Phosphorylated transcription factor PuHB40 mediates ROS-dependent anthocyanin biosynthesis in pear exposed to high light.

Plants are increasingly vulnerable to environmental stresses because of global warming and climate change. Stress-induced reactive oxygen species (ROS) accumulation results in plant cell damage and even cell death. Anthocyanins are important antioxidants that scavenge ROS to maintain ROS homeostasis. However, the mechanism underlying ROS-induced anthocyanin accumulation is unclear. In this study, we determined that the HD-Zip I family member transcription factor PuHB40 mediates ROS-dependent anthocyanin biosynthesis under high-light stress in pear (Pyrus ussuriensis). Specifically, PuHB40 induces the PuMYB123-like-PubHLH3 transcription factor complex for anthocyanin biosynthesis. PuHB40-mediated transcriptional activation depends on its phosphorylation level, which is regulated by protein phosphatase PP2A. Elevated ROS content maintains high PuHB40 phosphorylation levels, while also enhancing PuHB40-induced PuMYB123-like transcription by decreasing PuPP2AA2 expression, ultimately leading to increased anthocyanin biosynthesis. Our study reveals a pathway regulating ROS-induced anthocyanin biosynthesis in pear, further clarifying the mechanism underlying abiotic stress-induced anthocyanin biosynthesis, which may have implications for improving plant stress tolerance.

Photonic-assisted wideband microwave frequency measurement based on optical heterodyne detection.

A photonic-assisted microwave frequency measurement (MFM) method based on optical heterodyne detection is proposed and experimentally demonstrated. In the proposed MFM system, a linearly chirped optical waveform (LCOW) from a three-electrode distributed Bragg reflector laser diode (DBR-LD) and a multi-wavelength signal from a Mach-Zehnder modulator (MZM), where the signal under test (SUT) is modulated on an optical carrier from a distributed feedback laser diode (DFB-LD), are heterodyne detected by the photodetector (PD). A bandpass filter then filters the detected signal, and the envelope is detected by an oscilloscope. Then, frequency-to-time mapping is realized, and the signal frequency is measured. Thanks to the fast tuning rate and large tuning range of the DBR-LD, the proposed MFM system has a high measurement speed and a broad instantaneous measurement bandwidth. In the experimental demonstration, a measurement error below 39.1 MHz is achieved at an instantaneous bandwidth of 20 GHz and a measurement speed of 1.12 GHz/µs. The MFM of a frequency-hopping signal is also experimentally demonstrated. The successful demonstration of the MFM system with a simple structure provides a new optical solution for realizing broadband and fast microwave frequency measurements.

Synthesis of Indole-Fused Pyrazino[1,2-a]quinazolinones by Copper(I)-Catalyzed Selective Hydroamination-Cyclization of Alkynyl-tethered Quinazolinones.

We described a copper(I)-catalyzed atom economic and selective hydroamination-cyclization of alkynyl-tethered quinazolinones to prepare a variety of indole-fused pyrazino[1,2-a]quinazolinones in good to excellent yields ranging from 39 %-99 % under mild reaction conditions. Control experiments revealed that coordination-directed method of quinazolinone moiety with copper(I) was important for the selective hydroamination-cyclization of alkynes at the N1-atom instead of N3-atom of quinazolinone. The reaction could be easily performed at gram scales and some prepared indole-fused pyrazino[1,2-a]quinazolinones with donating groups on the indole moiety showed a distinct fluorescence emission wavelength with blue shift under the acid conditions.

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics.

OBJECTIVE: Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI)of LN. METHODS: A retrospective collection of 136 patients with LN who had renal biopsy was retrospectively collected, and the patients were randomly divided into training set and validation set according to 7:3. Radiomics features are extracted from ultrasound images, independent factors are obtained by using LASSO dimensionality reduction, and then seven ML models were used to establish predictive models. At the same time, a clinical model and an US model were established. The diagnostic efficacy of the model is evaluated by analysis of the receiver operating characteristics (ROC) curve, accuracy, specificity, and sensitivity. The performance of the seven machine learning models was compared with each other and with clinical and US models. RESULTS: A total of 1314 radiomics features are extracted from ultrasound images, and 5 features are finally screened out by LASSO for model construction, and the average ROC of the seven ML is 0.683, among which the Xgboost model performed the best, and the AUC in the test set is 0.826 (95% CI: 0.681-0.936). For the same test set, the AUC of clinical model constructed based on eGFR is 0.560 (95% CI: 0.357-0.761), and the AUC of US model constructed based on Ultrasound parameters is 0.679 (95% CI: 0.489-0.853). The Xgboost model is significantly more efficient than the clinical and US models. CONCLUSION: ML model based on ultrasound radiomics features can accurately predict the chronic degree of LN, which can provide a valuable reference for clinicians in the treatment strategy of LN patients.

Synthesis of 4-(trichloromethyl)pyrido[2',1':3,4]pyrazino[2,1-b]quinazolinones through a cyclized dearomatization and trichloromethylation cascade strategy.

A variety of 4-(trichloromethyl)pyrido[2',1':3,4]pyrazino[2,1-b]quinazolinones were prepared in moderate to good yields with high regioselectivity through intramolecular 6-endo-dig cyclization and trichloromethylation of N3-alkynyl-2-pyridinyl-tethered quinazolinones in chloroform. Mechanistic studies revealed that chloroform might serve as a trichloromethyl anion precursor. Furthermore, the reaction could be easily performed on gram scales and an estrone-derived 4-(trichloromethyl)pyrido[2',1':3,4]pyrazino[2,1-b]quinazolinone was prepared over five steps. The present method features broad substrate scope, good functional group tolerance, new dearomatization of pyridine rings, and chloroform as the trichloromethylation reagent.

Efficacy and safety of dabigatran and rivaroxaban in atrial fibrillation patients with impaired liver function: a multicenter retrospective cohort study.

BACKGROUND: The efficacy and safety of direct oral anticoagulants (DOACs) in atrial fibrillation (AF) patients with impaired liver function (ILF) have not been sufficiently studied. The aim of this study was to evaluate the efficacy and safety of DOACs for stroke prevention in patients with AF and ILF. METHOD: This study was based on data from 15 centers in China, including 4,982 AF patients. The patients were divided into 2 subgroups based on their liver function status: patients with normal liver function (NLF)(n = 4213) and patients with ILF (n = 769). Logistic regression analysis was used to investigate the risk of total bleeding, major bleeding, thromboembolism, and all-cause deaths in AF patients with NLF and ILF after taking dabigatran or rivaroxaban, respectively. RESULTS: Among AF patients treated with dabigatran or rivaroxaban, patients with ILF were associated with significantly higher major bleeding, compared with NLF patients (aOR: 4.797; 95% CI: 2.224-10.256; P < 0.001). In patients with NLF, dabigatran (n = 2011) had considerably lower risk of total bleeding than rivaroxaban (n = 2202) (aOR: 1.23; 95% CI: 1.002-1.513; P = 0.049). In patients with ILF, dabigatran (n = 321) significantly favored lower risks of major bleeding compared with rivaroxaban(n = 448) (aOR: 5.484; 95% CI: 1.508-35.269; P = 0.026). CONCLUSION: After using dabigatran or rivaroxaban, patients with ILF had remarkably increased risk of major bleeding compared with patients with NLF. In AF patients with NLF, dabigatran had the distinct strength of significantly reduced risk of total bleeding compared with rivaroxaban. In patients with AF and ILF, dabigatran use was associated with lower risk for major bleeding compared with rivaroxaban.

The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio with type 2 diabetes mellitus: recent findings from NHANES 2007-2018.

