Drug-Drug Interactions in Patients with Breast Cancer

The research paper investigates the intricate landscape of drug-drug interactions (DDIs) within the context of breast cancer treatment, with a particular focus on the elderly population and the use of complementary and alternative medicine (CAM). The study underscores the heightened susceptibility of elderly patients to DDIs due to the prevalence of polypharmacy and the widespread utilization of CAM among breast cancer patients. The potential ramifications of DDIs, encompassing adverse drug events and diminished treatment efficacy, are elucidated. The paper accentuates the imperative for healthcare providers to comprehensively understand both conventional and CAM therapies, enabling them to provide patients with informed guidance regarding safe and efficacious treatment options, culminating in enhanced patient outcomes.

Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such novelties.Drug-drug interaction(DDI)significantly contributed to drug administration and development.It continues as the main obstacle in offering inexpensive and safe healthcare.It normally happens for patients with extensive medication,leading them to take many drugs simultaneously.DDI may cause side effects,either mild or severe health problems.This reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery time.Several efforts were made to formulate new methods for DDI prediction to overcome this issue.In this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data environment.In the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for prediction.The input data is preprocessed at the primary level to improve its quality.Next,the salp swarm optimization algorithm(SSO)is used to select features.In this study,the deep belief network(DBN)model is exploited to predict the DDI accurately.The SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the work.The experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

Optimizing hepatitis C virus treatment through pharmacist interventions: Identification and management of drug-drug interactions

To quantify drug-drug-interactions (DDIs) encountered in patients prescribed hepatitis C virus (HCV) treatment, the interventions made, and the time spent in this process.METHODSAs standard of care, a clinical pharmacist screened for DDIs in patients prescribed direct acting antiviral (DAA) HCV treatment between November 2013 and July 2015 at the University of Colorado Hepatology Clinic. HCV regimens prescribed included ledipasvir/sofosbuvir (LDV/SOF), paritaprevir/ritonavir/ombitasvir/dasabuvir (OBV/PTV/r + DSV), simeprevir/sofosbuvir (SIM/SOF), and sofosbuvir/ribavirin (SOF/RBV). This retrospective analysis reviewed the work completed by the clinical pharmacist in order to measure the aims identified for the study. The number and type of DDIs identified were summarized with descriptive statistics.RESULTSSix hundred and sixty four patients (83.4% Caucasian, 57% male, average 56.7 years old) were identified; 369 for LDV/SOF, 48 for OBV/PTV/r + DSV, 114 for SIM/SOF, and 133 for SOF/RBV. Fifty-one point five per cent of patients were cirrhotic. Overall, 5217 medications were reviewed (7.86 medications per patient) and 781 interactions identified (1.18 interactions per patient). The number of interactions were fewest for SOF/RBV (0.17 interactions per patient) and highest for OBV/PTV/r + DSV (2.48 interactions per patient). LDV/SOF and SIM/SOF had similar number of interactions (1.28 and 1.48 interactions per patient, respectively). Gastric acid modifiers and vitamin/herbal supplements commonly caused interactions with LDV/SOF. Hypertensive agents, analgesics, and psychiatric medications frequently caused interactions with OBV/PTV/r + DSV and SIM/SOF. To manage these interactions, the pharmacists most often recommended discontinuing the medication (28.9%), increasing monitoring for toxicities (24.1%), or separating administration times (18.2%). The pharmacist chart review for each patient usually took approximately 30 min, with additional time for more complex patients.CONCLUSIONDDIs are common with HCV medications and management can require medication adjustments and increased monitoring. An interdisciplinary team including a clinical pharmacist can optimize patient care.

Facial and bilateral lower extremity edema due to drug-drug interactions in a patient with hepatitis C virus infection and benign prostate hypertrophy: A case report

BACKGROUND New direct-acting antivirals(DAAs)-based anti-hepatitis C virus(HCV)therapies are highly effective in patients with HCV infection.However,safety data are lacking regarding HCV treatment with DAAs and drugs for comorbidities.CASE SUMMARY Herein,we reported a case of HCV-infection in a 46-year-old man with benign prostatic hypertrophy.The patient received sofosbuvir/velpatasvir as well as methadone maintenance therapy for drug abuse.The viral load became negative at week 1 post treatment.He developed facial and bilateral lower extremity edema 48 h after starting receiving tamsulosin.Edema disappeared 10 d after treatment with oral furosemide and spironolactone.CONCLUSION In conclusion,this is the first case of an acute edema in the course of treatment with new DAAs,methadone and tamsulosin.These agents are useful in clinical management of patients with HCV infection,particularly in men with benign prostatic hypertrophy.Clinicians should be aware of potential drug-drug interactions in this subset of patients.

Multidisciplinary Approach to Drug-Drug Interactions between Tacrolimus and Sofosbuvir/Velpatasvir and Glecaprevir/Pibrentasvir in Kidney Transplant Patients during Hepatitis C Treatment:A Case Series Report

The direct acting antivirals(DAAs)are now the standard of care for hepatitis C virus(HCV)treatment with high and effective sustained virologic responserate(SVR)and great safety profile,including solid organ transplant patients.There are increasing reports showing DAAs are effective with high SVR rates and safety profile in kidney transplant recipients.There are reports on drug-drug interaction(DDI)between tacrolimus with DAAs.However,data remain lacking on potential DDIs between tacrolimus and DAA regimens and the management process.This case series reports three kidney transplant patients on tacrolimus who were successfully treated for HCV with multidisciplinary approach,although there was DDI between tacrolimus with sofosbuvir/velpatasvir and glecaprevir/pibrentasvir,which required tacrolimus dose adjustment to maintain therapeutic level during and after DAA treatment.Such DDIs should be aware of and closely monitored by pharmacist and physicians with tacrolimus dose adjustment as needed during and right after DAA treatment in post-kidney transplant patients.

Drug-Drug Interaction Studies of Levocetirizine with Atenolol

The objective of the study was to evaluate the drug-drug interaction studies of levoceterizine with atenolol. Calibration curve studies of working standard solutions of levocetirizine and atenolol （0.01-0.1 mmol） were scanned. Maxima appeared at 231 nm for levocetirizine and 224 nm for atenolol. The calibration curve obeyed Beer Lambert＇s Law. Lone availabilities of both the drugs were studied in pH 1, pH 4, pH 7.4 and pH 9 at 37℃ on B.P. （British Pharmacopoeia） dissolution apparatus. To study the drug-drug interaction of levocetirizine （5 mg tablet） and atenolol （100 mg tablet）, both the drugs were introduced to the dissolution apparatus in simulated gastric juice （pH 1）, pH 4, pH 7.4 and pH 9 at 37℃ at zero time and measured the absorbance maxima of both the drugs at the corresponding wavelength. Graphs were plotted for availability percentage （%） of drug versus time at each set of experiment. The availability percentage （%） of levocetirizine in the buffers of pH simulated to gastric pH 4, pH 7.4 and pH 9 in the presence of atenolol was 436.78%, 376.90%, 436.78% and 436.78%, respectively, but the availability of atenolol was increased up to 214.80%, 212.96%, 214.93% and 231.51% in simulated to gastric pH and in the buffers ofpH 4, pH 7.4 and pH 9, respectively. On the basis of these studies, it is concluded that levocetirizine forms a charge-complex with atenolol; therefore, co-administration of these drugs should be avoided.

Prevalence of Potential Drug-Drug Interactions in Hospitalized Surgical Patients

The objective of this study is to estimate the prevalence and describe the characteristics of pDDIs （potential drug-drug interactions） in medical prescriptions of hospitalized surgical patients. In this cross-sectional study, we analyzed 370 medical prescriptions from the surgery unit of a Mexican public teaching hospital. The identification and classification of potential drug-drug interactions were performed with the Micromedex 2.0 electronic drug information database. Results were analyzed with descriptive statistics and we estimated OR （odds ratio） to determine associated risk factors. From the study, it was found that the prevalence of potential drug-drug interactions was 45.9%. A total of 385 interactions were identified. Of these, 54.3% were classified as major and 60.5% as pharmacodynamic. Prescriptions for more than seven drugs （OR =7.33, CI （confidence interval） = 4.59-11.71） and advanced age 〉 60 years, （OR = 1.79, CI = 1.06-2.74） were positively associated with the presence of potential drug-drug interactions. We found a high prevalence of clinically relevant pDDIs in the surgery unit. In view of this outcome, the safety of drug combinations in hospitalized surgical patients should be evaluated during the prescription process in order to prevent adverse events.

