Identification of metabolic components of carotid plaque in high-risk patients utilizing liquid chromatography-tandem mass spectrometry.

OBJECTIVE: Carotid atherosclerosis is a chronic progressive vascular disease that can be complicated by stroke in severe cases. Prompt diagnosis and treatment of high-risk patients are quite difficult due to the lack of reliable clinical biomarkers. This study aimed to explore potential plaque metabolic markers of stroke-prone risk and relevant targets for pharmacological intervention. METHOD: Carotid intima and plaque sample tissues were obtained from 20 patients with cerebrovascular symptoms of carotid origin. An untargeted metabolomics approach based on liquid chromatography-tandem mass spectrometry was utilized to characterize the metabolic profiles of the tissues. Multivariate and univariate analysis tools were used. RESULTS: A total of 154 metabolites were significantly altered in carotid plaque when compared with thickened intima. Of these, 62 metabolites were upregulated, whereas 92 metabolites were downregulated. Support vector machines identified the 15 most important metabolites, such as N-(cyclopropylmethyl)-N'-phenylurea, 9(S)-HOTrE, ACar 12:2, quinoxaline-2,3-dithiol, and l-thyroxine, as biomarkers for high-risk plaques. Metabolic pathway analysis showed that abnormal purine and nucleotide metabolism, amino acid metabolism, glutathione metabolism, and vitamin metabolism may contribute to the occurrence and progression of carotid atherosclerotic plaque. CONCLUSIONS: Our study identifies the biomarkers and related metabolic mechanisms of carotid plaque, which is stroke-prone, and provides insights and ideas for the precise prevention and targeted intervention of the disease.

Discovery of meisoindigo derivatives as noncovalent and orally available Mpro inhibitors: their therapeutic implications in the treatment of COVID-19.

The progressive emergence of SARS-CoV-2 variants has necessitated the urgent exploration of novel therapeutic strategies to combat the COVID-19 pandemic. The SARS-CoV-2 main protease (Mpro) represents an evolutionarily conserved therapeutic target for drug discovery. This study highlights the discovery of meisoindigo (Mei), derived from the traditional Chinese medicine (TCM) Indigo naturalis, as a novel non-covalent and nonpeptidic Mpro inhibitor. Substantial optimizations and structure-activity relationship (SAR) studies, guided by a structure-based drug design approach, led to the identification of several Mei derivatives, including S5-27 and S5-28, exhibiting low micromolar inhibition against SARS-CoV-2 Mpro with high binding affinity. Notably, S5-28 provided significant protection against wild-type SARS-CoV-2 in HeLa-hACE2 cells, with EC50 up to 2.66 µM. Furthermore, it displayed favorable physiochemical properties and remarkable gastrointestinal and metabolic stability, demonstrating its potential as an orally bioavailable drug for anti-COVID-19 therapy. This research presents a promising avenue for the development of new antiviral agents, offering hope in the ongoing battle against COVID-19.

Microwave-assisted Pyrolysis-A new way for the sustainable recycling and upgrading of plastic and biomass:A review.

The efficient utilization of organic solid waste resources can help reducing the consumption of conventional fossil fuels, mitigating environmental pollution, and achieving green sustainable development. Due to its dual nature of being both a resource and a source of pollution, it is crucial to implement suitable recycling technologies throughout the recycling and upgrading processes for plastics and biomass, which are organic solid wastes with complex mixture of components. The conventional pyrolysis and hydropyrolysis were summarized for recycling plastics and biomass into highvalue fuels, chemicals, and materials. To enhance reaction efficiency and improve product selectivity, microwave-assisted pyrolysis was introduced to the upgrading of plastics and biomass through efficient energy supply especially with the aid of catalysts and microwave absorbers. This review provides a detail summary of microwave-assisted pyrolysis for plastics and biomass from the technical, applied, and mechanistic perspectives. Based on the recent technological advances, the future directions for the development of microwave-assisted pyrolysis technologies are predicted.

Gestational and Postpartum Exposure to PM2.5 Components and Glucose Metabolism in Chinese Women: A Prospective Cohort Study.

