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Abstract Objectives Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach. Methods Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman’s correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis. Results Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD. Conclusions The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Abstract Background Serotonin syndrome and Parkinson’s disease (PD) are two diseases whose symptoms partially overlap; this poses challenges in distinguishing them in clinical practice. Early manifestations such as tremor, akathisia, diaphoresis, hypertonia and hyperreflexia are common in mild-to-moderate serotonin syndrome and can also occur in PD. Without prompt recognition and treatment, serotonin syndrome can rapidly progress, potentially leading to severe complications such as multiple organ failure within hours. Given their disparate treatment strategies, accurate clinical distinction is crucial for effective treatment. This case study explores a patient with serotonin syndrome triggered by escitalopram in the context of PD psychosis (PDP), providing insights into diagnosis and treatment planning. Case presentation A 75-year-old Asian woman with a one-year history of PD, a two-month history of PDP, and a six-year history of depression presented with symptoms including hyperreflexia, tremor, hypertonia, impaired level of consciousness, and inappropriate behavior following a recent one-month adjustment in medication. Initially suspected of being drug-induced parkinsonism or worsening PD, therapeutic drug monitoring revealed warning levels of escitalopram. Subsequent diagnoses confirmed serotonin syndrome. This syndrome may result from increased cortical serotonin activity at the serotonin2A receptor due to dopamine and serotonin imbalances in PDP, compounded by increased dopamine-mediated serotonin release. Additionally, being an intermediate metabolizer of cytochrome P450 enzyme 2C19, the patient experienced excessive escitalopram accumulation, exacerbating her condition. Conclusions This case underscores the critical need to differentiate between symptoms of serotonin syndrome and PD, particularly in manifestations like tremor and hypertonia. Careful consideration of receptor profiles in patients with PDP is essential when selecting antidepressants to mitigate the risk of serotonin syndrome.

ABSTRACTIntroduction COVID-19 was an unprecedented challenge worldwide; however, disease epidemiology has evolved, and COVID-19 no longer constitutes a public health emergency of international concern. Nonetheless, COVID-19 remains a global threat and uncertainties remain, including definition of the end of the pandemic and transition to endemicity, and understanding true rates of SARS-CoV-2 infection/transmission.Areas covered Six international experts convened (April 2023) to interpret changing COVID-19 epidemiology and public health challenges. We report the panel’s recommendations and knowledge gaps in COVID-19 epidemiology, SARS-CoV-2 evolution, and future vaccination strategies, informed by peer-reviewed publications, surveillance data, health authority assessments, and clinical experience.Expert opinion High population SARS-CoV-2 immunity indicates the likely end to the pandemic’s acute phase. Continued emergence of variants/sublineages that can evade the vaccine-induced antibody response are likely, but widespread immunity reduces the risk of disease severity. Continued surveillance is required to capture transition to endemicity, seasonality, and emergence of novel variants/sublineages, to inform future vaccination strategies. COVID-19 vaccination should be integrated into routine vaccination programs throughout life. Co-circulation with other respiratory viruses should be monitored to avoid a combined peak, which could overrun healthcare systems. Effective, combined vaccines and improved education may help overcome vaccine hesitancy/booster fatigue and increase vaccination uptake.

ABSTRACTEveryone has a different level of interest in a trending topic posted on social media, which may affect user’s behaviour. In order to find out the way it affects the process of information transmission, we construct an interest intensity-based susceptible-forwarding-immune[Formula: see text] propagation dynamic model and two parameters birth rate[Formula: see text] and death rate[Formula: see text] are introduced to represent the users who newly join the group of disseminated information and the users who leave this population. And we give different birth rates to people with various levels of interest, which helps us to determine the interest intensity of potential users to a certain extent. We use the forwarding data of the real topic on Chinese Sina-microblog for data fitting, which can accurately parameterize the model and quantify the impact of interest intensity. And sensitivity analyses also give some strategies for increasing the impact of information dissemination process from the perspective of interest intensity.

