Established Method Research Articles

First classification criteria for diseases caused by calcium pyrophosphate deposition (CPPD)-Translation, explanation and assessment

For diseases caused by calcium pyrophosphate deposition (CPPD), validated classification criteria were previously lacking. In this article the recently developed and validated classification criteria are translated, explained, and assessed. In recent years a multinational research group developed classification criteria for CPPD disease with the support by the European Alliance of Associations for Rheumatology (EULAR) and the American College of Rheumatology (ACR), following an established method. The developed criteria were finally validated in an independent cohort. The translation and annotation of the new first classification criteria were carried out in an iterative procedure in consensus with the authors. The presence of a crowned dens syndrome or calcium pyrophosphate crystals in the synovial fluid in patients with pain, swelling or sensitivity of the joints (entry criterion) is sufficient for the classification as CPPD disease, where the symptoms cannot be completely explained by another rheumatic disease (exclusion criterion). If these symptoms are not present, a count of more than 56 points based on weighted criteria comprised of clinical features and the results of laboratory and imaging investigations can be included for classification as a CPPD disease. These criteria had a sensitivity of 92.2% and a specificity of 87.9% in the derivation cohorts (190 CPPD cases and 148 mimics), whereas the sensitivity was 99.2% and the specificity 92.5% in the validation cohorts (251 CPPD cases and 162 mimics). The ACR/EULAR classification criteria 2023 of a CPPD disease will facilitate clinical research in this field. The use in the clinical routine will show how practical the criteria are.

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

AN OVERVIEW PHARMACIST'S ROLE IN PREVENTING MEASURES TOWARD ANTIBIOTIC RESISTANCE

The issue of antimicrobial medication resistance is a significant global concern. Drug discovery plays a crucial role in addressing present treatment gaps and enhancing established therapeutic methods. However, due to the extensive research duration and substantial expenses involved in introducing novel drugs to the market, focusing on the development of new drugs that target drug-resistant microbes with harmful effects may not be the most optimal approach. The literature was systematically evaluatesing databases to identify all relevant research published until the beginning of 2022. In order to uncover novel classes of antibiotics, there is a pressing need for the development of innovative methodologies in rational design and screening-based approaches. The advancement of efficient molecular methodologies for the identification of resistance genes, as well as the exploration of diagnostic biomarkers such as procalcitonin for the purpose of guiding the discontinuation of antibiotic treatment, holds significant value in the effort to mitigate antibiotic usage.

Interactions between the metabolic syndrome and alcohol consumption increases the risk of liver disease.

Alcohol-related liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD, recently renamed metabolic dysfunction-associated steatotic liver disease [MASLD]) share many features, including certain pathophysiological mechanisms, susceptibility genes, and histological lesions. However, the natural history of the two diseases, studied separately, is significantly different, with ALD being associated with a higher risk of cirrhosis and liver-related mortality. Moreover, evidence suggests an interactive effect between ALD and metabolic risk factors that are associated with NAFLD on the risk of progressive fibrosis and development of cirrhosis. Patients with both a high consumption of alcohol and metabolic risk factors, such as obesity or diabetes, should therefore be considered a particularly high-risk group for cirrhosis. Additional studies regarding the efficacy of screening for advanced liver fibrosis or cirrhosis in these risk groups are needed. The most effective and established method for reducing the risk of progression in ALD is alcohol abstinence, whereas weight loss is effective in NAFLD. In this narrative review, we introduce the reader to the literature of the field and present key studies showing this interactive effect.

Prevalence and clinical profiles of anti-myelin-associated glycoprotein neuropathy in Japan: A nationwide survey study of 133 patients.

