High-speed and large-scale intrinsically stretchable integrated circuits.

Intrinsically stretchable electronics with skin-like mechanical properties have been identified as a promising platform for emerging applications ranging from continuous physiological monitoring to real-time analysis of health conditions, to closed-loop delivery of autonomous medical treatment1-7. However, current technologies could only reach electrical performance at amorphous-silicon level (that is, charge-carrier mobility of about 1 cm2 V-1 s-1), low integration scale (for example, 54 transistors per circuit) and limited functionalities8-11. Here we report high-density, intrinsically stretchable transistors and integrated circuits with high driving ability, high operation speed and large-scale integration. They were enabled by a combination of innovations in materials, fabrication process design, device engineering and circuit design. Our intrinsically stretchable transistors exhibit an average field-effect mobility of more than 20 cm2 V-1 s-1 under 100% strain, a device density of 100,000 transistors per cm2, including interconnects and a high drive current of around 2 µA µm-1 at a supply voltage of 5 V. Notably, these achieved parameters are on par with state-of-the-art flexible transistors based on metal-oxide, carbon nanotube and polycrystalline silicon materials on plastic substrates12-14. Furthermore, we realize a large-scale integrated circuit with more than 1,000 transistors and a stage-switching frequency greater than 1 MHz, for the first time, to our knowledge, in intrinsically stretchable electronics. Moreover, we demonstrate a high-throughput braille recognition system that surpasses human skin sensing ability, enabled by an active-matrix tactile sensor array with a record-high density of 2,500 units per cm2, and a light-emitting diode display with a high refreshing speed of 60 Hz and excellent mechanical robustness. The above advancements in device performance have substantially enhanced the abilities of skin-like electronics.

High-brightness all-polymer stretchable LED with charge-trapping dilution.

Next-generation light-emitting displays on skin should be soft, stretchable and bright1-7. Previously reported stretchable light-emitting devices were mostly based on inorganic nanomaterials, such as light-emitting capacitors, quantum dots or perovskites6-11. They either require high operating voltage or have limited stretchability and brightness, resolution or robustness under strain. On the other hand, intrinsically stretchable polymer materials hold the promise of good strain tolerance12,13. However, realizing high brightness remains a grand challenge for intrinsically stretchable light-emitting diodes. Here we report a material design strategy and fabrication processes to achieve stretchable all-polymer-based light-emitting diodes with high brightness (about 7,450 candela per square metre), current efficiency (about 5.3 candela per ampere) and stretchability (about 100 per cent strain). We fabricate stretchable all-polymer light-emitting diodes coloured red, green and blue, achieving both on-skin wireless powering and real-time displaying of pulse signals. This work signifies a considerable advancement towards high-performance stretchable displays.

SWATH-MS Based Secretome Proteomic Analysis of Pseudomonas aeruginosa Against MRSA.

The study uses Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH)-MS in conjunction with secretome proteomics to identify key proteins that Pseudomonas aeruginosa secretes against methicillin-resistant Staphylococcus aureus (MRSA). Variations in the inhibition zones indicated differences in strain resistance. Multivariate statistical methods were applied to filter the proteomic results, revealing five potential protein biomarkers, including Peptidase M23. Gene ontology (GO) analysis and sequence alignment supported their antibacterial activity. Thus, SWATH-MS provides a comprehensive understanding of the secretome of P. aeruginosa in its action against MRSA, guiding future antibacterial research.

Construction of the Glycolysis Metabolic Pathway Inside an Artificial Cell for the Synthesis of Amino Acid and Its Reversible Deformation.

