Proper Validation Research Articles

Multiple dimensions of soil food-web research: History and prospects

Soil food webs are at the nexus of soil biodiversity, functioning, and stability. With a research history of over 35 years, soil food-web research remains a challenging and relatively specialised field. Initially receiving wide attention after the general model of William H. Hunt and colleagues in 1987, the field diversified over the last two decades across ecosystem and community ecology, empirical and theoretical approaches, network and energy flux analyses. Here we reflect on the history, status, and trends in soil food-web research and identify major perspectives and synthesis directions. After briefly reviewing modelling approaches, structure, and quantification of functioning, we distinguish modern trends in tools that can streamline food-web research, a need for proper empirical validation, collaborative approaches to push the field forward, and conclude with application perspectives. In the light of increasing data availability and public awareness about soil biodiversity, we call for synthesis across multiple dimensions of soil food-web research (e.g. different methodologies and disciplines, various spatiotemporal scales, multiple trophic levels and phyla of life), integrating the soil food-web approach to biodiversity and environmental studies, and making it more accessible to a wider community of scientists.

Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning.

Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI. Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC). The data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions. Machine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.

Local variations of landslide factors in Pulau Pinang, Malaysia

A landslide is one of the most notorious natural disasters, resulting in massive losses and significant damages. Thus, this paper aims to analyze the spatial heterogeneity of the influencing factors which later inspect the relationship between the factors and landslide occurrences. A total of 988 landslides historical data and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, including distance to roads, distance to rivers, distance to faults, slope angle, curvature, slope aspect, land use, and lithology. Geographically Weighted Logistic Regression (GWLR) is introduced in this paper to carry out the local analysis, resulting in the slope angle and the slope aspect playing the most significant role in influencing landslides. The Akaike’s information criterion (AICc) of GWLR is 824.51 which has a lower value than the global regression represented as 906.09 revealing that GWLR is the best model. Other evaluation criteria such as deviance, local percent deviance explained (pdev), and Bayesian information criterion (BIC) also validate the significance of the GWLR model. The GWLR results show the degree of spatial variation in the relationship between landslides and the influencing factors in the study area as the coefficient values of every factor are inconsistent, providing a reference for managers to formulate targeted decision-making measures. In the meantime, urgent action to sustain this natural disaster as suggested in the SDG 13 has to be taken earnestly to avoid bigger impacts on both society and the environment.

English

This present study presents distinctive models that capture the nonlinear longitudinal and transverse vibrations of branched, slightly curved embedded nanotubes that convey nanofluids and operate in thermal-magnetic environments. Euler beam equation and Hamilton and Erigen’s theorems are employed for the development of the system’s governing equations of motion. Subsequently, these equations are solved with the aid of PDE tools and PDE solvers MATLAB. The solutions obtained via the employment of these tools are used for parametric study and visualization after proper and adequate verification and validation. From the results, it is obvious that an augmentation in the branched angle increases the nanotube’s instability. Besides, the magnetic term attenuates the vibrations of the slightly curved branched nanotubes by over 20%. It is anticipated that the outcomes of this present study will give improved insights into the vibration analyses of straight and branched nanotubes that rest on elastic foundations and operate in thermal-magnetic and thermal-nanofluidic environments.

Genetic group and swim-up processing affect SYBR Green real-time PCR-based porcine semen sex ratio in Landrace and its crossbred boars.

Determination of factors affecting sex ratio is important while considering application of sex ratio enrichment approach. Present study aimed to design a SYBR Green qPCR-based method for measurement of primary sex ratio and to evaluate different factors (genetic group, sire, spermiogenic cycle and processing layer) affecting boar sperm sex ratio. The qPCR was based on relative copy number analysis of sex chromosome-specific single copy gene fragments with an autosomal gene as reference and was evaluated using DNA dilution series from pigs with numerically normal karyotype. The sex ratio was estimated from genomic DNA samples isolated from boar semen collected from different genetic groups at different time points and different processing layers. The X chromosome frequencies of semen samples revealed significant effect of genetic group. However, significant variation was observed neither within same genetic group nor between ejaculates of different spermatogenic cycles. Among the processing techniques studied, swim-up technique produced a significant X sperm enrichment in comparison to control whereas, Percoll density gradient failed to show any significant difference among layers. The lower layer in swim-up technique was found to contain higher proportion of X sperms. The designed qPCR is found to be an easy, less time-consuming method and does not require high end laboratory facilities or the specialized expertise. The lower layer of swim-up processing has a scope for X sperm enrichment in boar semen with proper validation.

