Traditional Laboratory Methods Research Articles

Serovar-level identification of bacterial foodborne pathogens from full-length 16S rRNA gene sequencing.

The resolution of variation within species is critical for interpreting and acting on many microbial measurements. In the key foodborne pathogens Salmonella and Escherichia coli, the primary subspecies classification scheme used is serotyping: differentiating variants within these species by surface antigen profiles. Serotype prediction from whole-genome sequencing (WGS) of isolates is now seen as comparable or preferable to traditional laboratory methods where WGS is available. However, laboratory and WGS methods depend on an isolation step that is time-consuming and incompletely represents the sample when multiple strains are present. Community sequencing approaches that skip the isolation step are, therefore, of interest for pathogen surveillance. Here, we evaluated the viability of amplicon sequencing of the full-length 16S rRNA gene for serotyping Salmonella enterica and E. coli. We developed a novel algorithm for serotype prediction, implemented as an R package (Seroplacer), which takes as input full-length 16S rRNA gene sequences and outputs serovar predictions after phylogenetic placement into a reference phylogeny. We achieved over 89% accuracy in predicting Salmonella serotypes on in silico test data and identified key pathogenic serovars of Salmonella and E. coli in isolate and environmental test samples. Although serotype prediction from 16S rRNA gene sequences is not as accurate as serotype prediction from WGS of isolates, the potential to identify dangerous serovars directly from amplicon sequencing of environmental samples is intriguing for pathogen surveillance. The capabilities developed here are also broadly relevant to other applications where intraspecies variation and direct sequencing from environmental samples could be valuable.IMPORTANCEIn order to prevent and stop outbreaks of foodborne pathogens, it is important that we can detect when pathogenic bacteria are present in a food or food-associated site and identify connections between specific pathogenic bacteria present in different samples. In this work, we develop a new computational technology that allows the important foodborne pathogens Escherichia coli and Salmonella enterica to be serotyped (a subspecies level classification) from sequencing of a single-marker gene, and the 16S rRNA gene often used to surveil bacterial communities. Our results suggest current limitations to serotyping from 16S rRNA gene sequencing alone but set the stage for further progress that we consider likely given the rapid advance in the long-read sequencing technologies and genomic databases our work leverages. If this research direction succeeds, it could enable better detection of foodborne pathogens before they reach the public and speed the resolution of foodborne pathogen outbreaks.

DIAGNOSTIC APPROACHES FOR SEXUALLY TRANSMITTED INFECTIONS IN CLINICAL MICROBIOLOGY: A COMPREHENSIVE REVIEW

Accurate and timely diagnostic methods for the management of sexually transmitted infections (STIs) are very esssential because such diseases pose a big threat to public health. This systematic review gives a detailed overview of the wide range of diagnostic approaches, technologies, challenges and future possible ways in the STI diagnosis. A review of the traditional laboratory methods, nucleic acid amplification tests (NAATs), point-of-care testing (POCT) and also new diagnostic technologies discuss their significant contributions to the revolution in STI diagnostics. Challenges including the antimicrobial resistance, evolving pathogens and also poor access to testing quality assurance have been identified which highlights the need for continued interventions aimed at addressing these challenges. Over the horizon, integrated syndromic approaches and further development of point-of-care testing technologies as well as precision medicine strategies based on novel biomarkers and technology present many exciting possibilities for improving the accuracy, accessibility, diagnoses usefulness. This review seeks to guide current and future practices in the clinical microbiology toward STI diagnosis, highlighting innovations and challenges that are key for patient welfare advancements in public health efforts against the STIs.

