Conventional Laboratory Methods Research Articles

Non‐destructive characterization of pearl millet [Pennisetum glaucum (L.) R. Br.] composition using single‐kernel NIR spectroscopy

AbstractAs a gluten‐free cereal with high nutritional properties, pearl millet [Pennisetum glaucum (L.) R. Br.] has been increasingly regarded as an alternative dryland resilient food crop with enriched grain nutritional value. This paper explores the potential of single‐kernel near‐infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non‐destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains. Samples harvested from two consecutive years (2021 and 2022) were evaluated under dryland and irrigated conditions in Kansas State University, Agricultural Research Center, Hays (ARCH), KS and were analyzed using SKNIR and conventional laboratory methods. Model calibrations were developed using partial least squares regression. Results showed satisfactory performance of models with standard errors cross‐validation of 1.04%, 0.17%, 0.39%, 0.21%, and 0.16%, respectively, for protein, moisture, fat, fiber, and ash content. The findings suggest that SKNIR can be a potential tool for high‐throughput pearl millet composition screening efficiently, which will assist breeders and grain processors to optimize grain properties and enhance the grain quality and products.

Epidemiology of pediatric meningitis and encephalitis in Japan: a cross-sectional study.

Information on the epidemiology and cause-specific clinical features of pediatric meningitis and encephalitis is limited, owing to conventional laboratory methods' limitations in identifying causative pathogens. The FilmArray Meningitis/Encephalitis (FA-M/E) panel, a molecular diagnostic tool, can detect 14 pathogens within 1 hour. We investigated meningitis and encephalitis epidemiology among children in Japan and FA-M/E panels' utility in their clinical management. This cross-sectional study was conducted among children aged 0-18 years admitted to seven regional hospitals in western Japan between October 2022 and September 2023. Cerebrospinal fluid (CSF) samples were collected from 221 children and tested using the FA-M/E panel. Clinical and microbiological data were reviewed retrospectively. Fifty-eight patients tested positive using the FA-M/E panel. Viral and bacterial pathogens were detected in 49 and nine cases, respectively. Human parechovirus and enterovirus occurred mainly in epidemic clusters during the summer and were primarily detected in young infants. Patients who tested positive for human parechovirus had a significantly higher frequency of sepsis-like manifestations and a lower frequency of CSF pleocytosis than did those who tested positive for enterovirus. CSF pleocytosis was absent in 30 patients who tested positive using the FA-M/E panel. Among the patients who tested positive for bacteria, three of the nine were not diagnosed using conventional culture methods, owing to prior antimicrobial therapy. The FA-M/E panel can identify bacterial and viral pathogens causing pediatric meningitis and characterize the epidemiology in local communities.IMPORTANCECulture and polymerase chain reactions have traditionally been used to identify microorganisms causing pediatric meningitis and encephalitis. However, the methods currently used to identify the causative microorganisms are limited, particularly in general hospitals. The FilmArray Meningitis/Encephalitis (FA-M/E) panel, a fully automated genetic testing system, can detect 14 pathogens using the multiplex polymerase chain reaction method. This study described the epidemiology of pediatric meningitis and encephalitis in Japan. The microorganisms causing acute meningitis and encephalitis in children in Japan were identified using the FilmArray Meningitis/Encephalitis panel. Testing cerebrospinal fluid using the FA-M/E panel is useful for the identification of the pathogen in children with community-acquired acute meningitis and encephalitis. This increases knowledge on the epidemiology and clinical manifestations of acute meningitis and encephalitis caused by specific pathogens and can be used to facilitate optimal patient management.

Analytical performance evaluation of the GreenCare A1c and Cera-Stat HbA1c point-of-care testing assays.