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review.

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

Modelling of AgrA inhibitors to combat anti-microbial resistance in Staphylococcus aureus

Staphylococcus aureus is a Gram-positive bacterium found on human skin that causes skin and soft tissue infections, as well as pneumonia, osteomyelitis, and endocarditis. The prevalence of antibiotic resistant strains has made the treatments less effective. An efficient alternate method for battling these contagious diseases is anti-virulence strategy. The AgrA protein, a key activator of Accessory Gene Regulator system in S. aureus, is vital to the virulence of the organism and, consequently, its pathogenesis. Using a Machine Learning algorithm, the Support Vector Machine (SVM), and a ligand-based pharmacophore modelling method, prediction models of AgrA inhibitors were developed. The metrics of the SVM model were inadequate, hence it was not used for virtual screening. For ligand-based pharmacophore modelling, 14 of 29 compounds were removed from the active set due to a lack of shared pharmacophore properties, and 504 compounds were designated as decoys. A 3D pharmacophore model was created using LigandScout 4.4.5, with a fit score of 57.48, including a positive ionizable group, one hydrogen bond donor, and three hydrogen bond acceptors. The model after further validation was used to virtually screen an external database which resulted in six hits. These compounds were docked with the AgrA domain crystal structure to determine the inhibitor activity. Further, each docked complex was subjected to a 100 ns molecular dynamics simulation. CID238 and CID20510252 demonstrated potent inhibitory binding interactions and hence can be used to develop AgrA inhibitors in future after proper validation. Communicated by Ramaswamy H. Sarma

Assessment of the Psychometric Properties of the Attitude Scale for Nursing Profession (ASNP)

Background and Objective: The attitude towards the nursing profession is one of the most important topics in all areas of care provision from the time of choosing this profession to the end of the service period. The present study was conducted to determine the psychometric indicators of the Attitude Scale for the Nursing Profession (ASNP). Materials and Methods: This descriptive methodological study initially obtained permission from the original author to use the tool. The sample included 301 nurses who were selected using the convenience sampling method and working at hospitals affiliated with the Golestan University of Medical Sciences. Following that, the Attitude Scale for Nursing Profession (ASNP) developed by Ipek and Coban was translated into the Persian language according to the International Quality of Life Assessment (IQOLA) protocol. The validity assessment of the scale was carried out using a quantitative and qualitative method based on content validity (Content Validity Ratio [CVR] and Content Validity Index [CVI]) and utilizing the opinions of 11 nursing experts. Moreover, reliability was evaluated by two methods of internal consistency (Cronbach's alpha), test-retest, and the intra-class correlation coefficient (ICC). Data analysis was conducted using exploratory and confirmatory factor analysis in SPSS (version 22) and AMOS (version 23) software. Results: In the current study, the CVR score of the entire questionnaire was calculated as 0.72; however, statements 16 and 19 scored less than the acceptable level; therefore, they did not have proper validity and were removed. The CVI of all items was more than 0.79. The Cronbach's alpha coefficient of ASNP was obtained at 0.96. The ICC was determined at 0.93, and the stability reliability in the two stages of the test-retest method was 0.789 (P=0.001). The results of exploratory factor analysis showed that the Persian version of the scale has a 3-factor structure (characteristics of the nursing profession, preference for the nursing profession, and the general position of the nursing profession). These factors were confirmed by confirmatory factor analysis. (RMSEA=0.092, AGFA=0.90, and GFI=0.93). The estimation of standard factor loadings in confirmatory factor analysis showed that this scale has an acceptable fit in Iranian culture and has good construct validit. Conclusion: The results showed that the ASNP has the necessary validity and reliability after removing two items and can be used as a comprehensive and reliable tool to investigate the attitude towards the nursing profession in Iran.