Advanced prediction of soil shear strength parameters using index properties and artificial neural network approach

This study embarks on developing predictive models for soil shear strength parameters, cohesion (c) and angle of internal friction (ϕ), in Bishoftu town, employing Artificial Neural Networks (ANN). It aims at offering a cost-effective and time-saving alternative to traditional, often expensive, and labor-intensive laboratory methods. The research utilizes soil index properties such as Sand %, Fines %, Liquid Limit, Plastic Limit, and Plasticity Index to construct separate ANN models for c and ϕ. These models use a multi-layer perceptron network with feed-forward back propagation, varying the number of hidden layers to optimize performance. The study's dataset comprises 316 soil test results, encompassing both primary and secondary data, conforming to ASTM Standards. Soil cohesion and internal friction angle were determined using the direct shear box method. The models demonstrated remarkable success in predicting shear strength parameters, evidenced by correlation values of approximately 0.99 for cohesion and 0.98 for internal friction angle, surpassing the capabilities of existing empirical methods. Further examination of the models included comparison with existing correlation techniques and cross-validation using primary soil test data. This validation process confirmed the ANN method's superior accuracy and fit for predicting shear strength parameters over selected empirical methods. This research substantiates the efficiency of ANN in geotechnical engineering, particularly for areas with limited resources for extensive soil testing. It establishes ANN as a powerful, efficient tool for estimating soil shear strength parameters, with significant implications for future planning, design, and construction projects in similar environments.

Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development

In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management.

Unveiling the peptidases of parasites from the office chair - The endothelin-converting enzyme case study.

The emergence of high-throughput methodologies such as next-generation sequencing and proteomics has necessitated significant advancements in biological databases and bioinformatic tools, therefore reshaping the landscape of research into parasitic peptidases. In this review we outline the development of these resources along the -omics technologies and their transformative impact on the field. Apart from extensive summary of general and specific databases and tools, we provide a general pipeline on how to use these resources effectively to identify candidate peptidases from these large datasets and how to gain as much information about them as possible without leaving the office chair. This pipeline is then applied in an illustrative case study on the endothelin-converting enzyme 1 homologue from Schistosoma mansoni and attempts to highlight the contemporary capabilities of bioinformatics. The case study demonstrate how such approach can aid to hypothesize enzyme functions and interactions through computational analysis alone effectively and emphasizes how such virtual investigations can guide and optimize subsequent wet lab experiments therefore potentially saving precious time and resources. Finally, by showing what can be achieved without traditional wet laboratory methods, this review provides a compelling narrative on the use of bioinformatics to bridge the gap between big data and practical research applications, highlighting the key role of these technologies in furthering our understanding of parasitic diseases.

ПРИЧИНЫ НЕУДОВЛЕТВОРИТЕЛЬНЫХ РЕЗУЛЬТАТОВ ЛЕЧЕНИЯ ОСТРЫХ ГНОЙНО-ДЕСТРУКТИВНЫХ ЗАБОЛЕВАНИЙ ЛЁГКИХ У БОЛЬНЫХ С SARS-COV-2

Objective: To identify the reasons for unsatisfactory treatment results for acute purulent destructive lung diseases (APDLD) in patients with SARS-CoV-2. Methods: Sixty-five patients with APDLD on the background of SARS-CoV-2 infection were included in the study; 37 (57%) patients (Group 1) with APDLD were treated in a Tashkent Specialized Clinic for Infectious Diseases, and 28 (43%) patients with APDLD (Group 2) were hospitalized in the clinic of the General and Children’s Surgery Department of the Tashkent Medical Academy with a history of a recent SARS-CoV-2 infection. The patients were investigated using clinical, laboratory, and instrumental methods; the results were processed using statistical methods. Results: Complete recovery of patients was achieved in 16% of patients of Group 1 and 32% of Group 2 (p>0.05); clinical recovery was shown in 30% and 43% of patients in Groups 1 and 2 respectively (p>0.05). In Group 1 the results of treatment were more frequently disappointing than in Group 2 (54% vs. 25%, p=0.036), of which 19% were fatal in Group 1 and 11% in Group 2 (p>0.05). For the fatal outcome, the most dangerous was the early period after hospitalization. Morphological changes in the lungs of deceased patients included homogeneous fibrin deposits, which were combined with pronounced interstitial edema and capillary microthrombi around purulent foci. Conclusion: Traditional clinical and laboratory methods for assessing the severity of the condition of patients of both groups during and after SARSCoV-2 infection do not reflect the complete objective picture of the disease, as an accurate picture can only be obtained by applying the criteria for septic complications. Traditional treatment for APDLD is acceptable for patients of Group II. In patients of Group I with a severe, complicated, and progressive course, many methods of conventional therapy proved to be ineffective. Keywords: Acute purulent destructive diseases of the lungs, SARS-CoV-2, endothelial dysfunction of the lungs, treatment results, complications.