Abstract Background The national volume-based procurement (NVBP) policy has significantly decreased prices and increased the accessibility of NVBP drugs. Nevertheless, issues such as heightened adverse reactions and suboptimal efficacy have arisen. Concerns regarding the quality of low-cost medications and the absence of long-term research have been widely recognized. This has led to caution among patients with late-life depression (LLD) due to their delicate health and the severity of their condition. This study evaluated the choice intention for NVBP drugs and associated factors in older patients with LLD. Methods A weighted sample of 408 participants between December 2022 and March 2023 were included. Data were collected via face-to-face interviews and questionnaires. To identify significant associated factors of choice intention, a multilevel logistic regression model was employed. Results Over half (53.68%) of older patients with LLD intended to choose NVBP drugs. Associated factors included self-assessed poor economy, higher out-of-pocket expenses, monthly household income exceeding CNY 6000, absence of other non-communicable chronic diseases, ordinary registration, urban employee medical insurance, no requirements for brand-name drugs, adverse reactions after using NVBP drugs, and rejection of physicians’ recommendation for NVBP drugs. The interaction effect between the real economic condition and patients self-assessed economy significantly influences choice intention for NVBP drugs. Among 124 patients with self-assessed poor economy, 75 showed a higher intention to use NVBP drugs. In these patients, age, medical insurance reimbursement, and brand awareness were significantly associated with choice intention. Conclusion Economic factors, physical conditions, medical needs, and physician recommendations significantly influenced the choice intention for NVBP drugs. The choice intention can be improved by strengthening physician-patient communication, increasing the scope and proportion of medical insurance reimbursement, improving substitution studies, and conducting post-marketing re-evaluations of NVBP drugs.

Abstract Enterovirus D68 (EV-D68) has emerged as a significant cause of acute respiratory illness in children globally, notably following its extensive outbreak in North America in 2014. A recent outbreak of EV-D68 was observed in Ontario, Canada, from August to October 2022. Our phylogenetic analysis revealed a notable genetic similarity between the Ontario outbreak and a concurrent outbreak in Maryland, USA. Utilizing Bayesian phylodynamic modeling on whole genome sequences (WGS) from both outbreaks, we determined the median peak time-varying reproduction number (Rt) to be 2.70, 95% HPD (1.76, 4.08) in Ontario and 2.10, 95% HPD (1.41, 3.17) in Maryland. The Rt trends in Ontario closely matched those derived via EpiEstim using reported case numbers. Our study also provides new insights into the median infection duration of EV-D68, estimated at 7.94 days, 95% HPD (4.55, 12.8) in Ontario and 10.8 days, 95% HPD (5.85, 18.6) in Maryland, addressing the gap in the existing literature surrounding EV-D68’s infection period. We observed that the estimated Time since the Most Recent Common Ancestor (TMRCA) and the epidemic’s origin coincided with the easing of COVID-19 related social contact restrictions in both areas. This suggests that the relaxation of non-pharmaceutical interventions, initially implemented to control COVID-19, may have inadvertently facilitated the spread of EV-D68. These findings underscore the effectiveness of phylodynamic methods in public health, demonstrating their broad application from local to global scales and underscoring the critical role of pathogen genomic data in enhancing public health surveillance and outbreak characterization.

Abstract Objective To assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication. Methods We developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters. Results We found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing. Conclusion Studies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64).

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness.Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

BackgroundAccording to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants.MethodsWe used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries.Results21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central.ConclusionOverall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

Abstract Cervical cancer is a leading cause of gynecological cancer death in the world. Human papillomavirus (HPV) is the most causative factor of cervical cancer. In addition, many genetic factors are involved in cervical cancer development. Most studies focus on cervical samples to do research work about cervical cancer and precancerous lesions, but no sensitive or specific biomarkers were found. High-throughput genomic technologies are able to capture information from tumors and precancerous lesions in blood, thus providing a new way for the early diagnosis of cervical precancer and cervical cancer. Blood is an ideal specimen for detecting cancer biomarkers because it contains a lot of information, such as circulating tumor cells and circulating tumor DNA (ctDNA). This article reviews the clinical use and challenges of blood ctDNA testing in patients with cervical precancer and cervical cancer.

Abstract Objectives Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. Methods A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. Results Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. Conclusions These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

The stress environments of gob-side roadways (GSRs) are becoming increasingly complex during deep coal mining under thick and hard roofs. This leads to strong strata behaviors, including roadway floor heave, roof subsidence, and even coal bursts. Among them, coal bursts pose the greatest threat to production safety in coal mines. Coal bursts in a GSR strongly correlate with the load characteristics and stress-energy evolution laws of the roadway. This study analyzes the roof structures of double working faces (DWFs) during the initial weighting stage (IWS) and full mining stage (FMS) of gob-side working faces (GSWFs). This study also explores how varying roof structures affect the stability of GSRs. Three-dimensional roof structure models of DWFs and mechanical models of dynamic and static loads superposition on a GSR throughout the IWS and FMS of a GSWF were developed. An analysis identified the primary stress sources affecting the GSR throughout various mining stages of the GSWF. Subsequently, the principle of “three-load” superposition was developed. A novel method was proposed to quantify the stress state in the GSR surrounding rock across different mining stages of the GSWF. The method quantitatively characterizes the load of the GSR surrounding rock. Based on this, the criterion for judging the burst failure of the roadway was established. Numerical simulations are used to analyze the stress-energy evolution laws of the working face, coal pillar, and GSR surrounding rock during the mining process of the GSWF. These findings offer valuable references for studying and preventing coal bursts in GSRs under equivalent geological situations.