Pregnant women are physiologically prone to glucose intolerance, while the puerperium represents a critical phase for recovery. However, how air pollution disrupts glucose homeostasis during the gestational and early postpartum periods remains unclear. This prospective cohort study conducted an oral glucose tolerance test and measured the insulin levels of 834 pregnant women in Guangzhou, with a follow-up for 443 puerperae at 6-8 weeks postpartum. Residential PM2.5 and five chemical components were estimated by an established spatiotemporal model. The adjusted linear model showed that an IQR increase in gestational PM2.5 exposure was associated with an increase of 0.17 mmol/L (95% CI: 0.06, 0.28) in fasting plasma glucose (FPG) and 0.24 (95% CI: 0.05, 0.42) in the insulin resistance index. Postpartum PM2.5 exposure was linked to a 0.17 mmol/L (95% CI: 0.05, 0.28) elevation in FPG per IQR, with a strengthened association found in women with gestational diabetes (Pinteraction = 0.003). In the quantile-based g-computation model, NO3- consistently contributed to the combined effect of PM2.5 components on gestational and postpartum FPG. This study was the first to suggest that PM2.5 components were associated with exacerbated gestational insulin resistance and elevated postpartum FPG. Targeted interventions reducing the emissions of toxic PM2.5 components are essential to improving maternal glucose metabolism.

Evaluating the performance of traffic conflict measures in real-time crash risk prediction using pre-crash vehicle trajectories.

The primary objective of this study was to evaluate the performance of traffic conflict measures for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained. Vehicle trajectories preceding the crash were segmented based on different time periods to represent varying crash conditions. The Extreme Value Theory (EVT) approach combined with a block maxima sampling method was then employed to investigate the generalized extreme value (GEV) distributions of extremely risky events under non-crash and crash conditions. The prediction performance was demonstrated by the differences in GEV distributions under these two conditions. Within the proposed modeling framework, the performances of Time-to-Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), and Absolute value of Derivative of Instantaneous Acceleration (ADIA) were examined and compared. The results revealed a decreasing trend in the prediction performances as the preceding time window before a crash increased. For any given length of crash conditions, TTC consistently outperformed DRAC and ADIA. Notably, TTC's reliability in crash risk prediction became more uncertain when forecasting crashes more than 2 s in advance. This study provided the optimal thresholds for TTC and ADIA for practical application in crash early warning. The methods and results in this study have the potential to be used for crash risk assessments in autonomous vehicles.

Potential Protective Effect of SeleniumEnriched Lactobacillus plantarum on Cadmium-Induced Liver Injury in Mice.

Cadmium (Cd) is a prevalent environmental contaminant that poses a potential hazard to the health of both humans and animals. In this study, biosynthesized selenium-enriched Lactobacillus plantarum and selenium nanoparticles (SeNPs) were developed and evaluated for their protective effects against Cd-induced hepatic injury in mice through oral administration for 4 weeks. Cadmium exposure resulted in severe impairment of liver function, as evidenced by increased levels of serum markers of liver injury and, oxidative stress and significant damage to liver tissue, and a notable decrease in the diversity of the intestinal microbiota. Oral administration of Se-enriched L. plantarum (LS) reduced cadmium accumulation in the liver by 49.5% and, restored other cadmium-induced damage markers to normal levels. A comparison of the effects with those of L. plantarum (L) and SeNPs isolated from LS revealed that LS could more effectively alleviate hepatic oxidative stress and reduce the intrahepatic inflammatory responses of the liver, further protecting against cadmiuminduced liver injury. These findings suggest that the development of LS may be effective at protecting the liver and intestinal tract from cadmium-induced damage.

Towards sustainable and humane dairy farming: A low-cost electrochemical sensor for on-site diagnosis of milk fever.

Milk fever is a metabolic disorder that predominantly affects dairy animals during the periparturient period and within four weeks of calving. Milk fever is primarily attributed to a decrease in the animal's serum Ca2+ levels. Clinical milk fever occurs when Ca2+ concentration drops below 1.5 mM (6 mg/dL). Without prompt intervention, clinical milk fever leads to noticeable physical symptoms and health complications including coma and fatality. Subclinical milk fever is characterized by Ca2+ levels between 1.5 and 2.12 mM (6-8.48 mg/dL). Approximately 50% of multiparous dairy cows suffer from subclinical milk fever during the transition to lactation. The economic impact of milk fever, both direct and indirect, is substantial, posing challenges for farmers. To address this issue, we developed a low-cost electrochemical sensor that can measure bovine serum calcium levels on-site, providing an opportunity for early detection of subclinical and clinical milk fever and early intervention. This calcium sensor is a scalable solid contact ion sensing platform that incorporates a polymeric calcium-selective membrane and ionic liquid-based reference membrane into laser-induced graphene (LIG) electrodes. Our sensing platform demonstrates a sensitivity close to the theoretical Nernstian value (29.6 mV/dec) with a limit of detection of 15.6 µM and selectivity against the species in bovine serum. Moreover, our sensor can detect Ca2+ in bovine serum with 91% recovery.

