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Exploring the antioxidant, antiglycation, and anti-inflammatory potential of Oroxylum indicum stem bark extracts

Degenerative diseases occur when humans suffer from oxidative stress, glycation and prolonged inflammation. This study explores the antioxidant, antiglycation, and anti-inflammatory effects of extracts of Oroxylum indicum, a plant used in traditional medicines in Asia. Several extracts from its stem bark were obtained using hexane, ethyl acetate, and ethanol as extraction solvents. The extracts were analyzed using high-performance liquid chromatography (HPLC). The HPLC chromatograms showed that the different O. indicum extracts contained three major flavonoid compounds, namely baicalein, chrysin, and oroxylin A, as well as a phenolic compound, p-coumaric acid. The total phenolic content (TPC) and total flavonoid content (TFC) of the extracts were also determined. The ethyl acetate fractionated extract (EAFE) possessed the highest TPC (172 ± 8 mg/g extract) and TFC (147 ± 1 mg/g extract), several times higher than those of crude ethanol extract, ethanol fractionated extract (EFE) and hexane fractionated extract (HFE), respectively. According to the highest levels of TPC and TFC, EAFE showed the highest antioxidant activity with Trolox equivalent antioxidant activity of 9.7 ± 0.1 mM/mg and β-carotene bleaching inhibition of 79.5 ± 1.1%. The activities for the natural antioxidant quercetin were 3.1 ± 0.1 mM/mg and 88.7 ± 0.1%, respectively. EAFE showed the highest antiglycation activity using a bovine serum albumin-methylglyoxal assay with 89.1 ± 0.7% inhibition. These findings indicate that TPC and TFC are the determining factors for the antioxidant and antiglycation activities of the extracts. An in silico analysis suggested that the anti-inflammatory activity of the extracts is due to the inhibition of toll-like receptor 4 (TLR4) activity by direct binding of the bioactive compounds to the TLR4 protein. Our findings provide scientific support for the use of O. indicum in traditional medicine and demonstrate that EAFE has potential in mitigating oxidative stress, glycation, and inflammation.

Author Correction: Structure and topography of the synaptic V-ATPase–synaptophysin complex

Association between sarcopenia and falls in Chinese older adults: Findings from the China health and retirement longitudinal study

Falling has become a significant factor in the mortality of elderly people. Little is known about whether sarcopenia can be a risk factor for falls in older adults. This study aims to assess the association between sarcopenia and falls among older Chinese according to the updated diagnostic guidelines of the Asian Working Group on Sarcopenia 2019 (AWGS 2019). We used data from the 2011 baseline and 2015 follow-up survey of the China Health and Retirement Longitudinal Study (CHARLS). This study examined the relationship between sarcopenia status and falls through cross-sectional analysis. Cox proportional hazards regression models were conducted to investigate the effect of sarcopenia status on subsequent falls, with the report of hazard ratio (HR). A total of 5,337 participants aged at least 60 years (51.3% men; mean age 67.6 ± 6.3) were enrolled in this analysis from the CHARLS 2011. The study revealed that the prevalence of falls was significantly higher in the possible sarcopenia and sarcopenia groups compared to the no sarcopenia group, with rates of 15.8%, 19.4%, and 24%, respectively. Logistic regression was utilized to investigate the association between sarcopenia and falls. Both possible sarcopenia (OR: 1.22, 95% CI: 1.03–1.45) and sarcopenia (OR: 1.64, 95% CI: 1.23–2.19) were positively associated with higher odds of falls (all p < 0.05). During the 4 years of follow-up, 1490 cases (29.9%) with incident falls were identified. In the longitudinal analysis, individuals with diagnosed sarcopenia (HR: 1.32, 95% CI: 1.11–1.57) were more likely to have new-onset incident falls than their no-sarcopenia peers. Sarcopenia in the elderly is an independent risk factor for falls, with health screening and intervention reducing fall risk and improving quality of life.

From spots to cells: Cell segmentation in spatial transcriptomics with BOMS

Imaging-based Spatial Transcriptomics methods enable the study of gene expression and regulation in complex tissues at subcellular resolution. However, inaccurate cell segmentation procedures lead to misassignment of mRNAs to individual cells which can introduce errors in downstream analysis. Current methods estimate cell boundaries using auxiliary DAPI/Poly(A) stains. These stains can be difficult to segment, thus requiring manual tuning of the method, and not all mRNA molecules may be assigned to the correct cells. We describe a new method, based on mean shift, that segments the cells based on the spatial locations and the gene labels of the mRNA spots without requiring any auxiliary images. We evaluate the performance of BOMS across various publicly available datasets and demonstrate that it achieves comparable results to the best existing method while being simple to implement and significantly faster in execution. Open-source code is available at https://github.com/sciai-lab/boms .

