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Summary: Background: The World Health Organisation (WHO) has identified a range of symptomatic manifestations to aid in the clinical diagnosis of post-COVID conditions, herein referred to as post-acute COVID-19 symptoms. We conducted an international network cohort study to estimate the burden of these symptoms in North American, European, and Asian populations. Methods: A federated analysis was conducted including 10 databases from the United Kingdom, Netherlands, Norway, Estonia, Spain, France, South Korea, and the United States, between September 1st 2020 and latest data availability (which varied from December 31st 2021 to February 28th 2023), covering primary and secondary care, nationwide registries, and claims data, all mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We defined two cohorts for the main analyses: a SARS-CoV-2 infection cohort [positive polymerase chain reaction (PCR) or rapid lateral flow test (LFT) result or clinical COVID-19 diagnosis] and a general population cohort. Individuals with less than 365 days of prior history or 120 days of follow-up were excluded. We estimated incidence rates (IRs) of the 25 WHO-proposed post-acute COVID-19 symptoms, considering symptoms that occurred ≥90 and ≤365 days after index date, excluding individuals with the respective symptoms 180 days prior to the index event. Stratified analyses were conducted by age and sex. Incidence rate ratios (IRRs) were calculated comparing rates in the infected cohort versus the general population. Results from the different databases were combined using random-effects meta-analyses. Findings: 3,019,408 individuals were included in the infection cohort. 1,585,160 of them were female and 1,434,248 of them male. 929,351,505 individuals were included in the general population group. 461,195,036 of them were female and 466,022,004 of them male. The 1-year IR of any post-acute COVID-19 symptom in the COVID-19 infection cohort varied significantly across databases, from 4.4 (95% CI 3.8–5.1) per 100 person-years to 103.9 (95% CI 103.2–104.7). The five most common symptoms were joint pain (from 1.6 (95% CI 1.3–1.9) to 14.3 (95% CI 14.1–14.6)), abdominal pain (from 0.3 (95% CI 0.1–0.5) to 9.9 (95% CI 9.7–10.1)), gastrointestinal issues (from 0.6 (95% CI 0.4–0.9) to 13.3 (95% CI 13.1–13.6)), cough (from 0.3 (95% CI 0.2–0.5) to 9.1 (95% CI 8.9–9.3)), and anxiety (from 0.8 (95% CI 0.6–1.2) to 11.4 (95% CI 11.2–11.6)); whereas muscle spasms (from 0.01 (95% CI 0.008–0.2) to 1.7 (95% CI 1.6–1.8)), pins and needles (from 0.05 (95% CI 0.03–0.0.9) to 1.5 (95% CI 1.4–1.6)), memory issues (from 0.03 (95% CI 0.02–0.06) to 0.8 (95% CI 0.7–0.8)), cognitive dysfunction (from 0.007 (95% CI 0.004–0.01) to 0.6 (95% CI 0.4–0.8)), and altered smell and/or taste (from 0.04 (95% CI 0.03–0.04) to 0.7 (95% CI 0.6–0.8)) were least common. Incidence rates of any post-acute COVID-19 symptoms generally increased with age, with certain symptoms peaking in middle-aged adults (anxiety, depressive disorders, headache, altered smell and taste) and others in pre-school children (gastrointestinal issues and cough). Females had higher incidence rates for most symptoms. Based on the random-effects model, the infected cohort had a higher incidence of any post-acute COVID-19 symptom than the general population, with a meta-analytic incidence rate ratio (meta-IRR) of 1.4 (1–2). A similar pattern was seen for all individual symptoms. The highest meta-IRRs were depressive disorder, 2.6 (1.7–3.9); anxiety, 2.3 (1.4–3.8); allergy, 2.1 (1.7–2.8) and sleep disorders, 2.1 (1.5–2.6). The meta-IRR for altered smell and/or taste was 1.9 (1.3–2.8). Interpretation: Post-acute COVID-19 symptoms, as listed by the WHO, were commonly observed following COVID-19 infection. However, even after standardising research methods, there was significant heterogeneity in the incidence rates from different healthcare settings and geographical locations. This is the first international study of the epidemiology of post-acute COVID-19 symptoms using the WHO-listed symptoms. Its findings contibute to understand the epidemiology of this condition from a multinational approach. Limitations of this study include the lack of consensus of the post-acute COVID-19 definition, as well as the difficulty to capture the impact on daily life of the post-acute COVID-19 symptoms in the available datasets. Funding: This work has been funded by the European Health Data Evidence Network (EHDEN) through an Evidence Generation Fund Grant and by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC).