The escalating prevalence of diabetes underscores the need for precise diagnostic tools to facilitate effective management. Hemoglobin A1c (HbA1c) is a crucial biomarker for long-term glycemic control in diabetic patients. Point-of-care testing (POCT) for HbA1c offers rapid, accessible alternatives to conventional laboratory methods, but uncertainties persist regarding the accuracy and reliability of POCT assays. This study evaluates the analytical performance of two boronate-affinity based HbA1c POCT assays, the GreenCare A1c and Cera-Stat HbA1c. Various analytical parameters including precision, linearity, comparison, and accuracy are assessed following guidelines from Clinical and Laboratory Standards Institute (CLSI), with results applied to certification criteria from the National Glycohemoglobin Standardization Program (NGSP) and International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Furthermore, 52 and 13 frozen EDTA whole blood samples were respectively used for additional evaluation of accuracy and interference due to Hb variants for the GreenCare A1c assay. Both GreenCare and Cera-Stat demonstrated good precision (repeatability CV% 1.5-1.9 and total imprecision CV% 1.6-2.2), linearity (R2 = 0.9996 & 0.9990), and correlation (r = 0.982 & 0.978) with an established HbA1c analyzer, the Bio-Rad D100. The GreenCare also exhibited good accuracy with frozen EDTA samples with known HbA1c values. Both assays met the certification criteria from NGSP and IFCC, classifying them as "standard" according to IFCC model for quality targets for HbA1c. This evaluation affirms the reliability of GreenCare and Cera-Stat POCT assays for HbA1c measurements, which can potentially reduce unnecessary referrals and enhance the overall quality of diabetes diagnosis and treatment.

M5c-iDeep: 5-Methylcytosine sites identification through deep learning

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

Evaluation of the Impact of Climate Variability on the Soil-Water Characteristics Curve

The hydro-mechanical behavior of unsaturated soil, particularly expansive soil, is influenced significantly by cyclic wetting and drying. Understanding the soil parameters is crucial when evaluating the performance of infrastructures constructed on expansive clay. As a result of extreme rainfall events, highway slopes containing highly expansive Yazoo clay in Mississippi, U.S., become vulnerable to volume change. The phenomenon creates perched water zones within the slopes and poses a risk of slope failure. The soil-water characteristic curve (SWCC) defines the relationship between water content and soil suction, which can be obtained from different laboratory procedures. However, conventional laboratory methods have some limitations. To address this, various analytical and predictive models have been developed, but they can only offer estimates based on soil characteristics and lack seasonal variations occurring in field conditions. Studying seasonal SWCC through field measurements can help understand soil responses to changing moisture conditions. The current study utilized field data from six highway slopes in Mississippi and classified the data into different seasons: spring, summer, and fall. After obtaining van Genuchten parameters from the fitted curve for each season, the finite element method was applied to evaluate the parameters for accurate numerical analysis of infrastructures containing expansive clay. The study observed the variations in flow parameters with seasonal change that cannot be achieved when data from only one season is considered. The findings underscore the importance of field instrumentation data for developing SWCC and the significance of seasonal flow parameters in infrastructure design.

Two-dimensional imaging of elastic properties of rock core samples from measurements of angle-dependent ultrasonic reflection coefficients

Laboratory and field measurements often fail to identify small-scale variations in rock elastic properties. Due to limited spatial resolution, conventional laboratory methods cannot properly describe rock formations exhibiting a high degree of heterogeneity, thereby masking differences between stiff and compliant layers. Continuous sample measurements can mitigate this problem but are not widely used by the industry. We build upon our previous work and apply laboratory angle-dependent ultrasonic reflection coefficient (ADURC) measurements to achieve detailed two-dimensional descriptions of the elastic properties of complex rock samples. This method successfully yields high-resolution information on P- and S-wave velocities, as well as bulk density, across the surface of rock samples. Elastic properties are estimated using a nonlinear inversion algorithm that matches laboratory measurements with numerical simulations. ADURC data acquired at various sample locations enable detailed rock descriptions, where the effective measurement area is determined by the size of the receiver, measurement frequency, and incidence angle. Consequently, the sampling area is smaller compared with triaxial loading and acoustic transmission tests, for which the resolution is controlled by sample size. Measurements conducted on samples exhibiting different levels of spatial complexity validate the capability of the ADURC method to identify small-scale heterogeneities. For the reported experiments, variations in angle-dependent reflectivity give rise to corresponding variations in the estimated P- and S-wave velocities and density, which can exceed 60%. These small-scale variations across heterogeneous rock samples often are overlooked by conventional laboratory methods.

