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Hypoxia plays a crucial role in cell physiology. Defining the oxygen level that induces cell death within 3D tissues is vital for understanding tissue hypoxia; however, obtaining accurate measurements has been technically challenging. In this study, we introduce a non-invasive, high-throughput methodology to quantify critical survival partial oxygen pressure (pO₂) with high spatial resolution within spheroids by employing a combination of controlled hypoxic conditions, semi-automated live/dead cell imaging, and computational oxygen modeling. The oxygen-permeable, micro-pyramid patterned culture plates created a precisely controlled oxygen condition around the individual spheroid. Live/dead cell imaging provided the geometric information of the live/dead boundary within spheroids. Finally, computational oxygen modeling calculated the pO₂ at the live/dead boundary within spheroids. As proof of concept, we determined the critical survival pO₂ in two types of spheroids: isolated primary pancreatic islets and tumor-derived pseudo-islets (2.43 ± 0.08 vs. 0.84 ± 0.04 mmHg), indicating higher hypoxia tolerance in pseudo-islets due to their tumorigenic origin. We also applied this method for evaluating graft survival in cell transplantations for diabetes therapy, where hypoxia is a critical barrier to successful transplantation outcomes; thus, designing oxygenation strategies is required. Based on the elucidated critical survival pO₂, 100% viability could be maintained in a typically sized primary islet under the tissue pO₂ above 14.5 mmHg. This work presents a valuable tool that is potentially instrumental for fundamental hypoxia research. It offers insights into physiological responses to hypoxia among different cell types and may refine translational research in cell therapies.

Abstract Purpose The potential efficacy of metformin in breast cancer (BC) has been hotly discussed but never conclusive. This genetics-based study aimed to evaluate the relationships between metformin targets and BC risk. Methods Metformin targets from DrugBank and genome-wide association study (GWAS) data from IEU OpenGWAS and FinnGen were used to investigate the breast cancer (BC)-metformin causal link with various Mendelian Randomization (MR) methods (e.g., inverse-variance-weighting). The genetic association between type 2 diabetes (T2D) and the drug target of metformin was also analyzed as a positive control. Sensitivity and pleiotropic tests ensured reliability. Results The primary targets of metformin are PRKAB1, ETFDH and GPD1L. We found a causal association between PRKAB1 and T2D (odds ratio [OR] 0.959, P = 0.002), but no causal relationship was observed between metformin targets and overall BC risk (PRKAB1: OR 0.990, P = 0.530; ETFDH: OR 0.986, P = 0.592; GPD1L: OR 1.002, P = 0.806). A noteworthy causal relationship was observed between ETFDH and estrogen receptor (ER)-positive BC (OR 0.867, P = 0.018), and between GPD1L and human epidermal growth factor receptor 2 (HER2)-negative BC (OR 0.966, P = 0.040). Other group analyses did not yield positive results. Conclusion The star target of metformin, PRKAB1, does not exhibit a substantial causal association with the risk of BC. Conversely, metformin, acting as an inhibitor of ETFDH and GPD1L, may potentially elevate the likelihood of developing ER-positive BC and HER2-negative BC. Consequently, it is not advisable to employ metformin as a standard supplementary therapy for BC patients without T2D.