Gated nanoprobe utilizing metal-organic frameworks for identifying and distinguishing between the wild strains and the vaccine strains of brucella.

In the assay for Brucella, the identification and differentiation of wild strains and vaccine strains present a significant challenge. Currently, there aren't any commercially available product to address this issue. In this study, we have developed a novel gated nanoprobe by utilizing Metal-Organic Frameworks (MOFs) as a scaffold and hairpin DNA as a "gating switch". Specifically, Probe 1 with hairpin structure (P1h) targets a gene that is present in both wild strains Y3 (B. melitensis biovar 3) and vaccine strains A19 (Brucella abortus strains A19). We successfully applied this probe to screen positive samples of Brucella without any cross-reactivity with other substances. Additionally, we identified another specific gene exclusively found in wild strains, which serves as Probe 2 with hairpin structure (P2h) to confirm the strain type. Simultaneous detachment of both P1h and P2h from the MOFs leads to the release of Rhodamine 6G (Rho 6G) and Fluorescein (Flu), specifically indicating the presence of wild strains. If only P1h detaches and the Flu signal is detected, it suggests the presence of vaccine strains. Importantly, this method offers high accuracy, with a detection rate of 90% and a recovery rate of 94.71% to 107.65%, while avoiding cross-reactions with MO and TB. This one-step experiment provides reliable identification and differentiation of Y3 and A19, addressing concerns related to long periodicity, interference from individual variations, and the complex design of primers in existing laboratory methods. Furthermore, our approach successfully detects target 1 (T1) and target 2 (T2) at concentrations ranging from 10-6 M to 10-9 M, with a detection limit of 6.7 × 10-10 M and 6.4 × 10-10 M, respectively. Importantly, our strategy is cost-effective (around $1) and offers higher detection efficiency compared to traditional laboratory methods.

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

Advancements in wearable technology for monitoring lactate levels using lactate oxidase enzyme and free enzyme as analytical approaches: A review

Lactate is a metabolite that holds significant importance in human healthcare, biotechnology, and the food industry. The need for lactate monitoring has led to the development of various devices for measuring lactate concentration. Traditional laboratory methods, which involve extracting blood samples through invasive techniques such as needles, are costly, time-consuming, and require in-person sampling. To overcome these limitations, new technologies for lactate monitoring have emerged. Wearable biosensors are a promising approach that offers non-invasiveness, low cost, and short response times. They can be easily attached to the skin and provide continuous monitoring. In this review, we evaluate different types of wearable biosensors for lactate monitoring using lactate oxidase enzyme as biological recognition element and free enzyme systems.

Implementation of hospital-based sickle cell newborn screening and follow-up programs in Haiti

AbstractOne in 120 children are born with sickle cell disease (SCD) in Haiti. However, health care challenges include isolated newborn screening (NBS) activities and lack of transcranial Doppler (TCD) ultrasound to assess stroke risk. The implementation activities of the Comparative Study of Children in Haiti and Miami with Sickle Cell Disease involved both NBS and TCD ultrasound implementations at 4 Haitian clinical sites. We hypothesized that hospital-based newborn SCD screening and follow-up programs would be feasible at Haiti. A traditional NBS laboratory method with dried blood samples was performed at 3 Port-au-Prince sites, and the traditional method plus point-of-care (POC) testing was used at the 2 northern sites. The rate of clinical follow-up for newborns with SCD as the outcome for the NBS intervention was compared with that of the NBS method. The NBS programs identified SCD in 0.77% of 8224 newborns over a 24-month period. In the rural hospital assigned to the combination screening, 56% of newborns identified with POC testing returned for follow-up, compared with 0% when POC was not available (P = .044). Newborns who tested positive for SCD and children aged <6 years with SCD at the clinical sites were eligible for study follow-up. Accrual was successful: 165 participants (mean age, 42 months; 53% males; 93% hemoglobin SS) were recruited and received oral penicillin. TCD ultrasound screening was hampered by poor internet connections and trained staff leaving Haiti, with only 1 active site conducting screening. Despite challenges, the implementation of NBS and sickle cell programs in Haiti is feasible. We are in the process of understanding how to mitigate implementation limitations.