IntroductionMachine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored.MethodsWe used real-world data comparing antihypertensive users to non-users with 69 negative control outcomes, and plasmode simulations to study the performance of ML methods in PS and DRS estimation. We conducted a cohort study using UK primary care records. Further, we conducted a plasmode simulation with synthetic treatment and outcome mimicking empirical data distributions. We compared four PS and DRS estimation methods: 1. Reference: Logistic regression including clinically chosen confounders. 2. Logistic regression with L1 regularisation (LASSO). 3. Multi-layer perceptron (MLP). 4. Extreme Gradient Boosting (XgBoost). Covariate balance, coverage of the null effect of negative control outcomes (real-world data) and bias based on the absolute difference between observed and true effects (for plasmode) were estimated. 632,201 antihypertensive users and nonusers were included.ResultsML methods outperformed the reference method for PS estimation in some scenarios, both in terms of covariate balance and coverage/bias. Specifically, XgBoost achieved the best performance. DRS-based methods performed worse than PS in all tested scenarios.DiscussionWe found that ML methods could be reliable alternatives for PS estimation. ML-based DRS methods performed worse than PS ones, likely given the rarity of outcomes.

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in “real-life” patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Fishes are considered a good biomarker, and bioindicator of pollution, Our current research aims to investigate the magnification of ( Pb and Cd ) and how both affect antioxidants and use fry carp Cyprinus carpio as a bioindicator. The samples were analyzed to find the possible biomagnification of metals in fish. The highest concentration in fry fish fed with Artemia used Dunaliella salina as food treated with Pb in Food type 1 for 28 days, with a significant difference. The highest concentration of Cd in fry fish fed with Artemia used D.salina as food treated with Cd in Food type 1 for 28 days, with a significant difference. The activity of the enzyme ( SOD - GSH - CAT– GST ) U / gm is affected by toxicity in fry fish mussels. The investigation has proved that exposure of fish to (lead and cadmium) induced a significant increase in content and increased activity of GST U / gm. For this purpose, an aquarium-based trial was conducted with two different types of food treated with Cd and Pb. The highest concentration of SOD U / gm enzyme of fry fish fed with Artemia used D.salina treated with Pb in Food type 1 for 28 days when fry fish fed on Artemia used food treated with Pb the results show a strong positive significant correlation between ( GPX, with SOD and CAT ) U / gm And ( SOD, with CAT )U / gm. While the concentration has a positive significant correlation with all enzymes, With Cd the highest concentration was in the SOD U/gm enzyme in fry fish fed with Artemia that used D.salina as food treated with Cd in Food type 2 for 28 days.

Abstract Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Traffic signs have great importance regarding smooth traffic flow and safe driving. However, due to many distractions and capricious factors, spotting and perceiving them may become hazardous. Traffic sign detection and recognition have gained popularity to put an end or to lessen the issue, and massive efforts have been realized in this regard. Despite considerable endeavors put together for traffic sign detection and recognition, there is a lack of attention in this area where these traffic signs contain text in them. A handful of studies may be found in state-of-the-art (SOTA) methods for text-based traffic sign detection, and particularly lesser for text recognition of detected text. The proposed method focuses on developing a robust semi-pipeline intelligent system to detect and understand text from traffic road signs boards in various weather conditions. For this purpose, a customized YOLOv5s is deployed for initial panel detection. Subsequently, MSER with preprocessing techniques is used for localization of text. Finally, OCR with NLP is utilized to recognize the text. The proposed method employed the ASAYAR dataset for training and different datasets for testing. The proposed approach produced satisfactory outcomes on them in contrast with SOTA approaches.