Abstract Background The development of alcohol-associated liver disease (ALD) is influenced by the amount and duration of alcohol consumption. The resulting liver damage can range from reversible stages, such as steatosis, steatohepatitis and alcoholic fibrosis, to the advanced and irreversible stage of cirrhosis. Aldo-keto reductase family 1 member A1 (AKR1A1) is a member of the aldo-keto reductase family that catalyzes the reduction of aldehyde groups to their corresponding alcohols in an NADPH-dependent manner. AKR1A1 was found to be downregulated in patients diagnosed with ALD. This study aims to interpret the protective effects of AKR1A1 on the development of ALD. Methods A 5% alcohol-fed (AF) Akr1a1 knockout (Akr1a1 −/−) mouse model and an AML12 hepatocyte model were used. The effects of AKR1A1 on liver function, inflammation, oxidative stress, lipid accumulation, and fibrosis were assessed by ELISA, western blotting, RT‒PCR, and a variety of histological staining methods in AF-induced wild-type (WT) and Akr1a1 −/− mice compared to control liquid diet-fed (PF) WT and Akr1a1 −/− mice. Results The results demonstrated that AF-WT mice expressed higher levels of AKR1A1 than WT mice fed a control diet, and they did not show any noticeable liver steatosis. However, AF-Akr1a1 −/− mice displayed a lower survival rate and more severe liver injury than AF-WT mice, as demonstrated by increased proinflammatory cytokines, oxidative stress, lipid accumulation, fibrosis, and reduced antioxidant enzymes in their livers. Additionally, elevated levels of 4-HNE and p53 phosphorylation were observed in AF-Akr1a1 −/− mice, suggesting that the loss of AKR1A1 led to increased 4-HNE accumulation and subsequent activation of p53, which contributed to the progression of ALD. Furthermore, in AML12 hepatocytes, Akr1a1 knockdown aggravated oxidative stress and steatosis induced by palmitic acid/oleic acid (P/O) inflammation induced by lipopolysaccharide (LPS), and fibrosis induced by TGF-β1. Conclusions This loss-of-function study suggests that AKR1A1 plays a liver-protective role during chronic alcohol consumption by reducing the accumulation of 4-HNE and inhibiting 4-HNE-mediated p53 activation.

Pancreatic islet transplantation is one of the clinical options for certain types of diabetes. However, difficulty in maintaining islets prior to transplantation limits the clinical expansion of islet transplantations. Our study introduces a dynamic culture platform developed specifically for primary human islets by mimicking the physiological microenvironment, including tissue fluidics and extracellular matrix support. We engineered the dynamic culture system by incorporating our distinctive microwell-patterned porous collagen scaffolds for loading isolated human islets, enabling vertical medium flow through the scaffolds. The dynamic culture system featured four 12 mm diameter islet culture chambers, each capable of accommodating 500 islet equivalents (IEQ) per chamber. This configuration calculates > five-fold higher seeding density than the conventional islet culture in flasks prior to the clinical transplantations (442 vs 86 IEQ/cm 2 ). We tested our culture platform with three separate batches of human islets isolated from deceased donors for an extended period of 2 weeks, exceeding the limits of conventional culture methods for preserving islet quality. Static cultures served as controls. The computational simulation revealed that the dynamic culture reduced the islet volume exposed to the lethal hypoxia (< 10 mmHg) to ~1/3 of the static culture. Dynamic culture ameliorated the morphological islet degradation in long-term culture and maintained islet viability, with reduced expressions of hypoxia markers. Furthermore, dynamic culture maintained the islet metabolism and insulin-secreting function over static culture in a long-term culture. Collectively, the physiological microenvironment-mimetic culture platform supported the viability and quality of isolated human islets at high-seeding density. Such a platform has a high potential for broad applications in cell therapies and tissue engineering, including extended islet culture prior to clinical islet transplantations and extended culture of stem cell-derived islets for maturation.

The need for maintaining cell-spheroid viability and function within high-density cultures is unmet for various clinical and experimental applications, including cell therapies. One immediate application is for transplantation of pancreatic islets, a clinically recognized treatment option to cure type 1 diabetes; islets are isolated from a donor for subsequent culture prior to transplantation. However, high seeding conditions cause unsolicited fusion of multiple spheroids, thereby limiting oxygen diffusion to induce hypoxic cell death. Here we introduce a culture dish incorporating a micropyramid-patterned surface to prevent the unsolicited fusion and oxygen-permeable bottom for optimal oxygen environment. A 400µm-thick, oxygen-permeable polydimethylsiloxane sheet topped with micropyramid pattern of 400µm-base and 200µm-height was fabricated to apply to the 24-well plate format. The micropyramid pattern separated the individual pancreatic islets to prevent the fusion of multiple islets. This platform supported the high oxygen demand of islets at high seeding density at 260 islet equivalents cm-2, a 2-3-fold higher seeding density compared to the conventional islet culture used in a preparation for the clinical islet transplantations, demonstrating improved islet morphology, metabolism and function in a 4 d-culture. Transplantation of these islets into immunodeficient diabetic mice exhibited significantly improved engraftment to achieve euglycemia compared to islets cultured in the conventional culture wells. Collectively, this simple design modification allows for high-density cultures of three-dimensional cell spheroids to improve the viability and function for an array of investigational and clinical replacement tissues.

