Reliable Method Research Articles

Suboptimal reliability of FIB-4 and NAFLD-fibrosis scores for staging of liver fibrosis in general population.

The burden and incidence of liver cirrhosis are increasing worldwide. Early detection of liver fibrosis would help in early interventions and preventing the progression of fibrosis and cirrhosis. The accepted noninvasive markers for liver fibrosis staging, namely fibrosis-4 (FIB-4) and nonalcoholic fatty liver disease fibrosis score (NFS), have shown inconsistent performance for detecting the fibrosis stage. We aimed to evaluate the efficacy of FIB-4 score and NFS for the detection of liver fibrosis in the general population. From a general population referred from a single, community-based family-physician clinic, we included study participants between the ages of 45 and 65 years, with no knowledge of liver disease and no record of alcohol consumption. Liver fibrosis was evaluated by the FIB-4 score and NFS using shear wave elastography (SWE) or transient elastography (TE) measurements as a reference. A total of 76 participants (aged 61.5 ± 0.37 years, 33% females) were included in the study cohort. We observed a nonsignificant correlation between liver stiffness measurement (LSM) and FIB-4 and NFS (r = 0.1, P = 0.37; r = 0.16, P = 0.15, respectively). Our results showed that only 5.2% with FIB-4 >3.25 and 9.7% with NFS >0.675 had LSM >12 kPa. The compatibility of fibrosis staging was 55% between FIB-4 score and LSM and only 18% between NFS and LSM. We found that FIB-4 and NFS are unreliable tools for liver fibrosis estimation in the general population. There is a need for more reliable noninvasive methods for the early detection of liver fibrosis.

Assessment of Vcheck® analyzer for rapid progesterone concentration measurement including recommendations for achieving the optimal breeding time in bitches

Background and Aim: Serum progesterone concentration plays critical role in determining the optimal breeding time in bitches and diagnosing reproductive-related issues. This study aimed to conduct a comparative analysis of serum progesterone results obtained from commercial point-of-care immunological analyzers, namely, Vcheck®, with those obtained using chemiluminescent microparticle immunoassay (CMIA). Our overarching goal was to evaluate these analyzers’ accuracy and establish standardized guidelines for optimal breeding timing. Materials and Methods: Ninety-four serum samples from bitches were analyzed using the Vcheck® analyzer and compared with CMIA. Thorough documentation included the mean, standard deviation, 95% confidence interval (CI), and minimum and maximum values of serum progesterone concentrations. Furthermore, Pearson’s correlation coefficient, Lin’s concordance correlation coefficient, and the bias correction factor were meticulously recorded. Results: The mean progesterone concentration measured using the Vcheck® analyzer was significantly lower than that measured using CMIA, with a mean difference of 1.26 ng/mL of serum. The Bias correction factor was 0.935, which was nearly 1.00, indicating that the line of best-fit was on the perfect line of agreement, providing insight into the measurement accuracy. Pearson’s correlation coefficient, a measure of precision, was also close to 1 (0.939), confirming the reliability of the data. Furthermore, Lin’s concordance correlation coefficient was 0.877, indicating a fair overall agreement between the Vcheck® and CMIA methods. These results support the validity of the Vcheck® analyzer’s results. The present study was developed by aligning with established CMIA guidelines and adapting them using the range and 95% CI derived from each set of results, ensuring a standardized and rigorous approach. Conclusion: The Vcheck® analyzer provides a rapid assessment of serum progesterone concentration in bitches, with results comparable to those measured using the CMIA technique. However, when considering the use of the Vcheck® analyzer, it is recommended that the results should be interpreted carefully and the interpretation guidelines should be followed. In conclusion, Vcheck® provides a reliable and convenient method for veterinarian practitioners to measure canine progesterone levels in a clinical/hospital setting. Keywords: chemiluminescent microparticle immunoassay, optimal breeding time, progesterone, Vcheck®.