The bottom-up construction of artificial cells is beneficial for understanding cell working mechanisms. The glycolysis metabolism mimicry inside artificial cells is challenging. Herein, the glycolytic pathway (Entner-Doudoroff pathway in archaea) is reconstituted inside artificial cells. The glycolytic pathway comprising glucose dehydrogenase (GDH), gluconate dehydratase (GAD), and 2-keto-3-deoxygluconate aldolase (KDGA) converts glucose molecules to pyruvate molecules. Inside artificial cells, pyruvate molecules are further converted into alanine with the help of alanine dehydrogenase (AlaDH) to build a metabolic pathway for synthesizing amino acid. On the other hand, the pyruvate molecules from glycolysis stimulate the living mitochondria to produce ATP inside artificial cells, which further trigger actin monomers to polymerize to form actin filaments. With the addition of methylcellulose inside the artificial cell, the actin filaments form adjacent to the inner lipid bilayer, deforming the artificial cell from a spherical shape to a spindle shape. The spindle-shaped artificial cell reverses to a spherical shape by depolymerizing the actin filament upon laser irradiation. The glycolytic pathway and its further extension to produce amino acids (or ATP) inside artificial cells pave the path to build functional artificial cells with more complicated metabolic pathways.

Determination of adulteration, geographical origins, and species of food by mass spectrometry.

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Development of a double antibody sandwich ELISA method for the quantitative detection of serum C-reactive protein based on nanobody.

In this study, we successfully developed a nanobody-based double antibody sandwich ELISA kit for the detection of clinical serum C-reactive protein (CRP) by using two novel CRP specific nanobodies. The developed method exhibited a linear detection range of approximately 6-200 ng/mL, with a detection limit of 1 ng/mL. Furthermore, the method demonstrated excellent specificity, as there was no cross-reactivity with interfering substances such as total bilirubin and hemoglobin and so on. To assess reproducibility, independent measurements of the samples were conducted under experimental conditions, resulting in intra- and inter-batch coefficients of variation below 10% and a recovery rate of 93%-102%. These results indicate robust reproducibility of the method. To evaluate the performance of the developed kit, we collected 90 clinical samples for correlation analysis with commercial kits. The results showed a high correlation coefficient value (R2) of 0.98, indicating accurate concordance between the developed and commercial kits. In conclusion, our study successfully developed a nanobody-based double antibody sandwich ELISA kit to detect clinical serum CRP. The utilization of nanobodies represents a significant advancement in the field of CRP immunoassay development. The developed kit demonstrates excellent performance characteristics and holds promise for clinical applications.

Skin electronics from scalable fabrication of an intrinsically stretchable transistor array.

Skin-like electronics that can adhere seamlessly to human skin or within the body are highly desirable for applications such as health monitoring, medical treatment, medical implants and biological studies, and for technologies that include human-machine interfaces, soft robotics and augmented reality. Rendering such electronics soft and stretchable-like human skin-would make them more comfortable to wear, and, through increased contact area, would greatly enhance the fidelity of signals acquired from the skin. Structural engineering of rigid inorganic and organic devices has enabled circuit-level stretchability, but this requires sophisticated fabrication techniques and usually suffers from reduced densities of devices within an array. We reasoned that the desired parameters, such as higher mechanical deformability and robustness, improved skin compatibility and higher device density, could be provided by using intrinsically stretchable polymer materials instead. However, the production of intrinsically stretchable materials and devices is still largely in its infancy: such materials have been reported, but functional, intrinsically stretchable electronics have yet to be demonstrated owing to the lack of a scalable fabrication technology. Here we describe a fabrication process that enables high yield and uniformity from a variety of intrinsically stretchable electronic polymers. We demonstrate an intrinsically stretchable polymer transistor array with an unprecedented device density of 347 transistors per square centimetre. The transistors have an average charge-carrier mobility comparable to that of amorphous silicon, varying only slightly (within one order of magnitude) when subjected to 100 per cent strain for 1,000 cycles, without current-voltage hysteresis. Our transistor arrays thus constitute intrinsically stretchable skin electronics, and include an active matrix for sensory arrays, as well as analogue and digital circuit elements. Our process offers a general platform for incorporating other intrinsically stretchable polymer materials, enabling the fabrication of next-generation stretchable skin electronic devices.

ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method.

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Ultra-Narrow Alkane Product Distribution in Polyethylene Waste Hydrocracking by Zeolite Micropore Diffusion Optimization.

