Therapeutic Potential of Mangosteen Pericarp Extract-Loaded Liposomes against Superficial Skin Infection Caused by Staphylococcus pseudintermedius in a Murine Model.

α-mangostin (α-MG) demonstrates antibacterial activity against Staphylococcus species. Therefore, this study aimed to explore the antibacterial activity of α-MG-rich mangosteen pericarp extract (MPE)-loaded liposomes against Staphylococcus isolates from companion animal skin diseases in vitro and evaluated their therapeutic potential in a murine model of superficial skin infection caused by S. pseudintermedius. α-MG-rich extract was purified from mangosteen pericarp and then complexed with γ-cyclodextrin (γ-CD), forming the inclusion complexes. Nanoliposomes containing MPE and γ-CD complexes were prepared by adding lecithin and casein. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of MPE-loaded liposomes were determined using agar dilution and broth microdilution methods. The therapeutic potential of MPE-loaded liposomes was evaluated in vivo on tape-stripped skin lesions infected with S. pseudintermedius. Purified MPE and MPE-loaded liposomes contained 402.43 mg/g and 18.18 mg/g α-MG, respectively. MPE-loaded liposomes showed antibacterial activity against clinical Staphylococcus isolates in vitro but did not show antibacterial activity against Gram-negative bacterial isolates. MPE-loaded liposomes demonstrated consistent MICs and MBCs against Staphylococcus isolates. These liposomes significantly reduced bacterial numbers and lesional sizes in a superficial skin infection model. Moreover, they reconstructed the epidermal barrier in skin lesions. The therapeutic concentrations of MPE-loaded liposomes did not induce cytotoxicity in canine progenitor epidermal keratinocyte cells. In conclusion, MPE-loaded liposomes hold promise for the development of a prospective topical formulation to treat superficial pyoderma in companion animals.

Acinetobacter baumannii under Acidic Conditions Induces Colistin Resistance through PmrAB Activation and Lipid A Modification.

Colistin is a last-resort antimicrobial agent for treating carbapenem-resistant Acinetobacter baumannii infections. The activation of PmrAB by several environmental signals induces colistin resistance in Gram-negative bacteria. This study investigated the molecular mechanisms of colistin resistance in A. baumannii under acidic conditions using wild-type (WT) A. baumannii 17978, ΔpmrA and ΔpmrB mutants, and pmrA-complemented strains. The pmrA or pmrB deletion did not affect the growth of A. baumannii under acidic or aerobic conditions. A. baumannii under acidic (pH 5.5) and high-iron (1 mM) conditions showed 32- and 8-fold increases in the minimum inhibitory concentrations (MICs) of colistin, respectively. The ΔpmrA and ΔpmrB mutants at pH 5.5 showed a significant decrease in colistin MICs compared to the WT strain at pH 5.5. No difference in colistin MICs was observed between WT and mutant strains under high-iron conditions. The pmrCAB expression significantly increased in the WT strain at pH 5.5 compared to the WT strain at pH 7.0. The pmrC expression significantly decreased in two mutant strains at pH 5.5 compared to the WT strain at pH 5.5. The PmrA protein was expressed in the ΔpmrA strain carrying ppmrA_FLAG plasmids at pH 5.5 but not at pH 7.0. Lipid A modification by the addition of phosphoethanolamine was observed in the WT strain at pH 5.5. In conclusion, this study demonstrated that A. baumannii under acidic conditions induces colistin resistance via the activation of pmrCAB operon and subsequent lipid A modification.

Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy.

Surface-enhanced Raman scattering (SERS) spectroscopy is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this article, we present a self-supervised deep learning-based information fusion technique to minimize the variance in the SERS measurements of multiple laboratories for the same target analyte. In particular, a variation minimization model, coined the minimum-variance network (MVNet), is designed. Moreover, a linear regression model is trained using the output of the proposed MVNet. The proposed model showed improved performance in predicting the concentration of the unseen target analyte. The linear regression model trained on the output of the proposed model was evaluated by several well-known metrics, such as root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and coefficient of determination (R2). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.

Complementary Regulation of BfmRS Two-Component and AbaIR Quorum Sensing Systems to Express Virulence-Associated Genes in Acinetobacter baumannii.