Using mid-infrared spectroscopy as a tool to monitor responses of acidic soil properties to liming: case study from a dryland agricultural soil trial site in South Australia

Soil acidification is an issue for agriculture that requires effective management, typically in the form of lime (calcium carbonate, CaCO3), application. Mid infrared (MIR) spectroscopy methods offer an alternative to conventional laboratory methods, that may enable cost-effective and improved measurement of soil acidity and responses to liming, including detection of small–scale heterogeneity through the profile. Properties of an acidic soil following lime application were measured using both MIR spectroscopy with Partial Least Squares Regression (MIR-PLSR) and laboratory measurements to (a) compare the ability of each method to detect lime treatment effects on acidic soil, and (b) assess effects of the different treatments on selected soil properties. Soil properties including soil pH (in H2O and CaCl2), Aluminium (Al, exchangeable and extractable), cation exchange capacity (CEC) and organic carbon (OC) were measured at a single field trial receiving lime treatments differing in rate, source and incorporation. Model performance of MIR-PLSR prediction of the soil properties ranged from R2 = 0.582, RMSE = 2.023, RPIQ = 2.921 for Al (extractable) to R2 = 0.881, RMSE = 0.192, RPIQ = 5.729 for OC. MIR-PLSR predictions for pH (in H2O and CaCl2) were R2 = 0.739, RMSE = 0.287, RPIQ = 2.230 and R2 = 0.788, RMSE = 0.311, RPIQ = 1.897 respectively, and could detect a similar treatment effect compared to laboratory measurements. Treatment effects were not detected for MIR-PLSR-predicted values of CEC and both exchangeable and extractable Al. Findings support MIR-PLSR as a method of measuring soil pH to monitor effects of liming treatments on acidic soil to help inform precision agricultural management strategies, but suggests that some nuance and important information about treatment effects of lime on CEC and Al may be lost. Improvements to prediction model performance should be made to realise the full potential of this approach.

Novel point-of-care rapid detection of monkeypox virus.

Monkeypox, a viral zoonotic disease caused by MPXV, has emerged as a significant global health concern since the first outbreak outside Africa in 2003. As of the current data, there have been 30 189 confirmed cases of monkeypox in 88 countries, with 29 844 cases reported in 81 countries. Given the absence of prior documented instances of the disease, swift and accurate testing is imperative to contain the spread of monkeypox. In this study, we developed a LAMP detection reagent for monkeypox and evaluated its performance in terms of sensitivity, specificity, repeatability, stability, linear range, and linearity, utilizing a commercial magnetic bead-based nucleic acid extraction system. This has led to the establishment of an integrated on-site detection platform for the monkeypox virus, utilizing a closed cartridge. The sensitivity was found to be 100 copies per μL, with no cross-reactivity observed with three other viruses, indicating robust performance. The parameters of repeatability, stability, linear range, and linearity were also assessed. For 28 simulated samples, the detection results obtained from the integrated system were consistent with those from conventional laboratory methods, specifically qPCR and LAMP detection following nucleic acid extraction. The entire process can be completed in approximately one hour, making it highly suitable for immediate rapid testing.

Shigella as a Cause of Diarrhea Hospitalization in Children Under Five: Evaluation by Conventional and Molecular Methods.

