Optimal Time Research Articles

Graphene Oxide Decorated with Nickel Cobaltite Nanoparticles as an Adsorbent for Cationic Methyl Green Dye: Kinetic, Isotherm, and Thermodynamic Studies

‎تم في هذه الدراسة ، تزيين ‏رقائق أكسيد الجرافين (‏GO‏) بجسيمات ‏كوبلتيت النيكل النانوية ‏NiCo2O4‎‏(‏NC‏) عن طريق الترسيب في ‏الموقع ، وتم استخدام المتراكب ‏المحضر (‏NC: GO‏) كسطح ماز لإزالة ‏صبغة الميثيل الخضراء ( ‏MG‏) من ‏المحاليل المائية. تم التحقق من ‏التغطية الناجحة لأوكسيد الجرافين ‏بجزيئات كوبلتيت النيكل النانوية ‏‏(‏NC‏) باستخدام دراسات ‏FT-IR‏ وحيود ‏الأشعة السينية (‏XRD‏). كانت أحجام ‏الجسيمات البلورية لل ‏NC‏ و ‏ لل ‏GO‏ المزين ب NCهي ‏‎10.53‎‏ نانوميتر ‏‏9.30 نانومتر على التوالي. تم دراسة ‏تأثير العديد من العوامل التجريبية ‏، بما في ذلك زمن التلامس ، وكمية ‏السطح الماز ، ودرجة الحرارة. كان ‏وقت التلامس الأمثل وكمية السطح 120 ‏دقيقة و 3 مجم / لتر على التوالي. ‏تتلائم بيانات الامتزاز بشكل أفضل مع ‏ايزوثيرم ‏Freundlich‏. تم استخدام ‏أربعة نماذج حركية لتتبع عملية ‏الامتزاز: معادلة زائفة من الدرجة ‏الأولى ، ومعادلة زائفة من الدرجة ‏الثانية ، ومعادلة الانتشار داخل ‏الجسيمات ، ومعادلة بويد. أظهرت ‏نمذجة البيانات التجريبية أن حركية ‏الامتزاز كانت ممثلة بشكل جيد ‏بنموذج الرتبة الثانية الزائفة (‏R2 ‎‎= 0.9945‎‏) مع معدل ثابت سرعة يبلغ 3.2 ‏‏× 10 - 3 (جم / مجم. دقيقة). يتم ‏امتصاص صبغة ‏MG‏ تدريجيًا بواسطة ‏الجسيمات النانوية ‏NC‏ من خلال ‏الانتشار داخل الجسيمات ويتم ‏الاحتفاظ بها بعد ذلك في مسام أصغر. ‏أظهرت قيم التحليل الديناميكي ‏الحراري أن عملية امتزاز صبغة ‏MG‏ ‏كانت ماصة للحرارة بطبيعتها ، و ‏تلقائية وعملية الامتزاز فيزيائية. الكلمات المفتاحية: الانتشار داخل الجسيمات ، المسافات البينية، النموذج الزائف من الدرجة الأولى ، معادلة بويد

Preventing evolutionary rescue in cancer.

First-line cancer treatment frequently fails due to initially rare therapeutic resistance. An important clinical question is then how to schedule subsequent treatments to maximize the probability of tumour eradication. Here, we provide a theoretical solution to this problem by using mathematical analysis and extensive stochastic simulations within the framework of evolutionary rescue theory to determine how best to exploit the vulnerability of small tumours to stochastic extinction. Whereas standard clinical practice is to wait for evidence of relapse, we confirm a recent hypothesis that the optimal time to switch to a second treatment is when the tumour is close to its minimum size before relapse, when it is likely undetectable. This optimum can lie slightly before or slightly after the nadir, depending on tumour parameters. Given that this exact time point may be difficult to determine in practice, we study windows of high extinction probability that lie around the optimal switching point, showing that switching after the relapse has begun is typically better than switching too early. We further reveal how treatment dose and tumour demographic and evolutionary parameters influence the predicted clinical outcome, and we determine how best to schedule drugs of unequal efficacy. Our work establishes a foundation for further experimental and clinical investigation of this evolutionarily-informed "extinction therapy" strategy.