Simultaneous quantification of 4-hydroxytamoxifen and hesperidin in liposomal formulations: Development and validation of a RP-HPLC method

Breast cancer treatment options are diverse, with tamoxifen commonly used as a selective estrogen receptor modulator (SERM) for hormone receptor-positive breast cancer. However, tamoxifen can have adverse systemic effects. Local transdermal therapy offers a potential solution by delivering the drug directly to the breast and minimizing systemic exposure. Hesperidin, a flavonoid, exerts synergistic effects when combined with anticancer agents. This combination therapy may be a more effective approach to breast cancer management. Analytical methods have been developed to quantify 4-Hydroxytamoxifen (4-HT) and hesperidin separately; however, no method currently exists for their simultaneous quantification in pharmaceutical formulations. This study aimed to develop and validate a reverse-phase high-performance liquid chromatography (RP-HPLC) method for the simultaneous quantification of 4-HT and hesperidin in liposomal formulations. A Design of Experiments (DoE) approach was employed using a Box-Behnken design (BBD) to optimize the RP-HPLC method. BBD allowed for a reduction in the number of required tests by creating a statistical model to estimate the significance of various factors and interactions. The methanol concentration, flow rate, and injection volume were considered as independent variables for optimization. A mobile phase (90:10 ratio of methanol: 0.1% v/v orthophosphoric acid) with a flow rate of 0.4 mL/min, and an injection volume of 10 μL was selected as optimized chromatographic condition. 4-HT showed a retention time (Rt) of 5.05 min and hesperidin showed an Rt of 7.11 min using an optimized analytical method and was detected at 275 nm. The developed RP-HPLC method was validated according to the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines, confirming its accuracy, precision, linearity, selectivity, and robustness. The validated method was then successfully applied to determine the entrapment efficiency and permeation of 4-HT and hesperidin into loaded liposomes. This study fills a gap in the literature by providing a simple and reliable RP-HPLC method for the simultaneous quantification of 4-HT and hesperidin in liposomal formulations.

Cervical artery tortuosity-a reliable semi-automated magnetic resonance-based method.

Assessments of subclinical connective tissue disorders depend on complex approaches, emphasizing the need for more accessible methods applicable to clinical routine. Therefore, we aimed to establish a reliable approach assessing cervical vessel tortuosity, which is known to be associated with such disorders. Magnetic resonance angiography (MRA) images of ReSect study participants [single-center prospective cohort of spontaneous cervical artery dissection (sCeAD) patients] were used. Each patient underwent the same magnetic resonance imaging (MRI) protocol. The segmentation procedure was done using MATrix LABoratory 9.4 [up-sampling of raw MRA images, distance metric (DM) calculation], ITK-SNAP [region of interest (ROI) determination, vessel segmentation] and Vascular Modelling ToolKit (centerline determination). To assess inter-user variability and validity, we (I) had two blinded independent users segment all arteries and we (II) compared the results of our method to visual appraisal of vessel tortuosity done by two blinded expert neuro-radiologists. A total of 526 extracranial cervical arteries were available for analysis. The inter-user variability of our method users was below 0.5% throughout. Overall, our method outperformed the visual tortuosity appraisal, as the visual grading underestimated the DM in 38.8% subjects when tasked to assess overall cervical artery tortuosity (both vertebral and internal carotid arteries) and in 16.6% and 33.3% respectively if tasked to grade anterior or posterior circulation separately. We present a reliable method to assess cervical artery tortuosity derived from MRA images applicable in clinical routine and future research investigating the potential correlation of sCeAD and connective tissue disorder.