Comparison between droplet digital PCR and reverse transcription-quantitative PCR methods to measure ecotoxicology biomarkers

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is currently the gold-standard technique for detecting and quantifying messenger RNA. However, without proper validation, the method may produce artefactual and non-reproducible cycle threshold values generating poor-quality data. The newer droplet digital PCR (ddPCR) method allows for the absolute quantification of targeted nucleic acids providing more sensitive and accurate measurements without requiring external standards. This study compared these two PCR-based methods to measure the expression of well-documented genes used in ecotoxicology studies. We exposed Mediterranean mussels (Mytilus galloprovincialis) to copper and analyzed gene expression in gills and digestive glands using RT-qPCR and ddPCR assays. A step-by-step methodology to optimize and compare the two technologies is described. After ten-fold serial complementary DNA dilution, both RT-qPCR and ddPCR exhibited comparable linearity and efficiency and produced statistically similar results. We conclude that ddPCR is a suitable method to assess gene expression in an ecotoxicological context. However, RT-qPCR has a shorter processing time and remains more cost-effective.

Genome-wide survey identified superior and rare haplotypes for plant height in the north-eastern soybean germplasm of China

The proper and efficient utilization of natural genetic diversity can significantly impact crop improvements. Plant height is a quantitative trait governing the plant type as well as the yield and quality of soybean. Here, we used a combined approach including a genome-wide association study (GWAS) and haplotype and candidate gene analyses to explore the genetic basis of plant height in diverse natural soybean populations. For the GWAS analysis, we used the whole-genome resequencing data of 196 diverse soybean cultivars collected from different accumulated temperature zones of north-eastern China to detect the significant single-nucleotide polymorphisms (SNPs) associated with plant height across three environments (E1, E2, and E3). A total of 33 SNPs distributed on four chromosomes, viz., Chr.02, Chr.04, Chr.06, and Chr.19, were identified to be significantly associated with plant height across the three environments. Among them, 23 were consistently detected in two or more environments and the remaining 10 were identified in only one environment. Interestingly, all the significant SNPs detected on the respective chromosomes fell within the physical interval of linkage disequilibrium (LD) decay (± 38.9 kb). Hence, these genomic regions were considered to be four quantitative trait loci (QTLs), viz., qPH2, qPH4, qPH6, and qPH19, regulating plant height. Moreover, the genomic region flanking all significant SNPs on four chromosomes exhibited strong LD. These significant SNPs thus formed four haplotype blocks, viz., Hap-2, Hap-4, Hap-6, and Hap-19. The number of haplotype alleles underlying each block varied from four to six, and these alleles regulate the different phenotypes of plant height ranging from dwarf to extra-tall heights. Nine candidate genes were identified within the four haplotype blocks, and these genes were considered putative candidates regulating soybean plant height. Hence, these stable QTLs, superior haplotypes, and candidate genes (after proper validation) can be deployed for the development of soybean cultivars with desirable plant heights.

A first-level customization study of SAPS II with Norwegian Intensive Care and Pandemic Registry (NIPaR) data.

Severity scores and mortality prediction models (MPMs) are important tools for benchmarking and stratification in the intensive care unit (ICU) and need to be regularly updated using data from a local and contextual cohort. Simplified acute physiology score II (SAPS II) is widely used in European ICUs. A first-level customization was performed on the SAPS II model using data from the Norwegian Intensive Care and Pandemic Registry (NIPaR). Two previous SAPS II models (Model A: the original SAPS II model and Model B: a SAPS II model based on NIPaR data from 2008 to 2010) were compared to the new Model C. Model C was based on patients from 2018 to 2020 (corona virus disease 2019 patients omitted; n=43,891), and its performances (calibration, discrimination, and uniformity of fit) compared to the previous models (Model A and Model B). Model C was better calibrated than Model A with a Brier score 0.132 (95% confidence interval 0.130-0.135) versus 0.143 (95% confidence interval 0.141-0.146). The Brier score for Model B was 0.133 (95% confidence interval 0.130-0.135). In the Cox's calibration regression and for both Model C and Model B but not for Model A. Uniformity of fit was similar for Model B and for Model C, both better than for Model A, across age groups, sex, length of stay, type of admission, hospital category, and days on respirator. The area under the receiver operating characteristic curve was 0.79 (95% confidence interval 0.79-0.80), showing acceptable discrimination. The observed mortality and corresponding SAPS II scores have significantly changed during the last decades and an updated MPM is superior to the original SAPS II. However, proper external validation is required to confirm our findings. Prediction models need to be regularly customized using local datasets in order to optimize their performances.

Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia

Landslide is one of the natural disasters that commonly occurs in terrestrial environments with slopes throughout the world. Located among countries with tropical climates, the hot and humid conditions expose Kuala Lumpur, Malaysia, to the risk of landslides. This paper aims to delineate the influencing physical characteristics of landslide occurrences in Kuala Lumpur. In this study, a 100 landslides historical data set and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, such as distance to roads, distance to streams, elevation, slope angle, curvature, slope aspect, land use, and lithology. These factors were processed using GIS as geospatial analysis provides a useful tool for planning, disaster management, and hazard mitigation. By using ArcMap 10.8.2, a GIS software, different spatial analyses in which maps for each physical factor were layered with landslide events distribution. The weights for each factor were determined using the ANN approach resulting in the slope angle having the highest relative importance with a 100.0% value. In comparison, 8.3% represents the slope aspect as the most insignificant factor out of the eight selected characteristics for this study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition have to be established for future mitigation action to support the agenda of SDG 15.

Non-targeted authentication of black pepper using a local web platform: Development, validation and post-analytical challenges of a combined NIR spectroscopy and LASSO method

Black pepper is a valuable commodity susceptible to economically motivated adulterations. This contribution describes the proper development, standardization, validation and late-stage validation of a non-targeted method for authentication of black pepper by near-infrared spectroscopy (NIR) coupled to a least absolute shrinkage and selection operator (LASSO). Note that this is the first successful attempt to apply the LASSO method to infrared spectroscopy data. We analysed 150 diverse samples of black pepper supplied by a well-recognized spice trader. A first batch of samples (n = 116) were split into training and test sets and then normalized by multiplicative scatter correction (MSC). While the test set was withheld for further testing of the model, the training set was submitted to the LASSO method with the aim of retrieving the discriminant spectral features and classifying the samples as authentic, exogenously-adulterated or endogenously-adulterated. The model was tested on the test set, achieving an overall accuracy of 94% with very high sensitivity and specificity rates. Furthermore, an R-based local web platform was created for immediate prediction of the samples from raw data. The NIR user simply uploads the raw data to the local web application and MSC normalization on the stored training set and interrogation of the LASSO classifier are performed. The online application facilitates the model testing and enables clear visualization of the outcomes. After the creation of the local web platform, the classifier was then validated with a new batch of independent samples, resulting in the correct prediction of 33/34 samples. The robustness of this non-targeted method and its stability over time was further established, and a late-stage validation of the classifier was successfully performed by an inexperienced user. Finally, the LASSO classifier was successfully challenged with a sample from an international inter-laboratory proficiency test. • A non-targeted method for authentication of black pepper by NIR & LASSO. • A web application was created for direct classification of NIR raw data. • Validation and late-stage challenges of the method were carried out. • Successful participation to an international proficiency test.

A Layer-Wise Coupled Thermo-Elastic Shell Model for Three-Dimensional Stress Analysis of Functionally Graded Material Structures