Does US financial uncertainty spill over through the (asymmetric) international credit channel? The role of market expectations

We show that the international credit channel is an important channel through which US financial uncertainty spills over internationally. Moreover, we find the asymmetric responses of domestic and international credit conditions to US financial uncertainty shocks, contingent upon market participants' expectations. During periods of pessimism regarding future economic conditions, the impact of US financial uncertainty on both domestic and international credit conditions intensifies significantly, leading to a severe global economic slowdown. Elevated US financial uncertainty disproportionately affects the left tails of the distribution more than the right tails, heightening the likelihood of negative global output growth. Conversely, in times of optimism, heightened US financial uncertainty exerts modest effects on international credit conditions and global economic conditions. Yet it stretches the conditional distribution substantially, signaling an increasingly uncertain future.

Lower Extremity Traumatic Wound Management: Relative Significance of Negative Pressure Wound Therapy in the Orthopedic Setting.

Significance: Lower extremity traumatic wounds are associated with numerous perioperative challenges. Their etiologies determine the characteristics and extent of the injury. The timing of subsequent surgical intervention and wound healing optimization after lower extremity trauma are integral to successful perioperative lower extremity wound management. Recent Advances: Managing trauma to the lower extremities uses a multidisciplinary surgical approach. The objective of this review is to summarize lower limb trauma assessment, advancements in lower extremity trauma management, and the clinical applications of advanced wound care in lower limb traumatic wounds. The advent of lower limb reconstruction and the development of advanced wound care modalities have helped to improve the management of these complex injuries. Critical Issues: The extensive involvement of bone, soft tissues, nerves, and blood vessels of severe lower extremity trauma wounds presents a challenge for clinicians in both the acute care setting and during patient rehabilitation. If not properly managed, these injuries may be subject to a decline in limb function and may possibly result in limb loss. To reveal developing limb-threatening conditions, serial examinations should be performed. Future Directions: The majority of lower limb traumatic wound will benefit from the perioperative administration of an appropriate negative pressure wound therapy (NPWT)-based system, which can help to promote granulation tissue and remove wound exudate before definitive closure and/or reconstruction. NPWT should be included as an important adjunct in the surgical management of lower limb traumatic wounds.

Robust Switching Time Optimization for Networked Switched Systems via Model Predictive Control.

This article presents a model predictive control (MPC) strategy to find the optimal switching time sequences of networked switched systems with uncertainties. First, based on predicted trajectories under exact discretization, a large-scale MPC problem is formulated; second, a two-level hierarchical optimization structure coupled with a local compensation mechanism is established to solve the formulated MPC problem, where the proposed hierarchical optimization structure is actually a recurrent neural network consisting of a coordination unit (CU) at the upper level and a series of local optimization units (LOUs) related to each subsystem at the lower level. Finally, a real-time switching time optimization algorithm is designed to calculate the optimal switching time sequences.

Optimal Time and Power Allocation for Phase-Shift Configuration and Downlink Channel Estimation in RIS-Aided Systems

Optimal Time and Power Allocation for Phase-Shift Configuration and Downlink Channel Estimation in RIS-Aided Systems

Optimal timing of postoperative radiotherapy initiation in maxillary sinus cancer patients

Optimal timing of postoperative radiotherapy initiation in maxillary sinus cancer patients

Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.

Comprehensive Analysis of Perioperative Factors Influencing the Risk of Biochemical Recurrence in Patients with Radical Prostatectomy.

