Tag: statistics

Med Sci Monit. 2023 Jul 8;29:e939482. doi: 10.12659/MSM.939482.

ABSTRACT

BACKGROUND Patients with insulin-resistant diabetes have the highest risk of kidney disease. The triglyceride glucose (TyG) index is considered a reliable and simple marker of insulin resistance. We studied the relationship between the TyG index, diabetic kidney disease (DKD), and related metabolic disorders in patients with type 2 diabetes. MATERIAL AND METHODS This retrospective study included a consecutive case series from January 2021 to October 2022 in the Department of Endocrinology at Hebei Yiling Hospital. In total, 673 patients with type 2 diabetes met the inclusion criteria. The TyG index was calculated by napierian logarithmic (ln) (fasting triglyceride×fasting glucose /2). Patient demographic and clinical indicators were obtained from medical records, and statistical analysis was conducted using SPSS version 23. RESULTS The TyG index was significantly related to metabolic indicators (low-density lipoprotein, high-density lipoprotein, alanine aminotransferase, plasma albumin, serum uric acid, triglyceride, and fasting glucose) and urine albumin (P<0.01) but not with serum creatinine and estimated glomerular filtration rate. In multiple regression analysis, an increase in the TyG index was revealed to be an independent risk factor for DKD (OR: 1.699, P<0.001). CONCLUSIONS The TyG index was independently related to DKD and related metabolic disorders; therefore, the TyG index can be used as an early sensitive target for clinical guidance in the treatment of DKD with insulin resistance.

PMID:37421131 | DOI:10.12659/MSM.939482

Micromachines (Basel). 2023 Apr 14;14(4):848. doi: 10.3390/mi14040848.

ABSTRACT

PURPOSE: This study aims to evaluate the efficiency of infrared LEDs with a magnetic solenoid field in lowering the quantity of gram-positive Staphylococcus aureus and gram-negative Escherichia coli bacteria, as well as the best exposure period and energy dose for inactivating these bacteria.

METHOD: Research has been performed on a photodynamic therapy technique called photodynamic inactivation (PDI), which combines infrared LED light with a wavelength range of 951-952 nm and a solenoid magnetic field with a strength of 0-6 mT. The two, taken together, can potentially harm the target structure biologically. Infrared LED light and an AC-generated solenoid magnetic field are both applied to bacteria to measure the reduction in viability. Three different treatments infrared LED, solenoid magnetic field, and an amalgam of infrared LED and solenoid magnetic field, were used in this study. A factorial statistical ANOVA analysis was utilized in this investigation.

RESULTS: The maximum bacterial production was produced by irradiating a surface for 60 min at a dosage of 0.593 J/cm², according to the data. The combined use of infrared LEDs and a magnetic field solenoid resulted in the highest percentage of fatalities for Staphylococcus aureus, which was 94.43 s. The highest percentage of inactivation for Escherichia coli occurred in the combination treatment of infrared LEDs and a magnetic field solenoid, namely, 72.47 ± 5.06%. In contrast, S. aureus occurred in the combined treatment of infrared LEDs and a magnetic field solenoid, 94.43 ± 6.63 percent.

CONCLUSION: Staphylococcus aureus and Escherichia coli germs are inactivated using infrared illumination and the best solenoid magnetic fields. This is evidenced by the rise in the proportion of bacteria that died in treatment group III, which used a magnetic solenoid field and infrared LEDs to deliver a dosage of 0.593 J/cm² over 60 min. According to the research findings, the magnetic field of the solenoid and the infrared LED field significantly impact the gram-positive bacteria S. aureus and the gram-negative bacteria E. coli.

PMID:37421081 | DOI:10.3390/mi14040848

Micromachines (Basel). 2023 Mar 31;14(4):793. doi: 10.3390/mi14040793.

ABSTRACT

When rotating at a high speed in a microscale flow field in confined spaces, rotors are subject to a complex flow due to the joint effect of the centrifugal force, hindering of the stationary cavity and the scale effect. In this paper, a rotor-stator-cavity (RSC) microscale flow field simulation model of liquid-floating rotor micro gyroscopes is built, which can be used to study the flow characteristics of fluids in confined spaces with different Reynolds numbers (Re) and gap-to-diameter ratios. The Reynolds stress model (RSM) is applied to solve the Reynolds averaged Navier-Stokes equation for the distribution laws of the mean flow, turbulence statistics and frictional resistance under different working conditions. The results show that as the Re increases, the rotational boundary layer gradually separates from the stationary boundary layer, and the local Re mainly affects the distribution of velocity at the stationary boundary, while the gap-to-diameter ratio mainly affects the distribution of velocity at the rotational boundary. The Reynolds stress is mainly distributed in boundary layers, and the Reynolds normal stress is slightly greater than the Reynolds shear stress. The turbulence is in the state of plane-strain limit. As the Re increases, the frictional resistance coefficient increases. When Re is within 10⁴, the frictional resistance coefficient increases as the gap-to-diameter ratio decreases, while the frictional resistance coefficient drops to the minimum when the Re exceeds 10⁵ and the gap-to-diameter ratio is 0.027. This study can enable a better understanding of the flow characteristics of microscale RSCs under different working conditions.

PMID:37421027 | DOI:10.3390/mi14040793

Micromachines (Basel). 2023 Mar 31;14(4):787. doi: 10.3390/mi14040787.