Purpose: This study aimed to translate and validate a Chinese version of the Comprehensive assessment of Acceptance and Commitment Therapy processes (CompACT-C), a 23-item questionnaire for measuring an individual's level of psychological flexibility among Chinese breast cancer survivors for utilisation of Acceptance and commitment therapy (ACT)-based interventions in breast cancer patients. Methods: Six translators translated the original English version into Chinese according to Brislin's Translation Model. Psychometric properties tests were conducted on the CompACT-C, including internal consistency, test–retest reliability (two-week interval), face validity, content validity, convergent validity with experiential avoidance, and construct validity (confirmatory factor analysis). Results: 308 Chinese breast cancer survivors with mastectomies were conveniently recruited. The translated scale showed satisfactory test–retest reliability (two-week interval, r = 0.53–0.72, p

Abstract Radiotherapy (RT) plays an important role in tumor therapy due to its noninvasiveness and wide adaptation. In recent years, radiation therapy has been discovered to induce an anti-tumor immune response, which arouses widespread concern among scientists and clinicians. In this review, we highlight recent advances in the applications of nano-biomaterials for radiotherapy-activated immunotherapy. We first discuss the combination of different radiosensitizing nano-biomaterials and immune checkpoint inhibitors to enhance tumor immune response and improve radiotherapy efficacy. Subsequently, various nano-biomaterials-enabled tumor oxygenation strategies are introduced to alleviate the hypoxic tumor environment and amplify the immunomodulatory effect. With the aid of nano-vaccines and adjuvants, radiotherapy refreshes the host's immune system. Additionally, ionizing radiation responsive nano-biomaterials raise innate immunity-mediated anti-tumor immunity. At last, we summarize the rapid development of immune modulatable nano-biomaterials and discuss the key challenge in the development of nano-biomaterials for tumor radio-immunotherapy. Understanding the nano-biomaterials-assisted radio-immunotherapy will maximize the benefits of clinical radiotherapy and immunotherapy and facilitate the development of new combinational therapy modality. Graphical Abstract

ObjectiveAtherosclerosis is a chronic immuno-inflammatory condition emerging in arteries and considered the cause of a myriad of cardiovascular diseases. Atherosclerotic lesion characterization through invasive imaging modalities is essential in disease evaluation and determining intervention strategy. Recently, electrical properties of the lesions have been utilized in assessing its vulnerability mainly owing to its capability to differentiate lipid content existing in the lesion, albeit with limited detection resolution. Electrical impedance tomography is the natural extension of conventional spectrometric measurement by incorporating larger number of interrogating electrodes and advanced algorithm to achieve imaging of target objects and thus provides significantly richer information. It is within this context that we develop Outward Electrical Impedance Tomography (OEIT), aimed at intravascular imaging for atherosclerotic lesion characterization.MethodsWe utilized flexible electronics to establish the 32-electrode OEIT device with outward facing configuration suitable for imaging of vessels. We conducted comprehensive studies through simulation model and ex vivo setup to demonstrate the functionality of OEIT.ResultsQuantitative characterization for OEIT regarding its proximity sensing and conductivity differentiation was achieved using well-controlled experimental conditions. Imaging capability for OEIT was further verified with phantom setup using porcine aorta to emulate in vivo environment.ConclusionWe have successfully demonstrated a novel tool for intravascular imaging, OEIT, with unique advantages for atherosclerosis detection.SignificanceThis study demonstrates for the first time a novel electrical tomography-based platform for intravascular imaging, and we believe it paves the way for further adaptation of OEIT for intravascular detection in more translational settings and offers great potential as an alternative imaging tool for medical diagnosis.

