Nursing management of intracranial hypertension in adults with severe brain injury in a neurosurgery intensive care unit: a best practice implementation project.

INTRODUCTION: The nursing management of intracranial hypertension in adult patients with severe brain injury is crucial for maintaining the stability of intracranial pressure, which ultimately improves patient outcomes. OBJECTIVES: This project aimed to implement evidence-based practices for the nursing management of intracranial hypertension in adult patients with severe brain injury. METHODS: This evidence implementation project was conducted in a neurosurgery intensive care unit in a large tertiary hospital in Guangzhou, China. The project was guided by the JBI Evidence Implementation Framework, which is an audit and feedback model with seven stages. The Ottawa Model of Research Use was used to identify barriers and facilitators to best practices and to develop improvement strategies. RESULTS: Thirty-three nurses and 50 patients with severe brain injury participated in the baseline and follow-up audits. After project implementation, follow-up audits revealed significantly improved compliance with best practices compared with baseline. Nurses' awareness of best practices increased (41% to 96%); nursing assessment, monitoring, and interventions related to intracranial hypertension rose significantly (from 82%, 75%, and 59% to 98%, 84%, and 87%, respectively); and patients' optic nerve sheath diameter was notably lower (6.002±0.677 mm to 5.698±0.730 mm). CONCLUSIONS: The systematic integration of consistent training and education, together with the refinement of care processes and the creation of relevant tools, led to a significant improvement in awareness and adherence to best practices. Further testing of this program in more hospitals is needed. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A243.

Picroside II disrupts IBDV replication via targeting VP1 polymerase.

Infectious bursal disease virus (IBDV) is a highly contagious virus with a dsRNA genome, predominantly infecting chickens and causing significant economic losses due to high mortality rates. The emergence of recombinant, novel variant, and highly virulent strains that evade current vaccines has led to frequent epidemics and outbreaks in the poultry industry. The lack of targeted antivirals for IBDV underscores the pressing requirement to develop potent therapeutic options. Within this framework, our research investigated the effectiveness of picroside II, a naturally derived iridoid glycoside, against viruses in DF-1 cells. Our findings demonstrate that picroside II significantly inhibits viral replication, with its efficacy increasing proportionally to the dosage administered. Through time-addition and antiviral duration analysis, we determined that picroside II therapeutically blocks IBDV replication, with its effects persisting for over 72 hours. Further investigation revealed that picroside II specifically inhibits the cellular replication stage of IBDV's lifecycle. Additionally, our findings indicate that picroside II impairs VP1 polymerase activity by binding to the active pocket, which significantly disrupts the interaction between VP1 and VP3. Mutations at three critical binding sites on VP1 not only impair virus replication but also hinder polymerase function and disrupt VP1-VP3 interactions. Collectively, these results demonstrate that picroside II, by inhibiting viral polymerase activity, represents a promising antiviral agent against IBDV.

A new lightweight network for efficient UAV object detection.

Optimizing the structure of deep neural networks is essential in many applications. Especially in the object detection tasks of Unmanned Aerial Vehicles. Due to the constraints of the onboard platform, a more efficient network is required to meet practical demands. Nevertheless, existing lightweight detection networks exhibit excessive redundant computations and may yield in a certain level of accuracy loss. To address these issues, this paper proposes a new lightweight network structure named Cross-Stage Partially Deformable Network (CSPDNet). The initial proposal consists of a Deformable Separable Convolution Block (DSCBlock), separating feature channels, greatly reducing the computational load of convolution, and applying adaptive sampling to the separated feature map. Subsequently, to establish information interaction between feature layers, a channel weighting module is proposed. This module calculates weights for the separated feature map, facilitating information exchange across channels and resolutions. Moreover, it compensates for the effect of point-wise (1 × 1) convolutions, filtering out more important feature information. Furthermore, a new CSPDBlock is designed, primarily composed of DSCBlock, establishing multidimensional feature correlations for each separated feature layer. This approach improves the ability to capture critical feature information and reconstruct gradient paths, thereby preserving detection accuracy. The proposed technology achieves a balance between model parameter size and detection accuracy. Furthermore, experimental results on object detection datasets demonstrate that our designed network, using fewer parameters, achieves competitive detection performance results compared to existing lightweight networks YOLOv5n, YOLOv6n, YOLOv8n, NanoDet and PP-PicoDet. The optimization effect of the designed CSPDBlock, using the VisDrone dataset, is validated when incorporated into advanced detection algorithms YOLOv5m, PPYOLOEm, YOLOv7, RTMDetm and YOLOv8m. In more detail, by incorporating the designed modules it was achieved that the parameters were reduced by 10-20% while almost maintaining detection accuracy.

Establishment and Validation of a Mouse Bacterial Vaginosis Model.

