Model Selection for Exposure-Mediator Interaction.

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators M and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

Guizhitongluo Tablet inhibits atherosclerosis and foam cell formation through regulating Piezo1/NLRP3 mediated macrophage pyroptosis.

BACKGROUND: Atherosclerosis (AS) is the main pathological basis for the development of cardiovascular diseases. Vascular inflammation is an important factor in the formation of AS, and macrophage pyroptosis plays a key role in AS due to its unique inflammatory response. Guizhitongluo Tablet (GZTLT) has shown clinically effective in treating patients with AS, but its mechanism is elusive. PURPOSE: This study was to determine the effects of GZTLT on atherosclerotic vascular inflammation and pyroptosis and to understand its underlying mechanism. MATERIALS AND METHODS: The active constituents of GZTLT were analysed by means of UPLC-HRMS. In vivo experiments were performed using ApoE-/- mice fed a high fat diet for 8 weeks, followed by treatment with varying concentrations of GZTLT orally by gavage and GsMTx4 (GS) intraperitoneally and followed for another 8 weeks. Oil red O, Haematoxylin-eosin (HE) and Masson staining were employed to examine the lipid content, plaque size, and collagen fibre content of the mouse aorta. Immunofluorescence staining was utilised to identify macrophage infiltration, as well as the expression of Piezo1 and NLRP3 proteins in aortic plaques. The levels of aortic inflammatory factors were determined using RT-PCR and ELISA. In vitro, foam cell formation in bone marrow-derived macrophages (BMDMs) was observed using Oil Red O staining. Intracellular Ca2+ measurements were performed to detect the calcium influx in BMDMs, and the expression of NLRP3 and its related proteins were detected by Western blot. RESULTS: The UPLC-HRMS analysis revealed 31 major components of GZTLT. Our data showed that GZTLT inhibited aortic plaque formation in mice and increased plaque collagen fibre content to stabilise plaques. In addition, GZTLT could restrain the expression of serum lipid levels and suppress macrophage foam cell formation. Further studies found that GZTLT inhibited macrophage infiltration in aortic plaques and suppressed the expression of inflammatory factors. It is noteworthy that GZTLT can restrain Piezo1 expression and reduce Ca2+ influx in BMDMs. Additionally, we found that GZTLT could regulate NLRP3 activation and pyroptosis by inhibiting Piezo1. CONCLUSION: The present study suggests that GZTLT inhibits vascular inflammation and macrophage pyroptosis through the Piezo1/NLRP3 signaling pathway, thereby delaying AS development. Our finding provides a potential target for AS treatment and drug discovery.

A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database.

Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan-Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients' overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery.

Patient care in complex Sociotechnological ecosystems and learning health systems.

The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report "Crossing the Quality Chasm," the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.

Strategic assessment model of smart stadiums based on genetic algorithms and literature visualization analysis: A case study from Chengdu, China.

This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.

Prognostic impact of tumor size in ureteral cancer following segmental ureterectomy.

OBJECTIVE: To evaluate the classification and prognostic effects of a 2 cm tumor size in patients with ureteral cancer (UC) undergoing segmental ureterectomy (SU). PATIENTS AND METHODS: A total of 75 patients with UC who underwent SU in our hospital between April 2013 and April 2023 were included in this study. The study population was grouped based on tumor size, which was defined as the maximum diameter of the pathological specimens, resulting in 30 patients (40.0%) with tumor size <2 cm and 45 patients (60.0%) with tumor size ≥2 cm. The clinicopathological variables, perioperative parameters, and oncological outcomes were compared between the 2 groups. The endpoints were recurrence-free survival (RFS), and cancer-specific survival (CSS). RESULTS: A tumor ≥2 cm was related to a higher positive rate of urine exfoliative cytology (P = 0.049) and fewer preoperative ureteroscopies (P = 0.033) than tumors <2 cm. After a follow-up of 6.3 to 128.7 months (median 40.2 months), 23 cases (30.7%) experienced recurrence and 11 patients (14.7%) succumbed to UC in the end. Compared to those with tumor size <2 cm, patients with tumor size ≥2 cm experienced more urothelial recurrence (P = 0.032). Kaplan-Meier analysis demonstrated that patients with tumor size ≥2 cm displayed inferior urothelial RFS than those with tumor size <2 cm (P = 0.026). Multivariate Cox analysis identified tumor size ≥2 cm, and pathological stage ≥T2 were significant prognostic factors of poor urothelial RFS (all P < 0.05). CONCLUSION: Tumor size ≥2 cm was associated with a high rate of urothelial recurrence and served as an independent prognostic factor of adverse urothelial RFS in SU-treated patients with UC. Patients are advised to select surgical treatments for UC following the EAU guidelines.

