Estimation Of Content Research Articles

Quantitative Estimation of Iron and Fat Content in Prostate Cancer by Multiparametric MRI and Its Application in Optimizing D'Amico Score.

The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI). To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients. Prospective. Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years). 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence. The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected. Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant. Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723). This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved. 2 TECHNICAL EFFICACY: Stage 2.

Fluorescent Staining and Quantification of Starch Granules in Chloroplasts of Live Plant Cells Using Fluorescein.

Plants use CO2, water, and light energy to generate carbohydrates through photosynthesis. During daytime, these carbohydrates are polymerized, leading to the accumulation of starch granules in chloroplasts. The catabolites produced by the degradation of these chloroplast starch granules are used for physiological responses and plant growth. Various staining methods, such as iodine staining, have previously been used to visualize the accumulation of chloroplast starch granules; however, these staining methods cannot be used to image live cells and/or provide confocal images with non-specific signals. In this study, we developed a new imaging method for the fluorescent observation of chloroplast starch granules in living plant cells by staining with fluorescein, a widely available fluorescent dye. This simple staining method, which involves soaking a leaf disk in staining solution, shows high specificity in confocal images. Fluorescent images of the stained tissue allow the cellular starch content of living cells to be quantified with the same level of accuracy as a conventional biochemical method (amyloglucosidase/α-amylase method). Fluorescein staining thus not only enables the easy and clear observation of chloroplast starch granules but also allows for precise quantification in living cells. Key features • Visualizes chloroplast starch granules stained with fluorescein in living cells. • Requires only simple specimen preparation with no reagents needed other than the staining solution. • Fluorescein is readily available worldwide. • Highly specific method for identifying chloroplast starch granules in confocal images. • Enables estimation of cellular starch content using fluorescent images.

Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.

Phytochemical and toxicological evaluation of aqueous leaf extract of Premna integrifolia L. in male balb/c mice.

BackgroundPhytoconstituents present in plants provide a natural and comprehensive method for combating oxidative stress and various ailments. Safety evaluation of these herbal medications is necessary, so acute and sub-acute toxicity of plant leaf extract Premna integrifolia has been performed to standardize its dose regarding its therapeutic application in male fertility. PurposeToxicological evaluation Aqueous leaf extract of Premna integrifolia (ALEPI), a known herbal medicine, for its utilization in male fertility enhancement use. Study design and methodsTo assess the safety and pharmacological potential of the ALEPI, acute and subacute toxicity studies were carried out in compliance with the OECD guidelines No. 425 and 407, respectively. Identification of pharmacologically active compounds in ALEPI was performed using UHPLC-HRMS analysis. In vitro,estimation of Polyphenol Content, Total Flavonoid Content, saponins and Antioxidant Properties of ALEPI was performed. The oral acute toxicity of the ALEPI was conducted using male mice. The study involved administering single doses of ALEPI at 300, 2000, and 5000 mg/kg b.wt., followed by a fourteen-day observation period to monitor any signs of toxicity or adverse effects. In the oral subacute toxicity study, mice received daily oral gavage of the ALEPI at doses of 400, 800, and 1000 mg/kg b.wt. for twenty-eight days. The parameters studied included total reactive oxygen species (ROS) production, apoptosis, cell viability percentage, and cellular morphology. ResultsThe UHPLC-HRMS analysis of the ALEPI reveals the presence of various bioactive compounds with significant antioxidant, anti-inflammatory, and anticancer properties. The acute and subacute toxicity studies of the ALEPI provided a comprehensive evaluation of its safety profile. Both studies demonstrated no mortality and only minor alterations in relative organ weights, hematology, and serum biochemistry, suggesting a low toxicity profile. Additionally, the normal histoarchitecture of vital organs indicates the absence of significant morphological changes. Significant reduction in total ROS, necrosis percentage, and apoptosis in testicular tissue compared to the control group highlights the antioxidant properties of the extract. ConclusionBased on the findings from both the acute and subacute toxicity studies, it is evident that the ALEPI does not actuate adverse effects of toxicological significance at the tested doses and hence can be used at appropriate concentrations in therapeutic application.