A new 3,4-dihydroisocoumarin and an antiinflammatory coumarin from the roots of Saposhnikovia divaricata (Turcz.) Schischk.

Phytochemical investigation of the roots of Saposhnikovia divaricata (Turcz.) Schischk resulted in the isolation of twelve coumarin derivatives including one new 3,4-dihydroisocoumarin (1) and eleven known 3,4-unsubstituted coumarins (2-12). Structural elucidation of compounds 1-12 was established by 1D and 2D NMR spectra referring to the literature, together with high-resolution mass spectrometric analysis. LPS-induced RAW264.7 inflammatory cell model was used to determine the potential antiinflammation activity of all the isolated compounds in vitro. The results showed that compound 3 significantly inhibited the production of lipopolysaccharide (LPS)-induced NO in macrophages (IC50 = 4.54 ± 1.71 µM), more active than the positive control (L-NMMA).

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

Dopamine promotes osteogenic differentiation of PDLSCs by activating DRD1 and DRD2 during orthodontic tooth movement via ERK1/2 signaling pathway.

Introduction: Orthodontic tooth movement (OTM) involves complex interactions between mechanical forces and periodontal tissue adaptation, mainly mediated by periodontal ligament cells, including periodontal ligament stem cells (PDLSCs), osteoblasts, and osteoclasts. Dopamine (DA), a neurotransmitter known for its critical role in bone metabolism, is investigated in this study for its potential to enhance osteogenic differentiation in PDLSCs, which are pivotal in OTM. This study examined the potential of DA to facilitate OTM by binding to DA receptors (D1R and D2R) and activating the ERK1/2 signaling pathway. We propose that DA's interaction with these receptors on PDLSCs could enhance osteogenic differentiation, thereby accelerating bone remodeling and reducing the duration of orthodontic treatments, which offering a novel approach to improve clinical outcomes in orthodontic care. Methods: This study utilized a rat OTM model, micro-CT, histological analyses, and in vitro assays to investigate dopamine's effect on osteogenesis. PDLSCs were cultured and treated with DA, and cytotoxicity, osteogenic differentiation, gene and protein expression assessed. Results: Dopamine administration significantly increased trabecular bone density and osteogenic marker expression in an OTM rat model. In vitro, DA at 10 nM optimally promoted human PDLSCs osteogenesis without affecting proliferation. Blocking DA receptors or inhibiting the ERK1/2 pathway attenuated these effects, underscoring the importance of dopaminergic signaling in tension-induced osteogenesis during OTM. Conclusion: Taken together, our study reveals that local dopamine administration at a concentration of 10 nM not only enhances tension-induced osteogenesis in vivo but also significantly promotes osteogenic differentiation of PDLSCs in vitro through D1 and D2 receptor-mediated ERK1/2 signaling pathway activation.

The causal association between artificial sweeteners and the risk of cancer: a Mendelian randomization study.

Artificial sweeteners (ASs) have been widely added to food and beverages because of their properties of low calories and sweet taste. However, whether the consumption of ASs is causally associated with cancer risk is not clear. Here, we utilized the two-sample Mendelian randomization (MR) method to study the potential causal association. Genetic variants like single-nucleotide polymorphisms (SNPs) associated with exposure (AS consumption) were extracted from a genome-wide association study (GWAS) database including 64 949 Europeans and the influence of confounding was removed. The outcome was from 98 GWAS data and included several types of cancers like lung cancer, colorectal cancer, stomach cancer, breast cancer, and so on. The exposure-outcome SNPs were harmonized and then MR analysis was performed. The inverse-variance weighted (IVW) with random effects was used as the main analytical method accompanied by four complementary methods: MR Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses consisted of heterogeneity, pleiotropy, and leave-one-out analysis. Our results demonstrated that ASs added to coffee had a positive association with high-grade and low-grade serous ovarian cancer; ASs added to tea had a positive association with oral cavity and pharyngeal cancers, but a negative association with malignant neoplasm of the bronchus and lungs. No other cancers had a genetic causal association with AS consumption. Our MR study revealed that AS consumption had no genetic causal association with major cancers. Larger MR studies or RCTs are needed to investigate small effects and support this conclusion.