Background: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. Methods: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000–17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program—Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000–16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. Findings: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10–4 for Pakistan, 4·86 × 10–6 for India, and 1·32 × 10–5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. Interpretation: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. Funding: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Abstract Endophytic fungi, particularly from higher plants have proven to be a rich source of antimicrobial secondary metabolites. The purpose of this study is to examine the antimicrobial potential of three endophytic fungi Aspergillus sp. SA1, Aspergillus sp. SA2, and Aspergillus sp. SA3, cultivated from Nigella sativa seeds against Staphylococcus aureus (ATCC 9144), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), Klebsiella pneumoniae (ATCC 13883), MRSA (ATCC 33591), and human pathogen Candida albicans (ATCC 10231). Furthermore, the most active cultivated endophytic fungi were molecularly identified via internal transcribed spacer (ITS) sequencing. HR-ESIMS guided approach has been used successfully in chemical profiling of 26 known bioactive secondary metabolites (1–26), which belongs to different classes of natural compounds such as polyketides, benzenoids, quinones, alcohols, phenols or alkaloids. Finally, in-silico interactions within active site of fungal Cyp51 and bacterial DNA gyrase revealed possibility of being a hit-target for such metabolites as antimicrobials.

Aim: It is a complex decision for patients and their families to select a qualified surgeon who meets their needs. Understanding these needs enables surgeons to build stronger relationship with patients. This study aimed to identify influencing factors, variables, and criteria that individuals consider when selecting surgeons for elective surgeries. Methods: This is a nationwide cross-sectional study conducted among patients who have undergone elective surgeries in Saudi Arabia. The data were collected with an anonymous self-administered pre-validated questionnaire. The collection of data was carried out through web-based questionnaires using google forms. The questionnaire contains socio-demographic characteristics (i.e., age, gender, education, etc.) and different factors to assess patients' perceptions in choosing a surgeon. Result: Patients' overall number was 3133 (56.2 % females vs 43.8 % males). The most common age group was 18 to 34 years old (63.7 %). The prevalence of patients who were able to choose the right surgeon to perform an operation was 79.8 %. Patients' top choice when selecting a surgeon was according to the surgeon's manner followed by the qualifications then the reputation. Females are choosing a surgeon according to his/her manner while males are choosing according to his/her qualifications. Conclusion: Surgeons' manner and qualifications are the most considered factors among when selecting a surgeon while the realistic factors such as accreditation of a facility and surgeon's scientific roles, quality improvement, and patient safety are ignored among the public. This requires condensed educational efforts and further research to determine the advertisements and social media effects on patients' decisions related to their health.

Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data. We studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression.Methods: Monte Carlo simulations were used to generate variable cluster-level confounding intensity [odds ratio (OR) = 1.01–2.5] and cluster size (20–1,000 patients per cluster). The following PS estimation strategies were compared: i) logistic regression omitting cluster-level confounders; ii) logistic regression including cluster-level confounders; iii) the same as ii) but including cross-level interactions; iv), v), and vi), similar to i), ii), and iii), respectively, but using REM instead of logistic regression. The same strategies were tested in a trial emulation of partial versus total knee replacement (TKR) surgery, where observational versus trial-based estimates were compared as a proxy for bias. Performance metrics included bias and mean square error (MSE).Results: In most simulated scenarios, logistic regression, including cluster-level confounders, led to the lowest bias and MSE, for example, with 50 clusters × 200 individuals and confounding intensity OR = 1.5, a relative bias of 10%, and MSE of 0.003 for (i) compared to 32% and 0.010 for (iv). The results from the trial emulation also gave similar trends.Conclusion: Logistic regression, including patient and surgeon-/hospital-level confounders, appears to be the preferred strategy for PS estimation.