Acinetobacter baumannii expresses various virulence factors to adapt to hostile environments and infect susceptible hosts. This study investigated the regulatory network of the BfmRS two-component and AbaIR quorum sensing (QS) systems in the expression of virulence-associated genes in A. baumannii ATCC 17978. The ΔbfmS mutant exhibited a significant decrease in surface motility, which presumably resulted from the low expression of pilT and A1S_0112-A1S_0119 gene cluster. The ΔbfmR mutant displayed a significant reduction in biofilm and pellicle formation due to the low expression of csu operon. The deletion of abaR did not affect the expression of bfmR or bfmS. However, the expression of abaR and abaI was upregulated in the ΔbfmR mutant. The ΔbfmR mutant also produced more autoinducers than did the wild-type strain, suggesting that BfmR negatively regulates the AbaIR QS system. The ΔbfmS mutant exhibited no autoinducer production in the bioassay system. The expression of the A1S_0112-A1S_0119 gene cluster was downregulated in the ΔabaR mutant, whereas the expression of csu operon was upregulated in this mutant with a high cell density. In conclusion, for the first time, we demonstrated that the BfmRS-AbaIR QS system axis regulated the expression of virulence-associated genes in A. baumannii. This study provides new insights into the complex network system involved in the regulation of virulence-associated genes underlying the pathogenicity of A. baumannii.

Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer.

Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC =0.697, p-value =0.031). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy.

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.

A novel REEP1 splicing mutation with broad clinical variability in a family with hereditary spastic paraplegia.

Hereditary spastic paraplegia (HSP) is a heterogeneous group of genetic disorders characterized by lower-limb spastic paralysis. We report on a family with three generations of autosomal dominant inheritance of HSP caused by a novel heterozygous splice-site mutation (c.303 + 2 T > C) in REEP1 that was confirmed by RFLP analysis. Carriers of the mutation, including one asymptomatic individual, showed a mild HSP phenotype with a wide range of intrafamilial variation. All symptomatic carriers had ankle contractures in addition to other classical clinical symptoms of HSP. Clinicians should suspect REEP1-related HSP in patients who show ankle contractures with other symptoms of HSP and should consider that these patients have asymptomatic carriers within their family.

An autosomal dominant ERLIN2 mutation leads to a pure HSP phenotype distinct from the autosomal recessive ERLIN2 mutations (SPG18).

Hereditary spastic paraplegia (HSP) is a heterogeneous inherited disorder that manifests with lower extremity weakness and spasticity. HSP can be inherited by autosomal dominant, autosomal recessive, and X-linked inheritance patterns. Recent studies have shown that, although rare, mutations in a single gene can lead to multiple patterns of inheritance of HSP. We enrolled the HSP family showing autosomal dominant inheritance and performed genetic study to find the cause of phenotype in this family. We recruited five members of a Korean family as study participants. Four of the five family members had pure HSP. Part of the family members underwent whole-exome sequencing (WES) to identify the causative mutation. As the result of WES and Sanger sequencing analysis, a novel missense mutation (c.452 C > T, p.Ala151Val) of ERLIN2 gene was identified as the cause of the autosomal dominant HSP in the family. Our study suggests that the ERLIN2 gene leads to both autosomal recessive and autosomal dominant patterns of inheritance in HSP. Moreover, autosomal dominant HSP caused by ERLIN2 appears to cause pure HSP in contrast to autosomal recessive ERLIN2 related complicated HSP (SPG18).

Robotic-assisted Esophagectomy vs Video-Assisted Thoracoscopic Esophagectomy (REVATE): study protocol for a randomized controlled trial.