Ceratophyllum demersum alleviates microplastics uptake and physiological stress responses in aquatic organisms, an overlooked ability.

It has been demonstrated that microplastics (MPs) may be inadvertently ingested by aquatic animals, causing harm to their physiological functions and potentially entering the food chain, thereby posing risks to human food safety. To achieve an environmentally friendly and efficient reduction of MPs in freshwater environments, this experiment investigates the depuration effect of C. demersum on MPs using three common aquatic animals: Macrobrachium nipponense, Corbicula fluminea, and Bellamya aeruginosa as research subjects. The amounts of MPs, digestive enzyme activity, oxidative stress index, and energy metabolism enzyme activity in the digestive and non-digestive systems of three aquatic animals were measured on exposure days 1, 3, and 7 and on depuration days 1 and 3. The results indicated that the depuration effect of C. demersum and the species interaction were significant for the whole individual. Concerning digestive tissue, C. demersum was the most effective in purifying B. aeruginosa. When subjected to short-term exposure to MPs, C. demersum displayed a superior depuration effect. Among non-digestive tissues, C. demersum exhibited the earliest purifying effect on C. fluminea. Additionally, C. demersum alleviated physiological responses caused by MPs. In conclusion, this study underscores C. demersum as a promising new method for removing MPs from aquatic organisms.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Background: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). Methods: One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. Results: We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. Conclusion: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Hepatocyte-specific loss of DDB1 attenuates hepatic steatosis but aggravates liver inflammation and fibrosis in MASH.

BACKGROUND: MASH is a common clinical disease that can lead to advanced liver conditions, but no approved pharmacotherapies are available due to an incomplete understanding of its pathogenesis. Damaged DNA binding protein 1 (DDB1) participates in lipid metabolism. Nevertheless, the function of DDB1 in MASH is unclear. METHODS: Clinical liver samples were obtained from patients with MASH and control individuals by liver biopsy. Hepatocyte-specific Ddb1-knockout mice and liver Hmgb1 knockdown mice were fed with a methionine-and choline-deficient diet to induce MASH. RESULTS: We found that the expression of DDB1 in the liver was significantly decreased in MASH models. Hepatocyte-specific ablation of DDB1 markedly alleviated methionine-and choline-deficient diet-induced liver steatosis but unexpectedly exacerbated inflammation and fibrosis. Mechanistically, DDB1 deficiency attenuated hepatic steatosis by downregulating the expression of lipid synthesis and uptake genes. We identified high-mobility group box 1 as a key candidate target for DDB1-mediated liver injury. DDB1 deficiency upregulated the expression and extracellular release of high-mobility group box 1, which further increased macrophage infiltration and activated HSCs, ultimately leading to the exacerbation of liver inflammation and fibrosis. CONCLUSIONS: These data demonstrate the independent regulation of hepatic steatosis and injury in MASH. These findings have considerable clinical implications for the development of therapeutic strategies for MASH.

Socioeconomic support, quality of life, and prognosis of frailty among the older adults.

