Wideband PRM: Highly Accurate and Sensitive Method for High-Throughput Targeted Proteomics.

Targeted mass spectrometry (MS) approaches, which are powerful methods for uniquely and confidently quantifying a specific panel of proteins in complex biological samples, play a crucial role in validating and clinically translating protein biomarkers discovered through global proteomic profiling. Common targeted MS methods, such as multiple reaction monitoring (MRM) and parallel-reaction monitoring (PRM), employ specific mass spectrometric technologies to quantify protein levels by comparing the transitions of surrogate endogenous (ENDO) peptides with those of stable isotope-labeled (SIL) peptide counterparts. These methods utilizing amino acid analyzed (AAA) SIL peptides warrant sensitive and precise measurements required for targeted MS assays. Compared with MRM, PRM provides higher experimental throughput by simultaneously acquiring all transitions of the target peptides and thereby compensates for different ion suppressions among transitions of a target peptide. However, PRM still suffers different ion suppressions between ENDO and SIL peptides due to spray instability, as the ENDO and SIL peptides were monitored at different liquid chromatography (LC) retention times. Here we introduce a new targeted MS method, termed wideband PRM (WBPRM), that is designed for high-throughput targeted MS analysis. WBPRM employs a wide isolation window for simultaneous fragmentation of both ENDO and SIL peptides along with multiplexed single ion monitoring (SIM) scans for enhanced MS sensitivity of the target peptides. Compared with PRM, WBPRM was demonstrated to provide increased sensitivity, precision, and reproducibility of quantitative measurements of target peptides with increased throughput, allowing more target peptide measurements in a shortened experiment time. WBPRM is a straightforward adaptation to a manufacturer-provided MS method, making it an easily implementable technique, particularly in complex biological samples where the demand for higher precision, sensitivity, and efficiency is paramount.

Dancing on the inside: A qualitative study on online dance learning with teacher-AI cooperation.

Recent technologies have extended opportunities for online dance learning by overcoming the limitations of space and time. However, dance teachers report that student-teacher interaction is more likely to be challenging in a distant and asynchronous learning environment than in a conventional dance class, such as a dance studio. To address this issue, we introduce DancingInside, an online dance learning system that encourages a beginner to learn dance by providing timely and sufficient feedback based on Teacher-AI cooperation. The proposed system incorporates an AI-based tutor agent (AI tutor, in short) that uses a 2D pose estimation approach to quantitatively estimate the similarity between a learner's and teacher's performance. We conducted a two-week user study with 11 students and 4 teachers. Our qualitative study results highlight that the AI tutor in DancingInside could support the reflection on a learner's practice and help the performance improvement with multimodal feedback resources. The interview results also reveal that the human teacher's role is essential in complementing the AI feedback. We discuss our design and suggest potential implications for future AI-supported cooperative dance learning systems.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry.

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor ß signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome.

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.

Effect of Bombyx mori on the Liver Protection of Non-Alcoholic Fatty Liver Disease Based on In Vitro and In Vivo Models.

Edible insects, Bombyx mori (silkworm; SW), which feed on mulberry leaves, have been consumed by humans for a long time as supplements or traditional medication. Non-alcoholic fatty liver disease (NAFLD) is a liver metabolic disorder that affects many people worldwide. We examined the hepatoprotective effects of SW using in vitro and high-fat and high-fructose (HFHF) diet-induced obese in vivo model mice by real-time PCR, immunoblot analysis, and fecal microbiota analysis. SW significantly reduced lipid accumulation and expression of the lipogenic genes in HepG2 cells and the livers of HFHF-induced mice. SW caused significant reductions in triglycerides, and total cholesterol in serum and upregulation of fatty acid oxidation markers compared to the HFHF group. Besides, SW significantly induced phosphorylation of AMPK and ACC in both models, suggesting roles in AMPK activation and the ACC signaling pathway. Furthermore, the gut microbiota analysis demonstrated that SW treatment reduced Firmicutes to Bacteroidetes ratios and the relative abundance of the Lachnospiraceae family compared to HFHF-induced obese mice. These results provide a novel therapeutic agent of hepatoprotective effects of SW for non-alcoholic hepatic steatosis that targets hepatic AMPK and ACC-mediated lipid metabolism.

Novel Capturer-Catalyst Microreactor System with a Polypyrrole/Metal Nanoparticle Composite Incorporated in the Porous Honeycomb-Patterned Film.

A composite of polypyrrole/metal nanoparticles (PPy/MNPs) was selectively incorporated into the pores of a honeycomb-patterned porous polycaprolactone polymer film to fabricate a novel capturer-catalyst microreactor system. This fabrication involved a modified breath figure method, where the polymer solution containing metal ions as an oxidizing agent was cast under humid conditions along with the pyrrole monomer through an interfacial reaction in a one-step in situ process. The higher hydrophilicity of the metal ions compared to the polymer solution led to their self-assembly around the pore surface, resulting in the selective incorporation of the PPy/MNP composite into the porous film. Copper (Cu), silver (Ag), and gold (Au) were used for the PPy/MNP fabrication. Various methods characterized the fabricated film. Strong catalytic degradations of methylene blue and methyl orange were obtained with PCL-PPy/MNPs. Recycling experiments showed no loss of activity even after five cycles of recycling. Comparative analysis of PCL-PPy, PCL-MNP, and PCL-PPy/MNP results indicated the synergistic action of PPy and MNPs in dye degradation. High-performance liquid chromatography and mass spectroscopy analyses confirmed dye degradation after treatment with a fabricated microreactor. PPy might have acted as a capturer of the dye molecule and MNPs as a catalyst, thereby enhancing the efficiency of dye degradation. Additionally, the PCL-PPy/Cu composite exhibited strong antimicrobial properties against Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli) with no cytotoxicity as measured by the MTT assay. Therefore, the fabricated microreactor film has promising applications in various fields.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Introduction: Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. Methods: The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. Results: The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. Discussion: The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Vertical Alignment of Liquid Crystals on Phenylphenoxymethyl-Substituted Polystyrene-PS Derivatives Structurally Similar to LC Molecules.

A series of polystyrene derivatives containing precursors of liquid crystal (LC) molecules, phenylphenoxymethyl-substituted polystyrene (PPHE#; # = 5, 15, 25, 50, 75, and 100)-where # is the molar content of 4-phenylphenol using polymer modification reactions-were prepared in order to examine the effect of the polymer film, which possess similar LC molecular structure on the LC alignment properties. It was found that the Tg values of the PPHE# were higher than 100 °C due to their aromatic structure in the biphenyl-based PHE moiety. The LC cells fabricated with PPHE5 and PPHE15 films exhibited planar LC alignment. Conversely, LC molecules showed a vertical alignment in LC cells made using the polymer films with phenylphenoxymethyl side groups in the range of 25-100 mol %. The polar surface energies on the PPHE# films can be associated with the vertical LC alignment on the PPHE# films. For example, vertical LC alignment was exhibited when the polar surface energy of the polymer films was less than approximately 4.2 mJ/m2. Aligning stability was observed at 200 °C and UV irradiation of 20 J/cm2 for LC cells made using the PPHE100 film. Therefore, it was found that biphenyl, one of the LC precursors, modified polystyrene derivatives and can produce a next-generation vertical LC alignment system.

Mulberry (Morus alba L.) Leaf Extract and 1-Deoxynojirimycin Improve Skeletal Muscle Insulin Resistance via the Activation of IRS-1/PI3K/Akt Pathway in db/db Mice.

Mulberry (Morus alba L.) leaves have been used to lower blood glucose in patients with diabetes. We evaluated the effects of mulberry leaves extract (MLE) and 1-deoxynojirimycin (1-DNJ) in improving insulin resistance through the activation of the IRS-1/PI3K/Akt pathway in the skeletal muscle of db/db mice. Histological analysis revealed an amelioration of muscle deformation and increased muscle fiber size. MLE and 1-DNJ positively raised the protein expression of related glucose uptake and increased the translocation of glucose transporter type 4 (GLUT4) to the membrane. Furthermore, MLE and 1-DNJ activated the IRS-1/PI3K/Akt pathway in the skeletal muscle and, subsequently, modulated the protein levels of glycogen synthase kinase-3beta (GSK-3ß) and glycogen synthase (GS), leading to elevated muscle glycogen content. These findings suggest that MLE and 1-DNJ supplementation improves insulin resistance by modulating the insulin signaling pathway in the skeletal muscle of db/db mice.

N-Doped Carbon Nanorods from Biomass as a Potential Antidiabetic Nanomedicine.

Insufficient glucose control remains a critical challenge for type 2 diabetes mellitus (T2DM) patients with currently used therapeutic drugs, which can also have detrimental side effects. The facile synthesis of nitrogen-doped carbon nanorods (N-CNRs) as therapeutic agents in a T2DM transgenic db/db mouse model is reported herein. N-CNRs are synthesized from silkworm powder without the assistance of any template and possess a hollow graphitic nature, rough surface, and good aqueous solubility, which make them ideal candidates for fabricating nanomedicines. N-CNRs are employed to reduce fasting blood glucose and ameliorate serum biomarker levels linked to oxidative stress and inflammation. Interestingly, through the downregulation of enhanced expression of glutathione peroxidase, superoxide dismutase, and catalase as well as inflammatory responses, N-CNRs reverse pancreatic dysfunction and normalize the secretory functions of pancreatic cells. Moreover, hepatic steatosis is attenuated by downregulating lipogenesis and increasing fatty acid oxidation. This finding may help in designing novel therapeutics for T2DM treatment.

DNA repair and cholesterol-mediated drug efflux induce dose-dependent chemoresistance in nutrient-deprived neuroblastoma cells.

Neuroblastoma is a solid, heterogeneous pediatric tumor. Chemotherapy is widely used to treat neuroblastoma. However, dose-dependent responses and chemoresistance mechanisms of neuroblastoma cells to anticancer drugs remain challenging. Here, we investigated the dose-dependent effects of topotecan on human neuroblastoma cells (SK-N-SH, SH-SY5Y, and SK-N-BE) under various nutrient supply conditions. Serum-starved human neuroblastoma cells showed reduced toxicity. Their survival rate increased upon treatment with a high concentration (1 µM) of topotecan. Quantitative profiling of global and phosphoproteome identified 12,959 proteins and 48,812 phosphosites, respectively, from SK-N-SH cells. Network analysis revealed that topotecan upregulated DNA repair and cholesterol-mediated topotecan efflux, resulting in topotecan resistance. Results of DNA damage assay, cell cycle, and quantitative analyses of membrane cholesterol supported the validity of these resistance factors and their applicability to all neuroblastoma cells. Our results provide a model for high dose-dependent chemoresistance in neuroblastoma cells that could enable a patient-dependent chemotherapy screening strategy.

Artificial Intelligence and Computational Approaches for Epilepsy.

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.