Evaluation of taste score and fungiform papillae quantification using digital image analysis in COVID-19 patients with smell and taste dysfunction

Background: The COVID-19 pandemic which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a major global health crisis in recent years. Despite this, there have been few studies that have utilized reliable methods to assess changes in taste and smell perception. Therefore, our study aims at the number of fungiform papillae and objective measures of taste perception relationship among COVID-19 patients with olfactory and gustatory disorders. Materials and Methods: This was a cross-sectional analytical study in which 57 COVID-19 patients were recruited who confirmed the dysfunction of taste and smell. Objective assessment of the sense of taste was evaluated using four different standardized solution preparations, and the scores were given according to the patient's statements. Digitalized quantification of fungiform papillae was counted. The data were analyzed with the Pearson's correlation coefficient using the SPSS version. 23 [Licensed JSSAHER, Mysuru, Karnataka, India], and the level of significance was set at <0.001. Results: In terms of altered or reduced taste and smell, male patients exhibited a higher incidence compared to females. Compared to the sour taste, a substantial number of COVID-19 patients have displayed a notable decrease in their ability to taste sweet, salty, and bitter flavors. However, a statistically significant positive correlation was observed between taste scores and fungiform papillae density (r = 0.518, P < 0.001). Conclusion: Our Study demonstrated that the quantitative evaluation of taste perception and the count of fungiform papillae can serve as important indicators of SARS-CoV-2 infection, and could potentially help in the early detection and treatment of COVID-19 patients, as reduced taste function is a significant marker of the disease.

TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet

Background: Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis. Objective: We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset. Methods: (1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters. Results: The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results. Conclusion: TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning method to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.

Independent data collectors decrease bias in the measurement of adherence to anterior cruciate ligament injury prevention programs

ObjectivesStudies on adherence to neuromuscular training (NMT) for anterior cruciate ligament (ACL) injury prevention are frequently biased due to the use of self-reporting by coaches or the athletes themselves. Few NMT studies use data collectors (aside from the athletes or the individuals administering the NMT program) to decrease bias when assessing the adherence of coaches and sports teams. We hypothesized that the use of a data collector who is independent of the team to evaluate adherence to NMT programs would be reliable. MethodsIn a prior a cluster-randomized controlled trial evaluating adherence to NMT training trial, twelve boys' and nine girls' high school athletic teams in a variety of sports were enrolled. Eight data collectors (unaffiliated with the NMT program) were hired specifically to record adherence of the athletes to the NMT exercises at each team's warm-ups 2–3 times a week, prior to practices and games. In addition to the data collectors, a control group of independent observers made visits throughout the season to also record adherence (solely for the purpose of this study, alongside the data collectors and in the same fashion) in order to evaluate the data collectors' performance and determine inter-observer reliability. The inter-observer reliability between data collectors and independent observers was measured using the Kappa statistic. ResultsA total of 399 warm-ups for practices or games were observed by data collectors to obtain adherence data. Independent observers also measured adherence at 58 practices or games for inter-observer reliability. Exercise instruction and alignment cues for 29 different exercises were analysed. The Kappa values ranged from 0.63 to 1.0, indicating substantial to perfect agreement. The overall Kappa values of 0.89 and 0.90 for exercise instruction and alignment cues, respectively, indicated almost perfect agreement. ConclusionThe use of a data collector who is independent of the team to evaluate adherence to NMT programs (rather than athlete or coach self-reporting), was shown to be a reliable method for measurement of adherence in studies of NMT for injury prevention. Avoiding self-reporting in adherence research to NMT training may decrease bias. Level of EvidenceI.

Bioengineered small extracellular vesicles deliver multiple SARS-CoV-2 antigenic fragments and drive a broad immunological response.

The COVID-19 pandemic highlighted the clear risk that zoonotic viruses pose to global health and economies. The scientific community responded by developing several efficacious vaccines which were expedited by the global need for vaccines. The emergence of SARS-CoV-2 breakthrough infections highlights the need for additional vaccine modalities to provide stronger, long-lived protective immunity. Here we report the design and preclinical testing of small extracellular vesicles (sEVs) as a multi-subunit vaccine. Cell lines were engineered to produce sEVs containing either the SARS-CoV-2 Spike receptor-binding domain, or an antigenic region from SARS-CoV-2 Nucleocapsid, or both in combination, and we tested their ability to evoke immune responses in vitro and in vivo. B cells incubated with bioengineered sEVs were potent activators of antigen-specific T cell clones. Mice immunised with sEVs containing both sRBD and Nucleocapsid antigens generated sRBD-specific IgGs, nucleocapsid-specific IgGs, which neutralised SARS-CoV-2 infection. sEV-based vaccines allow multiple antigens to be delivered simultaneously resulting in potent, broad immunity, and provide a quick, cheap, and reliable method to test vaccine candidates.