Shigella is an important cause of diarrhea in children under five, often missed by conventional laboratory methods. Blood in stools has always been a syndromic indicator for Shigella diarrhea, but most cases present with watery diarrhea without blood. This study aimed to determine the frequency of Shigella detected by molecular and conventional methods in children under five. Additionally, we aimed to study the clinical profile and outcome of children with Shigella diarrhea managed as per current diarrhea treatment guidelines. In this hospital-based prospective observational study, stool samples from 150 children (age range: one month to five years) with acute diarrhea (duration < seven days) were subjected to routine microscopic examination, stool culture, and DNA extraction. The extracted DNA from stored stool samples was subjected to polymerase chain reaction (PCR) amplification using a specific primer for the invasion plasmid antigen Hgenesequence (ipaH) gene at 424 bp. Results were interpreted in the context of the percentage of isolation of Shigella by molecular (PCR) and conventional methods (stool microscopy and culture) and the follow-up outcome in terms of recurrence of diarrhea or dysentery and growth faltering over three months after discharge. Shigella infection was diagnosed in stool samples by PCR from 13 (8.7%) children, whereas it was isolated by conventional stool culture in only one (0.7%) child. The sensitivity of culture was only 7.7% against PCR for the diagnosis of Shigella infection, whereas blood in stools had a sensitivity of 15.4%. The majority of Shigella PCR-positive cases (11 out of 13) presented with non-bloody diarrhea. None of the evaluated clinical predictors had a significant association with the Shigella infection. No statistically significant difference was found between PCR-positive and PCR-negative children at the end of follow-up (P>0.05). The majority of children with Shigella infection present with watery diarrhea rather than bloody diarrhea, and a history of blood in stools is a poor marker for the diagnosis of shigellosis. The diagnostic performance of stool culture is also very low compared to stool PCR for the diagnosis of Shigella diarrhea.

Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks.

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties-Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah-during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR-SWIR spectrum (350-2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input-multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.

Detection of monkeypox virus using helicase dependent amplification and recombinase polymerase amplification combined with lateral flow test

The monkeypox virus (MPXV) is a zoonotic DNA virus that belongs to the poxvirus family. Conventional laboratory methods for detecting MPXV are complex and expensive, making them unsuitable for detecting the virus in regions with limited resources. In this study, we using the Helicase dependent amplification (HDA) method and the Recombinase polymerase amplification (RPA) technique in combination with the lateral flow test (LFT), together with a self-designed qPCR technique for the detection of the MPXV specific conserved fragment F3L, to compare the sensitivity and specificity of the three assays. By analyzing the sensitivity detection results using Probit, it can be seen that the limit of detection (LOD) of the HDA-LFT detection target is 9.86 copies/µL (95% confidence interval, CI 7.52 copies/µL lower bound), the RPA-LFT detection target is 6.97 copies/µL (95% CI 3.90 copies/µL lower bound), and the qPCR detection target is 479.24 copies/mL (95% CI 273.81 copies/mL lower bound). The specificity test results showed that the specificity of the three methods mentioned above was higher than 90% in detecting pseudoviruses of the same genus of MPXV. The simple, highly sensitive, and specific MPXV assay developed in this study is anticipated to provide a solid foundation for future applications in the early screening, diagnosis, and evaluation of the efficacy of MPXV. This is the first time the HDA-LFT assay has been utilized to detect MPXV infection.

Comparison of laboratory methods for diagnosis of human visceral leishmaniasis in the Center-West Region of São Paulo State, Brazil

Visceral leishmaniasis (VL) is a neglected disease and represents an important public health problem. The present study aimed to compare the conventional laboratory methods with the molecular method for VL diagnosis in human blood samples. Direct parasitological test of bone marrow aspirate (direct parasitological test), rapid immunochromatographic test (RIT), indirect immunofluorescent antibody test (IFAT) and polymerase chain reaction (PCR) were used to evaluate 26 human samples with clinical suspicion of VL. The positivity rates obtained for direct parasitological test, RIT, IFAT and PCR were 80.8%, 80.8%, 73.1% and 84.6%, respectively. PCR had greater positivity and agreement with the other methodologies. Therefore, the use of PCR in the diagnostic routine should be encouraged to complement the serological results.

