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As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.
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Electrocardiografía , Frecuencia Cardíaca , Redes Neurales de la Computación , Procesamiento de Señales Asistido por Computador , Humanos , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/fisiopatología , Arritmias Cardíacas/clasificaciónRESUMEN
This study evaluated the effect of simulated pulpal pressure (SPP) conditions and storage time on contemporary adhesive systems' microtensile bond strength (µTBS) to dentin. Extracted human molars were prepared and randomly divided into four groups according to the adhesives: Clearfil Megabond 2 (CSE), Beautibond Xtreme Universal (BXU), G2-Bond (G2B), and Scotchbond Universal Plus (SBP). Each adhesive group was further divided following the SPP conditions: control with no simulation (SPP-CTR), SPP with distilled water (SPP-DTW), and SPP with fetal bovine serum (SPP-FBS). Resin composite build-ups were prepared, and teeth were stored in water (37 °C) for 24 h (24 h) and 3 months (3 m). Then, teeth were sectioned to obtain resin-dentin bonded beams and tested to determine the µTBS. Data were analyzed using three-way ANOVA, Tukey post hoc tests (=0.05), and Weibull failure analysis. Failure mode was observed using scanning electron microscopy. The µTBS response was affected by adhesive systems, simulated pulpal pressure conditions, and storage time. SPP-CTR groups presented a higher overall bond strength than SPP-DTW and SPP-FBS, which were not significantly different from each other. Only for SBP, the SPP-FBS group showed higher µTBS than the SPP-DTW group. The Weibull analysis showed that the bonding reliability and durability under SPP-DTW and SPP-FBS were inferior to SPP-CTR, and the 24 h bonding quality of adhesives to dentin was superior to that of 3 m. SPP drastically reduced the µTBS of all adhesives to dentin regardless of solution (distilled water or fetal bovine serum). Storage after 3 m also decreased µTBS despite the SPP condition.
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Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow crack and low segmentation accuracy, this paper proposes an improved U-net network pavement crack segmentation method. VGG16 and Up_Conv (Upsampling Convolution) modules are introduced as backbone network and feature enhancement network respectively, and the more abstract features in the image are extracted by using the Block depth separable convolution blocks, and the multi-scale features are captured and enhanced by higher level semantic information to improve the recognition accuracy of narrow cracks in the road surface. The improved network embedded the Ca(Channel Attention) attention mechanism in U-net's jump connection to enhance the crack portion to suppress background noise. At the same time, DG_Conv(Depthwise GSConv Convolution) module and UnetUp(Unet Upsampling) module are added in the decoding part to extract richer features through more convolutional layers in the network, so that the model pays more attention to the detailed part of the crack, so the segmentation accuracy can be improved. In order to verify the model's ability to detect cracks in complex backgrounds, experiments were carried out on CFD and Deepcrack datasets. The experimental results show that compared with the traditional U-net network F1-score and mIoU have increased by 13.6% and 9.9% respectively. Superior to advanced models such as U-net, Segnet and Linknet in accuracy and generalization ability, the improved model provides a new method for asphalt pavement crack detection. The model is more conducive to practical application and ground deployment, and can be applied in road maintenance projects.