Background: Although socioeconomic support is recommended for frailty management, its association with the prognosis of frailty is unclear. Methods: Using data from participants aged ≥65 years in the Chinese Longitudinal Healthy Longevity Survey (2008-2018), the associations between socioeconomic support (source of income, medical insurance, community support, living status), onset of prefrailty/frailty, and worsening of prefrailty, were analyzed using multinominal logistic regression models. The associations between self-reported low quality of life (QoL) and reversion of prefrailty/frailty were analyzed using multivariate logistic regression models. Associations with mortality risk were analyzed using Cox proportional hazard regression models. Results: A total of 13,859 participants (mean age: 85.8 ± 11.1 years) containing 2056 centenarians were included. Financial dependence was a risk factor for low QoL among prefrail/frail individuals, but not among robust individuals. Having commercial or other insurance, and receiving social support from the community were protective factors for low QoL among prefrail/frail individuals and for the worsening of prefrailty. Continuing to work was a risk factor for low QoL, but a protective factor for worsening of prefrailty. A negative association between continuing to work and mortality existed in prefrail individuals aged <85 years and ≥85 years. Living alone was a risk factor for low QoL, but was not significantly associated with frailty prognosis. Conclusions: Prefrail and frail individuals were vulnerable to changes in socioeconomic support and more sensitive to it compared with robust individuals. Preferential policies regarding financial support, social support, and medical insurance should be developed for individuals with frailty.

Preparation of 4-Amino-3-hydrazino-1,2,4-triazol-5-thiol-Modified Graphene Oxide and Its Greatly Enhanced Selective Adsorption of Gallium in Aqueous Solution.

Efficient recovery of gallium (Ga) from vanadium slag processing residue (VSPR) solution is of great significance for environmental protection and resource utilization, but improving its selective adsorption against the coexisting Sc3+ and In3+ is still challenging. Herein, a novel adsorbent consisting of 4-amino-3-hydrazino-1,2,4-triazol-5-thiol (AHTZT)-modified graphene oxide (GO-AHTZT) was successfully synthesized that exhibits a higher adsorption selectivity for Ga3+ in VSPR solution with coexisting Sc3+ and In3+. Under optimal conditions, the adsorption capacity of GO-AHTZT for Ga3+ can reach 23.92 mg g-1, which is 4.9 and 12.6 times higher than that for Sc3+ (4.87 mg g-1) and In3+ (1.90 mg g-1) adsorption, indicating the excellent anti-interference ability of GO-AHTZT against Sc3+ and In3+. The process and mechanism of Ga3+ adsorption onto GO-AHTZT was also studied and discussed in detail. By measuring the adsorption process and by characterizing the adsorbent before and after adsorption, we demonstrate that the selective interaction between the Ga3+- and N-containing groups in AHTZT is the main reason for the improved adsorption selectivity. This work opens up an avenue for the design and synthesis of highly selective adsorbents for Ga3+ in complex VSPR solutions.

Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Association between household size and risk of incident dementia in the UK Biobank study.

Currently, the relationship between household size and incident dementia, along with the underlying neurobiological mechanisms, remains unclear. This prospective cohort study was based on UK Biobank participants aged ≥ 50 years without a history of dementia. The linear and non-linear longitudinal association was assessed using Cox proportional hazards regression and restricted cubic spline models. Additionally, the potential mechanisms driven by brain structures were investigated by linear regression models. We included 275,629 participants (mean age at baseline 60.45 years [SD 5.39]). Over a mean follow-up of 9.5 years, 6031 individuals developed all-cause dementia. Multivariable analyses revealed that smaller household size was associated with an increased risk of all-cause dementia (HR, 1.06; 95% CI 1.02-1.09), vascular dementia (HR, 1.08; 95% CI 1.01-1.15), and non-Alzheimer's disease non-vascular dementia (HR, 1.09; 95% CI 1.03-1.14). No significant association was observed for Alzheimer's disease. Restricted cubic splines demonstrated a reversed J-shaped relationship between household size and all-cause and cause-specific dementia. Additionally, substantial associations existed between household size and brain structures. Our findings suggest that small household size is a risk factor for dementia. Additionally, brain structural differences related to household size support these associations. Household size may thus be a potential modifiable risk factor for dementia.