A field‐based evaluation of portable XRF to screen for toxic metals in seafood products

AbstractPortable X‐Ray Fluorescence (XRF) has become increasingly popular where traditional laboratory methods are either impractical, time consuming, and/or too costly. While the Limit of Detection (LOD) is generally poorer for XRF compared to laboratory‐based methods, recent advances have improved XRF LODs and increased its potential for field‐based studies. Portable XRF can be used to screen food products for toxic elements such as lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), manganese, (Mn), zinc (Zn), and strontium (Sr). In this study, 23 seafood samples were analyzed using portable XRF in a home setting. After XRF measurements were completed in each home, the same samples were transferred to the laboratory for re‐analysis using microwave‐assisted digestion and Inductively Coupled Plasma Tandem Mass Spectrometry (ICP‐MS/MS). Four elements (Mn, Sr, As, and Zn) were quantifiable by XRF in most samples, and those results were compared to those obtained by ICP‐MS/MS. Agreement was judged reasonable for Mn, Sr, and As, but not for Zn. Discrepancies could be due to (1) the limited time available to prepare field samples for XRF, (2) the heterogeneous nature of “real samples” analyzed by XRF, and (3) the small beam spot size (~1 mm) of the XRF analyzer. Portable XRF is a cost‐effective screening tool for public health investigations involving exposure to toxic metals. It is important for practitioners untrained in XRF spectrometry to (1) recognize the limitations of portable instrumentation, (2) include validation data for each specific analyte(s) measured, and (3) ensure personnel have some training in sample preparation techniques for field‐based XRF analyses.

Optimal Perioperative Antimicrobial Management Strategies of Kidney Transplant Recipients Guided by Metagenomic Next-Generation Sequencing of Deceased Donors' Microbiology Samples.

There is no consensus on the optimal use of perioperative antibiotics prophylaxis after kidney transplantation, but there is a common trend to limit the duration of antibiotic use worldwide. Metagenomic next-generation sequencing (mNGS) has emerged as a novel technology for pathogen detection in clinical practice due to its noninvasive, rapid, precise and high susceptibility to detect infectious pathogens. However, data are lacking on whether mNGS analyses could be used to detect pathogens and guide anti-infection regimens in kidney transplant donors and recipients. We conducted a retrospective study to review all clinic data of mNGS and traditional laboratory methods (TMs) for pathogen detection in kidney transplant recipients and their corresponding deceased donors from August 1, 2021 to October 30, 2022 in our center. A total of 57 donors and 112 of their corresponding recipients were included. The antimicrobial strategy mainly depended on mNGS results combined with traditional pathogen culture and clinical conditions. The percentages of positive pathogen detected by mNGS in blood, urine, bronchoalveolar lavage fluid (BALF) and preservation fluids (PFs) were 50.9% (29/57), 35.1% (20/57), 84.2% (48/57) and 54.4% (31/57) respectively, and were 24.6% (14/57), 15.8% (9/57), 57.9% (33/57) and 14.1% (8/57) respectively when using TMs. mNGS could detected all of pathogens which were detected by TMs. However, samples with negative TMs testing can be additionally detected as positive by mNGS (15/43 in blood, 11/48 in urine, 15/24 in BALF and 23/49 in PFs). Drug resistance genes were detected in 9 donors by mNGS,which were consistent with 6 donors by TMs. There was only one case of donor-derived infection in this study. This study showed that it is effective to combine mNGS with traditional pathogen detection methods and clinical features to develop optimal perioperative antimicrobial management strategies for deceased donor kidney transplantation.

Clinical evaluation of a dermatophyte RT-PCR assay and its impact on the turn-around-time: a prospective study.