Objective: To characterize the trend of opioid use (number of users, dispensations and oral morphine milligram equivalents) in Catalonia (Spain).Design, setting, and participants: This population-based cohort study included all individuals aged 18 years or older, registered in the Information System for Research in Primary Care (SIDIAP), which covers >75% of the population in Catalonia, Spain, from 1 January 2007, to 31 December 2019.Main exposure and outcomes: The exposures were all commercialized opioids and their combinations (ATC-codes): codeine, tramadol, oxycodone, tapentadol, fentanyl, morphine, and other opioids (dihydrocodeine, hydromorphone, dextropropoxyphene, buprenorphine, pethidine, pentazocine). The main outcomes were the annual figures per 1,000 individuals of 1) opioid users, 2) dispensations, and 3) oral morphine milligram equivalents (MME). Results were stratified separately by opioid types, age (5-year age groups), sex (male or female), living area (rural or urban), and socioeconomic status (from least, U1, to most deprived, U5). The overall trends were quantified using the percentage change (PC) between 2007 and 2019.Results: Among 4,656,197 and 4,798,114 residents from 2007 to 2019, the number of opioid users, dispensations and morphine milligram equivalents per 1,000 individuals increased 12% (percentage change: 95% confidence interval (CI) 11.9–12.3%), 105% (95% confidence interval 83%–126%) and 339% (95% CI 289%–390%) respectively. Tramadol represented the majority of opioid use in 2019 (61, 59, and 54% of opioid users, dispensations, and total MME, respectively). Individuals aged 80 years or over reported the sharpest increase regarding opioid users (PC: 162%), dispensations (PC: 424%), and MME (PC: 830%). Strong opioids were increasingly prescribed for non-cancer pains over the years.Conclusion: Despite the modest increase of opioid users, opioid dispensations and MME increased substantially, particularly in the older population. In addition, strong opioids were incrementally indicated for non-cancer pains over the years. These findings suggest a transition of opioid prescriptions from intermittent to chronic and weak to strong and call for more rigorous opioid stewardship.

Background: Phobia is an extreme form of anxiety or fear which is triggered by a given situation or object or even danger associated with a situation or object for more than 6 months. There are three main forms of phobia: social phobia (social anxiety disorder), specific phobia, or agoraphobia. Agoraphobia is a form of anxiety disorder where one fears and avoids situations or places that might cause them to panic. The main purpose of this study is to investigate phobia among residents in broad training programs in Abha city and compare phobia between residents in broad training programs and general practitioners in Abha city. Methods: It is a cross-sectional study. The study was conducted in Abha city of Saudi Arabia from December 2019 to March 2020. It included a representative sample of medical doctors under broad training programs and general practitioners (none trained) in all the health-care centers. A link for the survey questionnaires was created and shared with the respondents. The questionnaire included sociodemographics of the participants, the Kutcher Generalized Social Anxiety Disorder Scale, and the Psychometric Properties and Clinical Utility of the Specific Phobia Questionnaire in an Anxiety Disorders Sample for specific phobia. Results: The study included 300 physicians. Majority of them (81%) were in the age group between 25 and 30 years. Males represent 54% of them. There was no statistically significant difference between resident physicians and general practitioners regarding the avoidance of different social situations. Conclusion: Overall, the residents in board training programs in Abha city expressed lower levels of discomfort, anxiety, distress, avoidance, fear, and life interference of some social and specific situations compared to general practitioners.

Objective: To identify post-operative risk factors for the development of chronic pain after knee replacement. Design: Primary knee replacements in persons aged ≥18 years between April 2008 and December 2016 from the National Joint Registry, linked with English Hospital Episode Statistics data, and Patient Reported Outcome Measures. The outcome was chronic pain 6-months after surgery (Oxford Knee pain score). Logistic regression modelling identified risk factors for chronic pain outcome. Results: 258,386 patients; 56.7% women; average age 70.1 years (SD ​± ​8.8 years). 43,702 (16.9%) were identified as having chronic pain 6-months post-surgery. Within 3 months of surgery complications were uncommon: intra-operative complications 1224 (0.5%); ≥1 medical complication 6073 (2.4%)); 32,930 (12.7%) hospital readmissions; 3848 (1.5%) re-operation; 835 (0.3%) revision. Post-surgical risk factors of chronic pain were: mechanical complication of prosthesis odds ratio (OR) 1.56 (95% Confidence Interval 1.35, 1.80); surgical site infection OR 1.13 (0.99, 1.29); readmission OR 1.47 (1.42, 1.52); re-operation OR 1.39 (1.27, 1.51); revision OR 1.92 (1.64, 2.25); length of stay e.g. 6+ vs.

