenyl-bisoxindoles Facilitated by C6' Steric Bulk of Cinchona Alkaloid.

A C6' bulky substituted quinine-catalyzed SNV reaction between 3-substituted oxindole and (E)-3-(nitromethylene)-oxindole was developed. This enantioselective C(sp3)-C(sp2) coupling furnished bisoxindole scaffolds featuring a vinyl-substituted all-carbon quaternary stereocenter with high stereoselectivities. In addition, the gram-scale synthesis and synthetic post-transformations were conducted to demonstrate the potential synthetic usefulness.

Direct Seawater Electrolysis: From Catalyst Design to Device Applications.

Direct seawater electrolysis (DSE) for hydrogen production, using earth-abundant seawater as the feedstock and renewable electricity as the driving source, paves a new opportunity for flexible energy conversion/storage and smooths the volatility of renewable energy. Unfortunately, the complex environments of seawater impose significant challenges on the design of DSE catalysts, and the practical performance of many current DSE catalysts remains unsatisfactory on the device level. However, many studies predominantly concentrate on the development of electrocatalysts for DSE without giving due consideration to the specific devices. To mitigate this gap, the most recent progress (mainly published within the year 2020-2023) of DSE electrocatalysts and devices are systematically evaluated. By discussing key bottlenecks, corresponding mitigation strategies, and various device designs and applications, the tremendous challenges in addressing the trade-off among activity, stability, and selectivity for DSE electrocatalysts by a single shot are emphasized. In addition, the rational design of the DSE electrocatalysts needs to align with the specific device configuration, which is more effective than attempting to comprehensively enhance all catalytic parameters. This work, featuring the first review of this kind to consider rational catalyst design in the framework of DSE devices, will facilitate practical DSE development.

Systemic administration of mesenchymal stem cells loaded with a novel oncolytic adenovirus carrying a bispecific T cell engager against hepatocellular carcinoma.

We previously established a hepatocellular carcinoma (HCC) targeting system of conditionally replicative adenovirus (CRAd) delivered by human umbilical cord-derived mesenchymal stem cells (HUMSCs). However, this system needed to be developed further to enhance the antitumor effect and overcome the limitations caused by the alpha-fetoprotein (AFP) heterogeneity of HCC. In this study, a bispecific T cell engager (BiTE) targeting programmed death ligand 1 controlled by the human telomerase reverse transcriptase promoter was armed on the CRAd of the old system. It was demonstrated on orthotopic transplantation model mice that the new system had a better anti-tumor effect with no more damage to extrahepatic organs and less liver injury, and the infiltration and activation of T cells were significantly enhanced in the tumor tissues of the model mice treated with the new system. Importantly, we confirmed that the new system eliminated the AFP-negative cells on AFP heterogeneous tumor models efficiently. Conclusion: Compared with the old system, the new system provided a more effective and safer strategy against HCC.

The application of artificial intelligence in the management of sepsis.

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.