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Hidrocarburos , Redes Neurales de la Computación , Hidrocarburos/análisis , Algoritmos , Materiales de Construcción/análisis , HumanosRESUMEN
AIM: Calcium hydroxide (CH) has been considered as a direct pulp capping materials (DPC) for the last decades despite having some limitations. Phosphorylate pullulan (PPL) incorporated with CH (CHPPL) is a novel biomaterial that was introduced as a promising DPC material. Thus, the aim of the study was to evaluate the inflammatory response and mineralized tissue formation (MTF) ability of PPL-based CH formulations on rat molars after DPC. METHODOLOGY: This study consisted of six groups: CH with 1% PPL (CHPPL-1); 3% PPL (CHPPL-3); 5% PPL (CHPPL-5); Dycal and NEX MTA Cement (N-MTA) as the positive control, and no capping materials (NC). One hundred twenty maxillary first molar cavities were prepared on Wistar rats. After capping, all the cavities were restored with 4-META/MMA-TBB resin and pulpal responses were evaluated at days 1, 7, and 28. Kruskal-Wallis followed by Mann-Whitney U-test was performed with a significance level of 0.05. Immunohistochemical expression of IL-6, Nestin, and DMP-1 was observed. RESULTS: At day 1, CHPPL-1, N-MTA, and Dycal exhibited no to mild inflammation, whilst CHPPL-3, CHPPL-5, and NC showed mild to moderate inflammation, and the results were significantly different (p < .05). At day 7, mild to moderate inflammation was observed in CHPPL-1, N-MTA, and Dycal, whereas CHPPL-3, CHPPL-5, and NC exhibited moderate to severe inflammation. Significant differences were observed between CHPPL-1 and N-MTA with NC (p < .05), CHPPL-1 and CHPPL-3 with CHPPL-5 and Dycal (p < .05), and CHPPL-3 with N-MTA (p < .05). A thin layer of mineralized tissue formation (MTF) was observed in all groups. At day 28, CHPPL-1, Dycal, and N-MTA showed no to mild inflammation, whilst CHPPL-3, CHPPL-5, and NC exhibited mild to severe inflammation, and statistically significant difference was detected (p < .05). CHPPL-1, Dycal, and N-MTA exhibited continuous MTF, whilst CHPPL-3, CHPPL-5, and NC had thicker and interrupted MTF. Significant differences were observed between CHPPL-1, CHPPL-3, and N-MTA with NC group (p < .05). Variable expressions of IL-6, Nestin, and DMP-1 indicated differences in the materials' impact on odontoblast-like cell formation and tissue mineralization. CONCLUSIONS: These findings suggest that CHPPL-1 has the potential to minimize pulpal inflammation and promote MTF and had similar efficacy as MTA cement.
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Hidróxido de Calcio , Recubrimiento de la Pulpa Dental , Glucanos , Materiales de Recubrimiento Pulpar y Pulpectomía , Ratas Wistar , Animales , Glucanos/farmacología , Hidróxido de Calcio/farmacología , Ratas , Materiales de Recubrimiento Pulpar y Pulpectomía/farmacología , Recubrimiento de la Pulpa Dental/métodos , Compuestos de Calcio/farmacología , Compuestos de Aluminio , Combinación de Medicamentos , Masculino , Silicatos/farmacología , Pulpa Dental/efectos de los fármacos , Pulpa Dental/metabolismo , Fosforilación , Óxidos , Diente Molar , MineralesRESUMEN
Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.
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Cuerpo Humano , Personal Militar , Humanos , Radar , Algoritmos , Aprendizaje AutomáticoRESUMEN
Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.
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Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention. We evaluate our proposed emotion model on the IEMOCAP and Emo-DB corpora. The experimental results show that the network model based on Bi-GRU and multi-head attention is significantly better than the traditional network model at detecting multiple evaluation indicators. At the same time, we also apply the model to a speech sentiment analysis task. On the CH-SIMS and MOSI datasets, the model shows excellent generalization performance.
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Percepción , Habla , Acústica , Emociones , Reconocimiento en PsicologíaRESUMEN
BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.
Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.
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The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 µm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found.
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Algoritmos , Hígado , Microscopía de Fuerza Atómica/métodos , Línea Celular , Hepatocitos , Viscosidad , ElasticidadRESUMEN
OBJECTIVE: To assess whether photobiomodulation therapy (PBMT) enhances the benefits of exercise in older adults. DATA SOURCES: PubMed, Scopus, Medline, and Web of Science, dated to February 2023. STUDY SELECTION: All included studies were randomized controlled trials of PBMT combined with exercise co-intervention in persons 60 years and older. OUTCOME MEASURES: Western Ontario and McMaster University Osteoarthritis Index (WOMAC-total, pain, stiffness and function), perceived pain intensity, timed Up and Go (TUG) Test, 6-min walk test (6MWT), muscle strength, and knee range of motion were included. DATA EXTRACTION: Two researchers independently performed data extraction. Article data were extracted in Excel and summarized by a third researcher. DATA SYNTHESIS: The meta-analysis included 14 of the 1864 studies searched in the database. No statistical differences were found between the treatment and control groups in terms of WOMAC-stiffness (mean difference [MD]=-0.31, 95% confidence interval [CI] -0.64 to 0.03), TUG (MD=-0.17, 95% CI -0.71 to 0.38), 6MWT (MD=32.2, 95% CI -44.62 to 109.01), or muscle strength (standardized mean difference=0.24, 95% CI -0.02 to 0.50). However, statistically significant differences were found for WOMAC-total (MD=-6.83, 95% CI -12.3 to -1.37), WOMAC-pain (MD=-2.03, 95% CI -4.06 to -0.01), WOMAC-function (MD=-5.03, 95% CI -9.11 to -0.96), visual analog scale/numeric pain rating scale (MD=-1.24, 95% CI -2.43 to -0.06), and knee range of motion (MD=1.47, 95% CI 0.07 to 2.88). CONCLUSIONS: In older adults who exercise regularly, PBMT can potentially provide additional pain relief, improve knee joint function, and increase knee joint range of motion.
