Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System.

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis.

Despite interest in the joint modeling of multiple functional responses such as diffusion properties in neuroimaging, robust statistical methods appropriate for this task are lacking. To address this need, we propose a varying coefficient quantile regression model able to handle bivariate functional responses. Our work supports innovative insights into biomedical data by modeling the joint distribution of functional variables over their domains and across clinical covariates. We propose an estimation procedure based on the alternating direction method of multipliers and propagation separation algorithms to estimate varying coefficients using a B-spline basis and an $L_2$ smoothness penalty that encourages interpretability. A simulation study and an application to a real-world neurodevelopmental data set demonstrates the performance of our model and the insights provided by modeling functional fractional anisotropy and mean diffusivity jointly and their association with gestational age and sex.

A novel model based on serum N-glycan markers for evaluating stage of liver necroinflammation in treatment-naïve chronic hepatitis B patients.

This study aimed to establish a novel noninvasive model based on the serum N-glycan spectrum for providing an objective value for determining the stage of liver necroinflammation related to chronic hepatitis B (CHB) patients. N-glycan profiles of the sera of 295 treatment-naïve CHB patients were analyzed. N-glycan profiles were tested for different liver necroinflammation stages using DNA sequence-assisted fluorophore-assisted carbohydrate electrophoresis. A serum N-glycan model named N-glycan-LI (NGLI) using support vector machine was selected to evaluate the classification of liver necroinflammation (G < 2 and G ≥ 2). The area under the receiver operating characteristic curves (AUROCs) was 0.898 (training set, n = 236) and 0.911 (validation set, n = 59) regardless of the stage of liver fibrosis (AUROC = 0.886 and 0.926, respectively, in S < 2 and S ≥ 2 group). The NGLI correspondingly had the highest specificity (SP) of 90.79% and negative predictive value of 92.00% in an inactive stage (including immune-tolerant [IT] and inactive-carrier [IC] stage), had the highest positive predictive value of 95.18% in stage immune-active, and had the highest SP of 93.94% in grey zone IT + IC. N-glycan profiles appear to correlate well with hepatic necroinflammation in CHB when compared with liver biopsy. The newly developed model appears to reliably predict liver damage in naïve-treatment patients with CHB.

Association of HBV serological markers with host antiviral immune response relevant hepatic inflammatory damage in chronic HBV infection.

The natural progression of chronic hepatitis B virus (HBV) infection is dynamic, but the longitudinal landscape of HBV serological markers with host antiviral immune response relevant hepatic inflammatory damage remains undetermined. To this issue, we studied the association of HBV serological markers with the severity of hepatic inflammatory damage and enumerated HBV-specific T cells using the cultured enzyme-linked immune absorbent spot (ELISpot). Five hundred and twenty-four treatment-naïve chronic HBV infection patients were enrolled. The Spearman correlation analysis revealed that in hepatitis B e antigen (HBeAg)-positive patients, all HBV virologic indicators negatively correlated with liver inflammatory damage and fibrosis (p < 0.01). Stronger correlations were accessed in the subgroup of HBeAg-positive patients with HBV DNA > 2 × 106 IU/mL (p < 0.01), whereas negative correlations disappeared in patients with HBV DNA ≤ 2 × 106 IU/mL. Surprisingly, in HBeAg-negative patients, the HBV DNA level was positively correlated with the hepatic inflammatory damage (p < 0.01). The relationship between type Ⅱ interferon genes expression and HBV DNA levels also revealed a direct shift from the initial negative to positive in HBeAg-positive patients with HBV DNA declined below 2 × 106 IU/mL. The number of HBV-specific T cells were identified by interferon γ ELISpot assays and showed a significant increase from HBeAg-positive to HBeAg-negative group. The host's anti-HBV immunity remains effective in HBeAg-positive patients with HBV DNA levels exceeding 2 × 106 IU/mL, as it efficiently eliminates infected hepatocytes and inhibits HBV replication. However, albeit the increasing number of HBV-specific T cells, the host antiviral immune response shifts towards dysfunctional when the HBV DNA load drops below this threshold, which causes more pathological damage and disease progression.