To analyze the perioperative factors that influence the risk of biochemical recurrence (BCR) in patients with localized PCa undergoing radical prostatectomy Materials and Methods: A total of 457 patients, operated by 2 surgeons in our high-volume oncological center were included in the initial database. Patients who underwent RP for clinically localized PCa in our clinic from 2016 to 2021 were included in the study. Perioperative data were retrospectively reviewed for this study. Follow-up data including post-operative PSA and adjuvant treatment was prospectively gathered by contacting the patients or from the follow-up consultation. Final database was composed of 366 patients who underwent open or 3D laparoscopic RP. Statistical analysis was performed to emphasize the most powerful parameters that influence the BCR. Results: Accounting for multivariable analysis, 4 parameters were statistically significant: initial PSA (iPSA), Gleason score, vascular involvement and positive surgical margins. For the group of patients with no positive margins, 3 parameters were statistically significant: iPSA above 10,98 ng/mL (AUC=0,71); lymph node involvement and Gleason score. Multivariable Cox regression showed that positive margins and iPSA had a significant impact on the time to BCR. Patients that received adjuvant therapy were excluded from the study. Out of the whole cohort, 27,3% of patients presented BCR. Perioperative factors need to be carefully analyzed and a detailed follow-up needs to be conducted in order to assess the risk of biochemical recurrence, resulting in the optimal time for adjuvant treatment implementation.

Postsurgical utility of copeptin for the prediction of postoperative arginine vasopressin deficiency.

Arginine vasopressin deficiency (AVD) following neurosurgical procedures for pituitary disorders is common and can delay discharge. Copeptin, a stable surrogate marker of arginine vasopressin, may predict postoperative AVD. The authors' aim was to assess the optimal postoperative sampling time and cut-point concentration of copeptin to predict the development of postsurgical AVD. Adults without preexisting AVD who were undergoing surgery for a pituitary lesion between February 2020 and April 2022 were eligible for study inclusion. Two samples were drawn from each patient postoperatively to assess the copeptin concentration using an immunofluorescent assay. Samples were denoted as "early" (within 6 hours of extubation) or "postoperative day 1" (POD1; within 10-30 hours of extubation). Patients were evaluated for the development of AVD. One hundred ninety-two patients (54.2% female) with a median age of 54.5 years (IQR 39.8-67.0 years) were included in the study. The median copeptin concentration at both time points was significantly lower in those with AVD (transient or permanent; n = 22, 11.5%) than in those without (early: 4.9 vs 18.7 pmol/L, p < 0.001; POD1: 3.4 vs 4.9 pmol/L, p < 0.001) but did not differ in those who developed transient versus permanent AVD. The optimal copeptin cut point for the prediction of AVD was < 8.5 pmol/L for early samples (sensitivity 0.70, specificity 0.80, positive predictive value [PPV] 0.29, negative predictive value [NPV] 0.96) and < 4.3 pmol/L for POD1 samples (sensitivity 0.82, specificity 0.63, PPV 0.22, NPV 0.96). In early samples, a copeptin cutoff of 22.9 pmol/L increased the sensitivity for the detection of AVD to 95% with an NPV of 99%. The proportion of patients who had AVD was higher (60.0% vs 8.8%, p < 0.001) and the copeptin concentration lower (early: 4.3 vs 17.0 pmol/L, p < 0.001; POD1: 2.7 vs 4.9 pmol/L, p < 0.001) among those who had undergone surgery for a craniopharyngeal duct pathology versus a pituitary adenoma. Although copeptin was lower in patients with persistent Cushing's disease than in those in remission, the difference did not reach statistical significance (early p = 0.11, POD1 p = 0.52). Furthermore, the copeptin concentration could not predict the development of syndrome of inappropriate secretion of antidiuretic hormone. Patients without AVD who had received stress dose steroids intraoperatively had lower median early copeptin (11.7 vs 19.1 pmol/L, p = 0.027). In early postoperative copeptin samples, the optimal copeptin cut point for AVD diagnosis was < 8.5 pmol/L, and a level > 22.9 pmol/L had predicative utility in excluding AVD. Caution should be used when interpreting copeptin results, as patients administered glucocorticoids intraoperatively without AVD had lower median copeptin concentrations.

Splenic gene expression of cytokines at multiple time points following lipopolysaccharide challenge in layers.