Has flood damage being reduced? A resident perspective on the effectiveness of flood management

Flood disasters have been studied intensively and extensively. However, studies to evaluate the long-term effectiveness of flood management from social perspectives are limited. Questions such as whether flood damage has been reduced or exacerbated have been insufficiently examined and poorly answered from the residents’ view angle. Usually, annual flood damage is used to quantify the economic impact of flood. However, as the estimation of flood-caused damage is affected by various uncertainties in methodology, the development of an indicator without ambiguity is needed for the assessment of flood management effectiveness. Moreover, annual flood damage is often the focus of researchers and administrators. The aim of this paper was to address the often-neglected question of what is the right perspective to better understand long-term changes in flood damage over time, and a related question of what perspective is most tangible to residents and can be used to promote public participation in flood risk management. Thus, the present work used Japan’s flood damage data over the past several decades to analyze various flood damage indices and identify the ones that can be used to detect significant changes in flood impacts without uncertainty, which can also bridge science with residents. The main finding is that the number of flooded houses and semi-damaged houses divided by the annual inundated residential area fit for the purpose of the present study. Since house inundation is more tangible to residents than annual flood economic loss, the findings suggested that the resident-oriented indicator can better reflect the change in flood impact and promote residents’ involvement in the building of coping capacity. In addition, the drivers for flood impact reduction in Japan were discussed and a framework for promoting citizen science for flood risk management was proposed. The overall value of the present work is that it has identified and attempted to fill the knowledge gap in flood impact assessment.

Publisher Correction: Imaging surface structure and premelting of ice Ih with atomic resolution

Syntactic complexity recognition and analysis in Chinese-English machine translation: A comparative study based on the BLSTM-CRF model

To enhance the recognition and preservation of syntactic complexity in Chinese–English translation, this study proposes an optimized Bidirectional Long Short-Term Memory–Conditional Random Field (BiLSTM-CRF) model. Based on the Workshop on Machine Translation (WMT) Chinese-English parallel corpus, an experimental framework is designed for two types of specialized data: complex sentences and cross-linguistic sentence pairs. The model integrates explicit syntactic features, including part-of-speech tags, dependency relations, and syntactic tree depth, and incorporates an attention mechanism to improve the model’s ability to capture syntactic complexity. In addition, this study constructs an evaluation framework consisting of eight indicators to assess syntactic complexity recognition and translation quality. These indicators encompass: (1) Average syntactic node depth (higher values indicate greater complexity; typically ranging from 1.0 to 5.0); (2) The number of embedded clause levels (higher values illustrate greater complexity; typically 0–5); (3) Long-distance dependency ratio (higher values indicate broader dependency spans; range 0–1, moderate values preferred); (4) Average branching factor (higher values show denser modifiers; range 1.0–4.0); (5) Syntactic change ratio (lower values demonstrate structural stability; range 0–1); (6) Translation alignment consistency rate (higher values indicate better alignment; range 0–1); (7) Syntactic tree reconstruction cost (lower values refer to smaller structural adjustment overhead; range 0–1); (8) Translation syntactic balance (higher values illustrate more natural syntactic rendering; range 0–1). This indicator system enables comprehensive evaluation of the model’s capabilities in syntactic modeling, structural preservation, and cross-linguistic alignment. Experimental results show that the optimized model outperforms baseline models across multiple core indicators. On the complex sentence dataset, the optimized model achieves a long-distance dependency ratio of 0.658 (moderately high), an embedded clause level of 3.167 (indicating complex structure), and an average branching factor of 2.897. The syntactic change ratio is only 0.432, all of which significantly outperform comparative models such as Syntax-Transformer and Syntax-Bidirectional Encoder Representations from Transformers (Syntax-BERT). On the cross-linguistic sentence dataset, the optimized model attains a syntactic tree reconstruction cost of only 0.214 (low adjustment overhead) and a translation alignment consistency rate of 0.894 (high alignment accuracy). This demonstrates remarkable advantages in structural preservation and adjustment. In contrast, comparison models show unstable performance on complex and cross-linguistic data. For example, Syntax-BERT achieves only 2.321 for the embedded clause level, indicating difficulty in handling complex syntactic structures. In summary, by introducing explicit syntactic features and a multidimensional indicator system, this study demonstrates strong modeling capacity in syntactic complexity recognition and achieves better preservation of syntactic structures during translation. This study offers new insights into syntactic complexity modeling in natural language processing and provides valuable theoretical and practical contributions to syntactic processing in machine translation systems.

