The mechanisms of pH regulation on promoting volatile fatty acids production from kitchen waste.

The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment. The results showed that pH 8 was considered to be the most suitable condition for acid production, especially for the formation of acetic acid and propionic acid. The average value of total volatile fatty acid at pH 8 was 8814 mg COD/L, 1.5 times of that under blank condition. The average yield of acetic acid and propionic acid was 3302 mg COD/L and 2891 mg COD/L, respectively. The activities of key functional enzymes such as phosphotransacetylase, acetokinase, oxaloacetate transcarboxylase and succinyl-coA transferase were all enhanced. To further explore the regulatory mechanisms within the system, the distribution of microorganisms at different levels in the fermentation system was obtained by microbial sequencing, results indicating that the relative abundances of Clostridiales, Bacteroidales, Chloroflexi, Clostridium, Bacteroidetes and Propionibacteriales, which were great contributors for the hydrolysis and acidification, increased rapidly at pH 8 compared with the blank group. Besides, the proportion of genes encoding key enzymes was generally increased, which further verified the mechanism of hydrolytic acidification and acetic acid production of organic matter under pH regulation.

Large-scale 2D heterostructures from hydrogen-bonded organic frameworks and graphene with distinct Dirac and flat bands.

The current strategies for building 2D organic-inorganic heterojunctions involve mostly wet-chemistry processes or exfoliation and transfer, leading to interface contaminations, poor crystallizing, or limited size. Here we show a bottom-up procedure to fabricate 2D large-scale heterostructure with clean interface and highly-crystalline sheets. As a prototypical example, a well-ordered hydrogen-bonded organic framework is self-assembled on the highly-oriented-pyrolytic-graphite substrate. The organic framework adopts a honeycomb lattice with faulted/unfaulted halves in a unit cell, resemble to molecular "graphene". Interestingly, the topmost layer of substrate is self-lifted by organic framework via strong interlayer coupling, to form effectively a floating organic framework/graphene heterostructure. The individual layer of heterostructure inherits its intrinsic property, exhibiting distinct Dirac bands of graphene and narrow bands of organic framework. Our results demonstrate a promising approach to fabricate 2D organic-inorganic heterostructure with large-scale uniformity and highly-crystalline via the self-lifting effect, which is generally applicable to most of van der Waals materials.

A novel NIR fluorescent probe for visualizing hydrogen sulfide in Alzheimer's disease.

Alzheimer's disease (AD) represents a devastating form of neurodegeneration, hallmarked by a relentless erosion of memory and cognitive faculties. One key player in this complex pathology is hydrogen sulfide (H2S), a gaseous neurotransmitter that is highly concentrated in the brain. Its fluctuating levels have been compellingly linked to the onset and progression of AD. Despite the availability of numerous fluorescent probes for detecting H2S, targeted imaging of this neurotransmitter within AD models remains underexplored. To bridge this gap, we have engineered an innovative near-infrared (NIR) "turn-on" fluorescent probe, designated as probe 1. Crafted around a dicyanoisophorone scaffold, the probe incorporates a strategic methoxy modification to facilitate a bathochromic spectral shift. Impressively, upon binding with H2S, probe 1 exhibited a robust 46-fold enhancement in fluorescence at a wavelength of 680 nm. We successfully deployed this probe to visualize both exogenous and endogenous H2S in living cells and zebrafish. Further, our pathogenic investigations have corroborated that diminished H2S levels are intricately linked to an escalation in amyloid plaque formation. Most crucially, we employed probe 1 to capture real-time images of H2S concentrations within the hippocampal tissue of AD mouse models. This revealed a significant depletion in H2S levels, thereby underscoring the probe's immense potential as an effective tool for the diagnosis and prevention of Alzheimer's disease.

Clinical adverse events to dexmedetomidine: a real-world drug safety study based on the FAERS database.

Objective: Adverse events associated with dexmedetomidine were analyzed using data from the FDA's FAERS database, spanning from 2004 to the third quarter of 2023. This analysis serves as a foundation for monitoring dexmedetomidine's safety in clinical applications. Methods: Data on adverse events associated with dexmedetomidine were standardized and analyzed to identify clinical adverse events closely linked to its use. This analysis employed various signal quantification analysis algorithms, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS). Results: In the FAERS database, dexmedetomidine was identified as the primary suspect in 1,910 adverse events. Our analysis encompassed 26 organ system levels, from which we selected 346 relevant Preferred Terms (PTs) for further examination. Notably, adverse drug reactions such as diabetes insipidus, abnormal transcranial electrical motor evoked potential monitoring, acute motor axonal neuropathy, and trigeminal cardiac reflex were identified. These reactions are not explicitly mentioned in the drug's specification, indicating the emergence of new signals for adverse drug reactions. Conclusion: Data mining in the FAERS database has elucidated the characteristics of dexmedetomidine-related adverse drug reactions. This analysis enhances our understanding of dexmedetomidine's drug safety, aids in the clinical management of pharmacovigilance studies, and offers valuable insights for refining drug-use protocols.