BACKGROUND: Bacterial vaginosis (BV) is a common vaginal infection without a reliable animal model. To establish a novel mouse BV model, we evaluated multiple parameters of various identified bacteria-infected mice, including Staphylococcus aureus (SA), Escherichia coli (EC), Streptococcus agalactiae, ß-Hemolytic streptococcus, and Gardnerella vaginalis (GV). METHODS: Mature female KM mice were randomly allocated to a vehicle group (group A, without any treatment) and experimental groups. After vaginal secretions were harvested, experimental groups were divided into phosphate buffer solution group (PBS, group B), control group including SA, and EC with a 1:1 ratio (group C), SA, EC, and Streptococcus agalactiae with a 1:2:1 ratio group (group D), SA, EC, and ß-Hemolytic streptococcus with a 1:2:1 ratio group (group E), and GV group (group F). The vaginal secretions of experimental mice were collected by flushing with 100 mL sterile PBS on days 2, 4, and 6. Vaginal secretions were examined by Gram staining, sialidase assay, ammonia test, and pH value measurement. IL-6 and IL-10 levels in mouse serum were detected by enzyme-linked immunosorbent assay. Hematoxylineosin staining and mouse cervicovaginal tissue histopathological scores were observed. The diagnostic test results were analyzed by logistic regression analysis and receiver operating characteristic curves. The Shapiro-Wilk analysis of variance, or rank-sum test, was used for normal distribution analysis. Pearson's correlation and chi-squared test determined the correlation and comparison data expressed as a percentage or frequency. RESULTS: There was less severe vaginal morphology in GV-infected mice compared to other bacteria-infected mice. The sialidase assay, the ammonia test, and the pH values of vaginal secretions showed significant differences between GV-infected and uninfected mice. Serum IL-6 and IL-10 levels and vaginal histological scoring increased in other bacteria-infected mice, but GV-infected mice showed only a mildly increasing trend of IL-10 levels and vaginal histological scoring compared to control mice. CONCLUSIONS: GV-infected mice showed clinical features similar to human BV infection, including vaginal anatomical and pathological indices, and biochemical and immune parameters. Serum IL-10 level has potential for use in BV diagnosis.

A Dimensionality-Reducible Operational Optimal Control for Wastewater Treatment Process.

Operational optimal control (OOC) is an essential component of wastewater treatment process (WWTP). The control variables usually are high-dimensional, nonlinear, and strongly coupled, which can easily fail traditional optimization control methods. Mathematically, these operational variables usually are in the unknown low-dimensional space embedded in the high-dimensional space. Therefore, the OOC problem of WWTP can be resolved as an optimization challenge involving low-dimensional space, and the unknown low-dimensional space is presented in the form of a set of controlled variables in a high-dimensional space, which is normal in real-world industries. Here, a dimension-reducible data-driven optimization control framework for WWTP is proposed. Considering the difficulty in elucidating the whole space of set points, a neural network is designed to approximate the constraint relationship between control variables. The search process is based on optimization methods in low-dimensional space embedded into Euclidean spaces. Furthermore, the convergence of the process is ensured via mathematical analysis. Finally, the experimental simulation of wastewater treatment revealed that this approach is effective for an optimal solution in control systems.

An Improved Circular Fringe Fourier Transform Profilometry.

Circular fringe projection profilometry (CFPP), as a branch of carrier fringe projection profilometry, has attracted research interest in recent years. Circular fringe Fourier transform profilometry (CFFTP) has been used to measure out-of-plane objects quickly because the absolute phase can be obtained by employing fewer fringes. However, the existing CFFTP method needs to solve a quadratic equation to calculate the pixel displacement amount related to the height of the object, in which the root-seeking process may get into trouble due to the phase error and the non-uniform period of reference fringe. In this paper, an improved CFFTP method based on a non-telecentric model is presented. The calculation of displacement amount is performed by solving a linear equation instead of a quadratic equation after introducing an extra projection of circular fringe with circular center translation. In addition, Gerchberg iteration is employed to eliminate phase error of the region close to the circular center, and the plane calibration technique is used to eliminate system error by establishing a displacement-to-height look-up table. The mathematical model and theoretical analysis are presented. Simulations and experiments have demonstrated the effectiveness of the proposed method.

Two-Photon Fluorescence Study of Olive Oils at Different Excitation Wavelengths.

Two-photon fluorescence (TPF) of olive oils is discovered and observed experimentally for the first time. Variations of the single-photon fluorescence (SPF) and TPF with the excitation wavelength are investigated for four different olive oils. The results show that fluorescence of the cosmetic olive oils (COO) is very weak and exhibits only one spectral peak around 490 nm. While for the ordinary edible oils (OEO) whether they are during their shelf life or not, their fluorescence spectra may exhibit multiple peak structures. The short-term natural expiration only slightly weakens TPF of OEO. Moreover, the excitation wavelength affects the OEO spectra considerably in terms of the spectral peak number, the spectral peak position, and spectral shapes. When the excitation wavelength decreases from 700 nm, the whole TPF of the OEO also decreases. Relatively, however, the short wave band will decrease and disappear more quickly. While for the SPF, the long wave band will decrease and disappear first. The optimal excitation wavelengths to make the TPF strongest are around 700 nm and 640 nm for OEOs and COO, respectively. And effects of temperature on SPF and TPF of extra virgin olive oil are also explored. This work may be of significance for its potential applications in TPF detection and two-photon laser.