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Terapia por Luz de Baja Intensidad , Humanos , Anciano , Tolerancia al Ejercicio , Ensayos Clínicos Controlados Aleatorios como Asunto , Articulación de la Rodilla , DolorRESUMEN
Geloina erosa is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in G. erosa. Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle. The results revealed a total of 1412 miRNAs, comprising 1047 known miRNAs, and 365 newly predicted miRNAs. These miRNAs exhibited distinct tissue-specific expression patterns. In the miRNA target gene prediction, a total of 7404 potential predicted targets were identified, representing approximately 33% of all unique transcripts associated with miRNAs. Further co-expression network analysis revealed nine modules, each showing a positive correlation with specific tissues (gills, hepatopancreas, or muscle). The blue module showed a significant correlation with gills (r = 0.83, p-value = 0.006), the black module was significantly related to the hepatopancreas (r = 0.78, p-value = 0.01), and the purple module was significantly correlated with muscle (r = 0.83, p-value = 0.006). Within these modules, related miRNAs tended to cluster together, while their correlations with other modules were relatively weak. Functional enrichment analysis was performed on miRNAs and their predicted targets in each tissue. In the gills, miRNAs primarily regulate immune-related genes, substance transport, and cytoskeletal organization. In the hepatopancreas, miRNAs suppressed genes involved in shell formation and played a role in cellular motor activity and metabolism. In muscle, miRNAs participate in metabolism and photoreceptive processes, as well as immune regulation. In summary, this study provides valuable insights into the tissue-specific regulation of miRNAs in G. erosa, highlighting their potential roles in immune response, metabolism, and environmental adaptation. These findings offer important clues for understanding the molecular mechanisms and biological processes in G. erosa, laying the foundation for further validation and elucidation of these regulatory relationships.
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BACKGROUND: Doxorubicin (DOX)-induced cardiotoxicity (DIC) is a major impediment to its clinical application. It is indispensable to explore alternative treatment molecules or drugs for mitigating DIC. WGX50, an organic extract derived from Zanthoxylum bungeanum Maxim, has anti-inflammatory and antioxidant biological activity, however, its function and mechanism in DIC remain unclear. METHODS: We established DOX-induced cardiotoxicity models both in vitro and in vivo. Echocardiography and histological analyses were used to determine the severity of cardiac injury in mice. The myocardial damage markers cTnT, CK-MB, ANP, BNP, and ferroptosis associated indicators Fe2+, MDA, and GPX4 were measured using ELISA, RT-qPCR, and western blot assays. The morphology of mitochondria was investigated with a transmission electron microscope. The levels of mitochondrial membrane potential, mitochondrial ROS, and lipid ROS were detected using JC-1, MitoSOX™, and C11-BODIPY 581/591 probes. RESULTS: Our findings demonstrate that WGX50 protects DOX-induced cardiotoxicity via restraining mitochondrial ROS and ferroptosis. In vivo, WGX50 effectively relieves doxorubicin-induced cardiac dysfunction, cardiac injury, fibrosis, mitochondrial damage, and redox imbalance. In vitro, WGX50 preserves mitochondrial function by reducing the level of mitochondrial membrane potential and increasing mitochondrial ATP production. Furthermore, WGX50 reduces iron accumulation and mitochondrial ROS, increases GPX4 expression, and regulates lipid metabolism to inhibit DOX-induced ferroptosis. CONCLUSION: Taken together, WGX50 protects DOX-induced cardiotoxicity via mitochondrial ROS and the ferroptosis pathway, which provides novel insights for WGX50 as a promising drug candidate for cardioprotection.