Enhanced Neuroprotection in Experiment Multiple Sclerosis through Combined Rosiglitazone and Probiotic-Loaded Solid Lipid Nanoparticles: Modulation of Cellular Signaling Pathways.

Multiple sclerosis (MS) is a persistent autoimmune condition characterized by inflammation and neurodegeneration. The current efficacy of treatments is limited, which has generated interest in developing neuroprotective strategies. Solid lipid nanoparticles (SLNs) and probiotics are potential drug delivery vehicles for targeting the CNS (Central nervous system), regulating immune responses, and supporting neuroprotection in neurological conditions. The study investigates how SLNs containing RSG (rosiglitazone) and probiotics can protect the nervous system in cases of MS. We administered toxin EtBr (Ethidium bromide) from day 1 to day 7, later followed by the treatment from day 8 to day 35. During this time interval, various behavioural parameters have been performed. Further, after 35th day, blood plasma of animals was collected to study complete CBC profiling and animals were sacrificed. Then, biochemical and molecular studies, gross morphology of brain sectioning, histopathological evaluation and estimation of fatty acid content in fecal matter were performed. RSG shows neuroprotective effects by blocking the STAT-3 and mTOR signaling pathways and increasing the production of PPAR-gamma. GW9662, a PPAR-gamma antagonist given at a dose of 2 mg/kg (i.p), was utilized to evaluate the role of PPAR-gamma and to compare the efficacy of RSG and probiotic-loaded SLNs in potentially providing neuroprotection. The relationship between RSG and the STAT-3, mTOR, and PPAR-gamma pathways in MS was confirmed and validated using in-silico analysis. RSG and probiotic-loaded SLNs modulate the complete blood profiling of rats and improve the symptoms of MS. We assessed the diagnostic capabilities of different biological samples such as cerebrospinal fluid, blood plasma, and brain homogenates (specifically from the hippocampus, striatum, cortex, and midbrain) to analyze neurochemical changes linked to neurobehavioral changes in the progression of MS. The study showed that combining RSG and probiotics in an experimental medication form improved symptoms of MS more effectively than using RSG alone. This improvement is likely due to changes in STAT-3, mTOR, and PPAR-gamma signaling pathways.

Investigation on the impact of the spatial arrangement of individuals in a standing-type in-vivo monitoring system

An automated standing type Quick Scan Whole Body Monitor (QS-WBM) has been developed for the measurement of internal radioactive contamination due to high energy photon (HEP) emitters (Eϒ >200keV). Individuals are monitored while standing on a platform inside QS-WBM at specified reference position. Instances may occur where individuals deviate from their monitoring position, potentially leading to errors in the measurement of the body content. Positional inaccuracies are estimated in three potential scenarios: lateral displacement of the individual, movement perpendicular to the detector face, and forward bending of the torso. The Monte Carlo particle transport code FLUKA is used for estimation of efficiencies for selected radionuclides across various geometrical positions for adult male radiation workers. Approximately, 8% variation in calibration factors (CFs) is noted for lateral movements up to 15 cm from the centerline regardless of the energy, while perpendicular movements from reference position exhibit variations ranging from 14 to 60 %. This study helps in quantifying uncertainties in the estimation of body content and dose, arising from deviations in position of individuals in QS-WBM enclosure during monitoring.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Examination of the Dry Density Importances in TDR Use and of the ASTM D6780 Standard Based on a New Calibration Procedure

