Target Values Research Articles

Phenotype-Specific Semi-Mechanistic Modelling of Florfenicol Time-Kill Curves in G. Parasuis Compared to Other Respiratory Pathogens.

This study examines the pharmacodynamics (PD) of florfenicol (FFN) for treating porcine respiratory diseases by comparing its effects on Glaesserella parasuis, Actinobacillus pleuropneumoniae and Pasteurella multocida. Invitro time-kill assays and semi-mechanistic PD modeling were used to assess bacterial growth and killing rates at varying FFN concentrations. Species-specific PD models indicated that fAUC/MIC was the best PK/PD index across all species. A. pleuropneumoniae and P. multocida had target values of 1.05 and 1.66 × MIC, respectively for bacteriostasis and 1.12 and 1.87 × MIC for 99.9% kill. Two phenotypes of G. parasuis emerged "fast-kill" and "slow-kill" which displayed distinct bacterial eradication rates despite similar MICs. For "slow-kill" isolates, an average free drug concentration (fAUC/MIC) of 1.46 and 1.63 × MIC (median, range: 1.53-1.69) was required for bacteriostasis and 99.9% kill. "Fast-kill" isolates needed an average free drug concentration of 1.38 × MIC for bacteriostasis and 1.51 × MIC for a 99.9% reduction. Indicating that the rate of kill influences the respective average free concentration required to achieve an equivalent antibacterial effect. Simulations of clinical dosing of FFN predicted bacterial eradication for all species, highlighting the value of phenotype-specific PD modeling in guiding treatment strategies for porcine respiratory infections.

Retrospective analysis of non-pharmacological factors associated with ineffective control of arterial hypertension in a clinical sample

Introduction. Arterial hypertension (HT) is the most common risk factor of cardiovascular diseases and leads to increased premature mortality. About half of HT patients do not reach the target values of blood pressure (BP).Aim: To assess the frequency of failure to achieve the target BP level and to identify non-pharmacological factors associated with inefficient BP control in a clinical sample of patients with hypertension.Material and Methods. A sequential clinical sample of patients (n = 105, 19–84 years old) with an established diagnosis of HT receiving antihypertensive therapy (AHT), was examined. Clinical data were analyzed in groups with efficient (ECHT, n = 29) and inefficient control of hypertension (ICHT, n = 76) by the time of admission to the hospital.Results. In the entire sample, the proportion of ICHT (72%) was higher compared to ECHT (28%). The groups did not differ in age, anthropometry, menopausal status in women, and duration of HT, but with an increase in the degree of hypertension, the proportion of patients with NCAH increased. There were more women in ECHT group, and there were equal proportions of men and women in ICHT group. In the ICHT group, we observed higher values of HDLC, more frequent chronic heart failure (CHF) and single atherosclerotic plaques (AP) of carotid and other peripheral arteries; in the ECHT group, the average levels of plasma glucose, glycated hemoglobin, the proportion of smokers were higher, and type 2 diabetes and multiple AP were more common. The AHT (doses, regimen, combinations) was more adequate in the ECHT group. About half of the patients received combination therapy.Conclusion. In the clinical sample, one third of HT patients receiving AHT, had blood pressure controlled to target at hospital admission. Inefficient blood pressure control was associated with male gender, a higher level of HDLC, the high degree of HT, single AP and CHF. In the efficient control group, smoking, comorbidity with type 2 diabetes, multiple AP, higher glucose levels and HbA1c were more often noted.

Using sensitivity analysis and soft calibration of geological regions to improve the representation of hydrological processes in a SWAT+ model

ABSTRACT Robust hydrological models require an accurate representation of hydrological processes, but modellers often rely solely on the streamflow simulation performance. This work presents an innovative zonal calibration workflow developed for a geologically heterogeneous basin, the Upper Tagus River Basin (Spain), towards achieving a realistic simulation of hydrological processes. A detailed SWAT+ model was built, and four geological regions were defined. The sensitivity of 10 parameters for two hydrological indices, runoff coefficient and groundwater contribution to the streamflow, was explored. Then, parameters were optimized for each region through a soft calibration to achieve an accurate simulation of these variables. Some parameters were identified as the most sensitive (cn2 and awc for runoff coefficient and perco for groundwater contribution). Sensitivity rankings and parameter adjustment varied among regions, revealing differences in the hydrological processes. The target values of the hydrological variables, previously estimated, were reached in every region, subsequently improving streamflow simulation performance.