Mobile applications available in Saudi Arabia for the management of Primary Dysmenorrhea: A quality review and content analysis

Background Primary dysmenorrhea (PD), common in women below 25 years, occurs as pain in the absence of any identifiable pelvic pathology. Menstrual tracking applications (MTAs) may help women manage their PD symptoms. No systematic assessment has been performed on MTA quality with respect to physical therapy management exercise. Objectives This study evaluated the quality of MTAs available in Saudi Arabia for mobile users in both the App Store and Google Play Store and assessed the quality and completeness of exercise regimens provided in these apps using the FITT principle as a guideline for managing PD symptoms. Methods In this cross-sectional study, apps were collected from the App Store and Google Play Store using two strategies for each store independently: Scraper and SimilarWeb. The app quality was evaluated using the Mobile Application Rating Scale (MARS), and exercise content was evaluated based on the recommended Frequency, Intensity, Time, and Type (FITT) principles. Results Final evaluation included 16 apps, of which 87.5% required subscription. The mean app quality score ranged from 2.54 (worst-rated app) to 4.45 (best-rated app) with a mean score of 3.54 ± 0.58. In addition, only three apps provided all the FITT components in the exercise content. Conclusion This study assessed the quality of exercise provided within these applications as interventions for managing PD symptoms. This evaluation contributes to the understanding of mobile health technologies for PD management in the region, and highlights areas for improvement in app development and content quality to better serve individuals with PD.

Societal factors influencing the implementation of AI-driven technologies in (smart) hospitals

Introduction The introduction of AI in healthcare promises benefits, but also faces challenges. Currently, one of these challenges is the lack of information on the societal aspects of implementing AI in healthcare. This study aims to: 1) identify which societal factors play a key role in the implementation of AI-driven technology in (smart) hospitals according to different stakeholder groups; 2) examine how these factors play a role within (smart) hospitals by discussing their facilitators, barriers, possibilities, and preconditions; and 3) develop a societal guide to serve as a roadmap for an implementation process of AI in a healthcare setting. Methods A survey was conducted, followed by four focus group interviews (FGIs). In the survey, participants (n = 7) assessed the relevance of factors for inclusion in the FGIs using a rating scale from 1 to 5 (1 = irrelevant, 5 = relevant). In each FGI, 2–3 participants discussed how these societal factors play a role in the implementation of AI technology in (smart) hospitals. By combining and categorizing these insights, a societal guide was set up to provide a structured approach for implementation of AI-driven healthcare innovation. Results The survey revealed that 9 out of 10 proposed factors were considered relevant (90%). The FGIs demonstrated uncertainty surrounding the (future) use of AI technologies within (smart) hospitals. As this field is still in its early stages, there are limited established methodologies and (regulatory and ethical) frameworks for implementation. While much knowledge exists on different factors concerning AI in (smart) hospitals, this knowledge is often siloed. This knowledge must be integrated across stakeholders to adequately prepare for the deployment of AI technologies. The societal guide developed addresses ethical and regulatory considerations, while also covering important human-centred factors for AI implementation in healthcare. Conclusion Engaging various stakeholders throughout different phases of AI implementation in (smart) hospitals (i.e., development, implementation, monitoring and evaluation phase) is key for fostering a collaborative approach. Recognizing the interdependence and collective impact of factors is essential for creating a successful implementation trajectory.

Physical restoration of a painting with a digitally constructed mask

ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime

High-fidelity numerical simulations such as Computational Fluid Dynamics (CFD) have been proven effective in analysing haemodynamics, offering insight into many vascular conditions. However, these methods often face challenges of high computational cost and long processing times. Data-driven approaches such as Reduced Order Modeling (ROM) and Machine Learning (ML) are increasingly being explored alongside CFD to advance biomechanical research and application. This study presents an integration of Proper Orthogonal Decomposition (POD)-based ROM with neural network-based ML models to predict Wall Shear Stress (WSS) in patient-specific vascular pathologies. CFD was used to generate WSS data, followed by POD to construct the ROM. The ML models were trained to predict the ROM coefficients from the inlet flowrate waveform, which can be routinely collected in the clinic. Two ML models were explored: a simpler flowrate-coefficients mapping model and a more advanced autoregressive model. Both models were tested against two case studies: flow in Peripheral Arterial Disease (PAD) and flow in Aortic Dissection (AD). Despite the limited training data sets (three flowrate waveforms for the PAD case and two for the AD case), the models were able to predict the haemodynamic indices, with the flowrate-coefficients mapping model outperforming the autoregressive model in both case studies. The accuracy is higher in the PAD case study, with reduced accuracy in the more complex case study of AD. Additionally, the computational cost analysis reveals a significant reduction in computational demands, with speed-up ratios in the order of 104 for both case studies. This approach shows an effective integration of ROM and ML techniques for fast and reliable evaluations of haemodynamic properties that contribute to vascular conditions, setting the stage for clinical translation.

Author Correction: A human brain map of mitochondrial respiratory capacity and diversity

Agreement of glomerular filtration rate estimation equations for chemotherapy dosing in cancer patients at a tertiary referral hospital in Sub-Saharan Africa

Introduction Narrow therapeutic indices of chemotherapeutic agents necessitate precise dosing to ensure efficacy and minimize nephrotoxicity. Due to the complexity of directly measuring Glomerular filtration rate (GFR), renal dosing is usually based on GFR estimation equations. The Cockcroft-Gault formula remains the most widely used equation in cancer patients, despite the availability of more precise kidney function estimation equations. Therefore, the aim of the study was to assess the agreement between Cr and cystatin-C (CysC) based GFR estimation equations and GFR estimated by Cockcroft-Gault for appropriate chemotherapy dosing in cancer patients undergoing assessment for first-Line chemotherapy at an oncology unit of St. Paul’s Hospital Millennium Medical College in Ethiopia. Methods A cross-sectional study was conducted among 136 adult cancer patients scheduled to initiate chemotherapy at the hospital between November 1, 2021, and April 30, 2022. GFR was calculated using 12 different GFR estimation equations to be compared with Cockcroft-Gault; MDRD, MDRD adjusted for ethnic factor (MDRDef), the 2009 CKD-EPI calculated based on serum creatinine (CKD-EPI 2009 Cr), the 2009 CKD-EPI Cr adjusted for ethnic factor (CKD-EPI 2009 Cref), the 2012 CKD-EPI calculated based on serum Cystatin C (CKD-EPI 2012 CysC), the 2021 CKD-EPI calculated based on serum creatinine and Cystatin C (CKD-EPI 2021 Cr-CysC), the 2021 CKD-EPI (CKD-EPI 2021), FAS calculated based on serum creatinine (FAS Cr)¸ FAS Cr adjusted for African coefficient (FAS Craf), FAS calculated based on serum Cystatin C (FAS CysC), FAS calculated based on serum creatinine and Cystatin C (FAS Cr-CysC), and FAS Cr-CysC adjusted for African coefficient (FAS Cr-CysCaf). To assess the level of agreement, bias (mean error/ME), precision, and accuracy (root-mean squared error/ RMSE) were analyzed for each equation, where for all measurements a value closer to 0 indicates minimal bias, high precision, and high accuracy demonstrating good agreement with Cockcroft-Gault. To confirm the significance of the recorded levels of agreement, a one-sample t-test, a Bland-Altman plot, and a linear regression analysis were performed step by step for variables which proved to have statistical agreement, where a p-value > 0.05 and the presence of heteroscedasticity indicates a non-significant difference and hence the presence of good agreement. Results The GFR estimation equations revealed variation, with some methods underestimating and others overestimating GFR. However, only four equations showed potential agreement with Cockcroft-Gault based on a one-sample t-test: MDRD, CKD-EPI 2009 Cr, CKD-EPI 2021, FAS Cr, and FAS Cr-CysCaf. Among these, CKD-EPI 2009 Cr exhibited the least bias (ME = 0.72 ml/min, 95% CI: −67.66, 69.10 ml/min), while FAS Cr-CysCaf demonstrated the highest precision (SD = 33.92) and accuracy (RMSE = 34.53). However, further analysis using Bland-Altman plots and linear regression to confirm agreement revealed no agreement between any of the formulas and Cockcroft-Gault. Conclusion The study revealed that the most recent and accurate GFR estimation equations that are recommended to be used in cancer patients did not show agreement with Cockcroft-Gault. This suggests that current GFR estimation practices in cancer patients might be inaccurate, potentially leading to improper chemotherapy dosing and poorer patient outcomes.

Analysis of influencing factors and scales of agglomerate fog on expressways

To enhance the monitoring accuracy of agglomerate fog on expressways, this paper takes the frequently occurring agglomerate fog data on Shandong’s expressways as an example. Based on the analysis of the spatiotemporal distribution characteristics of agglomerate fog, from the spatial perspective, it employs Geographic Weighted Regression (GWR) and Multi-scale Geographic Weighted Regression (MGWR) models to analyze the influence and scale of factors including Digital Elevation Model (DEM), DEM difference, water system density, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) difference, and precipitation on agglomerate fog. The main research conclusions are as follows: agglomerate fog frequently occurred in the early morning during autumn and winter when the temperature difference is large. Three concentration centers of agglomerate fog-prone road segments were identified along Shandong’s expressways, located near Jiaozhou Bay, within intermountain basins of the central region, and across the northern plain of Mount Tai (where the Yellow River traverses the concentration center). The impacts of various influencing factors on agglomerate fog are ranked as follows: DEM > DEM difference > LST difference > water system density > NDVI > precipitation, among which DEM difference and LST difference mainly promote fog formation, whereas other factors generally exhibit inhibitory effect. The influence range (adaptive scale) of precipitation is the largest, at 673 meters, followed by the water system with an influence range of 599 meters, and NDVI shows the smallest influence range at only 44 meters. It holds significant importance for reducing the accident rate on expressways.

Computer processors built from 2D materials

The role of nodes in controlling and observing complex networks

Dynamic processes on complex networks are closely associated with a variety of real-world systems. The controllability and observability of these networks are critical topics in the field of network science. Motivated by recent advancements in the study of structural controllability and observability, we investigate the roles of nodes in controlling and observing complex networks. Specifically, we categorize individual nodes into one of four types: driver nodes, sensor nodes, dual-identity nodes, and ordinary nodes. We propose a general framework for identifying the category of each node, thereby facilitating the exploration of the structural characteristics of these node types. Our findings indicate that these four types of nodes are prevalent in the control and observation of real networks. Through the analysis of their structural characteristics, we observe that nodes involved in controllability and observability are more likely to be low-degree nodes. Furthermore, we show that the proportions of these node categories are largely governed by the degree distribution of the network. Additionally, we present a theoretical analytical method to derive the proportions of the four node types, based on the network’s degree distribution.

Freeze-cast SiOC ceramics supporting the growth of industrially relevant microorganisms

Investigating the compatibility of ceramic support materials with industrially relevant microorganisms is a key starting point towards utilizing innovative ceramic frameworks for microbial culture support. This study demonstrates the biocompatibility of macroporous, freeze-cast SiOC monoliths with yeast Komagataella phaffii and bacteria Escherichia coli. In a first step, cultivations were carried out in the presence of non-macroporous SiOC materials pyrolyzed at 700 °C or 900 °C, which were further compared to Al2O3 and SiO2 as conventional ceramic and glass reference materials. Additionally, SiOC ceramics impregnated with 3 wt.% Cu were evaluated regarding cytotoxic effects, since Cu is recognized for its antimicrobial properties. Both E. coli and K. phaffii showed no growth inhibition in the presence of SiOC, yielding specific growth rates of 0.46 ± 0.01 h−1 and 0.088 ± 0.002 h−1, respectively, showing overall biocompatibility with SiOC. While E. coli showed growth inhibition in the presence of Cu via prolonged lag-phases, K. phaffii was resistant to Cu-modified SiOC. In the next step, adsorption of cells to macroporous SiOC was investigated after cultivation by electron microscopy of fracture surfaces of freeze-cast SiOC, structured with tert-butyl alcohol templating directional channels with pore opening diameters around 45 μm. Prevalent biofilm formation was observed within the channel walls with clear evidence for growth of K. phaffii as cell agglomerates. The study features promising results for promotion of the growth of E. coli and K. phaffii on freeze-cast SiOC ceramics, providing a versatile catalyst carrier design.

Determination of salt contents of bread types and estimation of salt intake from bread in Lebanon

Background High dietary salt intake is a major risk factor for hypertension, which strongly predisposes affected individuals to cardiovascular diseases and stroke. Most populations consume more salt than the upper limit set by the WHO at 5 g/day. Bread is a major contributor to salt intake, and reducing bread salt is the most effective approach for reducing the ingestion of salt by populations. Aims This work aims to determine the salt levels of bread marketed in Lebanon, bread consumption by the Lebanese population, and the bread’s contribution to daily salt intake. Methods One hundred and sixty-two samples of the breads consumed in Lebanon were collected from 45 bakeries, and their salt levels were determined by atomic absorption spectrophotometry. The bread consumption was estimated from a cross-sectional survey of 1048 individuals, and their salt intakes were computed using the determined levels of bread salt. The proportion of breads samples meeting the WHO-recommended salt levels was computed, and the salt intakes were determined and benchmarked against the WHO cut-offs. Results The least salty and saltiest breads were the white pita and markouk, with mean salt levels of 1.46g/100g and 2.77g/100g, respectively. The breads meeting the WHO-recommended salt levels ranged between 7.1% and 12%. The total bread consumption was 176.27 ± 216.73 g/day with white pita being the most consumed at 96.63 ± 175.44 g/day. The salt intake from bread at 2.86 ± 3.83 g/day amounted to 57.2% of the WHO limit for daily salt intake. Conclusions The breads spanned wide ranges of salt content and differed markedly in their contribution to salt intake. White pita was the most consumed and contained the least salt thereby making it the chief contributor to salt intake from bread. Interestingly, the analyzed breads indicated the availability of products that meet the WHO-recommended targets for salt thereby providing an impetus for reducing bread salt by stealth.

Anticancer potential of Thymoquinone from Nigella sativa L.: An in-silico and cytotoxicity study

Nigella sativa L. widely used spice cum medicinal plant in Asia and the middle east, is renowned for its seeds and oil which possess both culinary and therapeutic purposes. Its rich content of bioactive compounds, including metabolites and phenolics, with Thymoquinone, a monoterpene quinone, emerging as key therapeutic compound significantly consideration for its various pharmacological activity with lower toxicity compared to conventional chemotherapy. This study evaluated the anticancer potential of thymoquinone isolated from N. sativa L., through cytotoxicity and In Silico studies. Seeds from 38 accessions were collected across the country and screened for Thymoquinone content using HPTLC with the highest concentration identified in Ajmer Nigella 13 (247.60mg 100gm−1) accession. In Vitro MTT assay of Thymoquinone in human myelogenous leukemia (K562) cells demonstrated significant dose and time dependent cytotoxicity confirming Thymoquinone’s potential as a promising therapeutic candidate for leukemia and other cancer.

Comparative analysis of methods for identifying multimorbidity patterns among people with opioid use disorder: A retrospective single-cohort study

Background Multimorbidity, the presence of two or more (2+) chronic conditions, presents significant challenges for healthcare delivery, particularly among populations with opioid use disorder (OUD). Multimorbidity patterns among individuals with OUD are not well established, and minimal research exists examining the impact of clustering methods on identifying these patterns. Objective Our study aimed to assess multimorbidity prevalence, explore associated sociodemographic and clinical characteristics, and determine multimorbidity patterns using hierarchical cluster analysis (HCA) and K-means clustering among people receiving treatment for OUD in Ontario, Canada between 2011 and 2021. Methods Data from two prospective cohort studies were merged and linked to Ontario provincial health administrative databases. We identified 16 chronic conditions, used in prior research examining multimorbidity in Ontario, using ICD-10-CA diagnostic codes and the diagnostic codes of physician billing claims using a 2-year lookback. Multimorbidity was defined as the presence of 2+ of the above conditions, excluding the diagnosis of OUD. We conducted a retrospective cohort study, following the participants for eight years in the data holdings to ascertain the prevalence of multimorbidity. Sociodemographic and clinical characteristics were analyzed using modified Poisson regression models, and multimorbidity patterns were identified through HCA and K-means clustering. Results Among 3,430 people with OUD, 32.5% (n = 1,114, 95% confidence interval (CI)=30.9, 34.1) experienced multimorbidity over an eight-year period, with older age (Prevalence Ratio (PR)=3.39, 95% CI = 2.36, 4.87) and unemployment (PR = 1.31, 95% CI = 1.13, 1.54) associated with increased prevalence. HCA identified six distinct disease clusters, whereas K-means clustering identified four clusters. Both methods identified groupings of cardiovascular (coronary syndrome), cardiometabolic (diabetes, hypertension), and respiratory (chronic obstructive pulmonary disease) diseases, reflecting shared comorbidities among people with OUD. Discussion Our findings highlight the substantial burden of multimorbidity among populations with OUD, and the importance of considering sociodemographic factors in understanding multimorbidity prevalence. Moreover, the choice of clustering method significantly influences the identification and interpretation of multimorbidity patterns, with HCA providing more clinically meaningful groupings compared to K-means clustering. Our findings highlight the need for clinicians to tailor care plans and for policymakers to prioritize integrated healthcare delivery strategies to address the complex health needs of people with OUD.