Plastic pollution poses a pressing environmental challenge in modern society. Chemical catalytic conversion offers a promising solution for upgrading waste plastics into valuable liquid alkanes and other high value products. However, the current methods yield mixed products with a broad carbon distribution. To address this challenge, we introduce a tandem catalytic system that features matched acidic sites and confined metals for the conversion of low-density polyethylene (LDPE) into liquid alkanes. This system achieves a liquid alkane yield of 94.0%, with 84.8% of C5-C7 light alkanes. Combined with in situ FTIR and molecular dynamics simulation, the shape-selective mechanisms is elucidated, which ensures that only olefins of the appropriate size can diffuse to the encapsulated Pt sites within the zeolite for hydrogenation, resulting in an ultra-narrow product distribution. Furthermore, due to the rapid diffusion of olefins within the hierarchical zeolite, the catalyst exhibits higher catalytic efficiency and resistance to coking tendency. Our findings contribute to the design of efficient catalysts for plastic waste valorization.

Maternal adherence to healthy lifestyle and risk of depressive symptoms in the offspring: mediation by offspring lifestyle.

BACKGROUND: Adherence to healthy lifestyles can be beneficial for depression among adults, but the intergenerational impact of maternal healthy lifestyles on offspring depressive symptoms is unknown. METHODS: In total, 10 368 mothers in Nurses' Health Study II and 13 478 offspring in the Growing Up Today Study were paired. Maternal and offspring healthy lifestyles were defined as a composite score including a healthy diet, normal body mass index (BMI), never-smoking, light-to-moderate consumption of alcohol, and regular moderate-to-vigorous physical activity. Maternal lifestyles were assessed during their offspring's childhood. Offspring depressive symptoms were repeatedly assessed five times using the Center for Epidemiological Studies Depression Scale-10 (CESD-10); the offspring were between the ages of 14 and 30 when the first CESD-10 was assessed. Covariates included maternal variables (age at baseline, race/ethnicity, antidepressant use, pregnancy complications, etc.) and offspring age and sex. RESULTS: Children of mothers with the healthiest lifestyle had significantly fewer depressive symptoms (a 0.30 lower CESD-10 score, 95% confidence interval (CI) 0.09-0.50) in comparison with children of mothers with the least healthy lifestyle. The association was only found significant in female offspring but not in males. For individual maternal lifestyle factors, a normal BMI, never-smoking, and adherence to regular physical activity were independently associated with fewer depressive symptoms among the offspring. The association between maternal healthy lifestyles and offspring depressive symptoms was mediated by offspring's healthy lifestyles (mediation effect: 53.2%, 95% CI 15.8-87.3). CONCLUSIONS: Our finding indicates the potential mechanism of intergenerational transmission of healthy lifestyles to reduce the risk of depressive symptoms in offspring.

Biological characterization of coronavirus noncanonical transcripts in vitro and in vivo.

BACKGROUND: In addition to the well-known coronavirus genomes and subgenomic mRNAs, the existence of other coronavirus RNA species, which are collectively referred to as noncanonical transcripts, has been suggested; however, their biological characteristics have not yet been experimentally validated in vitro and in vivo. METHODS: To comprehensively determine the amounts, species and structures of noncanonical transcripts for bovine coronavirus in HRT-18 cells and mouse hepatitis virus A59, a mouse coronavirus, in mouse L cells and mice, nanopore direct RNA sequencing was employed. To experimentally validate the synthesis of noncanonical transcripts under regular infection, Northern blotting was performed. Both Northern blotting and nanopore direct RNA sequencing were also applied to examine the reproducibility of noncanonical transcripts. In addition, Northern blotting was also employed to determine the regulatory features of noncanonical transcripts under different infection conditions, including different cells, multiplicities of infection (MOIs) and coronavirus strains. RESULTS: In the current study, we (i) experimentally determined that coronavirus noncanonical transcripts were abundantly synthesized, (ii) classified the noncanonical transcripts into seven populations based on their structures and potential synthesis mechanisms, (iii) showed that the species and amounts of the noncanonical transcripts were reproducible during regular infection but regulated in altered infection environments, (iv) revealed that coronaviruses may employ various mechanisms to synthesize noncanonical transcripts, and (v) found that the biological characteristics of coronavirus noncanonical transcripts were similar between in vitro and in vivo conditions. CONCLUSIONS: The biological characteristics of noncanonical coronavirus transcripts were experimentally validated for the first time. The identified features of noncanonical transcripts in terms of abundance, reproducibility and variety extend the current model for coronavirus gene expression. The capability of coronaviruses to regulate the species and amounts of noncanonical transcripts may contribute to the pathogenesis of coronaviruses during infection, posing potential challenges in disease control. Thus, the biology of noncanonical transcripts both in vitro and in vivo revealed here can provide a database for biological research, contributing to the development of antiviral strategies.