Casting Simulation of Large-Volume Fluid Cementitious Materials: Effect of Material Properties and Casting Parameters.

The increasing pressure of traffic congestion on socio-economic development has made the construction of cross-water transportation ever more crucial. The immersed tunnel method is among the most extensively employed. However, a critical challenge of the immersed tunnel technique is to ensure the compactness and stability of concrete during the casting process. Conventional laboratory methods face challenges in achieving large-volume concrete casting, resulting in the notable waste of human and material resources. Hence, this study employs a simulation approach to investigate the casting parameters and the fresh properties of concrete, exploring their impacts on concrete stability and compactness. The results indicate that when the surface tension of concrete exceeds 0.03 N/m, and the yield stress and plastic viscosity are 50 Pa and 50 Pa·s, respectively, the concrete exhibits excellent casting compactness. A design incorporating three large and six small outlets, paired with a casting speed of 3 cm/s, achieves superior compactness. Additionally, when the yield stress of concrete exceeds 3 Pa, there is no segregation of aggregates. In cases where segregation occurs, the thixotropic property of the cement paste contributes to a significant reduction in the velocity of aggregate segregation.

Diagnostic Value of CD25, CD69, and CD134 on Tuberculosis-Specific Antigen-Stimulated CD4+ T Cells for Tuberculous Pleurisy.

Rapid and accurate methods for the diagnosis of tuberculous pleurisy (TP) are urgently needed. Activation markers of tuberculosis (TB)-reactive T cells are considered promising for the diagnosis of active TB (ATB). Different activation indexes may play different roles in the progression of TB, but there are few reports on T cell activation indicators, except for HLA-DR. Hence, we evaluated the expression of early (CD25 and CD69) and late (CD134) activation markers on TB antigen-stimulated CD4+ T cells in populations with different TB infection status and investigated their diagnostic value for ATB, particularly, for TP. Moreover, we compared the differences in the diagnostic efficacy among the indexes from peripheral blood (PB) and pleural fluid (PF) for TP. The expression of each activation marker was significantly increased in TB-infected populations (patients with ATB and latent TB infection vs. healthy individuals; patients with TP vs. non-TP) and was significantly higher in the PF than in the PB of patients with TP. The diagnostic performance of the coexpressed activation markers was superior to that of single expression markers in the differential diagnosis of ATB and non-TB, with CD25+CD134+ showing the best diagnostic efficiency (AUC: 0.93, 95% CI, 0.87-0.99; sensitivity: 86.7%, 95% CI, 72.5%-94.5%; and specificity: 94.0%, 95% CI, 82.5%-98.4%). Except for TB-IGRA, the activation indexes were more accurate than conventional laboratory methods for ATB diagnosis. In addition, the expression of CD25+CD134+ in PB and PF was the best values for differential diagnosis of TP and NTP, with AUCs of 0.87 (95% CI, 0.77-0.96) and 0.95 (95% CI, 0.90-1.00), respectively. Our study provides information on the diagnostic value of different activation markers for TB and shows that the expression of CD25+CD134+ on CD4+ T cells in PF can serve as a potential marker for TP diagnosis.

Laboratory evaluation of a prototype portable gas chromatograph (GC) with a flame ionization detector (FID) for toluene, ethylbenzene, and xylenes (TEX) analysis