Innovative plasticization technique for talc-powder reinforced wheat-starch biomass composite plastics with enhanced mechanical strength.

N-methyl-morpholine-N-oxide (NMMO) was initially created as a plasticizer for starch to produce thermoplastic wheat starch. Subsequently, talc powder was used as a reinforcing filler to enhance the mechanical strength of thermoplastic biomass-based composite plastics. The chemical structure, crystal structure, and microscopic morphology were analyzed using Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. Additionally, the thermal properties were explored through thermogravimetric analysis, differential scanning calorimetry, and dynamic mechanical analysis. The hydrated NMMO plasticizer demonstrated an outstanding plasticizing effect on starch, resulting in a composite with remarkable mechanical properties. In fact, the pure thermoplastic wheat starch plasticized with hydrated NMMO exhibited the highest mechanical strength recorded so far, with a tensile strength of up to 9.4 MPa. In addition, talcum powder displayed a noticeable reinforcing effect. When the talcum powder content reached 30 wt%, the targeted composite achieved a tensile strength of 20.5 MPa and a Young's modulus of 177.9 MPa. These values were 118 % and 48 % higher, respectively, than those of the pure thermoplastic starch sample. This innovative plasticizing method opens up a new avenue for the development of high-mechanical-strength thermoplastic biomass-based composite plastics with promising potential applications.

Construction and evaluation of near-infrared fluorescent probes for imaging lipid droplet and lysosomal viscosity.

Microenvironmental viscosity is a crucial parameter for biological systems, and its abnormal fluctuations are closely associated with various functional disorders and diseases. However, it is still important and urgent to develop improved near-infrared fluorescent probes for micro-viscosity with dual-organelle targeting properties, low background noise, and high sensitivity. Herein, two BODIPY-based small-molecule fluorescent probes were designed and synthesized, which were explored for their viscosity- and polarity-responsive properties, and were further applied to imaging sub-cellular viscosity in living cells. Interestingly, BSZ-Ph and BSZ-R displayed near-infrared fluorescence (more than 650 nm) and were sensitive to environmental viscosity and polarity due to the introduction of a benzothiazole at the 2-position and electron-rich aniline groups at the 5-position of the BODIPY core, respectively. The fluorescence intensity increased exponentially with the viscosity changes. Furthermore, the probe BSZ-Ph could successfully target lipid droplets and image cellular viscosity changes by treating lipopolysaccharides (LPS) and nystatin. Comparatively, the probe BSZ-R could successfully target the dual organelles of lipid droplets and lysosomes and image cellular viscosity changes by treating LPS and monensin. Therefore, in this work, we reported two new BODIPY-based near-infrared fluorescent probes, BSZ-Ph and BSZ-R, for cellular viscosity imaging, which could target lipid droplets and the dual organelles of lysosomes and lipid droplets, respectively. The study could provide a reference for the future development of fluorescent probes for viscosity in lipid droplets and lysosomes.

Low-frequency electroacupuncture exerts antinociceptive effects through activation of POMC neural circuit induced endorphinergic input to the periaqueductal gray from the arcuate nucleus.

