Advances in Flavonoid and Derivative Biosynthesis: Systematic Strategies for the Construction of Yeast Cell Factories.

Flavonoids, a significant group of natural polyphenolic compounds, possess a broad spectrum of pharmacological effects. Recent advances in the systematic metabolic engineering of yeast cell factories (YCFs) provide new opportunities for enhanced flavonoid production. Herein, we outline the latest research progress on typical flavonoid products in YCFs. Advanced engineering strategies involved in flavonoid biosynthesis are discussed in detail, including enhancing precursor supply, cofactor engineering, optimizing core pathways, eliminating competitive pathways, relieving transport limitations, and dynamic regulation. Additionally, we highlight the existing problems in the biosynthesis of flavonoid glucosides in yeast, such as endogenous degradation of flavonoid glycosides, substrate promiscuity of UDP-glycosyltransferases, and an insufficient supply of UDP-sugars, with summaries on the corresponding solutions. Discussions also cover other typical postmodifications like prenylation and methylation, and the recent biosynthesis of complex flavonoid compounds in yeast. Finally, a series of advanced technologies are envisioned, i.e., semirational enzyme engineering, ML/DL algorithn, and systems biology, with the aspiration of achieving large-scale industrial production of flavonoid compounds in the future.

[Mechanism of Qiwei Guibao Granules in treatment of premature ovarian failure based on metabolomics].

In this study, a mouse model of premature ovarian failure(POF) was constructed by injecting D-galactose(200 mg·kg~(-1)) into the back of the neck for 6 weeks. The mice were randomly divided into a normal group(group N), a model group(group M), and a Qiwei Guibao Granules group(group A, 12.87 g·kg~(-1)). Starting from the 11th day of modeling, group A was treated with Qiwei Guibao Granules by gavage for 32 days, while group M and group N were given equal volume of saline. Metabolomics analysis was used to explore the mechanism of action of Qiwei Guibao Granules in the treatment of POF. The results showed that compared with group N, the group M exhibited decreased wet weight of bilateral ovaries, increased levels of LH and FSH in serum, and significantly decreased levels of E_2 and PROG. After treatment with Qiwei Guibao Granules, compared with the group M, the group A showed a significant increase in the wet weight of bilateral ovaries, a significant decrease in the levels of FSH and LH in serum, and a significant increase in the level of E_2. Metabolomics analysis revealed 55 differential metabolites identified between group N and group M(14 upregulated and 41 downregulated compared with group N) and 82 differential metabolites identified between group M and group A(56 upregulated and 26 downregulated compared with group M), with 5 metabolites showing consistent changes between the group N vs group M. After excluding these 5 metabolites, 77 metabolites that changed after intervention with Qiwei Guibao Granules were focused on. These mainly involved histidine metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism. Among them, carnosine, 1-methyl-L-histidine, imidazoleacetic acid, choline, L-threonine, beta-hydroxypyruvic acid, phosphatidylcholine, and glycerol-3-phosphate were the major differential metabolites in these three metabolic pathways. Therefore, Qiwei Guibao Granules may exert therapeutic effects on POF mice by regulating amino acid metabolism and lipid metabolism in the mouse body.

Lower extremity function and cardiovascular disease risk in hemodialysis patients: A multicenter cross-sectional study.

Physical performance in hemodialysis patients declines and serves as a cardiovascular disease (CVD) incidence and mortality predictor. However, lower extremity function's role remains unclear. This study aimed to quantify the association between lower extremity function and CVD risk in hemodialysis patients. This was a multicenter cross-sectional study enrolling 868 participants (532 males, 336 females) from seven hemodialysis centers in Shanghai, China. Patients were divided into three groups per lower extremity function, evaluated by short physical performance battery (SPPB) scores: 0-6, 7-9, and 10-12. Upper extremity function was quantified through grip strength assessment. CVD risk was assessed using the Framingham Risk Score. Approximately 35% of hemodialysis patients had impaired lower extremity function (SPPB score < 10). Participants with high SPPB scores had stronger handgrip and lower Framingham CVD risk scores than those with low and moderate SPPB scores (p < 0.05). After adjusting clinical confounders, SPPB was independently associated with CVD risk, as a categorized variable (odds ratio: 0.577, 95% confidence interval [CI]: 0.388-0.857, p = 0.006) and as a continuous variable (odds ratio: 0.858, 95% CI: 0.772-0.953, p = 0.004). An SPPB score < 10 predicted an increased CVD risk (area under curve: 0.649, 95% CI: 0.599-0.699, p < 0.001). Causality between physical performance and CVD risk was not considered. Some upper limb results may not be generalizable to peritoneal dialysis and kidney transplant patients. Lower extremity function was significantly associated with CVD risk in hemodialysis patients. Further studies are needed to explore the long-term relationship between lower extremity function and CVD risk.