Onychomycosis is an important public health problem whose prevalence continues to grow and impact public health at several levels. Nevertheless, today the main diagnostic methods used in routine practice have many drawbacks. The aim of this study was to evaluate, for the first time, the clinical performance of a new multiplex PCR (Novaplex®) in the identification of the causative agent on nail samples, and its impact on the turnaround time, compared to our traditional laboratory methods. From 06/29/2022 to 12/21/2022, all nail samples sent to our laboratory for suspected onychomycosis were included in this prospective study. We collected for each sample the result obtained with the NovaplexࣨPCR method and with the traditional direct microscopy examination and culture. Each discordant result was checked using a third method, which is another PCR method (DermaGenius®kit) as resolver. For culture-positive samples, a turnaround time was calculated and compared to the one obtained with the Novaplex®method. A total of 131 samples were included. Among them, 5 were positive (3.8%) on direct microscopy, 33 positive (25.2%) after culture and 98 negative (74.8%). All positive (n=33) and negative (n=69) cultures were also positive/negative with the Novaplex®PCR. Twenty-nine samples were positive with the Novaplex®method but negative with culture (discordant results). The percentage agreement between the culture and the Novaplex® methods was 77.9% (102/131). While tested with the resolver (DermaGenius® PCR), 28 out 29 discordant results were similarly found positive. The percentage agreement between the 2 PCR methods (Novaplex® and DermaGenius®) was 96.6%. The Novaplex® PCR method evaluated proved to be very reliable and allowed the direct identification of 62/131 positive samples (47.3%) with the following distribution: 79.0% of Trichophyton rubrum complex, 11.3% of Trichophyton mentagrophytes complex, 6.5% of both Trichophyton rubrum complex and Trichophyton mentagrophytes complex and 3.2% Candida albicans. The median time [± 95% CI] for positive culture (between incubation and validation of the final identification) was 15 [12-23] days while the turnaround time for the Novaplex® method adapted to our clinical laboratory routine is ≤ 7 days. Laboratory confirmation of onychomycosis is crucial and should always be obtained before starting treatment. The evaluated PCR method offered a rapid, reliable, robust, and inexpensive method of identification of the causative agent compared to traditional methods.

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

Prediction of shale wettability using different machine learning techniques for the application of CO2 sequestration

Understanding the behavior of carbon dioxide (CO2) wetting in shale formations is crucial for various applications such as CO2-enhanced oil recovery, CO2 foam hydraulic fracturing, and CO2 storage in saline aquifers and shale formations. However, traditional laboratory methods to determine shale wettability, including contact angle measurements and nuclear magnetic resonance spectroscopy, can be time-consuming and expensive. This study aims to overcome these limitations by utilizing machine learning (ML) techniques to estimate shale wettability based on shale characteristics and experimental conditions.A dataset was collected, encompassing different shale samples under various conditions, including pressure, temperature, and brine salinity, to predict shale wettability. Pearson's correlation coefficient and linear regression analysis were employed to assess the relationship between the contact angle value and other input parameters. ML models, including decision tree, random forests, function networks, and gradient boosting regressor, were utilized for shale wettability prediction.Operating pressure, temperature, rock mineralogy, and total organic content were identified as influential factors on shale wettability. While the linear regression model showed limited accuracy, the ML models, especially random forests, decision tree, gradient boosting regressor, and function networks, effectively predicted the contact angle value with high R2 values. Gradient boosting regressor demonstrated the best performance, achieving R2 values of 0.99 and 0.98 for the training and testing datasets, respectively, with a root mean square error below 5 degrees. Sensitivity analysis revealed the significant impact of pressure on shale wettability, with low pressures resulting in water-wet shale regardless of other shale properties.The study highlights the efficacy of the developed ML models in predicting shale wettability in the context of CO2-water-shale systems, providing a faster and more affordable alternative to complex experimental analyses.