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Cardiotoxicidad , Ferroptosis , Ratones , Animales , Cardiotoxicidad/tratamiento farmacológico , Cardiotoxicidad/metabolismo , Cardiotoxicidad/patología , Especies Reactivas de Oxígeno/metabolismo , Miocitos Cardíacos/patología , Doxorrubicina/efectos adversos , Mitocondrias/metabolismo , Estrés Oxidativo , Antioxidantes/metabolismo , ApoptosisRESUMEN
This paper presents a novel Double-negative (DNG) metamaterial (MM) resonator with a mirror-symmetric configuration, designed to exhibit multiband resonances in the S, C, and X bands. The resonator is fabricated using advanced processing techniques on a Rogers 5880 substrate and features electrodeposited copper. It consists of four equal regions, each containing interconnected split-ring resonators that are connected through a cross-shaped structure to ensure mirror symmetry. The simulation results demonstrate six resonance points at frequencies of 2.45 GHz, 4.27 GHz, 6.86 GHz, 8.98 GHz, 10.69 GHz, and 11.65 GHz. These resonances are characterized by near-zero/negative permeability, negative permittivity, refractive index, and impedance. Furthermore, the cross-polarization effect of incident waves is investigated. Additionally, the potential for tunability of resonance frequencies is explored through a sandwiched configuration of the MM resonator, achieved by modifying the cover of the resonating patch. Moreover, the equivalent circuit model of the proposed MM resonator is in good agreement with practical measurements, validating the simulation results. The new tuning method for MM resonators holds the promise for future sensing applications and wireless communications.
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The magnetic chitosan/sludge biochar composite adsorbent was prepared using chitosan, Fe3O4, and sludge biochar as raw materials. The composite adsorbent was able to achieve rapid solid-liquid separation under an applied magnetic field. The morphology and microstructure of the composite adsorbent were characterized by FTIR, XRD, SEM, VSM, and BET analysis. The adsorption performance of the composite adsorbent on Cu2+ was investigated through static adsorption experiments, and the effects of adsorbent dosage, initial concentration of Cu2+, initial pH of the solution, and adsorption temperature on the adsorption efficiency of Cu2+ were discussed. The results showed that chitosan and Fe3O4 were successfully loaded on sludge biochar. When the initial concentration of Cu2+ was 30 mg/L, the dosage of the magnetic chitosan/sludge biochar composite material was 0.05 g, the adsorption time was 180 min, pH was 5, and the temperature was room temperature, the maximum removal rate of Cu2+ reached 99.77%, and the maximum adsorption capacity was 55.16 mg/g. The adsorption kinetics and adsorption isotherm data fitted well with the pseudo-second-order kinetic model and Langmuir adsorption isotherm model, indicating that the adsorption process was chemisorption with monolayer coverage.
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Osteoclasts uniquely resorb calcified bone matrices. To exert their function, mature osteoclasts maintain the cellular polarity and directional vesicle trafficking to and from the resorbing bone surface. However, the regulatory mechanisms and pathophysiological relevance of these processes remain largely unexplored. Bone histomorphometric analyses in Ccr5-deficient mice showed abnormalities in the morphology and functional phenotype of their osteoclasts, compared to wild type mice. We observed disorganized clustering of nuclei, as well as centrosomes that organize the microtubule network, which was concomitant with impaired cathepsin K secretion in cultured Ccr5-deficient osteoclasts. Intriguingly, forced expression of constitutively active Rho or Rac restored these cytoskeletal phenotypes with recovery of cathepsin K secretion. Furthermore, a gene-disease enrichment analysis identified that PLEKHM1, a responsible gene for osteopetrosis, which regulates lysosomal trafficking in osteoclasts, was regulated by CCR5. These experimental results highlighted that CCR5-mediated signaling served as an intracellular organizer for centrosome clustering in osteoclasts, which was involved in the pathophysiology of bone metabolism.