In this work, a coupled 3D thermo-elastic shell model is presented. The primary variables are the scalar sovra-temperature and the displacement vector. This model allows for the thermal stress analysis of one-layered and sandwich plates and shells embedding Functionally Graded Material (FGM) layers. The 3D equilibrium equations and the 3D Fourier heat conduction equation for spherical shells are put together into a set of four coupled equations. They automatically degenerate in those for simpler geometries thanks to proper considerations about the radii of curvature and the use of orthogonal mixed curvilinear coordinates α, β, and z. The obtained partial differential governing the equations along the thickness direction are solved using the exponential matrix method. The closed form solution is possible assuming simply supported boundary conditions and proper harmonic forms for all the unknowns. The sovra-temperature amplitudes are directly imposed at the outer surfaces for each geometry in steady-state conditions. The effects of the thermal environment are related to the sovra-temperature profiles through the thickness. The static responses are evaluated in terms of displacements and stresses. After a proper and global preliminary validation, new cases are presented for different thickness ratios, geometries, and temperature values at the external surfaces. The considered FGM is metallic at the bottom and ceramic at the top. This FGM layer can be embedded in a sandwich configuration or in a one-layered configuration. This new fully coupled thermo-elastic model provides results that are coincident with the results proposed by the uncoupled thermo-elastic model that separately solves the 3D Fourier heat conduction equation. The differences are always less than 0.5% for each investigated displacement, temperature, and stress component. The differences between the present 3D full coupled model and the the advantages of this new model are clearly shown. Both the thickness layer and material layer effects are directly included in all the conducted coupled thermal stress analyses.

The Need to Prioritize Model-Updating Processes in Clinical Artificial Intelligence (AI) Models: Protocol for a Scoping Review.

With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety. The objective of this scoping review was to evaluate and assess the model-updating practices of AI and ML clinical models that are used in direct patient-provider clinical decision-making. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the PRISMA-P protocol guidance in addition to a modified CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist to conduct this scoping review. A comprehensive medical literature search of databases, including Embase, MEDLINE, PsycINFO, Cochrane, Scopus, and Web of Science, was conducted to identify AI and ML algorithms that would impact clinical decision-making at the level of direct patient care. Our primary end point is the rate at which model updating is recommended by published algorithms; we will also conduct an assessment of study quality and risk of bias in all publications reviewed. In addition, we will evaluate the rate at which published algorithms include ethnic and gender demographic distribution information in their training data as a secondary end point. Our initial literature search yielded approximately 13,693 articles, with approximately 7810 articles to consider for full reviews among our team of 7 reviewers. We plan to complete the review process and disseminate the results by spring of 2023. Although AI and ML applications in health care have the potential to improve patient care by reducing errors between measurement and model output, currently there exists more hype than hope because of the lack of proper external validation of these models. We expect to find that the AI and ML model-updating methods are proxies for model applicability and generalizability on implementation. Our findings will add to the field by determining the degree to which published models meet the criteria for clinical validity, real-life implementation, and best practices to optimize model development, and in so doing, reduce the overpromise and underachievement of the contemporary model development process. PRR1-10.2196/37685.

Stability evaluation and validation of appropriate reference genes for real-time PCR expression analysis of immune genes in the rohu (Labeo rohita) skin following argulosis

Argulosis is one of the most unrestrained economically significant freshwater fish ectoparasitic diseases. Proper selection or normalization of the best reference gene governs the accuracy of results of gene expression studies using real-time PCR. Earlier studies in rohu carp (Labeo rohita) have used reference genes without proper validation. Here, seven candidate reference genes viz., acidic ribosomal protein (ARP0), glyceraldehyde 3-phosphate dehydrogenase, RNA polymerase II (RPo), elongation factor1α (EF1α), α- tubulin (AT), ribosomal protein L 10, and β-actin were evaluated using four algorithms (geNorm, BestKeeper, NormFinder and ∆Ct) followed by a comprehensive gene expression analysis using skin tissue of rohu at varied time points of experimental Argulus siamensis infection. ARP0 and EF1α were found to be the most stable whereas RPo and AT were considered as least stable genes based on basal expression level and variation in expression levels. Validation of candidate reference genes was undertaken by looking into the expression of six immune-related genes using the two most stable and two least stable genes as housekeeping genes in Argulus-infected rohu skin at different time points of infection. An increased expression of immune genes indicated the role of inflammation and the immune modulation process at the site of attachment of parasites in governing infection.

The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.

The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.