Comprehensive evaluation and validation of optimal reference genes for normalization of qPCR data in different caprine tissues.

Quantitative real-time PCR (qPCR) is a highly reliable method for validating gene expression data in molecular studies due to its sensitivity, specificity, and efficiency. To ensure accurate qPCR results, it's essential to normalize the expression data using stable reference genes. This study aimed to identify suitable reference genes for qPCR studies in goats by evaluating 18 candidate reference genes (ACTB, BACH1, B2M, GAPDH, HMBS, HPRT1, PGK1, PPIA, PPIB, RPLP0, RPL19, RPS9, RPS15, RPS28, SDHA, TBP, UXT, and YWHAZ) in 10 different caprine tissues (heart, intestine, kidney, liver, lung, muscle, rumen, skin, spleen, and testis). An integrated tool called RefFinder, which incorporates various algorithms like NormFinder, GeNorm, BestKeeper, and ΔCt, was used to assess the stability of expression among these genes. After thorough analysis, ACTB, PPIB, and B2M emerged as the most stable reference genes, while RPL19, RPS15, and RPS9 were found to be the least stable. The suitability of the selected internal control genes was further validated through target gene analysis, confirming their efficacy in ensuring accurate gene expression profiling in goats. The study determined that the geometric average of ACTB, PPIB, and B2M creates an appropriate normalization factor for gene expression studies in goat tissues.

Review—Electrostatic Spinning for Manufacturing Sensitive Layers of Flexible Sensors and Their Structural Design

Electrostatic spinning technology is widely used in the manufacturing of flexible sensors. It is a mature and reliable method to fabricate nanofibers with tailorable fiber diameter surface microstructure like porosities and specific surface areas. Based on these properties, the electrically conductive composite nanofiber mats achieved by functionalizing nanofibers with active conductive nanomaterials are used as a sensitive layer for flexible sensors with tunable sensing performance. However, it is crucial to select suitable materials and optimal electrospinning technology, as well as design of the sensitive layer structure, for tuning the mechano-electrical performance of flexible sensors. This paper first reviews the current methods for the fabrication of flexible sensors with a focus on preparation method based electrospinning technology. Then, we introduce in detail the types and properties of common substrate materials and conductive fillers used to make sensor sensitive layers, with emphasis on the design of sensitive layer structures for the properties of the materials themselves. Finally, there is a summary of improvements and derivations based on the traditional electrospinning technologies that have been reported in recent years. It is hoped that this review will provide both references and inspiration for researchers in the field of flexible sensors.

A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource Data

Depression has a large impact on one’s personal life, especially during the COVID-19 pandemic. People have been trying to develop reliable methods for the depression detection task. Recently, methods based on deep learning have attracted much attention from the research community. However, they still face the challenge that data collection and annotation are difficult and expensive. In many real-world applications, only a small number of or even no training data are available. In this context, we propose a Prompt-based Topic-modeling method for Depression Detection (PTDD) on low-resource data, aiming to establish an effective way of depression detection under the above challenging situation. Instead of learning discriminating features from a small amount of labeled data, the proposed framework turns to leverage the generalization power of pretrained language models. Specifically, based on the question-and-answer routine during the interview, we first reorganize the text data according to the predefined topics for each interviewee. Via the prompt-based framework, we then predict whether the next-sentence prompt is emotionally positive or not. Finally, the depression detection task can be achieved based on the obtained topicwise predictions through a simple voting process. In the experiments, we validate the effectiveness of our model under several low-resource data settings. The results and analysis demonstrate that our PTDD achieves acceptable performance when only a few training samples or even no training samples are available.