The association between visceral fat obesity and prefrailty in Chinese older adults: a cross-sectional study.

BACKGROUND: The prevalence of obesity is escalating. Previous research has concentrated on the link between frailty and obesity; however, the association between prefrailty and obesity has been less studied. Prefrailty screening and intervention may prevent or postpone frailty in older persons. OBJECTIVE: The study was to investigate into the relationship between prefrailty and several obesity indicators in Chinese community-dwelling older individuals. METHODS: This research employed the Frailty Screening Index to investigate the frailty phenotype of people living in Shanghai. Bioelectrical impedance analysis was used for evaluating body composition. RESULTS: There were 510 participants (39.0%) with high visceral adipose areas. Participants with a high visceral adipose area showed a higher risk of prefrailty (adjusted OR, 1.53; 95% CI, 1.19-1.96), according to multivariate models. When body mass index (BMI) and visceral fat area (VFA) were combined, it was discovered that having an overweight BMI with normal VFA was a protective factor for prefrailty (corrected OR, 0.62; 95% CI, 0.43-0.90), but having a normal weight but excess VFA increased the risk of prefrailty (corrected OR, 1.87; 95% CI, 1.15-3.03). CONCLUSION: Visceral fat obesity is an independent risk factor for prefrailty in Chinese older adults. Implementing targeted interventions, such as dietary modifications, increased physical activity, and other lifestyle changes, could play a crucial role in reducing the risk of prefrailty and improving overall health outcomes in this population.

Interfacial Wrinkling Structures Based on a Double Cross-Linking Strategy Enable a Dual-Mode Optical Information Encryption.

Surface wrinkling structures based on a bilayer system are widely employed in storing and encrypting specific optical information. However, constructing a stable wrinkling structure with high-level security remains an extensive challenge due to the delamination issue between the skin layer and the substrate. Herein, a double cross-linking strategy is introduced between a hydrogel layer doped with fluorescent molecules and polydimethylsiloxane to establish a stable interfacial wrinkling structure with dual-mode functionality, in which the light reflection of the wrinkles and fluorescence intensity of fluorescent molecules can be simultaneously regulated by the modulus ratio between the two layers. The spontaneous wrinkling structures with a physically unclonable function can enhance the photoluminescence emission intensity of the wrinkling area under ultraviolet radiation. Meanwhile, the skin layer constructed of acrylamide and acrylic acid copolymer protects the interfacial wrinkling patterns from the loss of a detailed structure for authentication due to external damage. The stable interfacial wrinkling structures with fluorescence can find potential applications in the fields of information storage and encryption.

Asymmetric bifunctionalization of allenes with aryl iodides and amino acids enabled by chiral aldehyde/palladium combined catalysis.

Even though catalytic asymmetric bifunctionalization of allenes has been extensively studied, almost all of the reported examples have been achieved in a two-component manner. In this study, we report a highly efficient asymmetric bifunctionalization of allenes with iodohydrocarbons and NH2-unprotected amino acid esters. The adopted chiral aldehyde/palladium combined catalytic system precisely governs the chemoselectivity, regioselectivity, and stereoselectivity of this three-component reaction. A wide range of substituted aryl iodides, allenes and amino acid esters can well participate in this reaction and deliver structurally diverse α,α-disubstituted α-amino acid esters with excellent experimental outcomes. One of the resulting products is utilized for the total synthesis of the molecule (S,R)-VPC01091.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development.