General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory.

Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.

Enhanced Light Absorption and Photo-Generated Charge Separation Efficiency for Boosting Photocatalytic H2 Evolution through TiO2 Quantum Dots with N-Doping and Concomitant Oxygen Vacancy.

Low-range light absorption and rapid recombination of photo-generated charge carriers have prevented the occurrence of effective and applicable photocatalysis for decades. Quantum dots (QDs) offer a solution due to their size-controlled photon properties and charge separation capabilities. Herein, well-dispersed interstitial nitrogen-doped TiO2 QDs with stable oxygen vacancies (N-TiO2-x-VO) are fabricated by using a low-temperature, annealing-assisted hydrothermal method. Remarkably, electrostatic repulsion prevented aggregation arising from negative charges accumulated in situ on the surface of N-TiO2-x-VO, enabling complete solar spectrum utilization (200-800 nm) with a 2.5 eV bandgap. Enhanced UV-vis photocatalytic H2 evolution rate (HER) reached 2757 µmol g-1 h-1, 41.6 times higher than commercial TiO2 (66 µmol g-1 h-1). Strikingly, under visible light, HER rate was 189 µmol g-1 h-1. Experimental and simulated studies of mechanisms reveal that VO can serve as an electron reservoir of photo-generated charge carriers on N-doped active sites, and consequently, enhance the separation rate of exciton pairs. Moreover, the negative free energy (-0.35 V) indicates more favorable thermodynamics for HER as compared with bulk TiO2 (0.66 V). This research work paves a new way of developing efficient photocatalytic strategies of HER that are applicable in the sustainable carbon-zero energy supply.

Astragaloside IV inhibits angiotensin II-induced atrial fibrosis and atrial fibrillation by SIRT1/PGC-1α/FNDC5 pathway.

Aims and objectives: Astragaloside IV (AS-IV) has been found to possess anti-oxidative, anti-inflammatory, and anti-apoptotic properties, but its effect on atrial fibrosis is yet to be determined. This research investigates the protective role of AS-IV in angiotensin II (Ang II)-induced atrial fibrosis and atrial fibrillation (AF). Methods: C57BL/6 male mice aged 8-10 weeks (n = 40) were subcutaneously administered Ang II (2.0 mg/kg/day) or saline, with AS-IV (80 mg/kg) intraperitoneally administered 2 h before Ang II infusion for 4 weeks. Biochemical, histological, and morphological analyses were carried out. Using transesophageal burst pacing, AF was generated in vivo. Results: Here, we report that AS-IV treatment inhibited Ang II-induced AF development in mice (58 ± 5.86 vs 15.13 ± 2.16 %, p < 0.001). Ang II + AS-IV therapy was effective in reducing the atrial fibrotic area and decreasing the increase in smooth muscle alpha-actin (α-SMA)-positive myofibroblasts brought on by Ang II treatment (fibrotic area: 26.25 ± 3.81 vs 8.62 ± 1.83 %, p < 0.001 and α-SMA: 65.62 ± 10.63 vs 17.25 ± 1.78 %, p < 0.001). The reactive oxygen species (ROS) production was reduced by pretreatment with Ang II + AS-IV (9.20 ± 0.92 vs 2.63 ± 0.22 %/sec, p < 0.001). In addition, Ang II + AS-IV treatment suppressed oxidative stress in Ang II-induced atrial fibrosis (malondialdehyde: 701.78 ± 85.01 vs 504.07 ± 25.62 pmol/mg protein, p < 0.001; superoxide dismutase: 13.82 ± 1.25 vs 29.54 ± 2.45 U/mg protein, p < 0.001 and catalase: 11.43 ± 1.19 vs 20.83 ± 3.29 U/mg protein, p < 0.001, respectively). Moreover, Ang II + AS-IV decreased the expression of α-SMA, collagen III and collagen I (3.32 ± 0.53 vs 1.41 ± 0.20 fold, p < 0.001; 3.41 ± 0.55 vs 1.48 ± 0.18 fold, p < 0.001; 2.34 ± 0.55 vs 0.99 ± 0.17 fold, p < 0.001, respectively) while increasing the protein expression of sirtuin 1 (SIRT1), peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α), and fibronectin type III domain-containing protein 5 (FNDC5) in Ang II-treated mice (0.22 ± 0.02 vs 0.57 ± 0.08 fold, p < 0.001; 0.28 ± 0.04 vs 0.72 ± 0.05 fold, p < 0.001; 0.38 ± 0.03 vs 0.68 ± 0.06 fold, p < 0.001, respectively). Conclusion: Our data led us to speculate that AS-IV may protect against Ang II-induced atrial fibrosis and AF via upregulation of the SIRT1/PGC-1α/FNDC5 pathway.