Assessment of matting agent effect on polyurethane coatings' perceived blackness through geometric properties modeling: An AI approach

This study investigates the effect of matting agent on the perceived blackness of polyurethane automotive finishes by examining their impact on geometric properties using an AI-based neural network model. Black polyurethane coatings were prepared with varying gloss levels by incorporating different amounts of matting agent. Observers ranked these samples based on perceived blackness using the pair comparison method. The data features included the colorimetric properties and geometric attributes of the black polyurethane panels, with visual scales obtained as target values. Various neural networks were employed: Single-Layer Neural Network for linear regression, Multi-Layer Neural Network, Deep Neural Network, and Dropout-Enhanced Deep Neural Network for non-linear regression. Feature selection was applied to eliminate irrelevant or redundant features. The findings indicated that the Multi-Layer Neural Network for non-linear regression, utilizing geometric features, achieved favorable evaluation metrics, including mean squared errors, and R2 values. Additionally, this model predicted the visual scale values more accurately compared to other neural network models. By simulating this behavior, the research effectively eliminates the need for time-consuming visual assessment experiments traditionally used for blackness evaluations in industrial settings. This method greatly simplifies the evaluation process, conserving both time and resources while maintaining reliable and precise assessments.

Antimicrobial Drug Use and Changing Trends From 2021 to 2023-A Case Study of a General Hospital in Sichuan Province.

Antibiotic resistance (AR) is a growing concern as a result of the widespread and excessive use of antibiotics. Because of this, China's health authorities have implemented a number of antibiotic control measures, including a requirement that the intensity of antibiotic usage stay within 40.00 DDDs. This study, which used a tertiary general hospital in Sichuan Province as an example, examined the hospital's trend in antibiotic use from 2021 to 2023, the relationship between antibiotic use and hospitalization days and CMI, and the viability of 40.000 DDDs in light of the evolving medical landscape. Data were obtained through the hospital information system (HIS) and the hospital DRG system. Boston matrix diagrams were used to find the departments that needed to be focused on control, Cochran-Armitage trend test, and ANOVA to analyze the trends of AUR and antimicrobial use density (AUD) in the hospital over time in the last 3 years and to analyze the factors affecting the changes in antibiotic consumption. Over the previous 3 years, the hospital's antibiotic use rate varied between 40% and 50%, and its intensity of use varied between 40.000 and 50.000 DDDs, both of which were rather steady. January 2023 had a significant rise, with an AUD of 59.38 DDDs and an AUR of 61.80%. The second quadrant has eight departments. Each department's AUD varied dramatically; neurosurgery saw an increase annually (p < 0.05), abdominal wall surgery and gastrointestinal hernia procedures, obstetrics and gynaecologist, and ophthalmology saw a drop annually (p < 0.05). The average number of preoperative hospitalization days (r = 0.1402, p < 0.01) and the CMI (r = 0.4864, p < 0.001) were strongly connected with the AUD of surgical and nonsurgical departments, respectively. Hospitals should concentrate on the issue of surgical departments' lengthy preoperative prophylactic medication times. AUD management should also be dynamically modified based on CMI, particularly in departments in the second quadrant (low CMI and high AUD). Furthermore, it is challenging to maintain the hospital AUD target value of 40.00 DDDs under the new medical model, given the decline in hospitalization days of discharged patients and the high prevalence of infectious diseases. It is also unclear whether this target value is still appropriate given the current state of medicine.

Biofeedback control of photosynthetic lighting using real-time monitoring of leaf chlorophyll fluorescence.