OBJECTIVE: This study aims to assess the relationship between NHHR (non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio) and Type 2 diabetes mellitus (T2DM) in US adults, using National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2018. METHODS: This study explored the connection between NHHR and T2DM by analyzing a sample reflecting the adult population of the United States (n = 10,420; NHANES 2007-2018). NHHR was characterized as the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol. T2DM was defined based on clinical guidelines. This research used multivariable logistic models to examine the connection between NHHR and T2DM. Additionally, it included subgroup and interaction analyses to assess variations among different groups. Generalized additive models, smooth curve fitting, and threshold effect analysis were also employed to analyze the data further. RESULTS: The study included 10,420 subjects, with 2160 diagnosed with T2DM and 8260 without. The weighted multivariate logistic regression model indicated an 8% higher probability of T2DM for each unit increase in NHHR (OR: 1.08, 95% CI: 1.01-1.15) after accounting for all covariates. Subgroup analysis outcomes were uniform across various categories, demonstrating a significant positive relationship between NHHR and T2DM. Interaction tests showed that the positive link between NHHR and T2DM remained consistent regardless of age, body mass index, smoking status, moderate recreational activities, hypertension, or stroke history, with all interaction P-values exceeding 0.05. However, participants' sex appeared to affect the magnitude of the connection between NHHR and T2DM (interaction P-value < 0.05). Also, a nonlinear association between NHHR and T2DM was discovered, featuring an inflection point at 1.50. CONCLUSIONS: Our study suggests that an increase in NHHR may be correlated with a heightened likelihood of developing T2DM. Consequently, NHHR could potentially serve as a marker for estimating the probability of T2DM development.

Assessment of the exposure to cytotoxic Bacillus cereus group genotypes through HTST milk consumption.

This study addresses the limited tools available for assessing food safety risks from cytotoxic Bacillus cereus group strains in contaminated food. We quantified the growth, in skim milk broth, of 17 cytotoxic B. cereus strains across 6 phylogenetic groups with various virulence gene profiles. The strains did not grow in HTST milk at 4 or 6°C. At 10°C, 15 strains exhibited growth; at 8°C, one strain grew; and all strains grew at temperatures ≥ 14°C. Using growth data from 16 strains, we developed linear secondary growth models and an exposure assessment model. This model, simulating a 5-stage HTST milk supply chain and up to 35 d of consumer storage with an initial contamination of 100 cfu/mL, estimated that 2.81 ± 0.66% and 4.13 ± 2.53% of milk containers would surpass 105 cfu/mL of B. cereus by d 21 and 35, respectively. A sensitivity analysis identified the initial physiological state of cells (Q0) as the most influential variable affecting predictions for specific isolates. What-if scenarios indicated that increases in mean and variability of consumer storage temperatures significantly affected the predicted B. cereus concentrations in milk. This model serves as an initial tool for risk-based food safety decision making regarding low-level B. cereus contamination.

Osthole Activates the Cholinergic Anti-Inflammatory Pathway via α7nAChR Upregulation to Alleviate Inflammatory Responses.

Osthole (also known as Osthol) is the main anti-inflammatory coumarin found in Cnidium monnieri and severs as the exclusive quality-controlled component according the Chinese Pharmacopoeia. However, its underlying anti-inflammatory mechanism remains unknown. In this study, we demonstrated that Osthole treatment significantly inhibited the generation of TNF-α, but not IL-6 in the classical LPS-stimulated RAW264.7 macrophage model. In addition, LPS induced the activation of both MAPK and NF-κB signalling pathways, of which the former was dose-dependently restrained by Osthole via suppressing the phosphorylation of JNK and P38 proteins, while the phosphorylation of IκB and P65 proteins remained unaffected. Interestingly, Osthole dose-dependently up-regulated the expression of the key cholinergic anti-inflammatory pathway regulator α7nAChR, and the TNF-α inhibition effect of Osthole was also significantly alleviated by the treatment of α7nAChR antagonist methylbetaine. These results demonstrate that Osthole may regulate TNF-α by promoting the expression of α7nAChR, thereby activate the vagus nerve-dependent cholinergic anti-inflammatory pathway.

Tandem Nitrate-to-Ammonia Conversion on Atomically Precise Silver Nanocluster/MXene Electrocatalyst.

Electrocatalytic reduction of nitrate (NO3 RR) to synthesize ammonia (NH3 ) provides a competitive manner for carbon neutrality and decentralized NH3 synthesis. Atomically precise nanoclusters, as an advantageous platform for investigating the NO3 RR mechanisms and actual active sites, remain largely underexplored due to the poor stability. Herein, we report a (NH4 )9 [Ag9 (mba)9 ] nanoclusters (Ag9 NCs) loaded on Ti3 C2 MXene (Ag9 /MXene) for highly efficient NO3 RR performance towards ambient NH3 synthesis with improved stability in neutral medium. The composite structure of MXene and Ag9 NCs enables a tandem catalysis process for nitrate reduction, significantly increasing the selectivity and FE of NH3 . Besides, compared with individual Ag9 NCs, Ag9 /MXene has better stability with the current density performed no decay after 108 hours of reaction. This work provides a strategy for improving the catalytic activity and stability of atomically precise metal NCs, expanding the mechanism research and application of metal NCs.

Musical protein: Mapping the time sequence of music onto the spatial architecture of proteins.

BACKGROUND AND OBJECTIVE: Music, the ubiquitous language across human cultures, is traditionally considered as a form of art but has been linked to biomolecules in recent years. However, previous efforts have only been addressed on sonification of nucleic acids and proteins to produce so-called life music, the soundscape from the basic building blocks of life. In this study, we attempted to, for the first time, conduct a reverse operation of this process, i.e. conversion of music to protein (CoMtP). METHODS: A novel notion termed musical protein (MP) -- the protein defined by music -- was proposed and, on this basis, we described a computational strategy to map the time sequence of music onto the spatial architecture of proteins, which considered that each note in the stave of a music (target) can be simply characterized by two acoustical quantities and that each residue in the primary sequence of a protein (hit) was represented by amino acid descriptors. RESULTS: A simulated annealing (SA) algorithm was applied to iteratively generate the best matched MP hit for a music target and structural bioinformatics was then used to model spatial advanced structure for the resulting MP. We also demonstrated that some small MPs derived from music segments may have potential biological functions, which, for example, can serve as antimicrobial peptides (AMPs) to inhibit clinical bacterial strains with moderate or high antibacterial potency. CONCLUSIONS: This work may benefit many aspects; for example, it would open a door for the hearing-impaired persons to 'listen' music in a biological vision and could be a mean of exposing students to the concepts of biomolecules at an earlier age through the use of auditory characteristics. The CoMtP would also facilitate the rational design of proteins with biological and medicinal significance.

Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma.

Medulloblastoma (MB) is a malignant brain tumour that is highly common in children and has a tendency to spread to the brain and spinal cord. MB is thought to be a metabolically driven brain tumour. Understanding tumour cell metabolic patterns and characteristics can provide a promising foundation for understanding MB pathogenesis and developing treatments. Here, by analysing RNA-seq data of MB samples from the Gene Expression Omnibus (GEO) database, 12 differentially expressed metabolic-related genes (DE-MRGs) were chosen for the construction of a predictive risk score model for MB. This model demonstrated outstanding accuracy in predicting the outcomes of MB patients and served as a standalone predictor. An evaluation of functional enrichment revealed that the risk score showed enrichment in pathways related to cancer promotion and the immune response. In addition, a high risk score was an independent poor prognostic factor for MB in patients with different ages, sexes, metastasis stages and subgroups (SHH and Group 4). Consistently, the metabolic enzyme ornithine decarboxylase (ODC1) was upregulated in MB patients with poor survival time. Inhibition of ODC1 in primary and metastatic MB cell lines decreased cell proliferation, migration and invasion but increased immune infiltration. This study could aid in identifying metabolic targets for MB as well as optimizing risk stratification systems and individual treatment plans for MB patients via the use of a metabolism-related gene prognostic risk score signature.

Identification of a novel HLA-C allele, HLA-C*02:246.

HLA-C*02 246 has one nucleotide change from HLA-C*02:02:02:01 at nucleotide 523 changing Arginine to Cysteine at residue 151.

Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology.

BACKGROUND: Clinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). OBJECTIVES: This study aims to develop a multi-expert knowledge-aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. METHODS: The MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. RESULTS: Both UPGY (z = -3.976, P < 0.001) and ENT-R (z = -2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = -1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = -4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. CONCLUSIONS: The MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

Imaging predictors of hemorrhagic progression of a contusion after traumatic brain injury: a systematic review and meta-analysis.