BACKGROUND: Radical lymph node dissection (LND) along the left recurrent laryngeal nerve (RLN) is surgically demanding and can be associated with substantial postoperative morbidity. The question of whether robot-assisted esophagectomy (RE) might be superior to video-assisted thoracoscopic esophagectomy (VATE) for performing LND along the RLN in patients with esophageal squamous cell carcinoma (ESCC) remains open. METHODS/DESIGN: We will conduct a multicenter, open-label, randomized controlled trial (Robotic-assisted Esophagectomy vs Video-Assisted Thoracoscopic Esophagectomy (REVATE)) enrolling patients with ESCC scheduled to undergo LND along the RLN. Patients will be randomly assigned to either RE or VATE. The primary outcome measure will be the rate of unsuccessful LND along the left RLN, which will be defined as: failure to remove lymph nodes along the left RLN (i.e., no identifiable nodes on pathology reports); or occurrence of permanent (duration > 6 months) left RLN palsy following LND. Secondary outcomes will include the number of successfully removed RLN nodes, postoperative recovery, length of hospital stay, 30-day and 90-day mortality, quality of life, and oncological outcomes. DISCUSSION: The REVATE study provides an opportunity to explore whether RE could facilitate LND along the left RLN-a complex surgical procedure that, as of now and with the use of VATE, remains difficult to perform and associated with a significant burden of morbidity. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03713749 . Registered on 22 October 2018.

Revisiting Primary Particles in Layered Lithium Transition-Metal Oxides and Their Impact on Structural Degradation.

Layered lithium transition-metal oxide materials, e.g., Li(Ni1- x - y Co x Mn y )O2 (NCM) and Li(Ni1- x - y Co x Al y )O2, are the most promising candidates for lithium-ion battery cathodes. They generally consist of ≈10 µm spherical particles densely packed with smaller particles (0.1-1 µm), called secondary and primary particles, respectively. The micrometer- to nanometer-sized particles are critical to the battery performance because they affect the reaction capability of the cathode. Herein, the crystal structure of the primary particles of NCM materials is revisited. Elaborate transmission electron microscopy investigations reveal that the so-called primary particles, often considered as single crystals, are in fact polycrystalline secondary particles. They contain low-angle and exceptionally stable special grain boundaries (GBs) presumably created during aggregation via an oriented attachment mechanism. Therefore, this so-called primary particle is renamed as primary-like particle. More importantly, the low-angle GBs between the smaller true primary particles cause the development of nanocracks within the primary-like particles of Ni-rich NCM cathodes after repetitive electrochemical cycles. In addition to rectifying a prevalent misconception about primary particles, this study provides a previously unknown but important origin of structural degradation in Ni-rich layered cathodes.

Direct Three-Dimensional Observation of Core/Shell-Structured Quantum Dots with a Composition-Competitive Gradient.

Synthesizing semiconductor nanoparticles through core/shell structuring is an effective strategy to promote the functional, physical, and kinetic performance of optoelectronic materials. However, elucidating the internal structure and related atomic distribution of core/shell structured quantum dots (QDs) in three dimensions, particularly at heterostructure interfaces, has been an overarching challenge. Herein, by applying complementary analytical techniques of electron microscopy and atom probe tomography, the dimensional, structural, topological, and compositional information on commercially available 11.8 nm-sized CdSSe/ZnS QDs were obtained. Systematic experiments at high resolution reveal the presence of a 1.8 nm-thick Cd xZn1 - xS inner shell with a composition gradient between the CdSe core and the ZnS outermost shell. More strikingly, the inner shell shows compositional variation because of competitive atomic configuration between Cd and ZnS, but it structurally retains a zinc-blende crystal structure with the core. The inner shell may grow through the decreased reactivity of S with Cd, followed by atomic diffusion-related processes. The composition-competitive gradient inner shell alleviates lattice misfit strain at heterostructure interfaces, thereby enhancing the quantum yield and photostabilty to a greater extent than those of other single-shell structures. Thus, this precise measurement approach could offer a potential pathway to develop a wide variety of three-dimensional core/shell-structured materials.

Graphene balls for lithium rechargeable batteries with fast charging and high volumetric energy densities.

Improving one property without sacrificing others is challenging for lithium-ion batteries due to the trade-off nature among key parameters. Here we report a chemical vapor deposition process to grow a graphene-silica assembly, called a graphene ball. Its hierarchical three-dimensional structure with the silicon oxide nanoparticle center allows even 1 wt% graphene ball to be uniformly coated onto a nickel-rich layered cathode via scalable Nobilta milling. The graphene-ball coating improves cycle life and fast charging capability by suppressing detrimental side reactions and providing efficient conductive pathways. The graphene ball itself also serves as an anode material with a high specific capacity of 716.2 mAh g-1. A full-cell incorporating graphene balls increases the volumetric energy density by 27.6% compared to a control cell without graphene balls, showing the possibility of achieving 800 Wh L-1 in a commercial cell setting, along with a high cyclability of 78.6% capacity retention after 500 cycles at 5C and 60 °C.