Optimizing photosynthetic lighting is essential for maximizing crop production and minimizing electricity costs in controlled environment agriculture (CEA). Traditional lighting methods often neglect the impact of environmental factors, crop type, and light acclimation on photosynthetic efficiency. To address this, a chlorophyll fluorescence-based biofeedback system was developed to adjust light-emitting diode (LED) intensity based on real-time plant responses, rather than using a fixed photosynthetic photon flux density (PPFD). This study used the biofeedback system to maintain a range of target quantum yield of photosystem II (ΦPSII) and electron transport rate (ETR) values and to examine if the adjustment logic (ΦPSII or ETR-based) and crop type influence LED light intensity. The system was tested in a growth chamber with lettuce (Lactuca sativa) 'Green Towers' and cucumber (Cucumis sativus) 'Diva' to maintain six ETR levels (30, 50, 70, 90, 110, 130 μmol·m-2·s-1) and five ΦPSII levels (0.65, 0.675, 0.7, 0.725, 0.75) during a 16-hour photoperiod. The ETR-based biofeedback quickly stabilized the target ETR within 30-45 minutes, whereas the ΦPSII-based system needed more time. The system adjusted light intensities according to target values, acclimation status, and crop-specific responses. For example, to maintain a target ETR of 130 μmol·m-2·s-1, the gradual increase in ΦPSII over time due to light acclimation allowed the required PPFD to decrease by 35 μmol·m-2·s-1. Lettuce showed higher photosynthetic efficiency and lower heat dissipation than cucumber, leading to higher PPFD adjustments for lettuce. This biofeedback system effectively controls LED light, optimizing photosynthetic efficiency and potentially reducing lighting costs.

A prospective study to evaluate high dose daptomycin pharmacokinetics and pharmacodynamics in Staphylococcus spp. infective endocarditis.

Daptomycin pharmacokinetics and pharmacodynamics data relative to higher doses in patients are necessary for clinical practice. A monocentric, prospective study that enrolled patients with a diagnosis of Staphylococcus spp. infective endocarditis treated with daptomycin according to clinical practice, to evaluate the pharmacokinetics/pharmacodynamics of different daptomycin daily doses (group A: 8-10 and group B: 11-12 mg/kg). A monocentric, prospective, cohort study that enrolled patients with a diagnosis of Staphylococcus spp. infective endocarditis treated with daptomycin. Daptomycin was administered by intravenous infusion over a 30-min period for at least five consecutive days before PK study. Twenty-two patients were included. Native valve infectious endocarditis (IE) was diagnosed in 9 patients, prosthetic valve IE was diagnosed in 10 patients and 3 patients had concomitant intracardiac device infections. All patients showed a microbiologic response with negative blood cultures by day 5 (1-3 interquartile rate (IQR) 3-8). The median calculated AUC0-24 was 1298 (1-3 IQR 1069-1484) and 1459 (1-3 IQR 1218-1711) µg*h/mL, with the corresponding clearance of 0.49 (1-3 IQR 0.37-0.57) and 0.57 (1-3 IQR 0.40-0.71) L/h, respectively. A value of area under the curve/minimum inhibitory concentration (AUC/MIC) > 666 was reached by all patients; however, 4 out of 15 patients in group A and 1 out of 14 patients in group B did not reach the pharmacokinetic/pharmacodynamic (PK/PD) target of 1061; therefore, AUC/MIC equal to or above 1061 was reached by 73.3% in group A and 92.9% in group B. From a PK/PD point of view, all patients reached the value of AUC/MIC > 666, while roughly 70% of patients in group A and 90% in group B reached the target value of AUC/MIC>1061. Even if this cut-off value is arbitrary, 11-12 mg/kg daily dose could be taken into consideration in case of serious infections characterised by a high inoculum or in cases of prosthetic valve infections.

Utilization of the Whale Optimization Algorithm in Finite State Automata Design for Advanced Pattern Recognition Systems

This research explores the application of the Whale Optimization Algorithm (WOA) in designing Finite State Automata (FSA) for advanced pattern recognition systems. Pattern recognition plays a crucial role in various fields, requiring high accuracy and efficiency. Traditional approaches to FSA design often face limitations in adaptability and optimization. By integrating WOA, a nature-inspired metaheuristic algorithm, this study aims to optimize FSA structures to improve recognition capabilities. The research process involves implementing WOA within the FSA design framework, testing it on multiple artificial pattern recognition tasks to assess effectiveness, and comparing results with other optimization methods. The findings reveal that after 10 iterations, the WOA achieved a best score of 14.01% error, indicating initial progress but room for further improvement. At 50 iterations, the performance plateaued, maintaining a score of 9.43% error, suggesting a need for additional exploration of the parameter space. However, by 100 iterations, the WOA produced a significantly improved score of 0.0022% error, demonstrating a highly optimized solution as the parameters converged closely to their target values. After 100 iterations, the error value did not decrease any further, indicating that the effective iteration count for optimization is 100 iterations. These results highlight the effectiveness of WOA in enhancing FSA performance, showcasing its potential as a robust solution for complex pattern recognition needs. This study contributes to the development of intelligent recognition systems, advancing the state of the art in pattern recognition technology.