The aim of this study was to determine the prevalence of anti-myelin-associated glycoprotein (MAG) neuropathy and the current status of such patients in Japan. We conducted a nationwide survey in 2021 using established epidemiological methods. Questionnaires were sent to all neurology and pediatric neurology departments throughout Japan to identify patients with anti-MAG neuropathy. An initial questionnaire was used to determine the number of patients, with a second one used to collect detailed clinical information. The estimated number of patients with anti-MAG neuropathy was 353, with a prevalence of 0.28 per 100,000 and an incidence of 0.05 per 100,000. The detailed clinical profiles of 133 patients were available. The median (range) age of onset was 67 (30-87) years, with a prominent peak in the age range 66-70 years, and the male-to-female ratio was 3.6. Most patients had distal sensory-predominant polyneuropathy, and neuropathic pain (50%), or sensory ataxia (42%), while 18% had Waldenström's macroglobulinemia or multiple myeloma. Intravenous immunoglobulin was the most frequently used treatment (65%), but the response rate was <50%, whereas rituximab was given in 32% of patients, and 64% of these showed improvement. At the last visit, 27% of patients could not walk independently. This study on anti-MAG neuropathy provides updated insights into the epidemiology of this disease, clinical profiles, and treatment approaches in Japan. Rituximab therapy, used for only one-third of the patients, demonstrated efficacy. During the final visit, a quarter of the patients were unable to walk independently. Further studies are warranted to determine the optimal management of this rare and intractable disorder.

The Fourier–Legendre Series of Bessel Functions of the First Kind and the Summed Series Involving 1F2 Hypergeometric Functions That Arise from Them

The Bessel function of the first kind JNkx is expanded in a Fourier–Legendre series, as is the modified Bessel function of the first kind INkx. The purpose of these expansions in Legendre polynomials was not an attempt to rival established numerical methods for calculating Bessel functions but to provide a form for JNkx useful for analytical work in the area of strong laser fields, where analytical integration over scattering angles is essential. Despite their primary purpose, one can easily truncate the series at 21 terms to provide 33-digit accuracy that matches the IEEE extended precision in some compilers. The analytical theme is furthered by showing that infinite series of like-powered contributors (involving 1F2 hypergeometric functions) extracted from the Fourier–Legendre series may be summed, having values that are inverse powers of the eight primes 1/2i3j5k7l11m13n17o19p multiplying powers of the coefficient k.

Underground Mine Safety and Health: A Hybrid MEREC-CoCoSo System for the Selection of Best Sensor.

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of "Ease of Installation" as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model's performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

Rapid multichannel fluorescent probe assay for CYP450 inhibition screening and drug interaction monitoring

Cytochrome P450 (CYP450) inhibition induced drug interactions (DDIs) are a primary cause of adverse drug reactions (ADRs). The rapid identification of CYP450 inhibitors is an important method for reducing potential ADRs. However, the existing methods are often intricate and time consuming, making them difficult to use for high-throughput rapid monitoring. Based on the specificity of CYP450 to the catalysis of different fluorescent substrate probes, an assay was developed to monitor the activity of six CYP450 isoforms simultaneously. The production rate of fluorescent metabolites characterized CYP450 activity. Method specificity was evaluated using inhibitors recommended by the FDA Guidelines for Drug Interaction Studies. We screened 28 clinically utilized medications for inhibition of CYP450 and evaluated the potential interaction by comparing inhibition constants (Ki) and maximum plasma concentration (Cmax). The analysis of the positive inhibitors using this method suggests that it allows for quick screening and identification of CYP450 inhibitors. Six drugs, including latoxefin, diclofenac, erythromycin lactate, labetalol, cimetidine, and droperidol, showed an interaction risk. The three isoforms of the CYP1A2, CYP3A4 and CYP3A5 metabolizing enzyme mediate a greater number of interactions, which should be specifically monitored in clinical applications. The established method permits rapid screening for inhibiting CYP1A2, CYP2E1, CYP2B6, CYP2A6, CYP3A4 and CYP3A5 with simple, stable, inexpensive, and high-throughput. This method will provide technical support for rapid discovery of clinical CYP450 inhibitors and risk avoidance of drug combination.

Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice

Abstract The majority of natural diabetic medications come from fruits and vegetables. These natural medications help protect humans from negative impacts of chemical antidiabetics by scavenging free radicals. The present study aimed to explore the antioxidant and antidiabetic properties of methanolic extract of fruits of Hibiscus sabdariffa Linn. (MEHSF) in alloxan-induced Swiss albino diabetic mice. The dried coarse powder of Hibiscus sabdariffa Linn. fruits was subjected to methanol extraction. The antidiabetic activity was determined by using alloxan-induced (80 mg/kg body weight) diabetic mice. Following a 15-day treatment period, serum biochemical parameters including total cholesterol (TC), triglyceride (TAG), LDL-cholesterol (LDL), HDL-cholesterol (HDL), serum glutamic pyruvic transaminase (SGPT), and serum glutamic-oxaloacetic transaminase (SGOT) enzymes were estimated. The antioxidant activity was evaluated through a DPPH and ABTS free radical scavenging assay. Total phenolic content and total flavonoid content were assessed using established methods. MESHF, containing polyphenolic and flavonoid compounds, exhibited antioxidant properties. A 100 and 200 mg/kg significantly (p &lt; 0.05) lowered the blood glucose levels and improved biochemical parameters such as TC, TAG, LDL, and HDL in diabetic mice. Further, MESHF significantly (p &lt; 0.05) reduced the activity of the SGPT and SGOT in diabetic mice compared to untreated diabetic mice. These results suggest that MEHSF with promising antioxidant and antidiabetic potentials can be considered to be a probable new resource of the antidiabetic agent.

Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.

Providing a Feature Selection Method for Lung Cancer Prediction Using Neural Network

Introduction: The remarkable growth of lung cancer and its associated impacts and consequences, along with the substantial costs it imposes on society, has driven the medical community to pursue programs aimed at further examination, prevention, early detection, and diagnosis. In medicine science, timely discovery and diagnosis of diseases can prevent many life-threatening conditions and save people's lives.Material and Methods: This study aims to predict lung cancer using a novel feature selection method integrated with a classifier. Our approach entails a comprehensive four-stage method. Initially, we calculate feature similarities within a lung cancer dataset using the absolute value of the Pearson correlation coefficient, followed by the clustering of initial features using the community detection algorithm called Louvain. Next, we employ techniques to determine the optimal subset of features using the concept of node centrality. Ultimately, lung cancer diagnosis is executed using the selected features, leveraging a classifier.Results: Comparative analysis reveals that our proposed method outperforms existing techniques in terms of reduced execution time and improved prediction accuracy. When compared with established methods, our approach demonstrates superior outcomes in terms of the number of selected features and classification accuracy. Our method reduced 12600 features to 118 features and its accuracy was 95.28, 95.49, 95.23 and 95.32 for Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier. The comparison of runtime shows that the proposed method is significantly improved with a runtime of 2.146 seconds compared to other methods.Conclusion: The proposed feature selection method successfully reduced the initial feature set and significantly decreased computational time. Moreover, the achieved prediction accuracies underscore the reliability of our approach. This significant reduction in feature space while maintaining consistently high prediction accuracies serves as a strong validation of the potency and practical applicability of our methodology in the domain of lung cancer prediction. These compelling results strongly advocate for the potential real-world impact of our approach.

Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software development

SummaryIn software development, developer turnover is among the primary reasons for project failures, leading to a great void of knowledge and strain for newcomers. Unfortunately, no established methods exist to measure how the problem domain knowledge is distributed among developers. Awareness of how this knowledge evolves and is owned by key developers in a project helps stakeholders reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of problem domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300 K issues and 1.3 M comments from 518 open‐source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the individual developers' concepts are with the team over time. We also investigate the impact of leaving developers on the frequency of concepts. Finally, we also evaluate the soundness of our approach on a closed‐source software project, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the problem domain knowledge within a project and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers correlates with a decline of their core concepts as the remaining developers cannot quickly familiarize themselves with those concepts.