CSNI status report “3D capabilities of thermalhydraulic system codes”: Lessons learned

In September 2015, an activity was endorsed by the OECD/CSNI/WGAMA to prepare a state of the art report presenting the 3D features of current thermal–hydraulic system codes. This activity was coordinated by IRSN and started in April 2016. This document is a synthesis of the full OECD report which is expected by 2023.Multi-dimensional phenomena that may have an important impact on the current thermalhydraulic models occur in the course of asymmetric scenarios such as feed water line break, main steam line break and loss of coolant accident. Most physical models were first established and validated in 1D geometry. The growing role of 3D modelling of reactor core and pressure vessel requires additional experimental data for a proper validation. Elements on available experimental support are proposed. However, the need of additional experimental support for validation has been identified in several directions such as the friction pressure losses, the diffusion–dispersion of momentum and energy dependency with space averaging, passive systems and the dispersion of void in core and downcomer. There are also several axes for progress, namely the development of phenomena identification and their ranking, the terms in the field equations and the mesh size dependence and the time step strategy.

Cultural Adaptation, Reliability and Validation of the Arabic Örebro Musculoskeletal Pain Questionnaire in Patients with Low Back Pain.

The Örebro Musculoskeletal Pain Questionnaire (ÖMPQ) assesses the psychosocial factors in people with complaints of musculoskeletal disorders and predicts those likely to develop persistent symptoms. To culturally-adapt and assess the validity of the ÖMPQ in an Arabic population with low back pain (LBP). This was a prospective cohort validation study of the ÖMPQ. The Arabic-ÖMPQ was created by forward translation, translation synthesis and backward translation in an Arabic population. Participants were included if they were 18 years or older, had acute or chronic LBP and were fluent in Arabic. Eighty-four patients completed the questionnaires at baseline, 2 days later and 3 months follow-up. We assessed specific agreement and test-retest reliability using the interclass correlation coefficient (ICC). We assessed predictive validity using linear regression and relative risk. We assessed content validity by investigating the ceiling and floor effects. To construct validity, the Arabic-ÖMPQ had a moderate (r≥0.3, <0.5) to high (r≥0.5) correlation with pain, disability, fear-avoidance and catastrophizing questionnaires. The test-rest reliability was high ICC2,1=0.92 (95% CI: 0.83-0.96). The Arabic-ÖMPQ score at baseline can significantly predict disability at 3 months F(1,82)=33.87, p<0.01; R2=0.29. The translation of the Arabic-ÖMPQ into Arabic was successful. The Arabic-ÖMPQ showed very good reliability and proper validity and thus can be used to predict the risk of developing persistent disability amongst patients with LBP in an Arabic population.

Ethnomedicinal survey of Bhalessa, Jammu and Kashmir, a remote region of Western Himalaya

Ethnopharmacological relevanceThe traditional knowledge about ethnomedicinal plants has a significant impact on human healthcare. However, across the globe especially in remote areas, this treasure-trove of traditional knowledge is being rapidly lost. Therefore, it is important to scientifically document and validate the folklore claims about medicinal plants, which will contribute to their bioprospection and sustainable use. Aims of the studyThe study was conducted to elucidate the traditional knowledge and practices about the usage of medicinal plants by the tribal population inhabiting Bhalessa, a remote area in district Doda, Jammu, and Kashmir, Western Himalaya. Material and methodsWe collected ethnomedicinal data through focused interviews of the local population in the study area by employing in situ and ex-situ survey methods. The collected data was subjected to statistical tools of informant consensus factor (ICF), use reports and comparison with previous studies. ResultsWe report a total of 80 medicinal plant species traditionally used against 17 disease categories in the area. The most frequently used plant parts were roots and leaves and the route of administration of the medicines was largely oral in the powdered form. The ICF values were indicative of a higher agreement among the users about the usage of plants in the treatment of disease categoriesmusculoskeletal and connective tissue, genitourinary and gastrointestinal disease categories. Based on the number of use report and number of taxa utilized, the most prevalent disease in the study area were joint pain and fever. ConclusionNovel information has been generated as the elderly people of Bhalessa possess precious knowledge of medicinal plants. These novel folklore claims about the various therapeutic values of medicinal plants require scientifically sound validation. Therefore, we call for focused phytochemical research and proper pharmacological validations of the reported medicinal uses in the study area, particularly of the Himalayan endemic species such as Aconitum lethale, Arisaema jacquemontii, and Saussurea costus.