Abstract The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low‐density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning‐directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL‐rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six‐point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL‐rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training.

Summary: Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods: Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings: The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation: The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding: This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.

AbstractBackground We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.Methods We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].Results β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.Conclusion This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

AbstractPurpose The purpose of this study was to estimate the clinical efficacy and identify the best beneficiaries of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) in hepatocellular carcinoma (HCC).Patients and methods A total of 749 HCC patients who underwent surgical resection (380 underwent PA-TACE, 369 had resection only) with a high risk of recurrence were reviewed retrospectively. Patients receiving PA-TACE were randomly split into development and validation cohorts. Univariate and multivariate analyses were performed in the development cohort. A novel model for PA-TACE-insensitivity prediction was built based on univariate and multivariate analysis and was multi-dimensionally validated in the validation set and all samples.Results After propensity score matching (PSM), in the early-recurrence group, no significant improvement in RFS was achieved with PA-TACE compared to radical hepatic resection alone. PA-TACE insensitive patients were considered as the PA-TACE non-benefit population and were associated with six clinicopathological factors: AFP, node number, tumor capsule, Ki-67 index, MVI, and complications in the development cohort. These factors were incorporated into a nomogram model, which reliably predicted PA-TACE insensitivity, with concordance indices of 0.874 and 0.897 for the development and validation cohort, respectively. In the overall sample, PA-TACE did not significantly improve patients’ RFS and OS in the high-score group, while the low-score group had statistical significance. Recurrence pattern diversity was also found to be a factor leading to PA-TACE insensitivity.Conclusion We constructed a new PA-TACE-insensitivity prediction model with potential clinical value. The good predictive performance and availability would allow this model to effectively screen PA-TACE beneficiaries.KEY MESSAGESThe independent influencing factors of PA-TACE insensitivity in patients who received PA-TACE were analyzed to construct a predictive model and its clinical application performance was verified with multi-dimensional methods.PA-TACE treatment should be avoided for patients with high scores according to this model, while it should be cautiously recommended for patients with low scores after multiple considerations.Compared with other related models, this model has obvious advantages in versatility and effectiveness. It can effectively screen the best benefit population of PA-TACE and provide a reliable reference for the selection of precise treatment plans for patients after radical resection of hepatocellular carcinoma.

The COVID-19 pandemic, caused by SARS-CoV-2, originated in China in late 2019, probably in the city of Wuhan (1,2). The outbreak of this unusual respiratory disease led to a wide [...]

The hole edge crack monitoring of metal structures with holes is of great significance for ensuring flight safety and enhancing the reliability of aircraft structures. In order to monitor the crack growth at the hole edge, the fatigue loading test of porous aluminum alloy plate containing the corner crack at the hole edge is carried out, and the a-N curve of the test piece of porous aluminum alloy plate and the center wavelength offset of the optical fiber grating strain sensor during the crack growth at the hole edge are obtained. The damage identification algorithms such as envelope analysis method and BP neural network are used to process and analyze the test data. The monitoring model that can identify the crack growth at the hole edge with the center wavelength offset of the optical fiber grating strain sensor is established, and verified with test parts. The results show that the established monitoring model can effectively identify the propagation and penetration of the corner crack at the hole edge, and the accuracy of monitoring the propagation length of the corner crack at the hole edge has reached 97.2%, which can be applied to the ground fatigue test of the whole aircraft, aircraft structure health monitoring and other scenarios in the future.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.