The genomic copy number determination of improved Cre in transgenic mice using standards derived from megaprimer PCR constructed tandem repeats

Determination of the genomic copy number of Cre transgene is critical for the establishment of transgenic animal models. We demonstrate a real-time quantitative polymerase chain reaction (RT-qPCR) based method for genomic copy number determination of improved Cre (iCre), which was tested in a transgenic mouse model of CYP19A1-iCre. The standards were derived from tandem repeats of iCre fragments which were constructed by a new application of megaprimer PCR. Two rounds of megaprimer PCR were used to obtain specific tandem repeats of iCre fragments, which were subsequently cloned into pUCm-T vectors. After the fragments of IL-2 (reference gene) were ligated into the same vectors, standards with copy number ratios (CNR) of 0.5, 1, 2, 4, 8, and 16 between iCre and IL-2 were completed. The R2 values of the standard curves exceeded 0.99. The calculated CNR value of the quality control sample was satisfactory. Multiple copies of iCre were detected in founder mice. After several passages, the copy number of iCre gradually decreased to a single copy, which was consistent with theoretical expectation. The established method is a universal and sensitive program for genomic copy number determination of iCre in transgenic mice.

Development of the Valued Living Questionnaire – Comprehension Support version (VLQ-CS) and validation in adults with acquired brain injury

Purpose Valued living (acting in accordance with personal values) is associated with better outcomes after acquired brain injury (ABI), but its measurement using the Valued Living Questionnaire (VLQ) may not be valid due to comprehension errors relating to structure and content. We aimed to modify the VLQ to improve its accessibility and evaluate construct validity and reliability in an ABI cohort. Materials and methods Adaptations made in the VLQ – Comprehension Support version (VLQ-CS) used established communication support methods and addressed common comprehension errors. 103 community-dwelling participants (34% female; mean age 52.17, range 19–79) with ABI (66% stroke, 16% TBI, 18% other) completed the VLQ-CS, and measures of convergent (valued living, mood, wellbeing, psychological inflexibility) and divergent validity (subjective memory). Test-retest reliability was evaluated with repeated administrations 6–8 weeks apart for a subset of participants (n = 44), using Intraclass Correlation Coefficients (ICCs). Results Convergent validity was supported; VLQ-CS scores were positively correlated with measures of valued living (r=.60–.65) and wellbeing (r=.64–.67), and negatively correlated with depression (r=-0.56–.58), anxiety (r=-0.35–.38) and psychological inflexibility (r=-0.37–.41). Divergent validity was marginal (r=-0.29). Test-retest reliability was good for the VLQ-CS Composite score (ICC=.80). Conclusions The VLQ-CS shows promise as a valid, reliable measure of valued living post-ABI. Future research should extend to neurotypical and other clinical populations.

Integrated analysis and separation of 5-lipoxygenase inhibitors from triterpenes of Poria cocos using bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking and target-based multiple complex networks

Poria cocos (Schw.) Wolf (P. cocos) has been widely used as medical plant in East Asia with remarkable anti-Alzheimer's disease (anti-AD) activity. However, the underlying mechanisms are still confused. In this study, based on the β-Amyloid deposition hypothesis of AD, an integrated analysis was conducted to screen and separation 5-lipoxygenase (5-LOX) inhibitors from triterpenoids of P. cocos and investigate the anti-AD mechanisms, containing bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking, and multiple complex networks. Five triterpenoids were identified as potential 5-LOX inhibitors, including Tumulosic acid, Polyporenic acid C, 3-Epi-dehydrotumulosic acid, Pachymic acid and Dehydrotrametenolic acid. Five potential 5-LOX inhibitors were screened by ultrafiltration affinity assay in P. cocos. The molecular docking simulation results are consistent with the ultrafiltration experimental results, which further verifies the accuracy of the experiment. The commercial 5-LOX inhibitor that Zileuton was used as a positive control to evaluate the inhibitory effect of active ingredients on 5-LOX. Subsequently, the established separation method allowed the five active ingredients (Pachymic acid, 3-Epi-dehydrotumulosic acid, Dehydrotrametenolic acid, Tumulosic acid and Polyporenic acid C) with high purity to be isolated. Targeting network pharmacology analysis showed that five active ingredients correspond to a total of 286 targets. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis found that target cells were mainly enriched in Pathways in cancer, Lipid and atherosclerosis. Our results indicate that P. cocos extract has the potential to be used in the prevention and treatment of neurodegenerative diseases. This will help elucidate the mechanisms of action of various medicinal plants at the molecular level and provide more opportunities for the discovery and development of new potential treatments from health food resources.