Development and Validation of a Sensitive UHPLC-MS/MS Method for the Measurement of Gardneramine in Rat Plasma and Tissues and its Application to Pharmacokinetics and Tissue Distribution Study.

As a novel monoterpenoid indole alkaloid, gardneramine has been confirmed to possess excellent nervous depressive effects. However, there have been no reports about the measurement of gardneramine in vitro and in vivo. The motivation of this study was to establish and validate a specific, sensitive, and robust analytical method based on UHPLC-MS/MS for quantification of gardneramine in rat plasma and various tissues after intravenous administration. The analyte was extracted from plasma and tissue samples by protein precipitation with methanol using theophylline as an internal standard (I.S.). The analytes were separated on an Agilent ZORBAX Eclipse Plus C18 column using a gradient elution of acetonitrile and 0.1% formic acid in water at a flow rate of 0.3 mL/min. Gardneramine and I.S. were detected and quantified using positive electrospray ionization in multiple reaction monitoring (MRM) mode with transitions of m/z 413.1→217.9 for gardneramine and m/z 181.2→124.1 for I.S.. Perfect linearity range was 1-2000 ng/mL with a correlation coefficient (r2) of ≥0.990. The lower limit of quantification (LLOQ) of 1.0 ng/mL was adequate for application to different preclinical studies. The method was successfully applied for determination of gardneramine in bio-samples.

A validated UHPLC-MS/MS method for measurement of pharmacokinetics and tissue distribution of trolline in rat.

As a novel alkaloid, trolline is a potential methionine aminopeptidase Ⅱ inhibitor. However, up to now, no informations about the quantification of trolline were available in biosamples. In this study, a simple, specific and sensitive analytical method based on UHPLC-MS/MS method has been established and validated for determination of trolline in rat plasma and tissues after intravenous administration. Sample preparation was carried out by a simple liquid-liquid extraction and carbamazepine was used as internal standard (I.S.). Chromatographic separation was achieved by using a Waters BEH C18 column and involving the optimized mobile phase of 0.1% formic acid aqueous solution and acetonitrile with gradient elution flow of 0.20 ml/min. Trolline and I.S. were detected by multiple reaction monitoring (MRM) modes with positive electrospray ionization and transitions at m/z 220.0→136.8 for trolline and m/z 237.0→193.9 for carbamazepine (I.S.). Good linearity was ranged from 10.0 ng/ml to 4000 ng/ml for trolline both in plasma and various tissues. The lower limit of quantification (LLOQ) was 10 ng/ml in all samples. The intra- and inter-day precision (RSD%) were below 11.3% and the accuracy (RE%) was ranged from -10.2% to 12.3%. The validated method was successfully applied to the pharmacokinetics and tissue distribution study of trolline after intravenous administration.

Effects of iron spin transition on the electronic structure, thermal expansivity and lattice thermal conductivity of ferropericlase: a first principles study.

The effects of the spin transition on the electronic structure, thermal expansivity and lattice thermal conductivity of ferropericlase are studied by first principles calculations at high pressures. The electronic structures indicate that ferropericlase is an insulator for high-spin and low-spin states. Combined with the quasiharmonic approximation, our calculations show that the thermal expansivity is larger in the high-spin state than in the low-spin state at ambient pressure, while the magnitude exhibits a crossover between high-spin and low-spin with increasing pressure. The calculated lattice thermal conductivity exhibits a drastic reduction upon the inclusion of ferrous iron, which is consistent with previous experimental studies. However, a subsequent enhancement in the thermal conductivity is obtained, which is associated with the spin transition. Mechanisms are discussed for the variation in thermal conductivity by the inclusion of ferrous iron and the spin transition.

An efficient self-organizing RBF neural network for water quality prediction.

This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure dynamically in order to maintain the prediction accuracy. The hidden neurons in the RBF neural network can be added or removed online based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency. The convergence of the algorithm is analyzed in both the dynamic process phase and the phase following the modification of the structure. The proposed FS-RBFNN has been tested and compared to other algorithms by applying it to the problem of identifying a nonlinear dynamic system. Experimental results show that the FS-RBFNN can be used to design an RBF structure which has fewer hidden neurons; the training time is also much faster. The algorithm is applied for predicting water quality in the wastewater treatment process. The results demonstrate its effectiveness.

Research on an online self-organizing radial basis function neural network.

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms.