Challenges In Interpretation Research Articles

Targeting immune cell recruitment in atherosclerosis.

Atherosclerosis is the primary underlying cause of myocardial infarction and stroke. Atherosclerotic cardiovascular disease is characterized by a chronic inflammatory reaction in medium-to-large-sized arteries, with its onset and perpetuation driven by leukocytes infiltrating the subendothelial space. Activation of endothelial cells triggered by hyperlipidaemia and lipoprotein retention in the arterial intima initiates the accumulation of pro-inflammatory leukocytes in the arterial wall, fostering the progression of atherosclerosis. This inflammatory response is coordinated by an array of soluble mediators, namely cytokines and chemokines, that amplify inflammation both locally and systemically and are complemented by tissue-specific molecules that regulate the homing, adhesion and transmigration of leukocytes. Despite abundant evidence from mouse models, only a few therapies targeting leukocytes in atherosclerosis have been assessed in humans. The major challenges for the clinical translation of these therapies include the lack of tissue specificity and insufficient selectivity of inhibition strategies. In this Review, we discuss the latest research on receptor-ligand pairs and interactors that regulate leukocyte influx into the inflamed artery wall, primarily focusing on studies that used pharmacological interventions. We also discuss mechanisms that promote the resolution of inflammation and highlight how major findings from these research areas hold promise as potential therapeutic strategies for atherosclerotic cardiovascular disease.

Exploring shoulder joint pain: a comparative analysis of dynamic ultrasonography and magnetic resonance imaging

BACKGROUND: To evaluate shoulder joint pain, the study concentrated on evaluating the diagnostic accuracy of ultrasonography and magnetic resonance imaging. AIM: By comparing the results from both modalities within the same patient group, the study aimed to identify potential challenges in image interpretation, underscoring the limitations of ultrasonography and magnetic resonance imaging. MATERIALS AND METHODS: Patients with shoulder joint pain were included through purposive sampling. These patients underwent ultrasonography and magnetic resonance imaging, and the results were correlated. Various shoulder pathologies, including tendon tears, bursitis, degenerative changes, calcifications, and impingement, were analyzed. RESULTS: The study comprised 35 patients who underwent ultrasonography and magnetic resonance imaging detecting pathologies such as subscapularis and supraspinatus tendon injuries, partial and full-thickness tears, peribicipital tendon fluid, subcoracoid and subacromial-subdeltoid bursitis, acromioclavicular joint degeneration, tendon calcification, and impingement. Comparative analysis showed varying sensitivities, specificities, positive predictive values, negative predictive values, and accuracy for different pathologies. CONCLUSION: Compared with ultrasonography, magnetic resonance imaging demonstrated greater sensitivity and specificity in identifying conditions causing shoulder pain. Ultrasonography’s affordability, real-time capabilities, and ability to compare results with the unaffected side make it a useful first diagnostic step for shoulder pain. Ultrasonography, although a quick and cost-effective initial diagnostic tool, has limitations, including operator dependence and lower sensitivity in certain conditions. In contrast, magnetic resonance imaging is employed as a confirming measure or in instances where diagnosis is challenging. The study emphasized the complementary roles of ultrasonography and magnetic resonance imaging in the diagnosis of shoulder joint pain, with magnetic resonance imaging as the more accurate and complete imaging modality.

Eye-tracking the processing of visual input of live transcripts in remote simultaneous interpreting

Abstract The Covid-19 pandemic has led to the booming of remote simultaneous interpreting (RSI), which poses new cognitive challenges for interpreters. This study explores how interpreters adapt to these challenges, specifically the functionality of live transcripts generated automatically by Zoom Meetings in RSI. The study aims to: (1) investigate whether access to live transcripts enhances the overall performance of interpreters; (2) explore whether RSI with live transcripts increases cognitive demands; (3) identify visual attention patterns in RSI with live transcripts. A group of professional interpreting trainees (N = 13) were recruited for the study to examine their interpreting with live transcripts and interpreting without live transcripts, which yields three preliminary findings: Firstly, using live transcripts significantly improves overall interpreting performance. Secondly, RSI with live transcripts is more cognitively demanding. Lastly, live transcripts attract the highest visual attention during the interpreting process.

Characterizing mudstones and fault structure in Bumita Quarry, Perlis through integrated geophysical techniques

The parameters of seismic refraction and electrical resistivity play a crucial role in geological studies, as they provide insights into the composition of rocks or soil beneath the Earth’s surface. Previous researchers have established reference ranges for seismic velocity and resistivity values based on different rock types, which are presented in tabulated forms. However, the wide variability in these values sometimes leads to challenges in interpretation due to overlapping ranges. In the region of Perlis, Malaysia, a comprehensive geophysical investigation involving seismic refraction and electrical resistivity methods was conducted within the Chepor Member of the Kubang Pasu Formation at the Bumita Quarry and Utan Aji. The results were then correlated with porosity and permeability data. The Chepor Member primarily comprises both red mudstone and grey mudstone. Interestingly, the seismic velocities of these two mudstone types are quite similar, differing only by a small margin of approximately 200 m/s. The resistivity method employed utilized a pole-dipole array configuration. In terms of resistivity values, the red mudstone exhibited lower readings (ranging from 15 to 100 Ωm) compared to the grey mudstone (ranging from 120 to 500 Ωm). Assessing porosity, the red mudstone displayed a value of 0.95%, alongside a permeability of 5.58×10-5 μd, while the grey mudstone indicated a slightly higher porosity value of 1.9% and a permeability of 2.06×10-5 μd. Consequently, the study successfully established seismic velocity and resistivity benchmarks for the mudstones within the Chepor Member geological unit.

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data

Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC’s robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC’s qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC, which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

Translation Strategies and challenges for translating Maithili Idioms and Proverbs into English

Translation of idioms and proverbs presents distinctive challenges due to their cultural and linguistic specificity, requiring strategies that go beyond literal translation to convey their intended meaning accurately. This study explores the translation strategies and challenges encountered in translating Maithili idioms and proverbs into English. Drawing from the Longman Dictionary of English Idioms and Larson's definition of idioms, present article highlights the significance of idiomatic expressions in preserving cultural shades and emotional connotations. Through an analysis of idiomatic expressions from Maithili literature and their English translations, the study examines the methods employed in translating these cultural artifacts. Utilizing Baker's framework, the article discusses various translation techniques and emphasizes the importance of context in conveying the essence of idiomatic expressions. The findings underscore the complexities involved in translating idioms, urging translators to prioritize conveying meaning over literal interpretation and to provide additional context when direct equivalence is lacking.

BEYOND WORDS: A COMPREHENSIVE EXPLORATION OF LEXICAL, GRAMMATICAL, CULTURAL, AND PRAGMATIC CHALLENGES IN TRANSLATION

Summary: This comprehensive article delves into the intricate world of translation challenges, exploring the dimensions of lexical, grammatical, cultural, and pragmatic hurdles faced by translators. From the nuances of vocabulary and sentence structures to the impact of cultural context and the subtleties of social dynamics, each challenge type is dissected, exemplified, and accompanied by strategies for effective navigation. Emphasizing the need for a holistic approach, the article calls for a nuanced understanding of the interconnected facets involved in the translation process. The conclusion resonates with a call to action, urging language enthusiasts and practitioners to recognize and navigate these challenges for the sake of fostering effective cross-cultural communication. This exploration underscores that successful translation is not just about transferring words; it's a transformative journey that requires cultural competence, linguistic finesse, and an appreciation for the intricate dance between languages and cultures. Keywords: translation challenges, lexical hurdles, grammatical complexity, cultural nuances, pragmatic dimensions, cross-cultural communication, holistic approach, effective translation strategies

Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Abstract P70: HIGH-THROUGHPUT IMAGING-BASED PHENOTYPIC PROFILING OF IMMUNE CELLS FROM BRCA2 VARIANT CARRIERS AND NON-CARRIERS

Abstract Mutated germline alleles in the DNA damage repair (DDR) genes BRCA1/2 are major risk factors for breast and ovarian cancers. Recent studies have found that BRCA1-mutant tumors tend to have higher abundance of immune cells within the tumor microenvironment, but much better response was observed from the BRCA2-mutant tumors when given immune-checkpoint inhibitors, suggesting possible functional and phenotypic effects of germline BRCA2 mutations on the immune cells. However, most existing studies focus on investigating the genomic instability and phenotypes of hereditary mutations in breast and ovarian cancer cells. Thus, the impact of these mutations on immune cells’ phenotypes and functions remains largely unknown. Here, we report a study of immune cells with and without BRCA2 mutations using High-throughput Imaging-based Phenotypic Profiling (HIPP), which can identify changes in cellular morphology and protein subcellular localization patterns that are not obvious from other traditional functional assays. We performed HIPP on immune cells from nine BRCA2 pathogenic variant carriers and eight non-carriers. Automatic quantification of ~200 phenotypic features from each single cell revealed that CD8+ T cells from BRCA2 variant carriers have distinct and quantifiable changes in their morphological features (such as cytoskeleton structures) and immunological features (such as Granzyme B expressions). Moreover, we found that BRCA2-mutant CD8+ T cells proliferate faster than normal CD8+ T cells. Our results suggest that BRCA2 mutations affect the functions and phenotypes of immune cells. Some of these phenotypic features and markers may be used to classify BRCA2 variants and predict the pathogenicity of variants of uncertain significance (VUS). Our study provides new insight into the immune phenotyping of cancer patients at individual level and may help to stratify patients, leading to the development of more effective therapeutic strategies. This method can potentially extend to other germline variants and overcome the challenge of VUS interpretation in clinical management. Citation Format: Guorui Zhong, Oscar Fu, Claresta Yeo, Siao Ting Chong, Sock Hoai Chan, Joanne Ngeow, Lit-Hsin Loo. HIGH-THROUGHPUT IMAGING-BASED PHENOTYPIC PROFILING OF IMMUNE CELLS FROM BRCA2 VARIANT CARRIERS AND NON-CARRIERS [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P70.

Systematic Review of Cerebrospinal Fluid Biomarker Discovery in Neuro-Oncology: A Roadmap to Standardization and Clinical Application.

Effective diagnosis, prognostication, and management of CNS malignancies traditionally involves invasive brain biopsies that pose significant risk to the patient. Sampling and molecular profiling of cerebrospinal fluid (CSF) is a safer, rapid, and noninvasive alternative that offers a snapshot of the intracranial milieu while overcoming the challenge of sampling error that plagues conventional brain biopsy. Although numerous biomarkers have been identified, translational challenges remain, and standardization of protocols is necessary. Here, we systematically reviewed 141 studies (Medline, SCOPUS, and Biosis databases; between January 2000 and September 29, 2022) that molecularly profiled CSF from adults with brain malignancies including glioma, brain metastasis, and primary and secondary CNS lymphomas. We provide an overview of promising CSF biomarkers, propose CSF reporting guidelines, and discuss the various considerations that go into biomarker discovery, including the influence of blood-brain barrier disruption, cell of origin, and site of CSF acquisition (eg, lumbar and ventricular). We also performed a meta-analysis of proteomic data sets, identifying biomarkers in CNS malignancies and establishing a resource for the research community.

Translation of Poetry: A Study of Translatability of Pragmatic and Cultural Elements

Poetry is a genre that states more in a few words because much of it is cultural content that is assumed to be comprehended by the reader(s). This very characteristic of poetry of being rooted in cultural ethos makes its translation complex and even "untranslatable." Imr-ul-Qais’s Muallaqa is an Arabic classic which has been a symbol of the shared Arab identity, values, and magnanimity. In addition to its unsurpassed poetic brilliance, it is an epitome of the culture of the pre-Islamic Arab world. There are many translations of this masterpiece into English, though each is unique in terms of interpretations, liberties, and constraints. This study examines three prominent translations of Imr-ul-Qais's Muallaqa by Arberry, Johnson, and Mumayiz with reference to Venuti's (1995) dichotomy of domestication and foreignization. The aim is to identify translators’ strategies in tackling the translational challenges, as well as the implications thereof, in order to bridge the linguistic and cultural divide as well as if and what is the nature of the loss of meaning in the process. Results showed that Arberry aims for a poetic rendition in blank verse, focusing on semantic and syntactic fidelity rather than rhyme and meter. Johnson employed transposition and modulation, resulting in a more prosaic translation that lacks the Arabic ethos. Both translators leaned towards domestication, prioritizing English comprehension over retaining the original sentiment. Mumayiz, a native speaker of Arabic, provides a more rhythmic translation, with greater effort to provide English readers with insights into the original text, hence leant more on foreignization than domesticaion.

Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network

AbstractAn ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract

Combining Artificial Intelligence and Simplified Image Processing for the Automatic Detection of Mycobacterium tuberculosis in Acid-fast Stain : A Cross-institute Training and Validation Study.

Tuberculosis (TB) poses a significant health threat in Taiwan, necessitating efficient detection methods. Traditional screening for acid-fast positive bacilli in acid-fast stain is time-consuming and prone to human error due to staining artifacts. To address this, we present an automated TB detection platform leveraging deep learning and image processing. Whole slide images from 2 hospitals were collected and processed on a high-performance system. The system utilizes an image processing technique to highlight red, rod-like regions and a modified EfficientNet model for binary classification of TB-positive regions. Our approach achieves a 97% accuracy in tile-based TB image classification, with minimal loss during the image processing step. By setting a 0.99 threshold, false positives are significantly reduced, resulting in a 94% detection rate when assisting pathologists, compared with 68% without artificial intelligence assistance. Notably, our system efficiently identifies artifacts and contaminants, addressing challenges in digital slide interpretation. Cross-hospital validation demonstrates the system's adaptability. The proposed artificial intelligence-assisted pipeline improves both detection rates and time efficiency, making it a promising tool for routine pathology work in TB detection.

The Potential Pharmacological Effects of Natural Product Withaferin A in Cancer: Opportunities and Challenges for Clinical Translation.

Cancer is one of the biggest health concerns with a complex pathophysiology. Currently, available chemotherapeutic drugs are showing deleterious side effects, and tumors often show resistance to treatment. Hence, extensive research is required to develop new treatment strategies to fight against cancer. Natural resources from plants are at the forefront of hunting novel drugs to treat various types of cancers. Withaferin A (WA) is a naturally occurring withanolide, a biologically active component obtained from the plant Ashwagandha. Various in vitro and in vivo oncological studies have reported that Withaferin A (WA) has shown protection from cancer. WA shows its activity by inhibiting the growth and proliferation of malignant cells, apoptosis, and inhibiting angiogenesis, metastasis, and cancer stem cells (CSCs). In addition, WA also showed chemo- and radio-sensitizing properties. Besides the beneficiary pharmacological activities of WA, a few aspects like pharmacokinetic properties, safety, and toxicity studies are still lacking, hindering this potent natural product from entering clinical development. In this review, we have summarized the various pharmacological mechanisms shown by WA in in vitro and in vivo cancer studies and the challenges that must be overcome for this potential natural product's clinical translation to be effective.

Advances in Nanodynamic Therapy for Cancer Treatment.

Nanodynamic therapy (NDT) exerts its anti-tumor effect by activating nanosensitizers to generate large amounts of reactive oxygen species (ROS) in tumor cells. NDT enhances tumor-specific targeting and selectivity by leveraging the tumor microenvironment (TME) and mechanisms that boost anti-tumor immune responses. It also minimizes damage to surrounding healthy tissues and enhances cytotoxicity in tumor cells, showing promise in cancer treatment, with significant potential. This review covers the research progress in five major nanodynamic therapies: photodynamic therapy (PDT), electrodynamic therapy (EDT), sonodynamic therapy (SDT), radiodynamic therapy (RDT), and chemodynamic therapy (CDT), emphasizing the significant role of advanced nanotechnology in the development of NDT for anti-tumor purposes. The mechanisms, effects, and challenges faced by these NDTs are discussed, along with their respective solutions for enhancing anti-tumor efficacy, such as pH response, oxygen delivery, and combined immunotherapy. Finally, this review briefly addresses challenges in the clinical translation of NDT.

Not Just Scraping By: Experimental Evidence for Large Cutting Tools in the High Lodge Non-handaxe Industry

The presence or absence of handaxes endures as the major criterion of Lower Palaeolithic classification, with contemporaneous core-and-flake industries modelled as simpler counterparts to Acheulean technology. This is based on the supposed absence of formal tools, particularly of large cutting tools (LCTs) which are understood to be important within Acheulean lifeways, functioning as butchery knives among other uses. Scrapers from the core-and-flake industry of High Lodge (MIS 13) evidence formalised flake-tool production techniques, geared towards large tools with long cutting edges and acute angles, comparable in many respects to Acheulean handaxes. A holistic set of experiments was designed to test the production, efficiency, and practical utility of these scrapers. The experiments compared these scraper forms against handaxes and Quina scrapers. Their use in roe deer butchery indicates functional differences but demonstrates the appropriacy of both large, refined scrapers, and handaxes for processing carcasses of this size. The results support the inclusion of High Lodge scraper forms within the standard definition of LCTs. This interpretation challenges perceived discrepancies between handaxe and non-handaxe industries and deterministic explanations for Acheulean material culture. The feasibility of alternative LCTs supports the argument that the Acheulean represents socially inherited behaviours rather than latent reinventions.

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

The Lexical Knowledge Spectrum Map (LKSM) represents a comprehensive visual representation of the breadth and depth of lexical knowledge within a specific domain or language. By categorizing words and phrases along a spectrum ranging from basic to advanced levels of complexity, the LKSM provides learners and educators with a clear overview of vocabulary proficiency and progression. This dynamic tool not only helps individuals track their language learning journey but also guides instructional planning by identifying areas of focus and potential gaps in lexical understanding. With its intuitive interface and adaptable framework, the LKSM serves as a valuable resource for promoting effective vocabulary acquisition and mastery across diverse linguistic contexts and educational settings. This paper introduces a novel translation algorithm designed specifically for handling proper nouns in English-Chinese translation, leveraging the Lexical Knowledge Spectrum Map (LKSM) and Feature Vector Optimization with Statistical (FVOS) techniques. Proper nouns pose a unique challenge in translation due to their cultural and contextual significance, often requiring specialized handling to ensure accuracy and coherence in the target language. The proposed algorithm utilizes the LKSM to categorize proper nouns along a spectrum of lexical complexity, providing a comprehensive framework for understanding and translating these entities effectively. Additionally, FVOS techniques are employed to optimize feature vectors for proper noun translation, enhancing the algorithm's ability to capture and preserve semantic nuances across languages. With FVOS model English proper nouns translated into Chinese, the proposed algorithm achieves an average accuracy of 85%, outperforming baseline translation methods by 15%. Moreover, specific proper noun categories exhibit notable improvements, with names of people achieving an accuracy of 90%, followed by locations at 85%, and organizations at 80%.

Identification of an ultra-rare Alu insertion in the CFTR gene: Pitfalls and challenges in genetic test interpretation

Cystic fibrosis (CF) is a life-limiting genetic disorder characterized by defective chloride ion transport due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Early detection through newborn screening programs significantly improves outcomes for individuals with CF by enabling timely intervention. Here, we report the identification of an Alu element insertion within the exon 15 of CFTR gene, initially overlooked in standard next-generation sequencing analyses. However, using traditional molecular techniques, based on polymerase chain reaction and Sanger sequencing, allowed the identification of the Alu element and the reporting of a correct diagnosis. Our analysis, based on bioinformatics tools and molecular techniques, revealed that the Alu element insertion severely affects the gene expression, splicing patterns, and structure of CFTR protein. In conclusion, this study emphasizes the importance of how the integration of human expertise and modern technologies represents a pivotal step forward in genomic medicine, ensuring the delivery of precision healthcare to individuals affected by genetic diseases.

Co-production processes for translation and validation of psychosocial assessments for older adults in aged care.

Given the diverse ethnic backgrounds of aged care clients, there is a critical requirement to translate psychosocial assessment tools into various languages to effectively evaluate social engagement and quality of life in older adults receiving aged care services. This study aimed to translate psychosocial tools into Turkish, Korean and Mandarin, the primary languages spoken by clients of an Australian community aged care provider. A co-development approach encompassing forward and backward translations of the Australian Community Participation Questionnaire and ICEpop CAPability measure for Older people tools, along with focus group discussions involving bilingual staff (n = 7) and clients (n = 16), was employed to ensure precision and cultural relevance. Multiple iterations were undertaken until linguistic, conceptual and scaling equivalence was achieved, with recorded sessions transcribed and analysed thematically. Cultural appropriateness significantly impacted the delivery of questions within the tools, emphasising translation challenges tied to specific queries. These difficulties included the lack of terms for unique places of worship, the use of outdated language (e.g., references to reading newspapers), and varying priorities in social and well-being matters between Western and Eastern/Asian cultures. Staff feedback identified that formal translated tool versions eased administration for culturally and linguistically diverse (CALD) clients, enabling them to independently interpret questions, resulting in improved questionnaire completion rates. Insights indicate the need for continued efforts in tailoring assessment tools to diverse cultural contexts to ensure accurate and meaningful data collection.