Energy efficiency potential determination for an oil treatment and stabilization unit at the field

Relevance. The desire to increase the energy efficiency of industrial enterprises and reduce greenhouse gas emissions from their activities. Reducing specific energy consumption at large oil refineries and petrochemical plants is not new and is quite widely used. Dozens of monographs and thousands of articles are devoted to these areas. But it should be taken into account that in the oil refining industry, all crude oil, even that which does not reach the refinery, necessarily passes through oil preparation and stabilization units at the fields. Therefore, in order to create energy-efficient and environmentally friendly processes throughout the entire oil refining chain, it is necessary to increase the energy efficiency of the oil preparation and stabilization units located at the fields. There are very few research works on thermal energy integration of oil preparation and stabilization units. Aim. Determination of target design and energy values for an energy-efficient retrofit project of the heat exchange network for the surveyed oil preparation and stabilization units and of its energy efficiency potential. Methods. Pinch analysis methods are used to determine the target values of an energy-efficient retrofit project. Mathematical modeling of heat exchange processes in the heat exchange network and economic analysis were performed using the Pinch 2.02 software; the Aspen HYSYS software package was used to create a simulation engineering model of the oil preparation and stabilization units. Results. When determining the target values of the heat exchange network retrofit project, the cost of energy and heat exchange equipment, the cost of collectors for splitting process streams, and technical restrictions on the placement of heat exchanger sections were taken into account. The inclusion of fuel gas in the process stream table has allowed the definition of target parameters for the optimal unit heat exchange network design to evolve. In the process of evolution of target values, the energy efficiency potential of the surveyed oil preparation and stabilization units was determined, which shows the possibility of reducing specific energy consumption by 77%. Upon implementation of the heat exchange network retrofit project and achievement of target parameters, the following economic results will be obtained: IRR=42%, NPV=7425780 USD, DPP 4 years. Also, CO2 emissions can be reduced by 30 thousand tons per year. It should be noted that the evolution of target designation and all target values for the reconstruction project were obtained before the implementation of the heat exchange network oil preparation and stabilization units project itself.

A study on misfire control using crank angular velocity combustion feedback considering in-cylinder gas properties

Internal combustion engines have been strongly required to improve thermal efficiency and utilize a variety of fuels derived from renewable energy sources to achieve carbon neutrality. In terms of engine development, the stable control of ignition and combustion is the primary challenge in adapting to diverse fuels for the recently emphasized premixed compression ignition combustion and super lean burn. In this study, a misfire index was used as a detector for a premixed type of diesel combustion engine. Combustion feedback control was performed to control the target value of EGR based on the difference between the misfire index and its target value. To apply crank angle velocity to feedback control, this study implemented corrections and limitations to the misfire index based on in-cylinder gas properties. This was made possible because, during misfire events, the in-cylinder pressure at top dead center (TDC) is not influenced by combustion pressure. Additionally, this research enabled model validation of the phenomena for the first time. A novel structure was also established to learn the EGR correction amount from the deviation of the misfire index, marking the first empirical demonstration of its effectiveness. This research not only presents control examples for misfire countermeasures, which are crucial for the development of engines capable of utilizing diverse fuels but also contributed to mass production and practical application.

Nanoencapsulated Optical Fiber-Based PEC Microelectrode: Highly Sensitive and Specific Detection of NT-proBNP and Its Implantable Performance.