The effect of intraoperative midazolam on postoperative delirium in older surgical patients: a prospective, multicentre cohort study.

BACKGROUND: Midazolam is a short-acting benzodiazepine frequently used in the perioperative setting. This study aimed to investigate the potential impact of intraoperative midazolam on postoperative delirium (POD) in older patients undergoing non-cardiac surgery. METHODS: This study included patients aged ≥ 65 years who received general anaesthesia between April 2020 and April 2022 in multiple hospitals across China. POD occurring within 7 days was assessed using the 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM). Univariable and multivariable logistic regression models based on the random effects were used to determine the association between midazolam administration and the occurrence of POD, presented as risk ratio (RR) and 95% confidence intervals (CI). Kaplan-Meier cumulative incidence curve was plotted to compare the distribution of time to POD onset between patients who received midazolam and those who did not. Subgroup analyses based on specific populations were performed to explore the relationship between midazolam and POD. RESULTS: In all, 5,663 patients were included, of whom 723 (12.8%) developed POD. Univariate and multivariable logistic regression analyses based on random effects of different hospitals showed no significant association between midazolam medication and POD among older population (unadjusted RR=0.96, 95% CI: 0.90-1.30, P=0.38; adjusted RR=1.09, 95% CI: 0.91-1.33, P=0.35). Kaplan-Meier curve showed no difference in the distribution of time to POD onset (Hazard ratio [HR]=1.02, 95%CI: 0.88-1.18, P=0.82). The results of subgroup analyses found that intraoperative midazolam treatment was not associated with POD in the specific subgroups of patients. CONCLUSIONS: Intraoperative administration of midazolam may not be associated with an increased risk of POD in older patients undergoing non-cardiac surgery.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

Dibenzofurans from nature: Biosynthesis, structural diversity, sources, and bioactivities.

Dibenzofurans are a small class of natural products with versatile biological activities that used to be thought to come mainly from lichens and ascomycetes. In fact, they are also distributed widely in higher plants, especially in the families Rosaceae and Myrtaceae. Dibenzofurans and derivatives from lichens and ascomycetes have been well reviewed, but dibenzofurans from all biological sources in nature have not been reviewed. In this review, dibenzofurans from all natural sources have been comprehensively reviewed, and a total of 211 dibenzofurans isolated and identified from organisms between 1843 and March 2023 are categorized and discussed, including their biosynthesis, structural diversity, sources, and bioactivities.

Adult-onset combined methylmalonic acidemia and hyperhomocysteinemia, cblC type with aortic dissection and acute kidney injury: a case report.

BACKGROUND: Combined methylmalonic acidemia (MMA) and hyperhomocysteinemia, cobalamin C (cblC) type, also named cblC deficiency is a rare autosomal recessive genetic metabolic disease. It progressively causes neurological, hematologic, renal and other system dysfunction. The clinical manifestations are relatively different due to the onset time of disease. CASE PRESENTATION: This report describes a rare case of a 26 year old man with cblC deficiency who developed life-threatening aortic dissection and acute kidney injury (AKI) and showed neuropsychiatric symptoms with elevated serum homocysteine and methylmalonic aciduria. After emergent operation and intramuscular cobalamin supplementation therapy, the male recovered from aortic dissection, neurological disorder and AKI. Finally, two previously published compound heterozygous variants, c.482G > A (p.R161Q) and c.658_660del (p.K220del) in the MMACHC gene were detected in this patient and he was confirmed to have cblC deficiency. CONCLUSIONS: Poor cognizance of presenting symptoms and biochemical features of adult onset cblC disease may cause delayed diagnosis and management. This case is the first to depict a case of adult-onset cblC deficiency with aortic dissection. This clinical finding may contribute to the diagnosis of cblC deficiency.

Association of sleep quality on the night of operative day with postoperative delirium in elderly patients: A prospective cohort study.

BACKGROUND: Sleep disturbances in the peri-operative period have been associated with adverse outcomes, including postoperative delirium (POD). However, research on sleep quality during the immediate postoperative period is limited. OBJECTIVES: This study aimed to investigate the association between sleep quality on the night of the operative day assessed using the Sleep Quality Numeric Rating Scale (SQ-NRS), and the incidence of POD in a large cohort of surgical patients. DESIGN: A prospective cohort study. SETTING: A tertiary hospital in China. PATIENTS: This study enrolled patients aged 65 years or older undergoing elective surgery under general anaesthesia. The participants were categorised into the sleep disturbance and no sleep disturbance groups according to their operative night SQ-NRS. MAIN OUTCOME MEASURES: The primary outcome was delirium incidence, whereas the secondary outcomes included acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. RESULTS: In total, 3072 patients were included in the analysis of this study. Among them, 791 (25.72%) experienced sleep disturbances on the night of operative day. Patients in the sleep disturbance group had a significantly higher risk of developing POD (adjusted OR 1.43, 95% CI 1.11 to 1.82, P  = 0.005). Subgroup analysis revealed that age 65-75 years; male sex; ASA III and IV; haemoglobin more than 12 g l -1 ; intra-operative hypotension; surgical duration more than 120 min; and education 9 years or less were significantly associated with POD. No interaction was observed between the subgroups. No significant differences were observed in the secondary outcomes, such as acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. CONCLUSIONS: The poor subjective sleep quality on the night of operative day was independently associated with increased POD risk, especially in certain subpopulations. Optimising peri-operative sleep may reduce POD. Further research should investigate potential mechanisms and causal relationships. TRIAL REGISTRY: chictr.org.cn: ChiCTR1900028545.

Dobinin K Displays Antiplasmodial Activity through Disruption of Plasmodium falciparum Mitochondria and Generation of Reactive Oxygen Species.

Dobinin K is a novel eudesmane sesquiterpenoids compound isolated from the root of Dobinea delavayi and displays potential antiplasmodial activity in vivo. Here, we evaluate the antiplasmodial activity of dobinin K in vitro and study its acting mechanism. The antiplasmodial activity of dobinin K in vitro was evaluated by concentration-, time-dependent, and stage-specific parasite inhibition assay. The potential target of dobinin K on Plasmodium falciparum was predicted by transcriptome analysis. Apoptosis of P. falciparum was detected by Giemsa, Hoechst 33258, and TUNEL staining assay. The reactive oxygen species (ROS) level, oxygen consumption, and mitochondrial membrane potential of P. falciparum were assessed by DCFH-DA, R01, and JC-1 fluorescent dye, respectively. The effect of dobinin K on the mitochondrial electron transport chain (ETC) was investigated by enzyme activity analysis and the binding abilities of dobinin K with different enzymes were learned by molecular docking. Dobinin K inhibited the growth of P. falciparum in a concentration-, time-dependent, and stage-specific manner. The predicted mechanism of dobinin K was related to the redox system of P. falciparum. Dobinin K increased intracellular ROS levels of P. falciparum and induced their apoptosis. After dobinin K treatment, P. falciparum mitochondria lost their function, which was presented as decreased oxygen consumption and depolarization of the membrane potential. Among five dehydrogenases in P. falciparum ETC, dobinin K displayed the best inhibitory power on NDH2 activity. Our findings indicate that the antiplasmodial effect of dobinin K in vitro is mediated by the enhancement of the ROS level in P. falciparum and the disruption of its mitochondrial function.

Fast Fusion Clustering via Double Random Projection.

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

A method for generating synthetic longitudinal health data.

Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.

A review of memristor: material and structure design, device performance, applications and prospects.

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

Effect of traditional Chinese and western medicine combined with lung rehabilitation training on pulmonary function in patients with chronic obstructive pulmonary disease complicated with chronic cor pulmonale and evaluation of efficacy.

OBJECTIVES: To investigate the effect and efficacy of traditional Chinese and western medicine combined with lung rehabilitation training on pulmonary function in patients with chronic obstructive pulmonary disease(COPD) complicated with chronic cor pulmonale. METHODS: Totally 200 COPD patients with chronic cor pulmonale in our hospital were selected as research objects. The 100 patients in control group were managed by conventional western medicine combined with lung rehabilitation training, and another 100 patients in observation group were treated with traditional Chinese and western medicine combined with lung rehabilitation training. The pulmonary function (modified British Medical Research Council (mMRC) Dyspnea Scale, COPD Assessment Tests (CAT), BODE index (body-mass, airflow obstruction, dyspnea, and exercise capacity), pulmonary function-related indexes (FEV1, FVC, FEV1/FVC, and FEV1/Pred), and blood gas analysis indicators (paO2, PaCO2, SaO2 and PH value)) and curative effect (the effectiveness, Satisfaction Test, Quality of Life Score, and TCM Syndrome Integral) before and after treatment within or between groups were compared. RESULTS: Before treatment, the pulmonary function and curative effect (Quality of Life Score and TCM Syndrome Integral) parameters showed no significant difference between the two groups, which indicated there was a comparability between the two groups. After treatment, all parameters of pulmonary function and curative effect displayed better results in observation group than in control group. Compared with the results before treatment, pulmonary function and curative effect revealed better in both groups after treatment, and more visible improvement trends were found in observation group. CONCLUSIONS: Traditional Chinese and western medicine combined with lung rehabilitation training displayed better results on lung function and curative effect than conventional western medicine combined with lung rehabilitation training in patients with COPD complicated with chronic cor pulmonale (Tab. 8, Ref. 43). Text in PDF www.elis.sk Keywords: traditional Chinese and western medicine combined, lung rehabilitation training, chronic obstructive pulmonary disease, pulmonary function, curative effect.

Temporal changes in cyclinD-CDK4/CDK6 and cyclinE-CDK2 pathways: implications for the mechanism of deficient decidualization in an immune-based mouse model of unexplained recurrent spontaneous abortion.

BACKGROUND: Deficient endometrial decidualization has been associated with URSA. However, the underlying mechanism is poorly understood. This study aimed to investigate the temporal cytokine changes and the involvement of CyclinD-CDK4/6 and CyclinE-CDK2 pathways in the regulation of the G1 phase of the cell cycle during decidualization in a murine model of URSA. METHODS: Serum and decidual tissues of mice were collected from GD4 to GD8. The embryo resorption and abortion rates were observed on GD8 and the decidual tissue status was assessed. In addition, PRL, Cyclin D, CDK6, CDK4, Cyclin E, CDK2 expression in mice were measured. RESULTS: URSA mice showed high embryo resorption rate and PRL, Cyclin D, Cyclin E CDK2, CDK4, CDK6 down-regulation during decidualization. The hyperactivated Cyclin D-CDK4/CDK6 and cyclin E/CDK2 pathways inhibit the decidualization process and leading to deficient decidualization. CONCLUSION: Insufficient decidualization is an important mechanism of URSA. which is related to the decrease of Cyclin D、Cyclin E、 CDK2、CDK4 and CDK6 in decidualization process of URSA.

Rapid Repurposing of Novel Combination Drugs for the Treatment of Heart Failure via a Computationally Guided Network Screening Approach.

Combination drugs, characterized by high efficacy and few side effects, have received extensive attention from pharmaceutical companies and researchers for the treatment of complex diseases such as heart failure (HF). Traditional combination drug discovery depends on large-scale high-throughput experimental approaches that are time-consuming and costly. Herein we developed a novel, rapid, and potentially universal computer-guided combination drug-network-screening approach based on a set of databases and web services that are easy for individuals to obtain and operate, and we discovered for the first time that the menthol-allethrin combination screened by this approach exhibited a significant synergistic cardioprotective effect in vitro. Further mechanistic studies indicated that allethrin and menthol could synergistically block calcium channels. Allethrin bound to the central cavity of the voltage-dependent L-type calcium channel subunit alpha-1S (CACNA1S) lead to a conformational change in an allosteric site of CACNA1S, thereby enhancing the binding of menthol to this allosteric site. In summary, we reported a potentially universal computational approach to combination drug screening that has been used to discover a new combination of menthol-allethrin against HF in vitro, providing a new synergistic mechanism and prospective agent for HF treatment.