A Systematic Investigation on the Effect of Carbon Nanotubes and Carbon Black on the Mechanical and Flame Retardancy Properties of Polyolefin Blends.

Due to high filler loading, clean, commercial, thermoplastic, flame-retardant materials are mechanically unstable when insulating wires and cables. In this study, composite formulations of linear low-density polyethylene (LLDPE)/ethylene-vinyl acetate (EVA) containing a flame retardant, such as magnesium hydroxide (MH; formula: Mg(OH)2) and huntite hydromagnesite (HH; formula: Mg3Ca(CO3)4, Mg5(CO3)4(OH)2·3H2O), were prepared. The influence of carbon nanotubes (CNTs) and carbon black (CB) on the mechanical properties and flame retardancy of LLDPE/EVA was studied. Three types of CNTs were examined for their compatibility with other materials in clean thermoplastic flame-retardant compositions. The CNTs had the following diameters: 10-15 nm, 40-60 nm, and 60-80 nm. Optimum mechanical flame retardancy and electrical properties were achieved by adding CNTs with an outer diameter of 40-60 nm and a length of fewer than 20 nm. Large-sized CNTs result in poor mechanical characteristics, while smaller-sized CNTs improve the mechanical properties of the composites. CB enhances flame retardancy but deteriorates mechanical properties, particularly elongation at break, in clean, black, thermoplastic, flame-retardant compositions. Obtaining satisfactory compositions that meet both properties, especially formulations passing the V-0 of the UL 94 test with a minimum tensile strength of 9.5 MPa and an elongation at break of 125%, is challenging. When LLDPE was partially substituted with EVA, the limiting oxygen index (LOI) increased. The amount of filler in the formulations determined how it affected flammability. This study also included a reliable method for producing clean, black, thermoplastic, flame-retardant insulating material for wire and cable without sacrificing mechanical properties.

Return on investment from educational research grant funding: deliverables and measurement

This paper investigates the return on investment (ROI) paradigm from fundamental educational research grant funding perspectives. The researchers conducted a wide-ranging literature search regarding the educational research grant funding ROI from public policy and economics viewpoints. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were utilized to identify and analyze research articles and original reports related to ROI from educational research grant funding. The research data were screened through a literature review based on the inclusion criteria, namely study focusing on return on investment of educational research funding and study published between 2001 and 2021 (as of December 2022). This study found evidence that ROI calculation from fundamental educational research grants is not straightforward and varies. Besides, most available research focuses on research impact rather than economic and intrinsic research value. Based on the compiled literature on research-related performance attributes, this study identified three distinct deliverables of educational research funding: tangible output, intangible output, and research outcome. The present research proposes more robust and reliable methods for measuring the ROI of fundamental educational research impact, potentially generating a much-inform decision-making and resource allocation in educational research grant funding.

Random vibration-based progressive fatigue damage monitoring in thermoplastic coupons: a preliminary investigation

In recent years there has been an increasing trend towards the utilization of composites, particularly thermoplastics, in various components of aerostructures. The prelusion of such materials has underscored the significance of investigating their fatigue behavior and developing reliable methods for detecting fatigue damage. In this context, vibration-based techniques hold significant potential as they leverage the inherent excitation provided by in-flight noise and turbulence. This study aims at assessing the progressively accumulated fatigue damage in thermoplastic coupons via random vibration signals while accounting for operational and inter-structural uncertainties. The experimental process consists of preliminary tension and fatigue tests, interrupted fatigue tests, C-Scan inspection tests, and non-contact random vibration tests. Consecutive fatigue states are obtained by performing fatigue tests at intervals of 10 000 cycles for a population of 7 coupons. At each interruption, ultrasonic C-Scan and vibration inspection tests are performed, allowing for the visualization of fatigue damage and random vibration signal analysis. Welch Power Spectral Density estimates are employed and are shown to have good potential for distinguishing among different fatigue states despite the inevitable population and experimental uncertainty. Furthermore, fatigue damage is found to progress symmetrically and laterally along the free edges of the test coupons, which is explained by the free edge effect.