Multiview Classification Through Learning From Interval-Valued Data.

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals. In this article, we focus on a highly challenging problem called learning from interval-valued data (LIND), where we aim to learn a classifier with high performance on interval-valued observations. First, we obtain the estimation error bound of the LIND problem based on the Rademacher complexity. Then, we give the theoretical analysis to show the strengths of multiview learning on classification problems, which inspires us to construct a new algorithm called multiview interval information extraction (Mv-IIE) approach for improving classification accuracy on interval-valued data. The experiment comparisons with several baselines on both synthetic and real-world datasets illustrate the superiority of the proposed framework in handling interval-valued data. Moreover, we describe an application of Mv-IIE that we can prevent data privacy leakage by transforming crisp-valued (raw) data into interval-valued data.

Porphyrin-based porous organic polymers synthesized using the Alder-Longo method: the most traditional synthetic strategy with exceptional capacity.

Porphyrin is a typical tetrapyrrole chromophore-based pigment with a special electronic structure and functionalities, which is frequently introduced into various porous organic polymers (POPs). Porphyrin-based POPs are widely used in various fields ranging from environmental and energy to biomedicine-related fields. Currently, most porphyrin-based POPs are prepared via the copolymerization of specific-group-functionalized porphyrins with other building blocks, in which the tedious and inefficient synthesis procedure for the porphyrin greatly hinders the development of such materials. This review aimed to summarize information on porphyrin-based POPs synthesized using the Alder-Longo method, thereby skipping the complex synthesis of porphyrin-bearing monomers, in which the porphyrin macrocycles are formed directly via the cyclic tetramerization of pyrrole with monomers containing multiple aldehyde groups during the polymerization process. The representative applications of porphyrin-based POPs derived using the Alder-Longo method are finally introduced, which pinpoints a clear relationship between the structure and function from the aspect of the building blocks used and porous structures. This review is therefore valuable for the rational design of efficient porphyrin-based porous organic polymer systems that may be utilized in various fields from energy-related conversion/storage technologies to biomedical science.

3D printable elastomers with exceptional strength and toughness.

Three-dimensional (3D) printing has emerged as an attractive manufacturing technique because of its exceptional freedom in accessing geometrically complex customizable products. Its potential for mass manufacturing, however, is hampered by its low manufacturing efficiency (print speed) and insufficient product quality (mechanical properties). Recent progresses in ultra-fast 3D printing of photo-polymers1-5 have alleviated the issue of manufacturing efficiency, but the mechanical performance of typical printed polymers still falls far behind what is achievable with conventional processing techniques. This is because of the printing requirements that restrict the molecular design towards achieving high mechanical performance. Here we report a 3D photo-printable resin chemistry that yields an elastomer with tensile strength of 94.6 MPa and toughness of 310.4 MJ m-3, both of which far exceed that of any 3D printed elastomer6-10. Mechanistically, this is achieved by the dynamic covalent bonds in the printed polymer that allow network topological reconfiguration. This facilitates the formation of hierarchical hydrogen bonds (in particular, amide hydrogen bonds), micro-phase separation and interpenetration architecture, which contribute synergistically to superior mechanical performance. Our work suggests a brighter future for mass manufacturing using 3D printing.

Toxic effect of fluorene on Perinereis aibuhitensis body wall and its corresponding defense mechanisms: A metabolomics perspective.

Fluorene is a coastal sediment pollutant with high ecological risk. Perinereis aibuhitensis is an ecotoxicological model used for polycyclic aromatic hydrocarbon bioremediation; however, the effects of fluorene on the physiological metabolism of P. aibuhitensis and its corresponding responses remain unclear. This study explored the tolerance and defense responses of P. aibuhitensis in sediments with different fluorene concentrations using histology, ecological biomarkers, and metabolic responses. Metabolomics analyses revealed that P. aibuhitensis has high tolerance to fluorene in sediments. Fluorene stress disrupted the normal metabolism of the P. aibuhitensis body wall, resulting in excessive glycosphospholipid and stearamide accumulation and elevated oxygen consumption rates. To mitigate this, P. aibuhitensis has adopted tail cutting, yellowing, and modulation of metabolite contents in the body wall. This study provides novel insights into the potential ecological risk of fluorene pollution in marine sediments and proposes the use of P. aibuhitensis in the bioremediation of fluorene-contaminated sediments.

