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Chirality, the property whereby an object or a system cannot be superimposed on its mirror image, prevails amongst nature over various scales. Especially in biology, numerous chiral building blocks and chiral-specific interactions are involved in many essential biological activities. Despite the prevalence of chirality in nature, it has been no longer than 70 years since the mechanisms of chiral-specific interactions drew scientific attention and began to be studied. Owing to the advent of chiral-sensitive equipment such as circular dichroism spectrometers or chiral liquid columns for chromatography, it has recently been possible to achieve a deeper understanding of the chiral-specific interactions and consequential impacts on the functionality and efficiency of nanomedicine. From this point of view, it is worthwhile to examine previously reported chiral biomaterials with their compositions and possible applications to achieve new paradigms of biomaterials. This review discusses chiral materials on various scales and their biological applications.

As deep learning technology paves its way, real-world applications that make use of it become popular these days. Edge computing architecture is one of the service architectures to realize the deep learning based service, which makes use of the resources near the data source or client. In Edge computing architecture it becomes important to manage resource usage, and there is research on optimization of deep learning, such as pruning or binarization, which makes deep learning models more lightweight, along with the research for the efficient distribution of workloads on cloud or edge resources. Those are to reduce the workload on edge resources. In this paper, a usage optimization method with batch and model management is proposed. The proposed method is to increase the utilization of GPU resource by modifying the batch size of the input of an inference application. To this end, the inference pipelines are identified to see how the different kinds of resources are used, and then the effect of batch inference on GPU is measured. The proposed method consists of a few modules, including a tool for batch size management which is able to change a batch size with respect to the available resources, and another one for model management which supports on-the-fly update of a model. The proposed methods are implemented on a real-time video analysis application and deployed in the Kubernetes cluster as a Docker container. The result shows that the proposed method can optimize the usage of edge resources for real-time video analysis deep learning applications.

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type ( wt ) and 10 with the BRCA1/2 variant ( var )) and 20 normal subjects (10 subjects with BRCA1/2 wt and 10 with BRCA1/2 var ) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2 var . If plasma SPARC concentrations increase over 337.35 ng/mL or plasma THBS1 concentrations increase over 65.28 μg/mL in a healthy BRCA1/2 carrier, oophorectomy may be suggested.

Localized surface plasmon resonance (LSPR)-based biosensors have recently garnered increasing attention due to their potential to allow label-free, portable, low-cost, and real-time monitoring of diverse analytes. Recent developments in this technology have focused on biochemical markers in clinical and environmental settings coupled with advances in nanostructure technology. Therefore, this review focuses on the recent advances in LSPR-based biosensor technology for the detection of diverse chemicals and biomolecules. Moreover, we also provide recent examples of sensing strategies based on diverse nanostructure platforms, in addition to their advantages and limitations. Finally, this review discusses potential strategies for the development of biosensors with enhanced sensing performance.

Colitis causes destruction of the intestinal mucus layer and increases intestinal inflammation. The use of antioxidants and anti-inflammatory agents derived from natural sources has been recently highlighted as a new approach for the treatment of colitis. Oxyresveratrol (OXY) is an antioxidant known to have various beneficial effects on human health, such as anti-inflammatory, antibacterial activity, and antiviral activity. The aim of this study was to investigate the therapeutic effect of OXY in rats with dextran sulfate sodium (DSS)-induced acute colitis. OXY ameliorated DSS-induced colitis and repaired damaged intestinal mucosa. OXY downregulated the expression of pro-inflammatory cytokine genes ( TNF- α, IL-6 , and IL-1 β) and chemokine gene MCP-1 , while promoting the production of anti-inflammatory cytokine IL-10. OXY treatment also suppressed inflammation via inhibiting cyclooxygenase-2 ( COX-2 ) and inducible nitric oxide synthase ( iNOS ) expression in the colon, as well as the activity of myeloperoxidase (MPO). OXY exhibited anti-apoptotic effects, shifting the Bax/Bcl-2 balance. In conclusion, OXY might improve DSS-induced colitis by restoring the intestinal mucus layer and reducing inflammation within the intestine.

A simple and cost-effective method is proposed herein for a plasmonic nanoantenna array (PNAA) for the fabrication of metal-enhanced fluorescence (MEF) substrates in which fluorophores interact with the enhanced electromagnetic field generated by a localized surface plasmon to provide a higher fluorescence signal. The PNAA is fabricated by the deposition of a silver (Ag) layer on an ultraviolet (UV) nanoimprinted nanodot array with a pitch of 400 nm, diameter of 200 nm, and height of 100 nm. During deposition, raised Ag nanodisks and a lower Ag layer are, respectively, formed on the top and bottom of the imprinted nanodot array, and the gap between these Ag layers acts as a plasmonic nanoantenna. Since the thickness of the gap within the PNAA is influenced by the thickness of Ag deposition, the effects of the latter upon the geometrical properties of the fabricated PNAA are examined, and the electromagnetic field intensity distributions of PNAAs with various Ag thicknesses are simulated. Finally, the fluorescence enhancement factor (FEF) of the fabricated PNAA MEF substrate is measured using spotted Cy5-conjugated streptavidin to indicate a maximum enhancement factor of ~22× for the PNAA with an Ag layer thickness of 75 nm. The experimental results are shown to match the simulated results.