It has been widely recognized that electroacupuncture (EA) inducing the release of ß-endorphin represents a crucial mechanism of EA analgesia. The arcuate nucleus (ARC) in the hypothalamus is a vital component of the endogenous opioid peptide system. Serving as an integration center, the periaqueductal gray (PAG) receives neural fiber projections from the frontal cortex, insular cortex, and ARC. However, the specific mechanisms how EA facilitates the release of ß-endorphin within the ARC, eliciting analgesic effects are yet to be elucidated. In this study, we conducted in vivo and in vitro experiments by transcriptomics, microdialysis, photogenetics, chemical genetics, and calcium imaging, combined with transgenic animals. Firstly, we detected 2 Hz EA at the Zusanli (ST36) increased the level of ß-endorphin and transcriptional level of proopiomelanocortin (POMC). Our transcriptomics profiling demonstrated that 2 Hz EA at the ST36 modulates the expression of c-Fos and Jun B in ARC brain nuclear cluster, and the transcriptional regulation of 2 Hz EA mainly occur in POMC neurons by Immunofluorescence staining verification. Meaning while, 2 Hz EA specifically activated the cAMP-PKA-CREB signaling pathway in ARC which mediating the c-Fos and Jun B transcription, and 2 Hz EA analgesia is dependent on the activation of cAMP-PKA-CREB signaling pathway in ARC. In order to investigate how the ß-endorphin produced in ARC transfer to integration center PAG, transneuronal tracing technology was used to observe the 2 Hz EA promoted the neural projection from ARC to PAG compared to 100 Hz EA and sham mice. Inhibited PAGGABA neurons, the transfer of ß-endorphin from the ARC nucleus to the PAG nucleus through the ARCPOMC-PAGGABA neural circuit. Furthermore, by manipulating the excitability of POMC neurons from ARCPOMC to PAGGABA using inhibitory chemogenetics and optogenetics, we found that this inhibition significantly reduced transfer of ß-endorphin from the ARC nucleus to the PAG nucleus and the effectiveness of 2 Hz EA analgesia in neurological POMC cyclization recombination enzyme (Cre) mice and C57BL/6J mice, which indicates that the transfer of ß-endorphin depends on the activation of POMC neurons prefect from ARCPOMC to PAGGABA. These findings contribute to our understanding of the neural circuitry underlying the EA pain-relieving effects and maybe provide valuable insights for optimizing EA stimulation parameters in clinical pain treatment using the in vivo dynamic visual investigating the central analgesic mechanism.

N vacancy and self-enhancement induced high anodic electrochemiluminescence of 3D g-C3N4 for sensitive staphylococcus aureus analysis.

Here, 3D g-C3N4 with dense N vacancy in its 3D porous interconnected open-framework was synthesized, and the co-reactive 3-(dibutylamino)propylamine (DBAPA) was further covalently coupled onto the surface, resulting in a strong self-enhanced anodic electrochemiluminescence (ECL). Through introduction of high-density N vacancy, for the obtained 3D g-C3N4-NV, the band gap was broadened and the electrical conductivity was enhanced, realizing an obvious ECL improvement. Moreover, after the covalent binding of co-reactive DBAPA, the obtained 3D g-C3N4-NV-DBAPA exhibited a more intensive self-enhanced ECL signal due to the higher co-reaction efficiency originated from shorter electron transfer distance and lower energy loss. Based on the high initial signal of the proposed 3D g-C3N4-NV-DBAPA, a sensitive ECL biosensor with signal "on-off" was fabricated in assistance with multiple horizontal ordered hybridization chain reaction (HO-HCR). Through orderly fixing the reacted DNA chains on the Y-shape DNA structure on the electrode could effectively decrease diffusion process and improve the reaction efficiency of HCR process, resulting in the formation of numerous long horizontal double-strand DNA that could immobilize abundant ferrocene-doxorubicin (Fc-Dox) with ECL quenching effect. Meanwhile, compared to the traditional vertical HCR, the HO-HCR could make the quench reagent closer to the ECL emitter on the electrode surface and obtain a more effective quenching effect to enhance the sensing sensitivity. As a result, the proposed ECL biosensor archived the sensitive measurement of staphylococcus aureus with a detection limit of 10.3 aM.

The Associations of Prenatal Exposure to Fine Particulate Matter and Its Chemical Components with Allergic Rhinitis in Children and the Modification Effect of Polyunsaturated Fatty Acids: A Birth Cohort Study.

BACKGROUND: Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter <2.5µm in aerodynamic diameter (PM2.5)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring. OBJECTIVES: We aimed to investigate the association of prenatal exposure to PM2.5 and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs. METHODS: This prospective birth cohort study involved 657 mother-child pairs from Guangzhou, China. Prenatal exposure to residential PM2.5 mass and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of PM2.5 components and examine the modification effects of maternal PUFA levels. RESULTS: Approximately 5.33% and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal PM2.5 was 35.50±5.31 µg/m3. PM2.5 was positively associated with the risk of developing AR [hazard ratio (HR)=1.85; 95% confidence interval (CI): 1.16, 2.96 per 5 µg/m3] and its symptoms (HR=1.79; 95% CI: 1.22, 2.62 per 5 µg/m3) after adjustment for confounders. Similar associations were observed between individual PM2.5 components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to PM2.5 and its components. CONCLUSION: Prenatal exposure to PM2.5 and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of PM2.5 on respiratory allergy. https://doi.org/10.1289/EHP13524.