Comprehensive quality evaluation of Magnolia officinalis leaves based on wavelength fusion fingerprint, content determination, and antioxidant activity analysis

Abstract A valid, novel, and comprehensive evaluation method for assessing the quality of Magnolia officinalis leaves (MOLs) from different origins has been established based on multiwavelength fusion fingerprint, quantitative analysis, and antioxidant activity screening. The four wavelength fingerprints for MOL samples at 258, 266, 290, and 327 nm were collected by high performance liquid chromatography, and then data fusion was conducted. In addition, the content of multiple components was determined by high performance liquid chromatography at four wavelengths. 1,1-Diphenyl-2-pyridyl hydrazine scavenging assay was applied to study in vitro antioxidant activity. The effective distinction of 18 batches of MOLs was realized based on hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis combined with multiwavelength fusion fingerprint. The orthogonal partial least squares discriminant analysis model exhibited huge advantages in distinguishing among different samples and screening three quality markers of the tested samples, including rutin, isoquercitrin, and chlorogenic acid. This established method of multitechnology fusion is simple, reliable, and efficient for the qualitative and quantitative bioactivity evaluation of MOLs. This study aims to investigate the development of new medicinal parts of traditional Chinese medicines (TCMs), so as to enhance the efficiency of utilizing TCM resources and promote the high-quality development of the TCM industry.

Effect of Crop Establishment Methods and Nitrogen Levels on Phenology, Quality, Nutrient Content and Uptake of Coarse Rice

A field experiment was conducted during the kharif season of 2020 at the farm of College of Agriculture, Kaul (Kaithal) of CCS Haryana Agricultural University, Hisar. The objective of the experiment was to study the response of a short-duration non-scented rice variety called HKR-48 to nitrogen fertilization under two different methods of crop establishment. The experiment followed a randomized complete block design (RBD) factorial design with the two establishment methods (direct seeding and transplanting) as main plot treatments and six levels of nitrogen (0, 30, 60, 90, 120, and 150 kg/ha) as sub-plot treatments, with three replications. The results of the experiment showed that the transplanted crop took longer to initiate tillering, anthesis (flowering), and reached maturity slightly earlier compared to the direct-seeded crop. However, both establishment methods showed similar timing for panicle emergence. The methods of crop establishment did not have a significant effect on kernel length, kernel breadth, NPK content, and protein content of the grains. However, the transplanted crop had improved hulling, milling, and head rice recovery compared to the direct-seeded crop. With increasing nitrogen levels, there was a delay in the days taken to attain tillering, panicle emergence, anthesis, and physiological maturity, following a graded response. The kernel length and breadth were not influenced by the graded doses of nitrogen. However, the quality parameters such as hulling, milling, head rice recovery, and protein content of the grains improved significantly with increasing nitrogen levels. The phosphorus (P) and potassium (K) content in the grain and straw remained unaffected by the different nitrogen doses, except for nitrogen content. The uptake of NPK by the crop (grain and straw) showed an increasing trend with higher nitrogen application

Enhancing Energy Efficiency and Classification Modeling Through a Combined Approach of LightGBM and Stratified KFold Cross-Validation