To investigate inflammatory responses to lipopolysaccharide (LPS) injection in layers. 33 40-week-old laying hens were used. 30 laying hens were divided into 2 groups: the first group was injected with 8 mg/kg LPS, while the second group was injected with sterile saline. At the start of the study, 3 birds served as a baseline and were used as the time 0 controls for both the saline and LPS-treated groups. Blood and spleen tissues were collected at 0 (before) and 1, 2, 3, 4, and 6 hours after injection. LPS administration increased splenic mRNA levels of IL-1β, IL-2, IL-6, IL-8, IL-10, interferon-γ, and tumor necrosis factor-α (P < .001) and serum IL-6 levels (P < .01) compared to saline injection. The mRNA expression of most cytokine genes increased rapidly toward peak values within 2 hours after the LPS injection, and then the difference between the saline and LPS treatments got smaller as time went on; serum IL-6 reached its highest concentration 2 hours after LPS administration. The magnitude of LPS-induced upregulation of gene expression was the highest for IL-6, followed by IL-1β and IL-8, and tumor necrosis factor-α was the least affected. The temporal and quantitative profile of these inflammatory mediators generated from this study provides valuable information in identifying the optimal time window and appropriate biomarkers for LPS-induced inflammation, which has significant implications in evaluating the effects of interventions on the immune system of chickens.

Coupling model predictive control and rules-based control for real-time control of urban river systems

Model Predictive Control (MPC) has the potential to enhance flood control in urban river systems. However, the computational burden of online optimization hinders the application of MPC to large-scale areas. To address this, we propose a method incorporating both Rules-Based Control (RBC) and MPC based on surrogate models. MPC optimizes the operation of gates and pumping stations in the area of interest, while RBC is implemented in a specific region that does not participate in online optimization, thereby reducing calculation time. We have successfully validated this approach in coastal urban river systems in China, reducing the decision variables from 109 to 24 and narrowing the simulation area from 165 km2 to 15 km2. During the Typhoon Lupit rainfall test, the total computation time and the highest online optimization time were only 33.9 % and 38.1 % of the full MPC system, respectively. Additionally, the method demonstrated impressive results, including a significant 94.62 % reduction in flood volume and a remarkable 96.5 % decrease in flood duration. These findings demonstrate that our method effectively addresses the challenge of long computation times for online optimization in urban river systems and presents a novel approach for implementing urban flood mitigation strategies.

DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework.

The global prevalence of childhood and adolescent obesity is a major concern due to its association with chronic diseases and long-term health risks. Artificial intelligence technology has been identified as a potential solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study highlights the importance of early identification and prevention of obesity-related health issues. To develop effective algorithms for the prediction of obesity rates and provide personalized feedback, factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information must be taken into account. Therefore, by collecting health datasets from 321 adolescents who participated in Would You Do It! application, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. Statistical analysis revealed that the performance of the proposed deep learning framework was more statistically significant (p 0.001) compared to the other general models. The proposed system has the potential to effectively address childhood and adolescent obesity.

355 Risk Factors for Intrathecal Baclofen Pump Implantation Infections: A Retrospective Database Analysis

INTRODUCTION: Baclofen, a GABAB receptor agonist, can be delivered via intrathecal baclofen (ITB) pumps for patients with intractable spasticity. Prior literature has shown infection rates ranging between 10-30%. METHODS: A multi-institutional de-identified database TriNetX was queried to identify patients with: 1) implantation of ITB pump + infection within 6 months or 2) implantation of ITB pump without infection within 6 months. Two populations were analyzed: only pediatric patients, then patients of all ages. Characteristics were compared using log-rank tests and independent-sample t-tests. RESULTS: 1,880 patients were identified in the pediatric cohort, of which 236 (12.6%) developed infection. 13,936 patients of all ages were identified, of which 1955 (14.0%) developed infection. In pediatric patients who developed infection, there were higher rates of diagnoses of overweight and obesity, malnutrition, and hypokalemia; the prevalence of diabetes was also higher, but this analysis was limited by small sample size. In patients of all ages, all medical diagnoses assessed were significantly more prevalent in patients with infection. Erythrocyte sedimentation rates and platelets were significantly higher in all patients with an infection. Additionally, the number of emergency department visits within 6 months was significantly higher in the infection group for pediatric (t = 3.571, p = 0.0004) and all-age (t = 7.882, p &lt; 0.0001) patients. CONCLUSIONS: This study suggests that patients with certain comorbidities are at increased risk for infection post-ITB implantation. Since prevalence of diabetes, abnormal coagulation profile, tracheostomy, and B-vitamin deficiency remained significantly different between infection vs. no-infection cohorts as sample size increased with the addition of adults, these diagnoses should be considered when calculating the infection risk of ITB pump. Studies are underway to investigate the time of highest infection risk post-ITB implantation and the optimal time for ITB reimplantation after removal for infection.