Microelectrodes offer exceptional sensitivity, rapid response, and versatility, making them ideal for real-time detection and monitoring applications. Photoelectrochemical (PEC) sensors have shown great value in many fields due to their high sensitivity, fast response, and ease of operation. Nevertheless, conventional PEC sensing relies on cumbersome external light sources and bulky electrodes, hindering its miniaturization and implantation, thereby limiting its application in real-time disease monitoring. To overcome these limitations, we developed a nanoencapsulated optical fiber (OF)-based PEC microelectrode. The microelectrode features TiO2/CdS nanocrystals and bis (2,2'-bipyridine) (10-methylphenanthroline [3,2-a:2'3'-c] pyridine ruthenium(II) dichloride ([Ru(bpy)2dppz]2+) @dsDNA/Au@epigallocatechin gallate nanoparticle (EGCG NP) layers. And its application for the detection of N-terminal pro-brain natriuretic peptide (NT-proBNP) as a biomarker of cardiovascular diseases was explored. An extensive linear range of 1-5000 pg mL-1 combined with a low detection limit of 0.36 pg mL-1 was achieved. This range covers not only the recommended threshold for excluding cardiovascular diseases in the clinical diagnosis of individuals across all age groups but also the prognostic target value. The sensor exhibited excellent selectivity and stability and notable labeling recovery capability in serum tests. Critically, the sensor successfully discriminated the alterations in NT-proBNP secretion levels within human smooth muscle cells, comparing pre- and poststimulation by platelet-derived growth factor-BB. Even more significantly, the skin puncture experiment conducted in mice demonstrated the remarkable implantability and biological compatibility of the OF-PEC microelectrode. This addresses critical challenges commonly faced by microelectrodes when used as implanted devices, such as minimizing invasive trauma, mitigating inflammation, and preventing biofouling, thereby firmly establishing their suitability for the development of advanced implantable sensing devices. Therefore, the present OF microelectrode PEC biosensor is not only cost-effective, easy to operate, and miniaturized but also holds significant potential for enabling more precise, more minimally invasive, and continuous monitoring of biological markers without causing inflammation. This capability is crucial for early disease detection, tracking disease progression, and facilitating personalized treatment strategies, which expands the practical application of PEC sensors.

Sum-Rate Maximization in Massive MIMO with Joint Antenna Selection and Power Allocation

Telecommunications improvements in the last decades have led to an increase in the available data rate and reliability, and to reduce latency. Massive MIMO (mMIMO) has emerged as a promising solution to replace simple antenna setups. This arrangement involves a large number of transmitter antennas compared to the served terminals in the covered area, enabling higher data rates through the key characteristics of mMIMO. Despite its advantages, the utilization of such large arrays is energy-intensive. With fewer antennas, it is possible to achieve high data rates with reduced power consumption. Antenna selection is crucial for energy efficiency while maintaining computational efficiency. Additionally, power allocation in the downlink for each served terminal is an important step to consider. This work aims to propose a joint algorithm for selecting antennas and distributing energy among terminals, simplifying the process through low-complexity algorithms. The results show the penalty in sum-rate capacity for each proposed method. The two proposed algorithms demonstrate an adequate approach to the target value asymptotically as the number of transmitter antennas increases. This implies the possibility of achieving the joint process with significantly lower computational costs compared to perform antenna selection and power allocation separately.

Calibration and verification of DEM parameters for asphalt mixture based on experimental design method and bulk experiments

In order to obtain the parameters in the discrete element simulation of asphalt mixture, a calibration of parameters was conducted based on pile-up experiments and the Johnson–Kendall–Roberts (JKR) model. By combining physical experiments with simulation tests, three parameters that significantly affect the angle of repose (AOR) were selected from eight parameters using the Plackett–Burman (PB) experiment, namely the particle-particle rolling friction coefficient, JKR surface energy, and particle-particle static friction coefficient. The steepest ascent experiment was then used to determine the optimal range of the three significant parameters. Finally, a second-order regression model of the AOR and significant parameters was established through Box–Behnken design (BBD) experiments based on the principle of response surface methodology (RSM), with a model P-value of <0.0001. Using the experimentally measured real AOR value of 37.05° as the target value, the significant parameters were optimized, resulting in the optimal parameter combination solution: particle-particle rolling friction coefficient of 0.103, JKR surface energy of 6.55 J/m2, and particle-particle static friction coefficient of 0.495. The optimal parameter combination was tested in three simulation experiments, yielding a mean AOR of 37.16°, with a relative error of 0.44% compared to the physical experiment AOR, thus verifying the reliability of the optimal parameter combination.

Factoring fu Variability Into Estimates of Unbound Drug Concentrations Negatively Biases the MIC Versus% Probability of Target Attainment Relationship of Antimicrobial Agents.