Possibility of Drug-Drug Interaction in Prescription Dispensed by Community and Hospital Pharmacy

Objective: To analyze the use of all subsidized prescription drugs including their use of drug combination generally accepted as carrying a risk of severe interactions. Methodology: In a cross sectional study, we analyzed all prescriptions (n = 1014) involving two or more drugs dispensed to the population (age range 4-85 years) from all pharmacies, clinics and hospitals. Data were stratified by age and sex, and frequency of common interacting drugs. Potential drug interactions were classified according to clinical relevance as significance of severity (types A: major, B: moderate, and C: minor) and documented evidence (types 1, 2, 3, and 4). Result and Discussion: The growing use of pharmacological agents means that drug interactions are of increasing interest for public health. Monitoring of potential drug interactions may improve the quality of drug prescribing and dispensing, and it might form a basis for education focused on appropriate prescribing. To make the manifestation of adverse interaction subside, management strategies must be exercised if two interacting drugs have to be taken with each other, involving: adjusting the dose of the object drug;spacing dosing times to avoid the interaction. The pharmacist, along with the prescriber has a duty to ensure that patients are aware of the risk of side effects and a suitable course of action they should take. Conclusion: It is unrealistic to expect clinicians to memorize the thousands of drug-drug interactions and their clinical significance, especially considering the rate of introduction of novel drugs and the escalating appreciation of the importance of pharmacogenomics. Reliable regularly updated decision support systems and information technology are necessary to help avert dangerous drug combinations.

<i>In Vitro</i>and <i>in Vivo</i>(Mouse) Evaluation of Drug-Drug Interactions of Repaglinide with Anti-HIV Drugs

Repaglinide is type 2 short acting anti-diabetic drug which is primarily metabolized by CYP2C8 and CYP3A4 and is also a substrate of influx transporter OATP1B1. HIV drugs are potent inhibitors of CYP3A4 and OATP transporters. Several drug-drug interactions (DDIs) were noticed when protease inhibitors (PIs) coadministered with drugs metabolized by CYP3A4. The PIs are also potent mechanism based inhibitors, out which ritonavir is most potent. In the current study we evaluated in vitro (mouse and human liver microsomes) and in vivo DDIs of repaglinide with anti-HIV drugs. Out of the following tested drugs (Amprenavir, Indinavir, Nelfinavir, Ritonavir, Saquinavir, Delavirdine, Maraviroc, Efavirenz, Nevirapine and Ketoconazole) Amprenavir (APV), Ritonavir (RTV) and Ketoconazole (KTZ) showed inhibition of OH-repaglinide formation in human and mouse liver microsomes. The positive reversible inhibitions were further tested for irreversible inhibitions where we didn’t observe any irreversible inhibitions. In vitro inhibitions were further evaluated in the in vivo pharmacokinetics (mouse) where repaglinide pharmacokinetics was altered by RTV and KTZ. The DDIs in both studies were very strong;the dose of repaglinide is reduced to 20 fold. In conclusion, there could be possible DDIs when RTV dosed with repaglinide;we have also demonstrated that mouse could be useful preclinical tool when used in conjunction with in vitro screening models for DDIs.

Short-Term Drug-Drug Interaction between Sildenafil and Bosentan under Long-Term Use in Patients with Pulmonary Arterial Hypertension

Sildenafil and bosentan are often co-administered for pulmonary arterial hypertension (PAH) treatment. The plasma concentration of sildenafil can be decreased by half if co-administered with bosentan. Many patients take these agents simultaneously in the morning and the evening. The aim of this study was to examine the pharmacokinetics of sildenafil which was interfered with bosentan administration to ascertain whether these agents should be given concomitantly or separately. A two-way crossover study was conducted in 6 PAH patients with combination therapy of sildenafil and bosentan. Participants underwent the sequence of treatment phases: phase S (sildenafil administered 3 h before bosen-tan);phase B (bosentan administered 3 h before sildenafil);and phase C (administered concomitantly). Blood samples were collected on the last day of each phase. There was no significant difference in maximum plasma concentration or area under the plasma concentration-time curve (AUC0-8) between phase C and phase S (95.5 ± 24.8 vs. 72.9 ± 40.9 (p = 0.07), 209.7 ± 81.8 vs. 180.2 ± 126.4 (p = 0.24), respectively) or between phases C and B (87.8 ± 42.0 vs. 99.6 ± 33.9 (p = 0.59), 197.2 ± 88.2 vs. 240.7 ± 121.8 (p = 0.19), respectively) (ng/mL, mean ± standard deviation). Large intra-and inter-individual variability in sildenafil concentration was noted. The timing of administration of sildenafil and bosentan does not significantly influence the plasma concentration of sildenafil. Physicians do not need to be overly concerned about the timing of administration of these drugs to maximize the sildenafil concentration.

Impact of Drug-Drug Interaction between CDK4/6 Inhibitors and Proton Pump Inhibitors on Survival Outcomes in the Treatment of Metastatic Breast Cancer—Real World Data from a Portuguese Center

Introduction: Proton pump inhibitors (PPi) are widely prescribed, including in patients undergoing treatment for advanced breast cancer (ABC). Due to the pharmacokinetic characteristics of the CDK4/6 inhibitor (Ci) palbociclib a drug interaction with PPi was hypothesized. It was shown in a retrospective study that this association was an independent predictive factor for worse progression-free survival (PFS). Objective: To verify the impact of concomitant administration of PPi with Ci on overall survival (OS) and PFS. Material and Methods: This is a retrospective cohort study of patients treated with Ci for HR+HER2-ABC in the period from Feb/2017 to Aug/2020. SPSS software was used for data processing. Univariate analysis was done by the Kaplan-Meier method and log-rank test, and multivariate analysis by COX regression. P-value < 0.05 was considered significant. Results: 80 patients were included. The median age at diagnosis of ABC was 56 years (25 - 75). Treatment with Ci was 1st line for ABC in 68.8%. Choice of Ci was palbociclib in 73.8% (n = 59) and ribociclib in 26.3% (n = 21). The hormone partner was a nonsteroidal aromatase inhibitor in 45.0%, and fulvestrant in 55.0% of cases. 37.5% of patients were on PPi, and 70.0% of them were during the entire treatment (23.3% omeprazole, 73.4% pantoprazole, 3.3% others). Patients taking concomitant PPi and Ci had lower OS (OS-3 years 42.6% vs. 63.4%, p = 0.254) and PFS (PFS med 15 m. vs. 21 m., p = 0.733), although with no statistically significant difference. Discussion: In the sample, there was a numerical difference, without the statistical significance in the use of PPi in the survival of patients under Ci. This difference could be more evident with a longer follow-up and a larger sample size. This study intends to alert to the growing importance of checking for drug interactions. Polymedication, advanced age and the presence of several comorbidities are real problems in patients with ABC. Conclusion: Real-world data from this center demonstrate a negative, non-statistically significant impact of PPi treatment on survival outcomes, in patients treated with Ci for HR+HER2-ABC.

Deep learning for drug-drug interaction prediction:A comprehensive review

The prediction of drug-drug interactions(DDIs)is a crucial task for drug safety research,and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy.Traditional wet chemical experiments for DDI are cumbersome and time-consuming,and are too small in scale,limiting the efficiency of DDI predictions.Therefore,it is particularly crucial to develop improved computational methods for detecting drug interactions.With the development of deep learning,several computational models based on deep learning have been proposed for DDI prediction.In this review,we summarized the high-quality DDI prediction methods based on deep learning in recent years,and divided them into four categories:neural network-based methods,graph neural network-based methods,knowledge graph-based methods,and multimodal-based methods.Furthermore,we discuss the challenges of existing methods and future potential perspectives.This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning.Deep learning models can scale to large-scale datasets and accept multiple data types as input,thus making DDI predictions more efficient and accurate.

A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction

Identifying drug–drug interactions(DDIs)is an important aspect of drug design research,and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects.Current substructure-based prediction methods still have some limitations:(i)The process of substructure extraction does not fully exploit the graph structure information of drugs,as it only evaluates the importance of different radius substructures from a single perspective.(ii)The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations.In this work,we propose a substructure-aware graph neural network incorporating relation features(RFSA-DDI)for DDI prediction,which introduces a directed message passing neural network with substructure attention mechanism based on graph self-adaptive pooling(GSP-DMPNN)and a substructure-aware interaction module incorporating relation features(RSAM).GSP-DMPNN utilizes graph self-adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures.RSAM interacts drug features with relation representations to enhance their respective features individually,highlighting substructures that significantly impact predictions.RFSA-DDI is evaluated on two real-world datasets.Compared to existing methods,RFSA-DDI demonstrates certain advantages in both transductive and inductive settings,effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability.The experimental results show that RFSA-DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction,and provide more reliable assistance for potential DDIs detection in drug development and treatment stages.

DDI-Transform:A neural network for predicting drug-drug interaction events

Drug-drug interaction(DDI)event prediction is a challenging problem,and accurate prediction of DDI events is critical to patient health and new drug development.Recently,many machine learning-based techniques have been proposed for predicting DDI events.However,most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information.To address these limitations,we propose a DDI-Transform neural network framework for DDI event prediction.In DDI-Transform,we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information.A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning,thus adaptively selecting the effective feature information for prediction.The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models.Results on different scale datasets confirm the robustness of the method.

DeepDrug:A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction

Background:Computational approaches for accurate prediction of drug interactions,such as drug-drug interactions(DDIs)and drug-target interactions(DTIs),are highly demanded for biochemical researchers.Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively,their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure.Methods:In this paper,we develop DeepDrug,a deep learning framework for overcoming the above limitation by using residual graph convolutional networks(Res-GCNs)and convolutional networks(CNNs)to learn the comprehensive structure-and sequence-based representations of drugs and proteins.Results:DeepDrug outperforms state-of-the-art methods in a series of systematic experiments,including binary-class DDIs,multi-class/multi-label DDIs,binary-class DTIs classification and DTIs regression tasks.Furthermore,we visualize the structural features learned by DeepDrug Res-GCN module,which displays compatible and accordant patterns in chemical properties and drug categories,providing additional evidence to support the strong predictive power of DeepDrug.Ultimately,we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2,where 7 out of 10 top-ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019(COVID-19).Conclusions:To sum up,we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

Strong metal–support interaction boosts the electrocatalytic hydrogen evolution capability of Ru nanoparticles supported on titanium nitride

Ruthenium(Ru)has been regarded as one of the most promising alternatives to substitute Pt for catalyzing alkaline hydrogen evolution reaction(HER),owing to its inherent high activity and being the cheapest platinum-group metal.Herein,based on the idea of strong metal–support interaction(SMSI)regulation,Ru/TiN catalysts with different degrees of TiN overlayer over Ru nanoparticles were fabricated,which were applied to the alkaline electrolytic water.Characterizations reveal that the TiN overlayer would gradually encapsulate the Ru nanoparticles and induce more electron transfer from Ru nanoparticles to TiN support by the Ru–N–Ti bond as the SMSI degree increased.Further study shows that the exposed Ru–TiN interfaces greatly promote the H_(2) desorption capacity.Thus,the Ru/TiN-300 with a moderate SMSI degree exhibits excellent HER performance,with an overpotential of 38 mV at 10 mA cm^(−2).Also,due to the encapsulation role of TiN overlayer on Ru nanoparticles,it displays super long-term stability with a very slight potential change after 24 h.This study provides a deep insight into the influence of the SMSI effect between Ru and TiN on HER and offers a novel approach for preparing efficient and stable HER electrocatalysts through SMSI engineering.

Electrostatic Interaction-directed Construction of Hierarchical Nanostructured Carbon Composite with Dual Electrical Conductive Networks for Zinc-ion Hybrid Capacitors with Ultrastability

Metal-organic framework(MOF)-derived carbon composites have been considered as the promising materials for energy storage.However,the construction of MOF-based composites with highly controllable mode via the liquid-liquid synthesis method has a great challenge because of the simultaneous heterogeneous nucleation on substrates and the self-nucleation of individual MOF nanocrystals in the liquid phase.Herein,we report a bidirectional electrostatic generated self-assembly strategy to achieve the precisely controlled coatings of single-layer nanoscale MOFs on a range of substrates,including carbon nanotubes(CNTs),graphene oxide(GO),MXene,layered double hydroxides(LDHs),MOFs,and SiO_(2).The obtained MOF-based nanostructured carbon composite exhibits the hierarchical porosity(V_(meso)/V_(micro)∶2.4),ultrahigh N content of 12.4 at.%and"dual electrical conductive networks."The assembled aqueous zinc-ion hybrid capacitor(ZIC)with the prepared nanocarbon composite as a cathode shows a high specific capacitance of 236 F g^(-1)at 0.5 A g^(-1),great rate performance of 98 F g^(-1)at 100 A g^(-1),and especially,an ultralong cycling stability up to 230000 cycles with the capacitance retention of 90.1%.This work develops a repeatable and general method for the controlled construction of MOF coatings on various functional substrates and further fabricates carbon composites for ZICs with ultrastability.

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.