The responses of activated sludge to membrane cleaning reagent H2O2 and protection of extracellular polymeric substances.

Hydrogen peroxide (H2O2) is evaluated as a potential replacement for chlorine to control biofouling in membrane bioreactors (MBRs). However, H2O2 might diffuse into the mixed liquor and damage microorganisms during membrane cleaning. This study comprehensively analyzed the impacts of H2O2 on microbes. Key enzymes involved in phenol biodegradation were inhibited with H2O2 concentration increased, and thus phenol degradation efficiency was decreased. Increase of lactic dehydrogenase (LDH) and intracellular reactive oxygen species (ROS) indicated more severe cell rupture with H2O2 concentration increased. At the same H2O2 concentration, Extracellular polymeric substances (EPS) extraction further led to inhibiting the activity of key enzymes, decreasing phenol degradation efficiency, and enhancing LDH release and ROS production, demonstrating that the existence of EPS moderated the adverse impacts on microbes. Spectroscopic characterization revealed the increase of H2O2 decreased tryptophan protein-like substances, protein-associated bonds and polysaccharide-associated bonds. Hydroxyl and amide groups in EPS were attacked, which might lead to the consumption of H2O2, indicated EPS protect the microorganism through sacrificial reaction with H2O2.

Phloridzin Highly Accumulated in Malus rockii Rehder and Its Structure Revision and Hypolipidemic Activity.

Phloridzin is a lead compound of the prestigious antidiabetic gliflozins. The present study found that phloridzin highly accumulated in Malus rockii Rehder. The content of phloridzin in M. rockii was the highest among wild plants, with the percentage of 15.54% in the dry leaves. The structure of phloridzin was revised by proton exchange experiments and extensive 2D NMR spectra. Phloridzin exhibited significant hypolipidemic activity in golden Syrian hamsters maybe by increasing the expression of CYP7A1, at the doses of 50 mg/kg and 200 mg/kg. The total performance of anti-hyperlipidemic effect of phloridzin may be superior to that of lovastatin, though lovastatin was more active than phloridzin. In addition, phloridzin exhibited moderate antimalarial activity with inhibition ratio of 31.3 ± 10.9% at a dose of 25 mg/kg/day, and showed moderate analgesic activity with 28.0% inhibition at a dose of 50 mg/kg.

Anti-Larval and Anti-Algal Natural Products from Marine Microorganisms as Sources of Anti-Biofilm Agents.

Bacteria growing inside biofilms are more resistant to hostile environments, conventional antibiotics, and mechanical stresses than their planktonic counterparts. It is estimated that more than 80% of microbial infections in human patients are biofilm-based, and biofouling induced by the biofilms of some bacteria causes serious ecological and economic problems throughout the world. Therefore, exploring highly effective anti-biofilm compounds has become an urgent demand for the medical and marine industries. Marine microorganisms, a well-documented and prolific source of natural products, provide an array of structurally distinct secondary metabolites with diverse biological activities. However, up to date, only a handful of anti-biofilm natural products derived from marine microorganisms have been reported. Meanwhile, it is worth noting that some promising antifouling (AF) compounds from marine microbes, particularly those that inhibit settlement of fouling invertebrate larvae and algal spores, can be considered as potential anti-biofilm agents owing to the well-known knowledge of the correlations between biofilm formation and the biofouling process of fouling organisms. In this review, a total of 112 anti-biofilm, anti-larval, and anti-algal natural products from marine microbes and 26 of their synthetic analogues are highlighted from 2000 to 2021. These compounds are introduced based on their microbial origins, and then categorized into the following different structural groups: fatty acids, butenolides, terpenoids, steroids, phenols, phenyl ethers, polyketides, alkaloids, flavonoids, amines, nucleosides, and peptides. The preliminary structure-activity relationships (SAR) of some important compounds are also briefly discussed. Finally, current challenges and future research perspectives are proposed based on opinions from many previous reviews.