ABSTRACT

Single-cell analysis provides a wealth of information regarding the molecular landscape of the tumor cells responding to extracellular stimulations, which has greatly advanced the research in cancer biology. In this work, we adapt such a concept for the analysis of inertial migration of cells and clusters, which is promising for cancer liquid biopsy, by isolation and detection of circulating tumor cells (CTCs) and CTC clusters. Using high-speed camera tracking live individual tumor cells and cell clusters, the behavior of inertial migration was profiled in unprecedented detail. We found that inertial migration is heterogeneous spatially, depending on the initial cross-sectional location. The lateral migration velocity peaks at about 25% of the channel width away from the sidewalls for both single cells and clusters. More importantly, while the doublets of the cell clusters migrate significantly faster than single cells (~two times faster), cell triplets unexpectedly have similar migration velocities to doublets, which seemingly disagrees with the size-dependent nature of inertial migration. Further analysis indicates that the cluster shape or format (for example, triplets can be in string format or triangle format) plays a significant role in the migration of more complex cell clusters. We found that the migration velocity of a string triplet is statistically comparable to that of a single cell while the triangle triplets can migrate slightly faster than doublets, suggesting that size-based sorting of cells and clusters can be challenging depending on the cluster format. Undoubtedly, these new findings need to be considered in the translation of inertial microfluidic technology for CTC cluster detection.

PMID:37421020 | DOI:10.3390/mi14040787

Micromachines (Basel). 2023 Mar 29;14(4):750. doi: 10.3390/mi14040750.

ABSTRACT

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al₂O₃ nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al₂O₃ nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

PMID:37420983 | DOI:10.3390/mi14040750

Micromachines (Basel). 2023 Mar 29;14(4):749. doi: 10.3390/mi14040749.

ABSTRACT

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

PMID:37420982 | DOI:10.3390/mi14040749

Micromachines (Basel). 2023 Mar 23;14(4):707. doi: 10.3390/mi14040707.

ABSTRACT

Here, a molecular imprinting technique was employed to create an SPR-based nanosensor for the selective and sensitive detection of organophosphate-based coumaphos, a toxic insecticide/veterinary drug often used. To achieve this, UV polymerization was used to create polymeric nanofilms using N-methacryloyl-l-cysteine methyl ester, ethylene glycol dimethacrylate, and 2-hydroxyethyl methacrylate, which are functional monomers, cross-linkers, and hydrophilicity enabling agents, respectively. Several methods, including scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle (CA) analyses, were used to characterize the nanofilms. Using coumaphos-imprinted SPR (CIP-SPR) and non-imprinted SPR (NIP-SPR) nanosensor chips, the kinetic evaluations of coumaphos sensing were investigated. The created CIP-SPR nanosensor demonstrated high selectivity to the coumaphos molecule compared to similar competitor molecules, including diazinon, pirimiphos-methyl, pyridaphenthion, phosalone, N-2,4(dimethylphenyl) formamide, 2,4-dimethylaniline, dimethoate, and phosmet. Additionally, there is a magnificent linear relationship for the concentration range of 0.1-250 ppb, with a low limit of detection (LOD) and limit of quantification (LOQ) of 0.001 and 0.003 ppb, respectively, and a high imprinting factor (I.F.4.4) for coumaphos. The Langmuir adsorption model is the best appropriate thermodynamic approach for the nanosensor. Intraday trials were performed three times with five repetitions to statistically evaluate the CIP-SPR nanosensor’s reusability. Reusability investigations for the two weeks of interday analyses also indicated the three-dimensional stability of the CIP-SPR nanosensor. The remarkable reusability and reproducibility of the procedure are indicated by an RSD% result of less than 1.5. Therefore, it has been determined that the generated CIP-SPR nanosensors are highly selective, rapidly responsive, simple to use, reusable, and sensitive for coumaphos detection in an aqueous solution. An amino acid, which was used to detect coumaphos, included a CIP-SPR nanosensor manufactured without complicated coupling methods and labelling processes. Liquid chromatography with tandem mass spectrometry (LC/MS-MS) studies was performed for the validation studies of the SPR.

PMID:37420940 | DOI:10.3390/mi14040707

Sensors (Basel). 2023 Jun 20;23(12):5756. doi: 10.3390/s23125756.

ABSTRACT

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities “in the wild” demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user’s movements in time series. However, frequency series contain more information about sensors’ signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model’s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.

PMID:37420921 | DOI:10.3390/s23125756

Sensors (Basel). 2023 Jun 20;23(12):5738. doi: 10.3390/s23125738.

ABSTRACT

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

PMID:37420900 | DOI:10.3390/s23125738

Sensors (Basel). 2023 Jun 18;23(12):5685. doi: 10.3390/s23125685.

ABSTRACT

Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore. As sensors are typically low-end devices, there is no built-in defense mechanism in sensors. It is common that system-level defense provides protection against security threats on sensors. Unfortunately, high-level countermeasures do not differentiate the root of cause and treat all anomalies with system-level recovery processes, resulting in high-cost overhead on delay and power consumption. In this work, we propose a secure architecture for temperature sensors with a transducer and a signal conditioning unit. The proposed architecture estimates the sensor data with statistical analysis and generates a residual signal for anomaly detection at the signal conditioning unit. Moreover, complementary current-temperature characteristics are exploited to generate a constant current reference for attack detection at the transducer level. Anomaly detection at the signal conditioning unit and attack detection at the transducer unit make the temperature sensor attack resilient to intentional and unintentional attacks. Simulation results show that our sensor is capable of detecting an under-powering attack and analog Trojan from a significant signal vibration in the constant current reference. Furthermore, the anomaly detection unit detects anomalies at the signal conditioning level from the generated residual signal. The proposed detection system is resilient against any intentional and unintentional attacks, with a detection rate of 97.73%.

PMID:37420851 | DOI:10.3390/s23125685