A real options approach to renewable energy module end-of-life decisions under multiple uncertainties: Application to PV and wind in China

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias.

Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm

Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications.

Ultrasound-Assisted Synthesis of High-Entropy Materials for Enhanced Oxygen Evolution Electrocatalysis

High-entropy materials (HEMs) play a significant role in the electrocatalytic oxygen evolution reaction (OER) due to their unique properties. However, there are still challenges in the preparation of HEMs for OER catalysts. In this study, the FeCoNiMnCr catalyst is synthesized for the first time using the ultrasonic hydrothermal-sintering technique and exhibits excellent performance for OER electrocatalysis. There is an optimal ultrasonic hydrothermal time and power for achieving the best OER performance. The results demonstrate that the performance of FeCoNiMnCr catalysts prepared through ultrasonic hydrothermal sintering (US-FeCoNiMnCr) is significantly improved compared with the traditional hydrothermal-sintering method. The US-FeCoNiMnCr catalyst exhibits an overpotential of 228 mV at the current density of 10 mA cm−2 and a Tafel slope as low as 45.39 mV dec−1 in an alkaline medium. Moreover, the US-FeCoNiMnCr catalyst demonstrates remarkable stability in electrocatalytic OER with a minimal potential increase observed even after 48 h. This work not only provides valuable insights into high-entropy material synthesis, but also presents a powerful electrocatalyst for water electrolysis.

Impact of Tracheostomy Timing Within the National Veterans Affairs Population.

There is a lack of a definitive study in the literature comparing early versus late tracheostomy and exploring the impact of tracheostomy timing on patient outcomes. This study may help guide treatment paradigms and contribute to a consensus for optimal tracheostomy timing. A retrospective review was performed comparing early versus late timing of tracheostomy placement and their respective outcomes. The authors used data provided by VA Informatics and Computing Infrastructure (VINCI) to find patients who received a tracheostomy at any VA Medical Center in the United States. There were a total of 25,334 tracheostomies in the database which satisfied our criteria. These occurred between the years 1999 and 2022. Propensity score matching assessed 17,074 tracheostomies, 8537 in either group. The median age of patients in the matched groups was 66 years, and approximately 97.4% of patients were male. Early tracheostomy timing was defined as the placement of the tracheostomy within 10 days of intubation. Outcomes included post-tracheostomy intensive care unit (ICU) days, post-tracheostomy hospital days, successful ventilator weaning, and all-cause mortality. Early tracheostomy was associated with significantly fewer ICU days and hospital days, and the early group experienced higher rates of successful ventilator weaning. Survival analysis of data within 5 years of tracheostomy showed that early tracheostomy was associated with significantly lower hazard for all-cause mortality. Our results add to the body of evidence that an earlier transition to mechanical ventilation by tracheostomy confers benefits in patient morbidity and mortality as well as resource utilization. 3 Laryngoscope, 134:3555-3561, 2024.

Isoquercitrin alleviates pirarubicin-induced cardiotoxicity in vivo and in vitro by inhibiting apoptosis through Phlpp1/AKT/Bcl-2 signaling pathway.