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

Bioactive Phenylpropanoid Glycosides, Dimers, and Heterodimers from the Bark of Cinnamomum cassia (L.) J.Presl.

Six new phenylpropanoid glycosides (1-6), two new phenylethanol glycosides (7 and 8), one new phenylmethanol glycoside (9), three new phenylpropanoid dimers (10-12), two new phenylpropanoid-flavan-3-ol heterodimers (13 and 14), and six known relevant compounds (15-20) were isolated and identified from the well-liked edible and medicinal substance (the bark of Cinnamomum cassia (L.) J.Presl). The structures of these isolates were determined by using spectroscopic analyses, chemical methods, and quantum chemical calculations. Notably, compounds 4-9 were rare apiuronyl-containing glycosides, and compounds 13 and 14 were heterodimers of phenylpropanoids and flavan-3-ols linked through C-9″-C-8 bonds. The antioxidant and α-glucosidase inhibitory activities of all isolates were evaluated. Compounds 10 and 12 exhibited DPPH radical scavenging capacities with IC50 values of 20.1 and 13.0 µM, respectively (vitamin C IC50 value of 14.3 µM). In the ORAC experiment, all these compounds exhibited different levels of capacity for scavenging free radicals, and compound 10 displayed extraordinary free radical scavenging capacity with the ORAC value of 6.42 ± 0.01 µM TE/µM (EGCG ORAC value of 1.54 ± 0.02 µM TE/µM). Compound 12 also showed significant α-glucosidase inhibitory activity with an IC50 of 56.3 µM (acarbose IC50 of 519.4 µM).

Flaxseed polyphenols: Effects of varieties on its composition and antioxidant capacity.

This study identified phenolic compounds in five flaxseed varieties and evaluated their antioxidant activities. Results showed significant variations in phenolic acids and flavonoids among the varieties. Longya 16 had the lowest flavonoid content, Longya 13 had the lowest phenolic acid content, while Longya 10 exhibited the highest content and diversity of polyphenols, including six flavonoids (vitexin, quercitrin, quercetin, apigenin, kaempfero1, (+)-dihydroquercetin) and five phenolic acids (gallic acid, vanillic acid, ferulic acid, sinapic acid, and 4-hydroxybenzoic acid). Antioxidant activity was assessed using DPPH and ABTS radical scavenging assays, and cell-based assays under tBHP-induced oxidative stress. Flaxseed polyphenol extracts significantly reduced ROS, MDA, and GSSG levels and increased SOD and CAT activities, preserving cell vitality and morphology. These findings confirmed the significant antioxidant activity of flaxseed polyphenols, providing a theoretical basis for their application in antioxidative functional areas.

Colorimetric sensor array for identifying antioxidants based on pyrolysis-free synthesis of Fe-N/C single-atom nanozymes.

Iron-anchored nitrogen/doped carbon single-atom nanozymes (Fe-N/C), which possess homogeneous active sites and adjustable catalytic environment, represent an exemplary model for investigating the structure-function relationship and catalytic activity. However, the development of pyrolysis-free synthesis technique for Fe-N/C with adjustable enzyme-mimicking activity still presents a significant challenge. Herein, Fe-N/C anchored three carrier morphologies were created via a pyrolysis-free approach by covalent organic polymers. The peroxidase-like activity of these Fe-N/C nanozymes was regulated via the pores of the anchored carrier, resulting in varying electron transfer efficiency due to disparities in contact efficacy between substrates and catalytic sites within diverse microenvironments. Additionally, a colorimetric sensor array for identifying antioxidants was developed: (1) the Fe-N/C catalytically oxidized two substrates TMB and ABTS, respectively; (2) the development of a colorimetric sensor array utilizing oxTMB and oxABTS as sensing channels enabled accurate discrimination of antioxidants such as ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), gallic acid (GA), and caffeic acid (CA). Subsequently, the sensor array underwent rigorous testing to validate its performance, including assessment of antioxidant mixtures and individual antioxidants at varying concentrations, as well as target antioxidants and interfering substances. In general, the present study offered valuable insights into the active origin and rational design of nanozyme materials, and highlighting their potential applications in food analysis.