The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified.

In this study, an ecological impact was assessed for the short-term leak scenario through the AQUATOX-EFDC model, which combines the proven ecological model AQUATOX with the hydrodynamic model EFDC. A case study of the coupled AQUATOX-EFDC model was conducted for 30-30,000 kg toluene leak scenarios in the Jeonju River in South Korea. A 21-day scenario simulation was conducted, and the impact of the toluene spill accident was evaluated by comparing the biomass between the control simulation and the perturbed simulation. As a result of the simulation, it was found that in the scenario in which 3000 kg of toluene was leaked for a day, a substantial change was expected in the range of 0-640 m from the accident site. Additionally, for a 30,000 kg leak, a substantial change was expected in the range of 0-2300 m from the accident site, and the greatest damage was observed for the fish species group, the top predators. As a result, the AQUATOX-EFDC simulation showed a significant ecological impact, and the proposed model will be helpful to understand the ecological impact and establish the management strategy for the ecological risk of the chemical spill.

Endoplasmic reticulum (ER) stress and its adaptive cellular response, the unfolded protein response (UPR), are involved in various diseases including neurodegenerative diseases, metabolic diseases, and even cancers. Here, we analyzed the novel function of ubiquitin-specific peptidase 14 (USP14) in ER stress. The overexpression of Drosophila USP14 protected the cells from ER stress without affecting the proteasomal activity. Null Hong Kong (NHK) and alpha-1-antitrypsin Z (ATZ) are ER-associated degradation substrates. The degradation of NHK, but not of ATZ, was delayed by USP14. USP14 restored the levels of rhodopsin-1 protein in a Drosophila model for autosomal dominant retinitis pigmentosa and suppressed the retinal degeneration in this model. In addition, we observed that proteasome complex is dynamically reorganized in response to ER stress in human 293T cells. These findings suggest that USP14 may be a therapeutic strategy in diseases associated with ER stress.

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p -value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers-support vector machine and random forest-showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

Background: MUC2, a major component of the mucus layer in the intestine, is associated with antimicrobial activity and gut immune system function. Currently, mucin is mainly known for its critical function in defense against toxic molecules and pathogens. In this study, we investigated the stimulatory effects of exogenous nicotinamide adenine dinucleotide (NAD + ) on the expression of MUC2 in LS 174T goblet cells., Methods: Genes related to MUC2 synthesis were measured by quantitative real-time PCR (qPCR). To analyze the gene expression profiles of NAD + -treated LS 174T goblet cells, RNA sequencing was performed. MUC2 expression in the cells and secreted MUC2 were measured by immunocytochemistry (ICC) and ELISA, respectively., Results: NAD + significantly stimulated MUC2 expression at mRNA and protein levels and increased the secretion of MUC2. Through RNA sequencing, we found that the expression of genes involved in arachidonic acid metabolism increased in NAD + -treated cells compared with the negative control cells. NAD + treatment increased phospholipase C (PLC)-δ and prostaglandin E synthase (PTGES) expression, which was inhibited by the appropriate inhibitors. Among the protein kinase C (PKC) isozymes, PKC-δ was involved in the increase in MUC2 expression. In addition, extracellular signal-regulated kinase (ERK)1/2 and cyclic AMP (cAMP) response element-binding protein (CREB) transcript levels were higher in NAD + -treated cells than in the negative control cells, and the enhanced levels of phosphorylated CREB augmented MUC2 expression., Conclusions: Exogenous NAD + increases MUC2 expression by stimulating the PLC-δ/PTGES/PKC-δ/ERK/CREB signaling pathway., Competing Interests: The authors declare that they have no competing interests.

A conventional approach to making miniature or microscale gas chromatography (GC) components relies on silicon as a base material and MEMS fabrication as manufacturing processes. However, these devices often fail in medium-to-high temperature applications due to a lack of robust fluidic interconnects and a high-yield bonding process. This paper explores the feasibility of using metal additive manufacturing (AM), which is also known as metal 3D printing, as an alternative platform to produce small-scale microfluidic devices that can operate at a temperature higher than that which polymers can withstand. Binder jet printing (BJP), one of the metal AM processes, was utilized to make stainless steel (SS) preconcentrators (PCs) with submillimeter internal features. PCs can increase the concentration of gaseous analytes or serve as an inline injector for GC or gas sensor applications. Normally, parts printed by BJP are highly porous and thus often infiltrated with low melting point metal. By adding to SS316 powder sintering additives such as boron nitride (BN), which reduces the liquidus line temperature, we produce near full-density SS PCs at sintering temperatures much lower than the SS melting temperature, and importantly without any measurable shape distortion. Conversely, the SS PC without BN remains porous after the sintering process and unsuitable for fluidic applications. Since the SS parts, unlike Si, are compatible with machining, they can be modified to work with commercial compression fitting. The PC structures as well as the connection with the fitting are leak-free with relatively high operating pressures. A flexible membrane heater along with a resistance-temperature detector is integrated with the SS PCs for thermal desorption. The proof-of-concept experiment demonstrates that the SS PC can preconcentrate and inject 0.6% headspace toluene to enhance the detector's response.