Abstract Esophageal cancer is a highly malignant disease with poor prognosis. Despite recent advances in the study of esophageal cancer, there has been only limited improvement in the treatment and prognosis. N6-methyladenosine (m6A), a type of RNA modification, has been extensively investigated and is involved in many biological behaviors, including tumorigenesis and progression. Thus, more research on m6A modification may increase our understanding of esophageal cancer pathogenesis and provide potential targets. In our study, we integrated the public data of esophageal cancer from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) databases. Unsupervised clustering analysis was used to classify patients into different groups. Gene set variation analysis (GSVA) was performed in a nonparametric and unsupervised mode. We evaluated immune cell infiltration by single sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) among m6A clusters were identified using Empirical Bayesian approach. Both multivariate and univariate Cox regression models were used for prognostic analysis. We provided an overview of gene variation and expression of 23 m6A regulators in esophageal cancer, as well as their effects on survival. Based on the overall expression level of m6A regulators, patients were classified into three m6A clusters (A-C) with different immune cell infiltration abundance, gene expression signatures and prognosis. Among m6A clusters, we identified 206 DEGs, according to which patients were classified into 4 gene clusters (A-D). Quantitative m6A score was calculated for each patient based on those DEGs with significant impact on survival. The infiltration of all types of immune cells except type 2 T helper (Th2) cells were negatively correlated with m6A score. M6Acluster C exhibited the lowest m6A score, the most abundant immune cell infiltration, and the worst prognosis, suggesting an immune excluded phenotype. Consistently, gene cluster D with the lowest m6A score showed the worst prognosis. In short, patients with esophageal cancer showed different m6A modification patterns. Quantitative scoring indicated that patients with the lowest m6A score exhibited the most abundant immune cell infiltration and the poorest prognosis. This m6A scoring system is promising to assess m6A modification pattern, characterize immune infiltration and guide personalized treatment and prognostic prediction.

Abstract Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.

Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.

Objective Restoration of the medial support is especially important for the treatment of proximal humeral fractures. The objective of this study was to investigate the radiographic and clinical outcomes of intramedullary nail fixation with a special focus on the presence of calcar comminution. Methods In this retrospective study of patients with displaced proximal humeral fractures that were treated by intramedullary nail between January 2018 and July 2021, fracture morphology and the calcar integrity were noted on preoperative radiographs. Patients were divided into two groups according to calcar integrity. During follow‐up, radiological assessment and functional outcome, including the deltoid tuberosity index (DTI), neck shaft angle (NSA), visual analog scale (VAS), the American Shoulder and Elbow Surgeons (ASES) score, the Simple Shoulder Test (SST) score, active and passive range of motion, were performed. A Student t‐test and univariate logistic regression analysis was used. Results A total of 83 patients (54 female, 29 male) had complete follow‐up (average, 12.8 months; range, 10 to 33 months) and functional assessment in our study. The average age was 58.6 years (range, 20 to 89 years). The mean loss of NSA was 4° (range, 0°–12°) and no significant difference was found between two groups (p = 0.27). DTI had an average of 1.50 ± 0.19 (range 1.13–2.04). Patients with intact calcar achieved greater range of forward elevation (129.06 ± 11.91 vs. 121.05 ± 11.97, p = 0.01), and higher SST scores (8.61 ± 1.85 vs. 7.37 ± 2.22, p = 0.02). Two groups showed similar outcomes in VAS, ASES score, and range of abduction. One patient demonstrated a proximal interlocking screw cutting through and osteonecrosis of the humeral head, who underwent a second surgery for screw removal. There were no cases of infection, malunion, nonunion, nerve injury, subacromial impingement, or rotator cuff tear during the study period. Conclusion Intramedullary nail can favorably be used to manage proximal humeral fractures with good early radiographic and functional outcomes, even for those with comminuted calcar.