Introduction: Due to the cardiotoxicity of pirarubicin (THP), it is necessary to investigate new compounds for the treatment of THP-induced cardiotoxicity. Isoquercitrin (IQC) is a natural flavonoid with anti-oxidant and anti-apoptosis properties. Thus, the present study aimed to investigate the influence of IQC on preventing the THP-induced cardiotoxicity in vivo and in vitro. Methods: The optimal concentration and time required for IQC to prevent THP-induced cardiomyocyte damage were determined by an MTT assay. The protective effect was further verified in H9c2 and HCM cells using dichlorodihydrofluorescein diacetate fluorescent probes, MitoTracker Red probe, enzyme-linked immunosorbent assay, JC-1 probe, and real time-quantitative polymerase chain reaction (RT-qPCR). Rats were administered THP to establish cardiotoxicity. An electrocardiogram (ECG) was performed, and cardiac hemodynamics, myocardial enzymes, oxidative stress indicators, and hematoxylin-eosin staining were studied. Voltage-dependent anion channel 1 (VDAC1), adenine nucleotide translocase 1 (ANT1), and cyclophilin D (CYPD) were detected by qRT-PCR, and the Phlpp1/AKT/Bcl-2 axis proteins were detected by western blot, confirming that IQC markedly increased cell viability and superoxide dismutase (SOD) levels, diminished the levels of ROS and MDA, and elevated mitochondrial function and apoptosis in vivo and in vitro. Results: Results showed that IQC reduced THP-induced myocardial histopathological injury, electrocardiogram (ECG) abnormalities, and cardiac dysfunction in vivo. IQC also decreased serum levels of MDA, BNP, CK-MB, c-TnT, and LDH, while increasing levels of SOD and GSH. We also found that IQC significantly reduced VDAC1, ANT1, and CYPD mRNA expression. In addition, IQC controlled apoptosis by modulating Phlpp1/AKT/Bcl-2 signaling pathways. IQC markedly increased H9c2 and HCM cell viability and SOD levels, diminished the levels of ROS and MDA, and elevated mitochondrial function in H9c2 and HCM cells to defend against THP-induced cardiomyocyte apoptosis in vitro. The AKT inhibitor IMQ demonstrated that IQC lacked antioxidant and anti-apoptotic properties. Moreover, our data showed that IQC regulates Phlpp1 expression, thereby influencing the expression levels of p-AKT, cytochrome c, caspase-3, caspase-9, Bcl-2, and Bax. Discussion: In conclusion, our results indicate that IQC protects the changes in mitochondrial membrane permeability in cardiomyocytes by regulating the Phlpp1/AKT/Bcl-2 signaling pathway, inhibits the release of cytc from the mitochondrial inner membrane to the cytoplasm, forms apoptotic bodies, induces cell apoptosis, and reduces THP induced cardiotoxicity.

A simple graphene oxide-based DNA purification strategy for plant pathogen detection.

The on-site molecular detection of plant pathogens is particularly important for the development of sustainable agriculture. Extracting DNA from plant tissues, microbes or coexisting environments is complex, labor-intensive and time-consuming. To facilitate this process, we propose a DNA purification strategy based on graphene oxide (GO). The excellent adsorption ability of GO was verified by visualizing changes in its microscopic surface and macroscopic mixture. To further optimize the DNA purification, we determined the optimal GO concentration and treatment time at 95 °C (2 mg mL-1 and 2 min, respectively). We confirmed that our strategy is effective on plant tissues and various microorganisms, and that the obtained DNA can be directly used for polymerase chain reaction amplification. Combining the proposed GO-based DNA purification method with the loop-mediated isothermal amplification method is superior, in terms of the required steps, time, cost and detection effect, to the cetyltrimethylammonium bromide method and a commercial kit for detecting plant pathogens. We present a feasible, rapid, simple and low-cost DNA purification method with high practical value for scientific applications in plant pathogen detection. This strategy can also provide important technical support for future research on plant-microbial microenvironments. © 2024 Society of Chemical Industry.