Abstract The use of time domain reflectometry (TDR) to monitor soil water content at either field or laboratory scales is highly common. Regarding the soil dry density, ρd, effect on the TDR application, three main approaches are found in the literature: (1) Ignore the density issue and use calibration to evaluate the water content regardless of the density. This approach is basic, yet common. (2) Include soil density in the calibration equation and use calibration for the water content as a function of both the TDR output and soil density. In this approach, the soil density should be provided from an independent source. (3) Determined ρd from the TDR waveform. In this case both water content and ρd are set from the TDR analysis. In cases where the ρd is not available, in order to use TDR for water content measurement, a decision has to be made whether to address the density issue although it requires resources, or to neglect it. This article reviews this issue and validates the density effect using an efficient TDR calibration methodology. A calibration scheme that controls ρd of the specimen has been developed and applied. The methodology creates continuous calibration curves over a range of volumetric water content from a single test specimen. Based on the results, it has been demonstrated that the soil density has a major effect on the TDR output and it cannot be ignored. In addition, this article presents a unique examination of ASTM D6780, Standard Test Methods for Water Content and Density of Soil In Situ by Time Domain Reflectometry (TDR). The ASTM dry density estimation was found to provide reliable results; however, the gravimetric water contents estimation gives unsatisfactory results, especially for low densities.

Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning

AbstractFusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.

Estimating ice water content for winter storms from millimeter-wavelength radar measurements using a synthesis of polarimetric and dual-frequency radar observations

Abstract The potential of millimeter-wavelength radar-based Ice Water Content (IWC) estimation is demonstrated using a Ka-band Scanning Polarimetric Radar (KASPR) for the U.S northeast coast winter storms. Two IWC relations for Ka-band polarimetric radar measurements are proposed: one that uses a combination of the radar reflectivity Z and the estimated total number concentration of snow particles Nt and the other based on the joint use of Z, specific differential phase KDP, and the degree of riming frim. A key element of the algorithms is to obtain the “Rayleigh-equivalent” value of Z measured at Ka band, i.e., the corresponding Z at longer radar wavelength for which Rayleigh scattering takes place. This is achieved via polarimetric retrieval of the mean volume diameter Dm and incorporating the relationship between the dual wavelength ratio DWRS/Ka and Dm. Those techniques allow for retrievals from single millimeter-wavelength radar measurements and do not necessarily require the DWR measurements, if the DWR-Dm relation and Rayleigh assumption for Ka-band KDP are valid. Comparison between the quasi-vertical profile product obtained from KASPR and the columnar vertical profile product generated from the nearby WSR-88D S-band radar measurements demonstrates that the DWRS/Ka can be estimated from the two close radars without the need for collocated radar beams and synchronized antenna scanning and can be used for determining the “Rayleigh-equivalent” value of Z. The performance of the suggested techniques is evaluated for seven winter storms using surface disdrometer and snow accumulation measurements.

Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

Machine learning‐driven microwave imaging for soil moisture estimation near leaky pipe

AbstractCharacterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%.

Selection of optimal spectral features for leaf chlorophyll content estimation

Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.

Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging

Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging

Scientifc Validation and Chromatographic Method Development for the Estimation of Piperine in Herbal Formulation Babbularishta: A Quality Control Strategy for Critical Quality Attributes

Background: Babbularishta is an Ayurvedic medicine in liquid form used for the treatment of phthisis, skin diseases, urinary disorders,, and breathing problems. It is also beneficial in cases of blood disorders, acne, dermatitis, hemoptysis, lung injury, asthma, persistent cough, whooping cough, etc. Objective: The functional role of this study is to develop a simple, novel, precise, accurate, reliable, and selective RP-HPLC method for the estimation of piperine content in herbal drug formulations (Babbularista). Methods: A traditional approach to method development could fail to meet the desired separation. Consideration of the current regulatory requirement for analytical method development and RP-HPLC method for routine analysis of piperine in the herbal formulation has been optimized using an analytical Quality by Design (QbD) approach. Three laboratory formulations and three marketed preparations of Babularishta were investigated for organoleptic parameters, physicochemical parameters, and validation. The mobile phase composition (methanol and water % as A), pH (B), and flow rate (mL/min as C) were selected as independent variable,. In contrast, retention time (min. as Y2), area (AU as Y1) is selected as dependent variables. Results: The fingerprinting method was developed and optimized by using the AQbD approach. Piperine content was found to be in Piper nigrum (Marica) 3.01±0.01527 % w/w and in Piper longum (Pipali) 2.74±0.01154% w/w in Babbularista. Conclusion: An attempt has been made to develop quality control parameters of Babbularista by developing a suitable and sensitive HPLC methods for the quantitation of piperine in herbal drug formulations (Babbularista). The developed method was found to be simple, accurate, precise,, and economical for the estimation of piperine in herbal formulations.