Acoustic monitoring yields informative bat population density estimates.

Bat population estimates are typically made during winter, although this is only feasible for bats that aggregate in hibernacula. While it is essential to measure summer bat population sizes for management, we lack a reliable method. Acoustic surveys should be less expensive and more efficient than capture surveys, and acoustic activity data are already used as indices of population size. Although we currently cannot differentiate individual bats by their calls, we can enter call counts, information on signal and detection angles, and weather data into generalized random encounter models to estimate bat density. We assessed the utility of generalized random encounter models for estimating Indiana bat (Myotis sodalis) population density with acoustic data collected at 51 total sites in six conservation areas in northeast Missouri, 2019-2021. We tested the effects of year, volancy period, conservation area, and their interactions on estimated density. Volancy period was the best predictor, with average predicted density increasing 60% from pre-volancy (46 bats/km2) to post-volancy (74 bats/km2); however, the magnitude of the effect differed by conservation area. We showed that passive acoustic surveys yield informative density estimates that are responsive to temporal changes in bat population size, which suggests this method may be useful for long-term monitoring. However, we need more information to choose the most appropriate values for the density estimation formula. Future work to refine this approach should include assessments of bat behavior and detection parameters and testing the method's efficacy in areas where population sizes are known.

Fabrication of novel (Cr) based (MIL-101) incorporated mixed matrix forward osmosis membranes for environmental remediation

This paper focusses on the fabrication of a thin film nanocomposite forward osmosis (FO) membrane using PA support. This was done through interfacial polymerization with MOF MIL-101(Cr). There are various methods which are used for the purification of water such as ion-exchange method; reverse osmosis, adsorption, solvent extraction but these are expensive, old and timing consuming. So there is need of a unique and reliable method which is cheap, easy, less time consuming, pollution free and can give better results. For this purpose, I have worked on FO membranes with the incorporation of novel MOF MIL-101(Cr). By using different characterization methods, the surface properties, chemical composition and structure of FO membranes based on MOF MIL-101(Cr) were studied. This coating has done by layer by method therefore has larger pores to higher selective layer roughness. It also enhanced the membrane permeability and heavy metal rejection because MOF MIL-101(Cr) has unique structures and desirable mechanical and chemical properties. This MOF has high thermal stability, greater surface area and high porosity. It was observed that TNF membranes provide ultrafast nano-channels for water transport, higher water flux and lower the solute reverse flux as compared to TFC membranes without MIL-101(Cr). The results of FO thin film nanocomposite membranes based on MOF MIL-101(Cr) have shown the 81 % heavy metal rejection than simple polyamide membranes. This FO method will offer further promising applications, opportunities and excellent separation performances to produce composite membranes with thin films on microfiltration substrates to mitigate concentration polarization.

Application of the multi-criteria analysis method mairca, spotis, comet for the optimisation of sustainable electricity technology development

The development of sustainable electricity technology is of utmost importance in addressing the increasing energy demand while mitigating greenhouse gas emissions. Fossil fuel-based electricity generation is the primary contributor to air pollution and climate change, necessitating a shift towards renewable energy sources. The efficient production, distribution, and utilization of energy resources, along with ensuring affordable energy access and environmental sustainability, are key policy objectives for any country's energy sector. However, assessing sustainable electricity technologies is a complex task due to the diverse range of evaluation criteria and impacts associated with the practical implementation of these solutions. To overcome this challenge, this study proposes a multi-criteria decision-making (MCDM) approach to select the optimal solution for the development of sustainable electricity technology. The study employs several reliable methods, including MAIRCA, SPOTIS, COMET, and the CRITIC weighting method, to perform ranking evaluations. Based on this, an evaluation Table of criteria using linguistic variables is constructed. Furthermore, a ranking of methods for developing sustainable electricity technology is established by combining MCDM optimization methods. The results indicate that future energy policies should prioritize sustainable energy technologies, particularly water and solar thermal solutions. These findings have significant implications for development policymakers as the transition towards a sustainable energy system becomes increasingly crucial. In the future, the findings of this research can be further developed on a regional level, enabling the identification of the most appropriate energy technologies for specific regions based on their unique characteristics and requirements