The clinical breakpoint for a drug-pathogen combination reflects the drug susceptibility of the pathogen wild-type population, the location of the infection, the integrity of the host immune response, and the drug-pathogen pharmacokinetic (PK)/pharmacodynamic (PD) relationship. That PK/PD relationship, along with the population variability in drug exposure, is used to determine the probability of target attainment (PTA) of the PK/PD index at a specified minimum inhibitory concentration (MIC) for a selected target value. The PTA is used to identify the pharmacodynamic cutoff value (COPD), which is one of the three components used to establish the clinical breakpoint. A challenge encountered when defining the COPD is that the available PK information typically reflects total (free plus protein-bound) plasma concentrations. However, it is the unbound drug concentrations that exert the therapeutic effects and how the population fraction unbound (fu) incorporated into the COPD assessments can markedly influence the COPD. Factors examined included the estimated population fu mean (risk of bias) and the incorporation of estimated fu population variability into the Monte Carlo simulations when converting total to unbound plasma concentrations (risk of inflating variability). In this in silico study, the drug fu, systemic clearance, and the variability of both were altered so that the relative impact of each could be explored. We demonstrate that incorporating fu variability into the estimation of fAUCback can bias the COPD assessment and that the magnitude of bias reflects the relative variability in systemic clearance and fu.

Large-Space Laser Tracking Attitude Combination Measurement Using Backpropagation Algorithm Based on Neighborhood Search

Large-space high-precision attitude dynamic measurement technology has urgent application needs in large equipment manufacturing fields, such as aerospace, rail transportation, automobiles, and ships. In this paper, taking laser tracking equipment as the base station, a backpropagation algorithm based on neighborhood search is proposed, which is applied to the fusion of multi-source information for solving the dynamic attitude angle. This paper firstly established a mathematical model of laser tracking attitude dynamic measurement based on IMU and CCD multi-sensor, designed a 6-11-3 back propagation network structure and algorithm flow, and realized the prediction of attitude angle through model training. Secondly, the method based on neighborhood search realizes the determination of the optimal training target value of the model, of which the MSE has a 34% reduction compared to the IMU determination method. Finally, the experimental platform is set up with the precision rotary table as the motion carrier to verify the effectiveness of the research method in this paper. The experimental results show that with the neighborhood-based backpropagation algorithm, the measurement results have a higher data update rate and a certain inhibition effect on the error accumulation of IMU. The absolute value of the system angle error can be less than 0.4° within 8 m and 0–50°, with an angle update rate of 100 Hz. The research method in this paper can be applied to the dynamic measurement of laser tracking attitude angles, which provides a new reference for the angle measurement method based on the fusion of multi-source information.

Comparison of statistical evaluation approaches for log-removal validation according to European water reuse regulations.

In 2020, the European Union published ordinance EU 2020/741, establishing minimum requirements for water reuse in agriculture. The ordinance differentiates between several water quality classes. For the highest water quality class (Class A), the ordinance mandates analytical validation of the treatment performance of new water reuse treatment plants (WRTP) related to the removal of microbial indicators for viral, bacterial, and parasitic pathogens. While the ordinance clearly defines the numeric target values for the required log10-reduction values (LRV), it provides limited to no guidance on the necessary sample sizes and statistical evaluation approaches. The main requirement is that at least 90% of the validation samples should meet the requirements. However, the interpretation of this 90% validation target can significantly impact the required sample size, efforts necessary, and the risk of misclassifying WRTPs in practice. The present study compares different statistical evaluation approaches that might be considered applicable for LRV validation monitoring. Special emphasis is placed on the use of tolerance intervals, which combine percentile estimations with sample size-based uncertainty and confidence regions. Tolerance interval-based approaches are compared with alternative methods, including a) a binomial evaluation and b) the calculation of empirical percentiles. The latter are already used in existing European and U.S. regulations for bathing water and irrigation water quality. Our study demonstrates that using tolerance intervals allows for the reliable validation of WRTPs that achieve high LRVs relative to regulatory targets with comparatively smaller sample sizes compared to the other two approaches, while reducing the risk of misclassification. Additionally, we show that simplified approaches, such as a "9 out of 10" approach, pose a substantial risk of misclassification and should not be applied. We illustrate the behavior of these different approaches through simulation experiments and application to real data collected in 2022 and 2023 at a large WRTP in Germany.

Monitoring and optimization of the microenvironment in a gravity-driven microfluidic system placed on a slow-tilting table.