Effects of Artificial Aging on Rice Lysophospholipids.

Lysophospholipids (LPLs) represent a major class of polar lipids crucial for rice's nutritional and functional properties. This study investigates the impact of varying storage temperatures (20, 30, and 40 °C) and humidity (50 and 95%) on the nonstarch and starch LPLs of paddy and milled rice. The findings revealed that the average nonstarch LPL content in paddy rice aged at 20 °C (82.6 µg/g) and 40 °C (83.6 µg/g) was significantly lower than that at 30 °C (95.0 µg/g). The nonstarch LPL content of milled rice aged at 20 °C (78.0 µg/g) was significantly higher than that at 30 and 40 °C. High storage temperature (40 °C) and humidity (95%) resulted in a significant reduction in rice total starch LPC and LPE content when compared to low humidity (50%). The ratio of rice starch/nonstarch LPL components such as LPC16:0 and LPC18:2 remarkably increased with increased storage temperature and humidity.

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics.

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.

Asymmetric three-component Tsuji-Trost allylation reaction enabled by chiral aldehyde/palladium combined catalysis.

Despite the long-standing exploration of the catalytic asymmetric Tsuji-Trost allylation reaction since the mid-20th century, most reported instances have adhered to a two-component approach. Here, we present a remarkably efficient three-component asymmetric allylation reaction enabled by the collaborative action of chiral aldehyde and palladium. A diverse array of NH2-unprotected amino acid esters, aryl or alkenyl iodides, and allyl alcohol esters exhibit robust participation in this reaction, resulting in the synthesis of structurally diverse non-proteinogenic α-amino acid esters with favorable experimental outcomes. Mechanistic investigations reveal the dominance of the allylation/Heck coupling cascade in reactions involving electron-rich aryl iodides, while the Heck coupling/allylation cascade emerges as the dominant pathway in reactions involving electron-deficient aryl iodides. This chiral aldehyde/palladium combining catalytic system precisely governs the chemoselectivity of C-allylation and N-allylation, the regioselectivity of linear and branched allylation, and the enantioselectivity of C-allylation products.

Associations of ACE I/D and AGTR1 rs5182 polymorphisms with diabetes and their effects on lipids in an elderly Chinese population.

BACKGROUND: Diabetes mellitus is generally accompanied by dyslipidaemia, but inconsistent relationships between lipid profiles and diabetes are noted. Moreover, genetic variations in insertion/deletion (I/D) polymorphisms at angiotensin-converting enzyme gene (ACE) and T/C polymorphisms in the angiotensin type 1 receptor gene (AGTR1) are related to diabetes and lipid levels, but the associations are controversial. Thus, the current research aimed to explore the effects of ACE I/D, AGTR1 rs5182 and diabetes mellitus on serum lipid profiles in 385 Chinese participants with an average age of 75.01 years. METHODS: The ACE I/D variant was identified using the polymerase chain reaction (PCR) method, whereas the AGTR1 rs5182 polymorphism was identified using the PCR-based restriction fragment length polymorphism (PCR-RFLP) method and verified with DNA sequencing. Total cholesterol (TC), triglyceride (TG), apolipoprotein A (ApoA), apolipoprotein B (ApoB), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels were measured using routine methods, and the lipid ratios were calculated. RESULTS: ACE I/D, but not AGTR1 rs5182, was a predictor of TG/HDL-C for the whole study population. Both ACE I/D and AGTR1 rs5182 were predictors of HDL-C and LDL-C levels in females but not in males. Moreover, in females, diabetes mellitus and ACE I/D were identified as predictors of TG and TG/HDL-C, whereas AGTR1 rs5182 and diabetes mellitus were predictors of TG/HDL-C. Moreover, diabetes mellitus and the combination of ACE I/D and AGTR1 rs5182 variations were predictors of TG and TG/HDL-C exclusively in females. CONCLUSIONS: The results demonstrated the potential for gender-dependent interactions of ACE I/D, AGTR1 rs5182, and diabetes on lipid profiles. These findings may serve as an additional explanation for the inconsistent changes of blood lipids in individuals with diabetes mellitus, thereby offering a novel perspective for the clinical management of blood lipid levels in diabetic patients.