Role of Culture Free Rapid ImmunoAssay (ELISA) and Genexpert (MTB/RIF) Technology in the Diagnosis of Tuberculosis

Introduction: Prolonged laboratory processes are major hurdles in the early diagnosis of tuberculosis. Prompt management of this disease is utmost necessary to control its spread. For this purpose, use of rapid and reliable laboratory testing methods for the diagnosis of Mycobacterium tuberculosis are crucial alongwith timely infection management and preventing its transmission to the close contact population. Aims and Objective: The aim of this study was to establish a diagnostic-connection between culture free cost effective rapid immuno-assay (IgG and IgM) and GeneXpert (MTB/RIF) technology for the detection of Mycobacterium tuberculosis within the shortest possible turnaround time. Place and Duration of Study: This cross-sectional study was carried out at Institute of Microbiology and Molecular Genetic, University of the Punjab, Pakistan in collaboration with the Department of Pulmonology and TB Outpatient Department (OPD) at Jinnah Hospital, Lahore over the time of one year (2019-2020). Material and Methods: To achieve the aim, a total of 180 (pulmonary and extra-pulmonary) samples along with blood samples were collected from clinical suspects of Tuberculosis. All collected samples were subjected to AFB microscopy, GeneXpert (MTB/RIF) assay and Enzyme-linked Immunosorbent Assay (ELISA) for IgG and IgM. The diagnostic sensitivity and specificity of both methods were determined by using the solid culture as the gold standard.Study data collected through questionnaire was entered and analyzed using Statistical Package for Social Sciences (SPSS version 20).p value of ? 0.05 was considered significant. Results: The overall sensitivity and specificity of GeneXpert (MTB/RIF) assay were 98.2 % and 82.4% respectively, whereas, for IgG (ELISA) antibodies, it was 50.0% and 76.4% and for IgM (ELISA) antibodies 50.4% and 97.1% respectively. Conclusion: It was concluded that with fully automated GeneXpert (MTB/RIF) technology, maximum sensitivity and specificity can be achieved for diagnosis of tuberculosis within 2 hours.

Molecular Characterization of Multidrug-Resistant Shigella flexneri.

Due to their propensity for causing diarrheal illnesses and their rising susceptibility to antimicrobials, Shigella infections constitute a serious threat to global public health. This extensive study explores the frequency, antibiotic resistance, genetic evolution, and effects of Shigella infections on vulnerable groups. The research covers a wide range of geographical areas and sheds information on how the prevalence of Shigella species is evolving.Shigella strain antimicrobial resistance patterns are thoroughly examined. Multidrug resistance (MDR) has been found to often occur in investigations, especially when older antimicrobials are used. The improper use of antibiotics in China is blamed for the quick emergence of resistance, and variations in resistance rates have been seen across different geographical areas. Shigella strains' genetic makeup can be used to identify emerging trends and horizontal gene transfer's acquisition of resistance genes. Notably, S. sonnei exhibits the capacity to obtain resistance genes from nearby bacteria, increasing its capacity for infection. The study also emphasizes the difficulties in accurately serotyping Shigella strains due to inconsistencies between molecular and conventional serology. These results highlight the necessity of reliable diagnostic methods for monitoring Shigella infections. In conclusion, this study emphasizes how dynamic Shigella infections are, with varying patterns of occurrence, changing resistance landscapes, and genetic adaptability. In addition to tackling the rising problem of antibiotic resistance in Shigella infections, these findings are essential for guiding efforts for disease surveillance, prevention, and treatment.

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.