Lactoferrin (LF) stands as one of the extensively investigated iron-binding glycoproteins within milk, exhibiting diverse biological functionalities. The global demand for LF has experienced consistent growth. Biotechnological strategies aimed at enhancing LF productivity through microbial expression systems offer substantial cost-effective advantages and exhibit fewer constraints compared to traditional animal bioreactor technologies. This study devised a novel recombinant plasmid, wherein the AOX1 promoter was replaced with a glucose-inducible G1 promoter (PG1) to govern the expression of recombinant porcine LF (rpLF) in Pichia pastoris GS115. High-copy-number PG1-rpLF yeast clones were meticulously selected, and subsequent induction with 0.05 g/L glucose demonstrated robust secretion of rpLF. Scaling up production transpired in a 5 L fermenter, yielding an estimated rpLF productivity of approximately 2.8 g/L by the conclusion of glycerol-fed fermentation. A three-step purification process involving tangential-flow ultrafiltration yielded approximately 6.55 g of rpLF crude (approximately 85% purity). Notably, exceptional purity of rpLF was achieved through sequential heparin and size-exclusion column purification. Comparatively, the present glucose-inducible system outperformed our previous methanol-induced system, which yielded a level of 87 mg/L of extracellular rpLF secretion. Furthermore, yeast-produced rpLF demonstrated affinity for ferric ions (Fe3+) and exhibited growth inhibition against various pathogenic microbes (E. coli, S. aureus, and C. albicans) and human cancer cells (A549, MDA-MB-231, and Hep3B), similar to commercial bovine LF (bLF). Intriguingly, the hydrolysate of rpLF (rpLFH) manifested heightened antimicrobial and anticancer effects compared to its intact form. In conclusion, this study presents an efficient glucose-inducible yeast expression system for large-scale production and purification of active rpLF protein with the potential for veterinary or medical applications.

Skeptics of Programme for International Student Assessment (PISA) and Trend for International Math and Science Study (TIMSS) argue that while US elementary and high school students are behind their peers in other nations, the US workforce is still excellent because of the high quality post-secondary educational institutions in the US. However, the Programme for the International Assessment of Adult Competencies (PIAAC) indicates that US adults are in fact far behind their international peers in literacy, numeracy, and technology-based problem solving. Through the use of data mining, this study explored the possible association between PIAAC scores and several constructs. Since the US, Canada, and New Zealand were considered culturally similar nations, according to cluster analysis, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Additionally, model comparison was utilized to decide between bagging and boosting in order to select the optimal model for each sample. In these samples, cultural engagement, readiness to learn, and social trust, respectively emerged as strong predictors for learning outcomes as they were assessed by PIAAC.

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex data sets. Although data mining techniques could alleviate this problem, it is often unclear to social science researchers which option is the most suitable one to a particular research problem. The main objective of this paper is to illustrate how the model comparison of two popular ensemble methods, namely, boosting and bagging, could yield an improved explanatory model.

Abstract Background Identifying common factors that affect public adherence to COVID-19 containment measures can directly inform the development of official public health communication strategies. The present international longitudinal study aimed to examine whether prosociality, together with other theoretically derived motivating factors (self-efficacy, perceived susceptibility and severity of COVID-19, perceived social support) predict the change in adherence to COVID-19 containment strategies. Method In wave 1 of data collection, adults from eight geographical regions completed online surveys beginning in April 2020, and wave 2 began in June and ended in September 2020. Hypothesized predictors included prosociality, self-efficacy in following COVID-19 containment measures, perceived susceptibility to COVID-19, perceived severity of COVID-19 and perceived social support. Baseline covariates included age, sex, history of COVID-19 infection and geographical regions. Participants who reported adhering to specific containment measures, including physical distancing, avoidance of non-essential travel and hand hygiene, were classified as adherence. The dependent variable was the category of adherence, which was constructed based on changes in adherence across the survey period and included four categories: non-adherence, less adherence, greater adherence and sustained adherence (which was designated as the reference category). Results In total, 2189 adult participants (82% female, 57.2% aged 31–59 years) from East Asia (217 [9.7%]), West Asia (246 [11.2%]), North and South America (131 [6.0%]), Northern Europe (600 [27.4%]), Western Europe (322 [14.7%]), Southern Europe (433 [19.8%]), Eastern Europe (148 [6.8%]) and other regions (96 [4.4%]) were analyzed. Adjusted multinomial logistic regression analyses showed that prosociality, self-efficacy, perceived susceptibility and severity of COVID-19 were significant factors affecting adherence. Participants with greater self-efficacy at wave 1 were less likely to become non-adherence at wave 2 by 26% (adjusted odds ratio [aOR], 0.74; 95% CI, 0.71 to 0.77; P