Abstract Background Approximately one in five patients undergoing knee replacement surgery experience chronic pain after their operation, which can negatively impact on their quality of life. In order to develop and evaluate interventions to improve the management of chronic post-surgical pain, we aimed to derive a cut-off point in the Oxford Knee Score pain subscale to identify patients with chronic pain following knee replacement, and to characterise these patients using self-reported outcomes. Methods Data from the English Patient-Reported Outcome Measures (PROMs) programme were used. This comprised patient-reported data from 128,145 patients who underwent primary knee replacement surgery in England between 2012 and 2015. Cluster analysis was applied to derive a cut-off point on the pain subscale of the Oxford Knee Score. Results A high-pain group was identified, described by a maximum of 14 points in the Oxford Knee Score pain subscale six months after surgery. The high-pain group, comprising 15% of the sample, was characterised by severe and frequent problems in all pain dimensions, particularly in pain severity, night pain and limping, as well as in all dimensions of health-related quality of life. Conclusions Patients with Oxford Knee Score pain subscale scores of 14 or less at six months after knee replacement can be considered to be in chronic pain that is likely to negatively affect their quality of life. This derived cut-off can be used for patient selection in research settings to design and assess interventions that support patients in their management of chronic post-surgical pain.

Fishes are considered a good biomarker, and bioindicator of pollution, Our current research aims to investigate the magnification of (Pb and Cd) and how both affect antioxidants and use fry carp Cyprinus carpio as a bioindicator. The samples were analyzed to find the possible biomagnification of metals in fish. The highest concentration in fry fish fed with Artemia used Dunaliella salina as food treated with Pb in Food type 1 for 28 days, with a significant difference. The highest concentration of Cd in fry fish fed with Artemia used D. salina as food treated with Cd in food type 1 for 28 days, with a significant difference. The activity of the enzyme (SOD - GSH - CAT– GST) U/gm is affected by toxicity in fry fish mussels. The investigation has proved that exposure of fish to (lead and cadmium) induced a significant increase in content and increased activity of GST U/gm. For this purpose, an aquarium-based trial was conducted with two different types of food treated with Cd and Pb. The highest concentration of SOD U/ gm enzyme of fry fish fed with Artemia used D.salina treated with Pb in food type 1 for 28 days when fry fish fed on Artemia used food treated with Pb the results show a strong positive significant correlation between (GPX, with SOD and CAT) U/gm and (SOD, with CAT) U/gm. While the concentration has a positive significant correlation with all enzymes, With Cd the highest concentration was in the SOD U/gm enzyme in fry fish fed with Artemia that used D. salina as food treated with Cd in food type 2 for 28 days. [ABSTRACT FROM AUTHOR]

Background: Laryngopharyngeal reflux (LPR) involves retrograde movement of gastric contents to the larynx and pharynx. It is well known among laryngologists to cause various upper aerodigestive tract symptoms; however, it is misdiagnosed in general clinical practice. With very limited studies conducted on LPR in Saudi Arabia, data related to its prevalence and risk factors in Saudi Arabia are lacking. Hence, in this study, we used the validated Arabic version of the reflux symptom index (RSI) as a tool to diagnose and estimate the prevalence of LPR. Objective: The current study aimed to identify the risk factors and the prevalence of LPR in patients attending otolaryngology clinics. Materials and Methods: This cross-sectional study was conducted among adult patients attending the otolaryngology outpatient clinic. Participants completed a two-part questionnaire before entering the clinic. The first part was designed to collect demographic data, whereas the second part consisted of the validated Arabic version of the RSI questionnaire. A score of 13 or above was considered diagnostic for LPR. Results: A total of 166 patients participated in the study. The prevalence of LPR among the study population was 51.2%. LPR was detected more frequently in patients between 21 and 35 years old, and individuals over 65 years were found to have a lower incidence. Smoking and gastroesophageal reflux disease were associated with an increased risk of LPR (P < 0.05). Conclusion: LPR is prevalent among patients attending otolaryngology clinics. Physicians should try to identify patients with risk factors and counsel them about lifestyle modifications, with an emphasis on weight reduction strategies and smoking cessation. [ABSTRACT FROM AUTHOR]