Granting analysis of the credit financing of the platform-type highway transportation supply chain.

Against the background of digital development, this study's research object is the platform-based highway transportation supply chain. It also analyzes two modes of supply chain financial credit financing, namely, upstream, and downstream enterprises of the platform, and network freight platform as the main financing body. Notably, the financial provider sets up a transaction credit based on the principle of business truth, and closed-loop transactions, determine the upper limit of the credit line based on the principle of financing self-compensation, build the expected profit maximization model, and establish the optimal credit line. Combined with the Highway Freight Index and Logistics Prosperity Index, the dynamic early warning value is established for the financing mode, where the platform as the main financing body. Through numerical analysis, the credit line and expected profit increase with the transaction credit, expected freight volume, and credit interest rate under the two modes, and the increase deriving from the credit interest rate is more significant. Finally, this paper describes the two-dimensional credit matrix of the financing subject via transaction credit and credit interest rate, which provides an intuitive credit reference for financial institutions to conduct the credit financing of the platform-based highway transportation supply chain.

Wavelength-tunable high-fidelity entangled photon sources enabled by dual Stark effects.

The construction of a large-scale quantum internet requires quantum repeaters containing multiple entangled photon sources with identical wavelengths. Semiconductor quantum dots can generate entangled photon pairs deterministically with high fidelity. However, realizing wavelength-matched quantum-dot entangled photon sources faces two difficulties: the non-uniformity of emission wavelength and exciton fine-structure splitting induced fidelity reduction. Typically, these two factors are not independently tunable, making it challenging to achieve simultaneous improvement. In this work, we demonstrate wavelength-tunable entangled photon sources based on droplet-etched GaAs quantum dots through the combined use of AC and quantum-confined Stark effects. The emission wavelength can be tuned by ~1 meV while preserving an entanglement fidelity f exceeding 0.955(1) in the entire tuning range. Based on this hybrid tuning scheme, we finally demonstrate multiple wavelength-matched entangled photon sources with f > 0.919(3), paving the way towards robust and scalable on-demand entangled photon sources for quantum internet and integrated quantum optical circuits.

Photocatalytically self-cleaning graphene oxide nanofiltration membranes reinforced with bismuth oxybromide for high-performance water purification.

Graphene oxide (GO) membranes have emerged as promising candidates for water purification applications, owing to their unique physicochemical attributes. Nevertheless, the trade-off between permeability and selectivity, coupled with their vulnerability to membrane fouling, poses significant challenges to their widespread industrial deployment. In this study, we introduce an innovative in-situ growth and layer-by-layer assembly technique for fabricating multilayer GO membranes reinforced with bismuth oxybromide (BiOBr) on commonly employed Nylon substrates. This method allows for the creation of two-dimensional lamellar membranes capable of photocatalytic self-cleaning and tunable nanochannel dimensions. The synthesized GO/BiOBr composite membranes exhibit remarkable water permeance rates (approximately 493.9 LMH/bar) and high molecular rejection efficiency (>99 % for Victoria Blue B and Congo Red dyes). Notably, these membranes showcase an enhanced photocatalytic self-cleaning performance upon exposure to visible light. Our work provides a viable route for the fabrication of functionalized GO-based nanofiltration membranes with BiOBr inclusions, offering a synergistic combination of high water permeability, modifiable nanochannels, and effective self-cleaning capabilities through photocatalysis.

Organic-Inorganic Hybrid Cuprous-Based Metal Halides with Unique Two-Dimensional Crystal Structure for White Light-Emitting Diodes.

Ternary cuprous (Cu+)-based metal halides, represented by cesium copper iodide (e.g., CsCu2I3 and Cs3Cu2I5), are garnering increasing interest for light-emitting applications owing to their intrinsically high photoluminescence quantum yield and direct bandgap. Toward electrically driven light-emitting diodes (LEDs), it is highly desirable for the light emitters to have a high structural dimensionality as it may favor efficient electrical injection. However, unlike lead-based halide perovskites whose light-emitting units can be facilely arranged in three-dimensional (3D) ways, to date, nearly all ternary Cu+-based metal halides crystallize into 0D or 1D networks of Cu-X (X = Cl, Br, I) polyhedra, whereas 3D and even 2D structures remain mostly uncharted. Here, by employing a fluorinated organic cation, we report a new kind of ternary Cu+-based metal halides, (DFPD)CuX2 (DFPD+ = 4,4-difluoropiperidinium), which exhibits unique 2D layered crystal structure. Theoretical calculations reveal a highly dispersive conduction band of (DFPD)CuBr2, which is beneficial for charge carrier injection. It is also of particular significance to find that the 2D (DFPD)CuBr2 crystals show appealing properties, including improved ambient stability and an efficient warm white-light emission, making it a promising candidate for single-component lighting and display applications.