Cardiograms and Electrography Are Crucial Diagnostic Procedures In Pregnant Women

Certain examinations place significant emphasis on the gestation period. Strictly passing every examination is of utmost importance for every woman, as failure to do so may result in the birth of children with various deformed and incapacitating disorders. The diagnostic procedures of cardiotomy, electrocardiography, ultrasonography, doppler ultrasonography, and phenocardiography are highly significant for pregnant women. Every expectant mother should undertake these tests comprehensively. The duration of pregnancy and developmental abnormalities like as hypotrophy, hydrocephaly, and microcephaly can be determined using these techniques. Furthermore, these techniques facilitate the surveillance of the foetal placenta and the estimation of the water content, particularly in cases of multiple pregnancies. Sonography examination is safe for both the pregnant woman and the developing foetus. Today, X-ray diagnostics is seldom employed. However, this approach strictly relies on the use of the vagina as the sole means of assessing the state and abnormalities of the foetus, in the absence of ultrasound equipment.

Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam

Accurate estimation of Soil Organic Carbon (SOC) is vital for assessing soil fertility, health, and carbon sequestration. Visible and Near-Infrared (Vis-NIR) spectroscopy has gained popularity worldwide for SOC estimation due to its cost-effectiveness and environmental benefits. However, inconsistencies arise from varying preprocessing techniques and regression models applied across different datasets and regions. Few studies explore combinations of spectral preprocessing, modeling algorithms, and resampling techniques. This study presents the first SOC estimation using Vis-NIR spectroscopy in the Red River Delta, Vietnam. We assessed estimation performances incorporating fifteen preprocessing techniques, four regression models, and three resampling methods to identify the most effective strategies. Standard Normal Variate (SNV) emerged as the top preprocessing technique, while Partial Least Squares Regression (PLSR) demonstrated the highest accuracy with minimal discrepancies between calibration and validation. Regarding resampling methods, repeated cross-validation (repeatedcv) proved most robust, with simple cross-validation as an alternative. By utilizing SNV, PLSR, and repeatedcv, we achieved the first successful Vis-NIR spectroscopy-based SOC estimation in the Red River Delta and Vietnam. This approach satisfied stringent statistical criteria for predictive models, yielding validation performance metrics of R2 = 0.740, RMSE = 0.166, RPD = 2.337, and RPIQ = 2.321. Our findings highlight the importance of optimizing preprocessing, regression, and resampling techniques for accurate Vis-NIR spectroscopy-based SOC prediction.

Novel oxygen vacancies-rich 3D porous ZnO for highly sensitive 3-hydroxy-2-butanone biomarker gas sensors

Listeriosis can cause human death via infection by Listeria monocytogenes (LMs) with a high fatality rate of 20–30 %. Detection of 3-hydroxy-2-butanone (3H-2B) biomarker of LMs can achieve estimation of LMs content, which is of great significance for human health. Exploring porous metal oxide semiconductors (MOSs) with rich vacancies towards 3H-2B sensing is quite promising for achieving this goal. Herein, we report a highly sensitive 3H-2B sensor based on novel 3D porous ZnO with rich oxygen vacancies which was facilely obtained by sacrificing Zn foam in oxalic acid and subsequent thermal decomposition. The ZnO sensor displays ultrahigh sensor response of Ra/Rg = 328@5 ppm, super-low detection limit of 0.001 ppm, excellent cycling stability and selectivity and good linear response at 120 °C, showing great potential for precise detection of 3H-2B biomarker. The porous structure and increased oxygen vacancies contribute greatly to the adsorption of active oxygen species therefore the sensing reactions on ZnO surface, which accounts for the high sensing performance. Our work provides an ingenious strategy to produce novel 3D ZnO with rich oxygen vacancies as well as new insights for boosting the detection of LMs.