A Scoring System to Predict Severe Acute Lower Respiratory Infection in Children Caused by Respiratory Syncytial Virus.

There were several factors associated with respiratory syncytial virus (RSV) severe acute lower respiratory infection (RSV-sALRI) in infants and young children. It is vital to develop a convenient scoring system to predict RSV-sALRI in children. Pediatric patients with RSV-ALRI from January 2009 to December 2021 were recruited retrospectively. Two-third of them were randomly grouped into the development set and one-third to the validation set. In the development set, risk factors for RSV-sALRI were transferred into the logistic regression analysis, then their receiver operating characteristic (ROC) curves were built to obtain the area under the ROC curve (AUC), and regression coefficients for each predictor were converted to points. Finally, the value of the scoring system was evaluated in the validation set. A total of 1 066 children with RSV-ALRI were recruited, including 710 in the development set and 356 in the validation set. By logistic regression analysis, six factors (younger than 2 years, gestational age <37 weeks, have siblings, birth weight ≤2500 g, artificial/mix feeding, CHD) showed statistical difference and then were scored with points according to the coefficient value (OR) in the development set. In the validation set, the sensitivity of the scoring system was 70.25%, the specificity 85.53%, the positive predictive value 71.43%, the negative predictive value 84.81%, and coincidence rate 0.80. The Kolmogorov-Smirnov test showed the distribution of AUC 0.765 (SE = 0.027; 95% CI = 0.713-0.818; p < 0.001). A simplified scoring system was developed in the study with high prediction value for RSV-sALRI in children.

Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research.

Objective: Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics. Methods: Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction. Results: 34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better. Conclusion: Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.

The p38/MAPK pathway as a therapeutic target to prevent therapeutic escape of breast cancer stem cells.

Cancer stem cells (CSCs) play an important role in metastasis development, tumor recurrence, and treatment resistance, and are essential for the eradication of cancer. Currently, therapies fail to eradicate CSCs due to their therapeutic stress-induced cellular escape, which leads to enhanced aggressive behaviors compared with CSCs that have never been treated. However, the underlying mechanisms regulating the therapeutic escape remain unknown. To this end, we established a model to isolate the therapeutic escaped CSCs (TSCSCs) from breast CSCs and performed the transcription profile to reveal the mechanism. Mechanistically, we demonstrated that the behavior of therapeutic escape was regulated through the p38/MAPK signaling pathway, resulting in TSCSCs exhibiting enhanced motility and metastasis. Notably, blocking the p38/MAPK signaling pathway effectively reduced motility and metastasis ability both in vitro and in vivo, which were further supported by downregulated motility-related genes and epithelial-mesenchymal transition (EMT)-related proteins vimentin and N-cadherin. The obtained findings reveal the p38/MAPK pathway as a potential therapeutic target for TSCSCs and would provide profound implications for cancer therapy.

Acid-resistant chitosan/graphene oxide adsorbent for Cu2+ removal: The role of mixed cross-linking and amino-functionalized.

Copper ions in wastewater pose a significant threat to human and ecological safety. Therefore, preparing macroscopic adsorbents with reusable and high adsorption performance is paramount. This paper used graphene oxide as the adsorbent and chitosan as the thickener. Additionally, a silane coupling agent was employed to enhance the acid resistance of chitosan, and amino-modification of graphene oxide was performed. Macroscopic adsorbents with high adsorption capacity were fabricated using 3D printing technology. The results show that all five proportions of inks exhibit good printability. Dissolution experiments revealed that all materials maintained structural integrity after 180 days across pH values. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Photoelectron Spectroscopy (XPS) confirmed the successful preparation of the materials. Adsorption experiments showed that the best performing material ratio was 8 wt% graphene oxide and 7 wt% chitosan. Adsorption kinetics and isothermal adsorption experiments demonstrated that the adsorption process occurred via monolayer chemisorption. The adsorption process was attributed to strong electrostatic forces, van der Waals forces, and nitrogen/oxygen-containing functional group coordination. Cycling experiments showed that the material retained good adsorption performance after 6 cycles, suggesting its potential for practical heavy metal treatment applications.