Drug repositioning for enzyme modulator based on human metabolite-likeness.

BACKGROUND: Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. METHODS: We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. RESULTS: In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence. CONCLUSIONS: This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Spatial analysis of the regional variation of hypertensive disease mortality and its socio-economic correlates in South Korea.

This paper presents a cross-sectional study based on the cause of death statistics in 2011 extracted from all 229 local governments in South Korea. The standardised hypertensive disease mortality rate (SHDMR) was defined by age- and sex-adjusted mortality by hypertensive diseases distinguished by International Classification of Disease- 10 (ICD-10). Variables taken into account were the number of doctors per 100,000 persons, the proportion with higher education (including university students and high school graduates), the number of recipients of basic livelihood support per 100,000 persons, the annual national health insurance premium per capita and the proportion of persons classified as high-risk drinkers. Ordinary least square (OLS) regression and geographically weighted regression (GWR) were applied to identify the potential associations. The statistical analysis was conducted with SAS ver. 9.3, while ArcGIS ver. 10.0 was utilised for the spatial analysis. The OLS results showed that the number of basic livelihood recipients per 100,000 persons had a significant positive association with the SHDMR, and the proportion with higher education had a significant negative one. GWR coefficients varied depending on region investigated and some regional variables had various directions. GWR showed higher adjusted R2 than that of OLS. It was found that the SHDMR was affected by socio-economic status, but as the effects observed were not consistent in all regions of the country, the development of health policies will need to consider the potential for regional variation.

Silicon carbide-free graphene growth on silicon for lithium-ion battery with high volumetric energy density.

Silicon is receiving discernable attention as an active material for next generation lithium-ion battery anodes because of its unparalleled gravimetric capacity. However, the large volume change of silicon over charge-discharge cycles weakens its competitiveness in the volumetric energy density and cycle life. Here we report direct graphene growth over silicon nanoparticles without silicon carbide formation. The graphene layers anchored onto the silicon surface accommodate the volume expansion of silicon via a sliding process between adjacent graphene layers. When paired with a commercial lithium cobalt oxide cathode, the silicon carbide-free graphene coating allows the full cell to reach volumetric energy densities of 972 and 700 Wh l(-1) at first and 200th cycle, respectively, 1.8 and 1.5 times higher than those of current commercial lithium-ion batteries. This observation suggests that two-dimensional layered structure of graphene and its silicon carbide-free integration with silicon can serve as a prototype in advancing silicon anodes to commercially viable technology.

A platform for large-scale graphene electronics--CVD growth of single-layer graphene on CVD-grown hexagonal boron nitride.

Direct chemical vapor deposition (CVD) growth of single-layer graphene on CVD-grown hexagonal boron nitride (h-BN) film can suggest a large-scale and high-quality graphene/h-BN film hybrid structure with a defect-free interface. This sequentially grown graphene/h-BN film shows better electronic properties than that of graphene/SiO2 or graphene transferred on h-BN film, and suggests a new promising template for graphene device fabrication.

Microfabricated ratchet structure integrated concentrator arrays for synthetic bacterial cell-to-cell communication assays.

We describe a microfluidic concentrator array device that is integrated with microfabricated ratchet structures to concentrate motile bacterial cells in desired destinations with required cell densities. The device consists of many pairs of concentrators with a wide range of spacing distances on a chip, and allows cells in one concentrator to be physically separated from but chemically connected to cells in the other concentrator. Therefore, the device facilitates quantification of the effect of spacing distance on the cell-to-cell communication of synthetically engineered bacterial cells. In addition, the device enables us to control the cell number density in each concentrator unit by adjusting the concentration time and the density of cell suspensions, and the basic concentrator unit of the device can be repeatedly duplicated on a chip. Hence, the device not only facilitates an investigation of the effect of cell densities on cell-to-cell communication, but it can also be further applied to an investigation of cellular communication among multiple types of cells. Lastly, the device can be easily fabricated using a single-layered soft-lithography technology so that we believe it would provide a simple but robust means for many synthetic and systems biologists to simplify and speed up their investigations of the synthetic genetic circuits in bacterial cells.