Mushroom poisoning of Panaeolus subbalteatus from Ningxia, northwest China, with species identification and tryptamine detection.

Mushroom poisoning is a significant contributor to foodborne disease outbreaks in China. This study focuses on two Panaeolus subbalteatus poisoning incidents accompanied by epidemiological investigations, species identification, and toxin detection in Ningxia, northwest China. In these two poisoning incidents, some patients exhibited gastrointestinal or neurological symptoms approximately 0.5 h after ingestion of a large amount of wild mushroom. Specifically, in Case 1, one of the three patients experienced nausea, vomiting, and numbness in the throat and limbs; in Case 2, one patient reported dizziness and an abnormal sense of direction. Through morphological and phylogenetic analyses, mushroom specimens were identified as P. subbalteatus. Psilocybin and psilocin were detected in mushroom samples, and only psilocin was detected in biological samples by liquid chromatography-triple quadrupole-linear ion trap mass spectrometry screening. The average psilocybin and psilocin contents in mushroom samples were 1532.2-1760.7 and 114.5-136.0 mg/kg (n = 3), respectively. Moreover, only psilocin was detected in blood and urine samples, with average concentrations 0.5-1.2 ng/mL (n = 3) and 2.5-3.1 ng/mL (n = 3), respectively. These findings provide technical support for managing similar incidents in the future.

DNA methylation analysis in plasma for early diagnosis in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) represents the most prevalent type of lung cancer. SHOX2 and RASSF1A methylation have been identified as important biomarkers for diagnosis and prognosis of lung cancer. Bronchoalveolar lavage fluid (BALF) exhibits good specificity and sensitivity in diagnosing pulmonary diseases, but its acquisition is challenging and may cause discomfort to patients. In clinical, plasma samples are more convenient to obtain than BALF; however, there is little research on the concurrent detection of SHOX2 and RASSF1A methylation in plasma. This study aims to assess the diagnostic value of a combined promoter methylation assay for SHOX2 and RASSF1A in early-stage LUAD using plasma samples. METHODS: BALF and blood samples were obtained from 36 early-stage LUAD patients, with a control group of nineteen non-tumor individuals. The promoter methylation levels of SHOX2 and RASSF1A in all subjects were assessed using the human SHOX2 and RASSF1A gene methylation kit. RESULTS: The methylation detection rate of SHOX2 and RASSF1A in plasma was 61.11%, slightly lower than that in BALF (66.7%). The Chi-square test revealed no significant difference in the methylation rate between BALF and plasma (P > 0.05). The area under the receiver operating characteristic (ROC) curve analysis for blood was 0.806 (95% CI, 0.677 to 0.900), while for BALF it was 0.781 (95% CI, 0.649 to 0.881). Additionally, we conducted an analysis on the correlation between SHOX2 and RASSF1A methylation levels in plasma with gender, age, tumor differentiation, pathologic classification, and other clinicopathological variables; however, no significant correlations were observed. CONCLUSIONS: Measurement of SHOX2 and RASSF1A methylation for early diagnosis of LUAD can be achieved with high sensitivity and specificity by using plasma as a substitute for BALF samples.

A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.

The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children's Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). A clinical-imaging prediction model was then constructed utilizing Log2Φ and clinical characteristics. Performance was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical model demonstrated AUC values of 0.810 and 0.782, while the imaging model showed AUC values of 0.764 and 0.769 in the training and test sets, respectively. The clinical-imaging model, incorporating Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD), achieved the highest AUC values of 0.897 and 0.895 in the training and test sets, respectively. A prognostic model developed through automated quantification of lung disease on CT scans, in conjunction with clinical data in MPP may be utilized for the early identification of RMPP.

Evaporate-casting of curvature gradient graphene superstructures for ultra-high strength structural materials.