The standard method to evaluate human exposure to volatile organic compounds (VOCs) is in general performed by sampling the air on sorbents followed by liquid extraction and detection using laboratory gas chromatograph (GC). The conventional method is time and labor intensive and employs a toxic solvent which adds a risk factor as well as waste. Hence, there have been increasing demands for portable GC instruments which allow near real-time, in-situ analysis. In this study, the potential use of a prototype, dual column portable GC (protoGC) with flame ionization detector (FID) was examined by comparing its performance with a conventional GC laboratory method. Four target concentration levels (1x, 2x, 4x, and 8x; x = 1.12 ± 0.01 ppm) of toluene, ethylbenzene, and o-, m-, and p-xylene were generated in an exposure chamber (24 ± 1 °C and 50 ± 5% RH). The challenge atmosphere was directly sampled and analyzed with protoGC while for the conventional method it was sampled on a sorbent tube and analyzed with a laboratory GC/FID. The results of protoGC correlated well with the conventional method (r = 0.991–0.999), indicating that protoGC has comparable performance with the conventional method within the test conditions. Although two-way ANOVA showed significant differences in mean concentrations between the methods, the differences were small. protoGC would be useful to monitor VOCs in air with high temporal resolution or to quickly determine the safety of the environment of interest due to the substantial time savings in sampling and analysis. Further examinations at various environmental conditions and other analytes will be necessary to thoroughly evaluate its performance.

Sodium Hypochlorite Diagnostic Performance compared to Ziehl-Nelseen method among Presumptive Tuberculosis Patients in Uganda

Background: Tuberculosis remains a major public health problem worldwide. Direct Ziehl Nelseen (ZN) is the conventional Laboratory method for diagnosis of TB. However, there are higher chances of missing TB positive cases due to insufficient sensitivity. This would eventually lead to TB transmission and hence mortality. Objective: To determine sensitivity and specificity of ZN diagnostic technique on concentrated and unconcentrated sputum samples as well as to assess how clinical, socio-demographic, and sample variables affect concentrated sputum ZN positivity. Method and Materials: This cross-sectional investigation was carried out in Mulago Hospital, covering adult TB presumptive patients. Sputum samples from 114 patients were collected and processed by both NaOCl concentration and direct ZN diagnostic techniques. Sensitivity and specificity were calculated and tabulated. Prediction of ZN positivity was assessed using Logistic regression analysis. Results: Out of the total 114 samples, 82 (71.9%) were positive by GeneXpert test (the gold standard), whereas by NaOCl concentration method 79 samples (69.3%) were found positive but with direct ZN method 65 samples (57.0%) were found positive, thus an increase of 14 samples (12.3%) in positivity was noted when used NaOCl concentration method which is highly significant with ρ&lt;0.05 (χ2 =95.38). NaOCl concentration method got a higher sensitivity than direct sputum smear with 97.5% and 80.2% respectively. The specificities were almost the same with NaOCl method 93.9% and direct sputum smear with 90.9%. NaOCl concentration method had a PPV of 0.98 and NPV of 0.94 while direct sputum smear had PPV of 0.95 and NPV of 0.65. Conclusion: NaOCl concentration method was found to be more sensitive than direct ZN though the specificity was almost the same. Clinical, socio-demographic and sample factors’ effect on ZN positivity was not statistically significant (ρ value=0.796). NaOCl concentration method should be adopted for effective diagnosis of pulmonary TB.

Paper-based biosensors as point-of-care diagnostic devices for the detection of cancers: a review of innovative techniques and clinical applications.

The development and rapid progression of cancer are major social problems. Medical diagnostic techniques and smooth clinical care of cancer are new necessities that must be supported by innovative diagnostic methods and technologies. Current molecular diagnostic tools based on the detection of blood protein markers are the most common tools for cancer diagnosis. Biosensors have already proven to be a cost-effective and accessible diagnostic tool that can be used where conventional laboratory methods are not readily available. Paper-based biosensors offer a new look at the world of analytical techniques by overcoming limitations through the creation of a simple device with significant advantages such as adaptability, biocompatibility, biodegradability, ease of use, large surface-to-volume ratio, and cost-effectiveness. In this review, we covered the characteristics of exosomes and their role in tumor growth and clinical diagnosis, followed by a discussion of various paper-based biosensors for exosome detection, such as dipsticks, lateral flow assays (LFA), and microfluidic paper-based devices (µPADs). We also discussed the various clinical studies on paper-based biosensors for exosome detection.