Effect of Nitrendipine and Amlodipine on Cognitive Functions of Patients with Arterial Hypertension

Background: Arterial hypertension (AH) is one of the main factors causing a high risk of cardiovascular complications and mortality. The existence of a relationship between high blood pressure (BP) and the risk of developing central nervous system pathology, primarily stroke, and cognitive impairment, has been shown. The purpose of this study was a comparative assessment of the effect of 12-month antihypertensive therapy (AHT) with the inclusion of nitrendipine (NIT) or amlodipine (AML) on cognitive functions (CF) in hypertensive patients. Methods and Results: The study included 111 patients of both genders aged 30-75 years with AH Grades 1-3 (ESC/ESH, 2018). All patients underwent the following examinations: assessment of traditional risk factors, physical examination, clinical and biochemical laboratory methods, 12-lead ECG, echocardiography, pulse contour analysis, and 24-hour ambulatory blood pressure monitoring, neuropsychological tests (Mini-Cog test, Montreal Cognitive Assessment (MoCA) test, Hospital Anxiety and Depression Scale (HADS), and self-assessment questionnaire for memory, attention, thinking, ability to cope with one's affairs, and ability to make decisions). After the screening stage, all patients were discontinued from previous therapy and assigned to the 2 regimes of AHT. Group 1 included 58 AH patients who received NIT as monotherapy or as part of combination AHT; Group 2 included 53 patients who received AML as monotherapy or as part of combination AHT. Correlation analysis between the parameters of DBPP and the MoCA test revealed a weak but statistically significant negative correlation between the total MoCA score and the average 24-h SBP (rs=-0.33, P=0.015). In addition, there was a weak but statistically significant negative correlation between the total MoCA score and the daytime SBP variability and daytime DBP variability (rs=-0.40 and rs=-0.35, respectively, P=0.000 in both cases). A weak but statistically significant negative correlation was found between the total Mini-Cog score and PWV and PPc (rs=-0.24, P=0.01 and rs=-0.27, P=0.007, respectively). Analysis of the office BP indicators showed high antihypertensive efficacy of 12-month therapy in both groups, regardless of the therapy regimens. A comparative analysis of the effect of AHT with the inclusion of NIT or AML on CF in AH patients showed the advantages of combined AHT with the inclusion of NIT. Amlodipine treatment did not significantly affect any test score. Thus, in Group 1, after 12 months of therapy, there was an increase in the total Mini-Cog score from 3.8±1.08 points to 4.55±0.75 points (P&lt;0.001), while in Group 2, there was a non-significant decrease in this score from 4.26±0.98 points to 3.92±0.95 points (P&gt;0.05). There was also an increase in the total MoCA score in Group 1 from 23.3±2.8 points to 25.08±2.6 points (P&lt;0.001), while in Group 2, there was a non-significant decrease in this score from 24.06±2.73 points to 23.07±2.7 points (P&gt;0.05). It should be noted that only in Group 1 did we find a significant improvement in CF, such as abstraction, delayed recall, memory, and attention, as well as a significant improvement in work-coping and decision-making. In Group 1, the HADS Depression score decreased from 4.6±3.7 points to 3.32±2.95 points (P&lt;0.05), HADS Anxiety score decreased from 7.01±5.37 points to 4.95±3.75 points (P&lt;0.02). At the same time, in Group 2, in contrast, the HADS Depression score and the HADS Anxiety score did not significantly change. Conclusion: A weak but statistically significant negative correlation was found between the total MoCA score and the daytime SBP/DBP variability in AH patients. A weak but statistically significant negative correlation was found between the total Mini-Cog score and pulse wave velocity and central pulse pressure. A pronounced antihypertensive efficacy of 12-month combination therapy was noted, with the inclusion of both NIT and AML. The NIT-based treatment contributed to a significant increase in the total Mini-Cog score and the total MoCA score and a substantial improvement in CF. Abstraction, delayed recall, memory, attention, work-coping, and decision-making significantly improved, compared to AML-based treatment. The presented data allow a differentiated approach to tactics for treating AH patients with severe cognitive impairment.

Geotechnical properties of cohesive soils used in the construction of subgrade for the development of the railways in the Azov-Black Sea region

This work is devoted to the determination and systematization of the properties of clay soils used in the construction of new railway tracks in order to develop the railway network in the Azov-Black Sea region of Russia. To this end, classification characteristics are determined by traditional laboratory methods, and the possibility of soil swelling under excessive moisture is estimated. In addition, the compressibility of soils is studied as the main factor ensuring the trouble-free operation of the subgrade of railways during their long-term operation. Soil samples for measurements were taken from open pits located near construction sites at an extended length of construction of 530 km. The new regression relations proposed in the work provide in some cases the accuracy of determining the soil characteristics close to the accuracy of laboratory tests. They may be in demand when monitoring the accuracy of laboratory tests of soil properties of other open pits and increasing the speed of pre-design surveys during further development of the railroad network in this region.