In contemporary manufacturing, the processing of structural materials plays a pivotal role in enabling the creation of robust, tailor-made, and precise components suitable for diverse industrial applications. Nonetheless, current material forming technologies face challenges due to internal stress and defects, resulting in a substantial decline in both mechanical properties and processing precision. We herein develop a processing strategy toward graphene superstructure with a curvature gradient, which allows us to fabricate robust structural materials with meticulously designed functional shapes. The structure consists of an arc-shaped assembly of graphene nanosheets positioned at co-axial curvature centers. During the dehydration-based evaporate-casting process, the assembly is tightened via capillary effect, inducing local bending. By precisely tuning the axis-center distance and tilt angle, we achieve accurate control over the shape of obtained structure. Notably, internal stress is harnessed to reinforce a designed mortise and tenon structure, resulting in a high joining strength of up to ~200 MPa. This innovative approach addresses the challenges faced by current material forming technologies and opens up more possibilities for the manufacturing of robust and precisely shaped components.

Identification and validation of a prognostic model based on four genes related to satellite nodules in hepatocellular carcinoma.

Satellite nodules is a key clinical characteristic which has prognostic value of hepatocellular carcinoma (HCC). Currently, there is no gene-level predictive model for Satellite nodules in liver cancer. For the 377 HCC cases collected from the dataset of Cancer Genome Atlas (TCGA), their original pathological data were analyzed to extract information regarding satellite nodules status as well as other relevant pathological data. Then, this study employed statistical modeling for prognostic model establishment in TCGA, and validation in International Cancer Genome Consortium (ICGC) cohorts and GSE76427. Through rigorous statistical analyses, 253 differential satellite nodules-related genes (SNRGs) were identified, and four key genes related to satellite nodules and prognosis were selected to construct a prognostic model. The high-risk group predicted by our model exhibited an unfavorable overall survival (OS) outlook and demonstrated an association with adverse worse clinical characteristics such as larger tumor size, higher alpha-fetoprotein, microvascular invasion and advanced stage. Moreover, the validation of the model's prognostic value in the ICGC and GSE76427 cohorts mirrored that of the TCGA cohort. Besides, the high-risk group also showed higher levels of resting Dendritic cells, M0 macrophages infiltration, alongside decreased levels of CD8+ T cells and γδT cells infiltration. The prognostic model based on SNRGs can reliability predict the OS of HCC and is likely to have predictive value of immunotherapy for HCC.

A multiscale 3D network for lung nodule detection using flexible nodule modeling.

BACKGROUND: Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. PURPOSE: Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. METHODS: To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions. RESULTS: Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules. CONCLUSIONS: The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.

Improving Lower Limb Function and Frailty in Frail Older Patients with Acute Myocardial Infarction After Percutaneous Coronary Intervention: A Randomized Controlled Study of Neuromuscular Electrical Stimulation.

Background: A global public health problem, frailty is closely associated with poor prognosis after percutaneous coronary intervention (PCI) in older patients with acute myocardial infarction (AMI). Although exercise intervention is the most commonly used method to reverse and alleviate frailty, its application is restricted in patients with acute myocardial infarction following PCI due to cardiovascular instability and autonomic imbalance. Consequently, there is a need for a new practical intervention to address frailty syndrome in these patients. Purpose: This study aimed to investigate the effect of neuromuscular electrical stimulation in frail older AMI patients post-PCI. Patients and Methods: A single-blind, randomized controlled trial was carried out in the Department of Cardiovascular Medicine from March to October 2023. A total of 100 eligible participants were randomly divided into two groups: experimental (n = 50) and control (n = 50) groups, respectively. Both groups received usual care. The experimental group underwent neuromuscular electrical stimulation (NMES) on bilateral quadriceps and gastrocnemius muscles for 30 minutes daily from day 1 to day 7 after surgery. The primary outcomes measured included the frailty score, lower limb muscle strength, and lower limb muscle quality. Secondary outcomes included the activities of daily living score, inflammatory markers, and length of hospital stay. All participants were included in an intention-to-treat analysis after the study ended. Results: The frailty scores of the two groups exhibited a gradual decrease over time, and the scores of the experimental group were lower than those of the control group at 4 and 7 days after surgery (P<0.001). Concurrently, the lower limb muscle strength showed an increasing trend over the time in the experimental group and a decreasing trend in the control group, and the scores of the experimental group surpassed those of the control group (p<0.001). Moreover, a statistical difference was observed in the lower limb muscle mass across the groups after 7 days postoperatively compared with baseline on both sides (p<0.05). Conclusion: Neuromuscular electrical stimulation has the potential to enhance lower limb function and alleviate frailty in elderly patients with acute myocardial infarction after PCI. These findings introduce a novel intervention approach for frailty management in the elderly population.