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Resorción Ósea , Osteoclastos , Receptores CCR5 , Animales , Ratones , Huesos/metabolismo , Matriz Ósea/metabolismo , Resorción Ósea/genética , Resorción Ósea/metabolismo , Catepsina K/metabolismo , Centrosoma/metabolismo , Osteoclastos/metabolismo , Receptores CCR5/metabolismoRESUMEN
BACKGROUND: The aim of this study was to compare the efficacy of ultrasound-guided PENG (pericapsular nerve group) block and drug therapy with intravenous flurbiprofen for early analgesia in elderly patients with hip fractures after hospitalization. METHODS: This is a single-center, observer-blinded, prospective, randomized, controlled trial. A total of 41 elderly patients (aged 60 or older) with hip fractures were enrolled in the current study. Patients were randomly assigned to two groups: Group P (ultrasound-guided PENG block, 20 mL of 0.375% ropivacaine) and Group F (intravenous flurbiprofen 50 mg). The primary outcome measure was the dynamic (passive straight leg raising 15°) NRS (numerical rating scale 0 to 10) pain scores at different time points. The secondary outcomes were the static NRS scores at different time points, the number of rescue analgesia sessions, patient satisfaction, and the incidence of complications. RESULTS: Patients in the two groups had comparable baseline characteristics. The group P had lower dynamic and static NRS scores at 15 min, 30 min, 6 h, and 12 h after intervention (P<0.05) than the group F. The highest NRS pain scores in the group P were still lower than the NRS scores in the group F at 30 min-12 h (Group F: 5.57±1.54 vs. Group P: 3.00±1.12, P<0.001), and there was no significant difference between the two groups at 12-24 h (Group F: 6.35±1.79 vs. Group P: 5.90±1.83, P>0.05). The group P had higher satisfaction scores (Group P: 9 (9,9) vs. Group F: 8 (7,8), P<0.001). There was no statistically significant difference in the number of rescue analgesics at 0-12 h or 12-24 h or the incidence of complications between the groups. CONCLUSIONS: Compared with intravenous flurbiprofen, ultrasound-guided PENG block provides better early analgesic effects in elderly patients with hip fractures, and a PENG block is safe for elderly patients with hip fractures after hospitalization. Trial registration This study was registered in the Chinese Clinical Trial Testing Center (ID: ChiCTR2200062400).
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Analgesia , Flurbiprofeno , Fracturas de Cadera , Anciano , Humanos , Flurbiprofeno/uso terapéutico , Estudios Prospectivos , Nervio Femoral , Dolor Postoperatorio/tratamiento farmacológico , Fracturas de Cadera/cirugía , Ultrasonografía IntervencionalRESUMEN
Here, we present Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping, a user-friendly pipeline for the registration and analysis of whole-brain images in larval zebrafish. We describe steps for pre-processing, registration, quantification, and visualization of whole-brain phenotypes in zebrafish mutants of genes associated with neurodevelopmental and neuropsychiatric disorders. By utilizing BioImage Suite Web, an open-source software package originally developed for processing human brain imaging data, we provide a highly accessible whole-brain mapping protocol developed for users with general computational proficiency. For complete details on the use and execution of this protocol, please refer to Weinschutz Mendes et al. (2023).1.
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Encéfalo , Pez Cebra , Humanos , Animales , Encéfalo/diagnóstico por imagen , Mapeo Encefálico , Larva , FenotipoRESUMEN
This study evaluated the effect of a 0.5% chloramine T solution on a chemical-cured universal adhesive by comparing the light-cured, one-step, self-etch adhesive for the bonding performance, mechanical properties, and resin-dentin interfacial characteristics. Caries-free human molars were randomly assigned into eight groups based on the bonding systems employed (Bond Force II, BF and Bondmer Lightless, BL), the immersion solutions used before bonding (0.5% chloramine T solution and distilled water), and the immersion durations (5 and 60 min). Microtensile bond strength (µTBS), nanoleakage evaluation, and nanoindentation tests were performed, and the surface morphology of the resin-dentin interface was examined using a focus ion beam/scanning ion microscopy system. Immersion in chloramine-T for 5 min significantly decreased the µTBS of Bondmer Lightless (from 22.62 to 12.87 MPa) compared with that in distilled water. Moreover, there was also a decreasing trend after immersing in chloramine-T for 60 min (from 19.11 to 13.93 MPa). Chloramine T was found to have no effect on the hardness, elastic modulus, or morphological characteristics of the ion-beam milled resin-dentin interfacial surfaces in the tested adhesives, suggesting that chloramine T might reduce the bond strength by interfering with the interaction and the sealing between the adhesive resin and dentin in the chemical-cured universal adhesive, albeit without affecting the mechanical properties.