Utility of multiplex real-time polymerase chain reaction for the detection of bacteria from sputum samples of community acquired pneumonia patients: A study from Dhaka, Bangladesh

Background: This study was carried out to evaluate the utility of multiplex real-time polymerase chain reaction (PCR) to identify the common bacterial agents of community acquired pneumonia (CAP). Methods: Sputum and blood samples were collected from 80 clinically suspected CAP patients in three tertiary-level hospitals in Dhaka city. Multiplex real-time PCR assay was carried out to simultaneously detect five common bacterial agents of CAP; Streptococcus pneumoniae, Haemophilus influenzae, Mycoplasma pneumoniae, Chlamydophila pneumoniae, and Legionella pneumophila. Routine microbiological methods and serology were carried out. The results of PCR were compared with culture, Gram stain and serology. Results: Among the 80 patients, sputum samples of 35 (43.7%) patients were positive by PCR, of which the most commonly detected bacteria were S. pneumoniae (25/35, 71.4%), followed by H. influenzae (9/35, 25.7%) and L. pneumophila (1/35, 2.9%). All 80 sputum samples were negative for both M. pneumoniae and C. pneumoniae by PCR. Out of the 26 culture positive sputum samples, 8 (30.7%) were positive for S. pneumoniae and 1 (3.8%) was positive for H. influenzae. Among the 52 Gram stain valid sputum samples, 24 (46.1%) were S. pneumoniae and 7 (13.5%) were H. influenzae. By serology, out of the 80 cases, M. pneumoniae was detected in 32 (40%) and C. pneumoniae in 24 (30%) of cases. Mixed infections comprised of 38.8% (31/80) cases. Conclusion: Multiplex real-time PCR is useful for the rapid and simultaneous detection of bacterial pathogens of CAP in sputum and can help support traditional laboratory methods for the accurate diagnosis of CAP patients.

Investigation to design, fabricate, and integrate a mobile radiation monitoring system in ocean

Ocean radiation monitoring and warning systems are essential in monitoring radioactive species' contamination levels in an oceanic environment and predicting the radioactive materials propagation caused by nuclear incidents in a marine area. Since the Fukushima nuclear disaster, different types of mobile radiation monitoring devices have been investigated based on modern technologies in electronic – communication. Those types of equipment can directly measure the radioactive contamination in the ocean instead of the traditional laboratory method. This study presents a complete process of researching, designing, manufacturing, and integrating a mobile radiation monitoring system (MRMS) in the ocean. The integrated radiation sensor can both measure radiation dose rate and identify radioactive isotopes on a floating device. The measured data from the sensor is processed and displayed on the electronic block by developed software, or it can be transmitted directly to the mainland by one of three different communication devices. This prototype system is the first product in Vietnam in the development of the MRMS, which is highly promising in terms of performance, low cost, and maintenance.

Advanced Online Broadband Impedance Spectrum Acquisition of Fuel Cells by S-Transform

Electrochemical impedance spectrum (EIS) applied in fuel cell characterization and diagnosis facilitates its system management in the vehicular application, but the first consideration of impedance-based management is how to obtain impedance online. The traditional laboratory method based on sine sweep is unsuitable for vehicular EIS measurement as limited by measured time and equipment price. In this article, we design an excitation device that can generate the square wave signal containing many harmonics as the excitation current. Besides, introduce S-transform (ST) for online broadband fuel cell impedance spectrum fast calculation, whose window width can be adaptively changed with frequency, and meanwhile, preserving high computational efficiency by fast Fourier transform operation. Based on this, the performance of ST and the previous method based on Morlet wavelet transform (MWT) is compared under different operating conditions. Extensive experimental results show that the ST can calculate impedance with the mean relative error of less than 3% based on appropriate excitation, while the computed time can be saved by nearly 95% as compared to MWT. Furthermore, the effectiveness and robustness of ST against different current densities, cell temperatures, fault scenarios with various fault degrees, and stable time are also verified through experimentation.