StMAPK10 gene functional identification and analysis in drought resistance of potato crop (Solanum tuberosum L.).

All over the world, potato (Solanum tuberosum L.) production is constrained by several biotic and abiotic factors. Many techniques and mechanisms have been used to overcome these hurdles and increase food for the rising population. In crop plants, the mitogen-activated protein kinase (MAPK) cascade, a significant regulator of the MAPK pathway under various biotic and abiotic stress conditions, is one of the targets to increase productivity. MAPK plays a significant role under drought stress in potato. However, the function of MAPK in drought resistance in potato is poorly understood. In this study, we wanted to identify the function of StMAPK10 in the drought resistance in potato. StMAPK10 was up-regulated under drought conditions and dynamically modulated by abiotic stresses. Over-expression and down-regulation of StMAPK10 revealed that StMAPK10 stimulated potato growth under drought conditions, as demonstrated by changes in SOD, CAT, and POD activity, as well as H2O2, proline, and MDA content. StMAPK10 up-regulation exaggerated the drought resistance of the potato plant by uplifting antioxidant activities and photosynthetic indices. Overexpressed-StMAPK10 potato lines showed highly significant results for physiological and photosynthetic indices in response to drought stress, while knockdown expression showed opposite outcomes. Additionally, subcellular localization and phenotypic analysis of transgenic and non-transgenic plants substantiated the role of the increased expression of StMAPK10 against drought stress. The results could provide novel insights into the functionality of StMAPK10 in drought responses and conceivable mechanisms.

GhMYB52 Like: A Key Factor That Enhances Lint Yield by Negatively Regulating the Lignin Biosynthesis Pathway in Fibers of Upland Cotton (Gossypium hirsutum L.).

In the context of sustainable agriculture and biomaterial development, understanding and enhancing plant secondary cell wall formation are crucial for improving crop fiber quality and biomass conversion efficiency. This is especially critical for economically important crops like upland cotton (Gossypium hirsutum L.), for which fiber quality and its processing properties are essential. Through comprehensive genome-wide screening and analysis of expression patterns, we identified a particularly high expression of an R2R3 MYB transcription factor, GhMYB52 Like, in the development of the secondary cell wall in cotton fiber cells. Utilizing gene-editing technology to generate a loss-of-function mutant to clarify the role of GhMYB52 Like, we revealed that GhMYB52 Like does not directly contribute to cellulose synthesis in cotton fibers but instead represses a subset of lignin biosynthesis genes, establishing it as a lignin biosynthesis inhibitor. Concurrently, a substantial decrease in the lint index, a critical measure of cotton yield, was noted in parallel with an elevation in lignin levels. This study not only deepens our understanding of the molecular mechanisms underlying cotton fiber development but also offers new perspectives for the molecular improvement of other economically important crops and the enhancement of biomass energy utilization.