Gravity-driven microfluidic chips offer portability and flexibility in different settings because pumps and connecting tubes are unnecessary for driving fluid flow. In a previous study, human induced pluripotent stem cells were cultured using gravity-driven microfluidics, with the liquid flow rate regulated by a tilting table. However, instability in cell culture has been observed, occasionally leading to cell death owing to unknown causes. This study measured the ability of a gravity-driven microfluidic system to maintain essential microenvironments, specifically the flow rate, CO2 levels, temperature, and humidity. The incubation procedure was improved to stabilize the parameters at target values. Improvements in the incubation process reduced the time required to reach the stabilized value for CO2, temperature, and humidity by 85, 67, and 5%, respectively, compared to previous methods. The system demonstrated a precise flow rate, confirmed by a consistent increase in the downstream tank's medium volume after 4h of perfusion. In addition, the adjustment of the tilting table maintained a steady angle and effectively regulated the flow rate, with the measured flow rate consistent with the theoretical value. The gravity-driven microfluidic system effectively facilitated the culture and differentiation of human iPSCs into the mesodermal lineage after bone morphogenetic protein 4 induction, as indicated by positive SSEA1 immunostaining, demonstrating its potential for stem cell research. Gravity-driven microfluidic systems satisfy these requirements and are suitable for stem cell culture experiments.

Multi-UAV cooperative task assignment based on multi-strategy improved DBO

Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.

In Situ Gels for Nasal Delivery: Formulation, Characterization and Applications

AbstractThe nasal route offers many advantages for drug delivery: quick onset of action, better patient compliance, avoidance of first‐pass metabolism and bypassing the blood‐brain barrier. Despite the potential of this route, several challenges exist, such as the short drug retention time caused by mucociliary clearance (MCC). In situ forming gels, which undergo a sol‐to‐gel transition with specific triggers at the site of action, present real opportunities in this field. They combine the intrinsic characteristics of hydrogels (elasticity and water‐holding capacity) with responsiveness, allowing easy application of drugs (spraying or extruding through a nozzle), as well as prolonged retention in the nasal cavity. The incorporation of mucoadhesive polymers, additives and nanocarriers can further tune the properties of in situ gels as nasal delivery platforms. This review summarizes advances in in situ gels for nasal drug delivery. We first describe challenges of the nasal route, target properties of in situ nasal gels, and then present both “classic” gelling polymers (poloxamers and polysaccharides) – which form the bulk of reported studies on nasal gels – as well as novel bespoke materials; we review characterization methods, highlighting the lack of standardization and accepted target values, then discuss applications by spraying, and conclude with future prospects.

The effect of blood glucose levels on serum triglyceride clearance in patients with hyperlipidemic acute pancreatitis

Preventing moderately severe and severe acute pancreatitis (MSAP & SAP) is the primary goal of the management of hyperlipidemic acute pancreatitis (HLAP). The main aim of this study was to investigate the factors affecting serum triglyceride (TG) clearance, particularly blood glucose (GLU) levels, which could potentially help to prevent the development of MSAP & SAP. The clinical data from 177 patients with MSAP & SAP and 195 patients with mild acute pancreatitis (MAP) on days 1–6 after the onset were collected and analyzed by multivariate logistic regression to identify the factors that have an impact on the severity of HLAP, especially TG. The optimal TG threshold was obtained by receiver operating characteristic (ROC) analysis to differentiate TG fast-clearance patients from TG slow-clearance patients, as defined in this study. Metabolism-related factors that may interfere with TG clearance, such as GLU, diabetes mellitus (DM), obesity, and uric acid, were further analyzed. Day 2 TG was an independent risk factor for MSAP & SAP in patients with HLAP (OR: 3.718, 95% CI: 2.042–6.77; P < 0.001). And patients with TG slow-clearance (day 2 TG ≥ 7.335 mmol/L) were more likely to develop MSAP & SAP (sensitivity 0.932, specificity 0.898), severe pancreatic necrosis, and death. DM (OR: 3.574, 95% CI: 1.13–11.308, P < 0.001) and day 2 GLU (OR: 1.537, 95% CI: 1.292–1.675; P < 0.001) were independent risk factors for TG slow-clearance but not for HLAP severity (OR: 1.728, P = 0.253 and OR: 1.119, P = 0.059). Day 2 GLU decreased below 13.07 mmol/L and 6.575 mmol/L in patients with DM and non-diabetes mellitus (NDM), respectively, contributing to the reduction of day 2 TG to 7.335 mmol/L. Day 2 TG levels had the most impact on the severity of HLAP, which also accurately predicted the occurrence of MSAP & SAP. It is worth noting whether the GLU can reach the target value on day 2, as it can directly affect the TG clearance rate and indirectly affect the severity of HLAP.