Identification of the protein coding capability of coronavirus defective viral genomes by mass spectrometry.

During coronavirus infection, in addition to the well-known coronavirus genomes and subgenomic mRNAs, an abundance of defective viral genomes (DVGs) can also be synthesized. In this study, we aimed to examine whether DVGs can encode proteins in infected cells. Nanopore direct RNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis were employed. With the protein databases generated by nanopore direct RNA sequencing and the cell lysates derived from the RNA-protein pull-down assay, six DVG-encoded proteins were identified by LC-MS/MS based on the featured fusion peptides caused by recombination during DVG synthesis. The results suggest that the coronavirus DVGs have the capability to encode proteins. Consequently, future studies determining the biological function of DVG-encoded proteins may contribute to the understanding of their roles in coronavirus pathogenesis and the development of antiviral strategies.

Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS).

Formal thought disorder (ThD) is a clinical sign of schizophrenia amongst other serious mental health conditions. ThD can be recognized by observing incoherent speech - speech in which it is difficult to perceive connections between successive utterances and lacks a clear global theme. Automated assessment of the coherence of speech in patients with schizophrenia has been an active area of research for over a decade, in an effort to develop an objective and reliable instrument through which to quantify ThD. However, this work has largely been conducted in controlled settings using structured interviews and depended upon manual transcription services to render audio recordings amenable to computational analysis. In this paper, we present an evaluation of such automated methods in the context of a fully automated system using Automated Speech Recognition (ASR) in place of a manual transcription service, with "audio diaries" collected in naturalistic settings from participants experiencing Auditory Verbal Hallucinations (AVH). We show that performance lost due to ASR errors can often be restored through the application of Time-Series Augmented Representations for Detection of Incoherent Speech (TARDIS), a novel approach that involves treating the sequence of coherence scores from a transcript as a time-series, providing features for machine learning. With ASR, TARDIS improves average AUC across coherence metrics for detection of severe ThD by 0.09; average correlation with human-labeled derailment scores by 0.10; and average correlation between coherence estimates from manual and ASR-derived transcripts by 0.29. In addition, TARDIS improves the agreement between coherence estimates from manual transcripts and human judgment and correlation with self-reported estimates of AVH symptom severity. As such, TARDIS eliminates a fundamental barrier to the deployment of automated methods to detect linguistic indicators of ThD to monitor and improve clinical care in serious mental illness.

A Design Strategy for Intrinsically Stretchable High-Performance Polymer Semiconductors: Incorporating Conjugated Rigid Fused-Rings with Bulky Side Groups.

Strategies to improve stretchability of polymer semiconductors, such as introducing flexible conjugation-breakers or adding flexible blocks, usually result in degraded electrical properties. In this work, we propose a concept to address this limitation, by introducing conjugated rigid fused-rings with optimized bulky side groups and maintaining a conjugated polymer backbone. Specifically, we investigated two classes of rigid fused-ring systems, namely, benzene-substituted dibenzothiopheno[6,5-b:6',5'-f]thieno[3,2-b]thiophene (Ph-DBTTT) and indacenodithiophene (IDT) systems, and identified molecules displaying optimized electrical and mechanical properties. In the IDT system, the polymer PIDT-3T-OC12-10% showed promising electrical and mechanical properties. In fully stretchable transistors, the polymer PIDT-3T-OC12-10% showed a mobility of 0.27 cm2 V-1 s-1 at 75% strain and maintained its mobility after being subjected to hundreds of stretching-releasing cycles at 25% strain. Our results underscore the intimate correlation between chemical structures, mechanical properties, and charge carrier mobility for polymer semiconductors. Our described molecular design approach will help to expedite the next generation of intrinsically stretchable high-performance polymer semiconductors.

eIF4E-eIF4G complex inhibition synergistically enhances the effect of sorafenib in hepatocellular carcinoma.