The hemorrhagic progression of a contusion (HPC) after Traumatic brain injury (TBI) is one of the important causes of death in trauma patients. The purpose of this meta-analysis was to evaluate the predictive effect of imaging features of Computed tomography (CT) on HPC after TBI. A comprehensive systematic search was performed using PubMed, EMBASE, and WEB OF SCIENCE databases to identify all relevant literature. A total of 8 studies involving 2543 patients were included in this meta-analysis. Meta-analysis showed that subarachnoid hemorrhage (OR 3.28; 95% CI 2.57-4.20), subdural hemorrhage (OR 4.35; 95% CI 3.29-5.75), epidural hemorrhage (OR 1.47;95% CI 1.15-1.89), contrast extravasation (OR 11.81; 95% CI 4.86-28.71) had a predictive effect on the occurrence of HPC. Skull fracture (OR 1.64; 95% CI 0.84-3.19) showed no statistical significance, and midline displacement > 5 mm (OR 4.66; 95% CI 1.87-11.62) showed high heterogeneity. The results of this meta-analysis showed that some imaging features were effective predictors of HPC after TBI. Well-designed prospective studies are needed to more accurately assess the effective predictors of HPC after TBI.

A scenario-based web app to facilitate patient education in lung tumor patients undergoing video-assisted thoracoscopic surgery: Development and usability testing.

Background: Patient education (PE) is essential for improving patients' knowledge, anxiety, and satisfaction, and supporting their postoperative recovery. However, the advantages of video-assisted thoracoscopic surgery (VATS)-smaller incisions and faster recovery-can result in shorter hospital stays, making PE more challenging to implement effectively. Multimedia PE can potentially enhance PE, but its effectiveness for patients undergoing VATS is unclear. Objective: This study developed a scenario-based PE web app for lung tumor patients undergoing VATS (SPE-VATS) to facilitate the PE process and evaluated its usability through a clinical trial. Methods: The SPE-VATS provided the experimental group (EG: 32 participants) with interactive scenario, query guidance, diagnostic analysis, experience sharing, and active reminder, while the control group (CG: 32 participants) used pamphlets and videos. The usability of SPE-VATS in terms of postoperative anxiety reduction and patient satisfaction with the app was evaluated using self-reported questionnaires based on the state-trait anxiety inventory, technology acceptance model, system usability scale, and task load index. Results: There was no statistically significant difference in postoperative anxiety reduction between the EG and CG, possibly because 90% of the participants underwent a low-risk surgical type, and VATS is known to be advantageous in alleviating surgical anxiety. However, females and higher educated EG participants showed a non-significant but favorable reduction than their CG counterparts. Moreover, the EG was highly satisfied with the app (rated 4.2 to 4.4 out of 5.0), with no significant gender and education level difference. They particularly valued the interactive scenario, experience sharing, and diagnostic analysis features of SPE-VATS. Conclusions: The SPE-VATS demonstrated its usability and high patient satisfaction, particularly for female and higher educated patients. Low-risk patient predominance and VATS's advantages may explain non-significant postoperative anxiety reduction, warranting further studies on high-risk patients to evaluate the impact of SPE-VATS on clinical practice.

Prediction Model of Lean Coal Adsorption of Power Plant Flue Gas.

To minimize errors in calculating coal flue gas adsorption capacity due to gas compressibility and to preclude prediction inaccuracies in abandoned mine flue gas storage capacity for power plants, it is imperative to account for the influence of compression factor calculation accuracy while selecting the optimal theoretical adsorption model. In this paper, the flue gas adsorption experiment of a power plant with coal samples gradually pressurized to close to 5 MPa at two different temperatures is carried out, and the temperature and pressure data obtained from the experiment are substituted into five different compression factor calculation methods to calculate different absolute adsorption amounts. The calculated adsorption capacities were fitted into six theoretical adsorption models to establish a predictive model suitable for estimating the coal adsorption capacity in power plant flue gas. Results reveal significant disparities in the absolute adsorption capacity determined by different compression factors, with an error range of 0.001278-7.8262 (cm3/kg). The Redlich-Kwong equation of state emerged as the most suitable for the flue gas of the selected experimental coal sample and the chosen composition ratio among the five compression factors. Among the six theoretical adsorption models, the Brunauer-Emmett-Teller model with three parameters demonstrated the highest suitability for predicting the adsorption capacity of coal samples in power plant smoke, achieving a fitting accuracy as high as 0.9922 at 49.7 °C.