Glycine betaine modulates extracellular polymeric substances to enhance microbial salinity tolerance.

High salinity inhibits microbial activity in the bioremediation of saline wastewater. To alleviate osmotic stress, glycine betaine (GB), an osmoprotectant, is added to enhance the secretion of extracellular polymeric substances (EPS). These EPS are pivotal in withstanding environmental stressors, yet the intricate interplay between GB supplementation and microbial responses through EPS modifications-encompassing composition, molecular architecture, and electrochemical features-remains elusive in hypersaline conditions. Here we show microbial strategies for salinity endurance by investigating the impact of GB on the dynamic alterations of EPS properties. Our findings reveal that GB supplementation at 3.5% salinity elevates the total EPS (T-EPS) content from 12.50 ± 0.05 to 24.58 ± 0.96 mg per g dry cell weight. The observed shift in zeta potential from -28.95 to -6.25 mV at 0% and 3.5% salinity, respectively, with GB treatment, indicates a reduction in electrostatic repulsion and compaction. Notably, the EPS protein secondary structure transition from ß-sheet to α-helix, with GB addition, signifies a more compact protein configuration, less susceptible to salinity fluctuations. Electrochemical analyses, including cyclic voltammetry (CV) and differential pulse voltammetry (DPV), reveal GB's role in promoting the release of exogenous electron shuttles, such as flavins and c-type cytochromes (c-Cyts). The enhancement in DPV peak areas (QDPV) with GB addition implies an increase in available extracellular electron transfer sites. This investigation advances our comprehension of microbial adaptation mechanisms to salinity through EPS modifications facilitated by GB in saline habitats.

PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry.

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS2MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS2MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS2MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS2MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts.

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning.

Chinese media companies are facing opportunities and challenges brought about by digital transformation. Media economics takes the evaluation of the business results of media companies as the main research topic. However, overcoming the internal differences in the industry and comprehensively predicting the digital transformation of Chinese media companies from multiple dimensions has become an important issue to be understood. Based on the "TOE-I" theoretical framework, this study innovatively uses machine learning methods to predict the digital transformation of Chinese media companies and to analyze specific modes of the main driving factors affecting the digital transformation, using data from China's A-share-listed media companies from 2010 to 2020. The study found that environmental drivers can most effectively and accurately predict the digital transformation of Chinese media companies. Therefore, under sustained and stable economic and financial policies, guiding inter-industry competition and providing balanced digital infrastructure conditions are keys to bridging internal barriers in the media industry and promoting digital transformation. In the process of transformation from traditional content to digital production, media companies should focus on policy changes, economic benefits, the decision-making role of core managers, and the training and preservation of digital technology talent.

Driving forces of digital transformation in chinese enterprises based on machine learning.

With advanced science and digital technology, digital transformation has become an important way to promote the sustainable development of enterprises. However, the existing research only focuses on the linear relationship between a single characteristic and digital transformation. In this study, we select the data of Chinese A-share listed companies from 2010 to 2020, innovatively use the machine learning method and explore the differences in the predictive effects of multi-dimensional features on the digital transformation of enterprises based on the Technology-Organization-Environment (TOE) theory, thus identifying the main drivers affecting digital transformation and the fitting models with stronger predictive effect. The study found that: first, by comparing machine learning and traditional linear regression models, it is found that the prediction ability of ensemble earning method is generally higher than that of tradition measurement method. For the sample data selected in this research, XGBoost and LightGBM have strong explanatory ability and high prediction accuracy. Second, compared with the technical driving force and environmental driving force, the organizational driving force has a greater impact. Third, among these characteristics, equity concentration and executives' knowledge level in organizational dimension have the greatest impact on digital transformation. Therefore, enterprise managers should always pay attention to the decision-making role of equity concentration and executives' knowledge level. This study further enriches the literature on digital transformation in enterprises, expands the application of machine learning in economics, and provides a theoretical basis for enterprises to enhance digital transformation.