This research article aims to develop a robust and interpretable model for energy efficiency and classification that overcomes the limitations of existing techniques. The study’s objectives are to address the challenges faced by the established methods such as linear regression, random forest, support vector machines, and others. These limitations include issues related to predictive accuracy, model interpretability, and computational efficiency. To bridge these gaps, we introduce a novel model, stratified KFold LightGBM ensemble (SKL-EEFE). SKL-EEFE leverages the strengths of stratified KFold cross-validation, LightGBM gradient boosting, domain-specific feature engineering, and ensemble learning to provide a comprehensive solution to classification tasks. Our key findings reveal that SKL-EEFE outperforms existing methods while maintaining model interpretability. The proposed model offers both accuracy and transparency, making it a valuable addition to the field of classification modeling. This research contributes to advancing the state of knowledge by offering an innovative approach to overcome the limitations of existing models and providing a practical solution for real-world classification problems.

Establishment of a steroid binding assay for goldfish membrane progesterone receptor (mPR) by coupling with graphene quantum dots (GQDs).

A homogeneous assay was developed to evaluate ligands that target the membrane progesterone receptor alpha (mPRα) of goldfish. This was achieved by employing graphene quantum dots (GQDs), a type of semiconductor nanoparticle conjugated to the goldfish mPRα. When progesterone-BSA-fluorescein isothiocyanate (P4-BSA-FITC) was combined with the other agents, fluorescence was observed through Förster resonance energy transfer (FRET). However, this fluorescence was quenched by binding between the ligand and receptor. This established method demonstrated the ligand selectivity of the mPRα protein. Then, the methylotrophic yeast Pichia pastoris was used to express the goldfish mPRα (GmPRα) protein. The recombinant purified GmPRα protein was coupled with graphene quantum dots (GQDs) to generate GQD-conjugated goldfish mPRα (GQD-GmPRα). Fluorescence at a wavelength of 520nm was observed through FRET upon the combination of P4-BSA-FITC and subsequent activation by ultraviolet (UV) light. Adding free P4 to the reaction mixture resulted in a decrease in fluorescence intensity at a wavelength of 520nm. The fluorescence was reduced by the administration of GmPRα ligands but not by steroids that do not interact with GmPRα. The findings indicated that the interaction between the ligand and receptor led to the formation of a complex involving GQD-GmPRα and P4-BSA-FITC. The interaction between the compounds and GQD-GmPRα was additionally validated by a binding experiment that employed the radiolabeled natural ligand [3H]-17α,20β-dihydroxy-4-pregnen-3-one. We established a ligand-binding assay for the fish membrane progesterone receptor that is applicable for screening compounds.

Economic evaluations of treatment of depressive disorders in adolescents: Protocol for a scoping review.

Depressive disorders in adolescents are common and impairing. Evidence-based treatments are available; however, at a cost. In the context of the COVID-19 pandemic, we anticipate increased demand for treatment services for adolescents with depression. We also anticipate that economic resources will be strained. Identifying cost-effective strategies to optimally treat depression in adolescents is imperative. This protocol for a scoping review aims to describe the literature with respect to economic evaluations of treatments for depression in adolescents. We will conduct a scoping review using established methods and reporting guidelines. MEDLINE, Embase, PsyclNFO, Econlit, and the International HTA Database will be searched from inception to June 13, 2023, with an update closer to time of manuscript submission, while the NHS Economic Evaluation Database archives will be searched from inception to December 2014. Publications that contain economic evaluations, in the context of a clinical trial or a model-based study, testing a treatment of depression in adolescents will be selected for inclusion. Extracted data items will include: economic evaluation perspectives, health outcome variables and costs used in economic evaluations, types of analyses performed, as well as quality of reporting and methodology. A narrative synthesis with summary tables will be used to describe our findings. Our findings will help identify gaps in the literature with respect to economic analyses for the treatment of depression such that these gaps can be filled with future research. Policy-makers, funders and administrators may also use our findings to inform their decisions around provision of various treatments for depression in adolescents. osf.io/5fteb (note that information on this link will be updated upon acceptance for publication based on reviewer comments).