Data-driven models to predict shale wettability for CO2 sequestration applications

The significance of CO2 wetting behavior in shale formations has been emphasized in various CO2 sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO2 wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R2 of 0.99, testing R2 of 0.98 and a validation R2 of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R2 of 0.99, testing R2 of 0.97 and a validation R2 of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R2 values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R2 of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.

Scientific basics to develop functional meat pâtés

The problems of providing consumers with healthy nutrition in sufficient quantities are typical for the entire modern world, and specialists are working hard to solve them, trying to harmonize the priorities of nutritional value with the basics of thrifty use of food resources. In this regard, it is important to develop functional food products, the composition and production technology of which rationally correspond to the nutritional needs of certain categories of consumers. The purpose of the article is to substantiate the use of the indicated vegetable raw materials in functional pâté products and to determine the quantitative parameters of their inclusion in formulations. To study the swelling and hydration of the samples, conventional laboratory methods were used, the water-retaining and fat-retaining capacities were determined by the Schoch method, the amino acid composition – by using the High-Performance Liquid Chromatography. The research has confirmed the feasibility of using plant flour and oils as sources of protein and fat to impart functional properties to meat products and to save valuable meat raw materials. Studies of the physical, chemical and functional-technological properties of flaxseed, rice, corn, sunflower flour showed that flaxseed and rice flour are the most suitable for creating formulations of functional meat products. The determination of the amino acid composition of these two types of flour showed a higher content of essential amino acids, including lysine, in flaxseed flour, which is very valuable for ensuring the proper functioning of the child's brain. For the rational conduct of research, a generalized scheme for planning formulations of functional pâté products was proposed, based on the properties of the used plant materials and the desired functional properties of these products. The practical value of the studies performed is that they made it possible to determine the necessary ratios of vegetable raw materials in the formulations of meat pâtés, which determine their functional properties

Clinical Validation of Stimulated Raman Histology for Rapid Intraoperative Diagnosis of Central Nervous System Tumors

Stimulated Raman histology (SRH) is an ex vivo optical imaging method that enables microscopic examination of fresh tissue intraoperatively. The conventional intraoperative method uses frozen section analysis, which is labor and time intensive, introduces artifacts that limit diagnostic accuracy, and consumes tissue. SRH imaging allows rapid microscopic imaging of fresh tissue, avoids tissue loss, and enables remote telepathology review. This improves access to expert neuropathology consultation in both low- and high-resource practices. We clinically validated SRH by performing a blinded, retrospective two-arm telepathology study to clinically validate SRH for telepathology at our institution. Using surgical specimens from 47 subjects, we generated a data set composed of 47 SRH images and 47 matched whole slide images (WSIs) of formalin-fixed, paraffin-embedded tissue stained with hematoxylin and eosin, with associated intraoperative clinicoradiologic information and structured diagnostic questions. We compared diagnostic concordance between WSI and SRH-rendered diagnoses. Also, we compared the 1-year median turnaround time (TAT) of intraoperative conventional neuropathology frozen sections with prospectively rendered SRH-telepathology TAT. All SRH images were of sufficient quality for diagnostic review. A review of SRH images showed high accuracy in distinguishing glial from nonglial tumors (96.5% SRH vs 98% WSIs) and predicting final diagnosis (85.9% SRH vs 93.1% WSIs). SRH-based diagnosis and WSI-permanent section diagnosis had high concordance (κ = 0.76). The median TAT for prospectively SRH-rendered diagnosis was 3.7 minutes, approximately 10-fold shorter than the median frozen section TAT (31 minutes). The SRH-imaging procedure did not affect ancillary studies. SRH generates diagnostic virtual histologic images with accuracy comparable to conventional hematoxylin and eosin-based methods in a rapid manner. Our study represents the largest and most rigorous clinical validation of SRH to date. It supports the feasibility of implementing SRH as a rapid method for intraoperative diagnosis complementary to conventional pathology laboratory methods.