The clinical efficacy of sorafenib in hepatocellular carcinoma (HCC) is disappointing due to its low response rate and high rates of adverse effects. The eukaryotic translation initiation factor 4F (eIF4F) complex, mainly consisting of eIF4E-eukaryotic translation initiation factor 4G (eIF4G) interaction, is involved in the induction of drug resistance. Herein, we aimed to demonstrate that eIF4E-eIF4G complex inhibition enhanced the effect of sorafenib. The antiproliferation effect of combined treatment was evaluated by MTT assay and colony formation assay. Flow cytometry was used to detect the early cell apoptosis and cell cycle. The specific mechanism was demonstrated using western blot and lentivirus transfection. The combination of sorafenib with eIF4E-eIF4G inhibitors 4E1RCat (structural) or 4EGI-1 (competitive) synergistically inhibited the cell viability and colony formation ability of HCC cells. Moreover, the combined treatment induced more early apoptosis than sorafenib alone through downregulating the Bcl-2 expression. Besides, the coadministration of sorafenib and 4E1RCat or 4EGI-1 synergistically inhibited the expressions of eIF4E, eIF4G and phospho-4E-BP1 in HCC cells while blocking the phosphorylation of 4E-BP1 with lentiviral transfection failed to increase the sensitivity of HCC cells to sorafenib treatment. PI3K-AKT-mTOR signaling was also inhibited by the combined treatment. In a word, eIF4E-eIF4G complex inhibition synergistically enhances the effect of sorafenib in HCC treatment.

A SHRINKAGE PRINCIPLE FOR HEAVY-TAILED DATA: HIGH-DIMENSIONAL ROBUST LOW-RANK MATRIX RECOVERY.

This paper introduces a simple principle for robust statistical inference via appropriate shrinkage on the data. This widens the scope of high-dimensional techniques, reducing the distributional conditions from sub-exponential or sub-Gaussian to more relaxed bounded second or fourth moment. As an illustration of this principle, we focus on robust estimation of the low-rank matrix Θ* from the trace regression model Y = Tr(Θ*⊤ X) + ϵ. It encompasses four popular problems: sparse linear model, compressed sensing, matrix completion and multi-task learning. We propose to apply the penalized least-squares approach to the appropriately truncated or shrunk data. Under only bounded 2+δ moment condition on the response, the proposed robust methodology yields an estimator that possesses the same statistical error rates as previous literature with sub-Gaussian errors. For sparse linear model and multi-task regression, we further allow the design to have only bounded fourth moment and obtain the same statistical rates. As a byproduct, we give a robust covariance estimator with concentration inequality and optimal rate of convergence in terms of the spectral norm, when the samples only bear bounded fourth moment. This result is of its own interest and importance. We reveal that under high dimensions, the sample covariance matrix is not optimal whereas our proposed robust covariance can achieve optimality. Extensive simulations are carried out to support the theories.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

BACKGROUND: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Proteomic analysis of "Oriental Beauty" oolong tea leaves with different degrees of leafhopper infestation.

RATIONALE: Oriental Beauty, a type of oolong tea native to Taiwan, is highly prized by connoisseurs for its unique fruity aroma and sweet taste. Leaves of Oriental Beauty vary in appearance, aroma, and taste, depending on the degree of tea green leafhopper (Jacobiasca formosana) infestation. In this study, the aim is to investigate the differential expression of proteins in leaves with low, medium, and high degrees of leafhopper infestation. METHODS: Proteomic techniques 2DE (two-dimensional electrophoresis) and nanoscale liquid chromatography/tandem mass spectrometry (LC/MS/MS) were used to investigate the differential expression of proteins in tea leaves with different degrees of leafhopper infestation. RESULTS: A total of 89 proteins were found to exhibit significant differences in expression. In a gene ontology analysis, most of these proteins participated in biosynthesis, carbohydrate metabolism, transport, responses to stress, and amino acid metabolism. CONCLUSIONS: These results indicated that the unique aroma and taste of the leaves might be influenced by their protein expression profiles, as well as related factors such as defensive responses to tea green leafhopper saliva.