Objective Implant failure is a disastrous complication of the operative treatment of midshaft clavicle fractures, and improving the osteosynthesis plate is a strategy for preventing this. We aimed to investigate whether canceling the notch and adding screw‐hole inserts enhanced the mechanical properties of the plate. Methods A clavicle model was generated based on the CT images of six adult volunteers (age range, 20–40 years; three males and three females; height range 160–175) using dedicated software, and a midshaft fracture model was created. The domestically made seven‐hole locking plate commonly used for midshaft clavicle fractures was simulated (Model I); modifications were made to the plate (Model II). Using 3D finite element analysis, we simulated the fracture construct under three different load conditions—downward cantilever bending, axial compression, and axial torsion—and compared the stress distribution. Results We found that under axial compression, Model II experienced its maximum stress on the plate at 551.9MPa, which was less than that in Model I (790.4 MPa). Moreover, a greater stress concentration at the fracture site was observed under axial torsion, despite the maximum stress of both the models being similar. Conclusion Canceling the notch and filling the screw holes near the fracture can ameliorate stress concentration on the internal fixation construct and enhance its reliability under axial compression. This improvement has substantial effects on the mechanical properties of implants and potentially prevents implant failure. Modern osteosynthesis anatomical implants need to be improved.

Objective Current X‐ray‐based classification methods cannot describe all distal clavicle fracture (DCF) patterns, especially the osteoligamentous injury pattern of DCFs. We aimed to develop a novel classification based on the osteoligamentous injury pattern of the DCFs and investigated its reliability. Methods All DCFs from January 2017 to January 2022 were respectively screened and 45 cases (mean age 20–78; male 31, female 14) met the including criteria and were enrolled. Based on their Zanca view X‐ray radiograph and three‐dimensional CT construction images, we analyzed the osteoligamentous injury pattern of each case, particularly the acromioclavicular (AC) and coracoclavicular ligaments and their bone attachment. Then we developed a novel classification method, five types in total, sorting all DCFs according to their lesion manifestations of osteoligamentous complex. Also, we investigated the inter‐ and intra‐observer reliability using kappa value. Results A novel classification method for DCF was developed, manifesting the avulsion or rupture of conoid and trapezoid ligaments, and involvement of AC joint. Forty‐five cases of DCFs were included in this study. Among them, 11 (24.4%) were Type 1 fracture, three (6.7%) cases were Type 2, six cases (13.3%) were Type 3, 21 (46.7%) were Type 4, four (8.9%) were Type 5. Kappa values for inter‐observer agreement were 0.57 after first evaluation and 0.61 after second evaluation. Intra‐observer agreement was 0.72 for experienced shoulder specialist and 0.63 for radiologist. Conclusion This new classification method is reliable to use, supplementary to current classification systems, and emphasizes on the osteoligamentous complex injury when opting for the treatment.

Background Proximal humeral fractures with increasing incidence are common over the past decades. Among them, the complicated and displaced ones, especially combined with the proximal third humeral shaft fractures, need to be treated surgically. Humeral intramedullary nail and locking plate are the most frequently used options. However, few studies address the application of nail‐plate combination for management. Case presentation A 49‐year‐old man struck by a car was referred to our emergency trauma center. The X‐ray and three‐dimensional computed tomography (CT) showed a complex proximal humeral fracture with proximal third shaft fracture. No evidence of other associated injuries and neurovascular symptom of the injured left upper limb was shown by physical examination. Nail‐plate fixation was performed to restore the anatomic integrity of proximal humerus. The injured arm was maintained in a broad arm sling for 14 days after surgery. The active as well as positive movements under the instruction were encouraged as long as the pain could be tolerated from postoperative day 2. One year after the surgery, the fracture was healed, and the patient regained normal shoulder motion. The good clinical outcomes were obtained by application of nail‐plate fixation to treat complex proximal third humeral shaft fractures. Thus, this surgical intervention could be performed as an alternative.

Abstract It is imperative that resources are channelled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the following research question: what is the role of community compliance as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities—examples, social distancing, face mask use, and sanitizing—coupled with efforts by health authorities in areas of vaccine provision and effective quarantine—showed that the best intervention in addition to implementing vaccination programs and effective quarantine measures, is the active incorporation of individuals’ collective behaviours, and that resources should also be directed towards community campaigns on the importance of face mask use, social distancing, and frequent sanitizing, and any other collective activities. We also demonstrated that collective behavioral response of individuals influences the disease dynamics; implying that recommended health policy should be contextualized.

In this paper, we establish a spatial heterogeneous SEIRS patch model with asymmetric mobility kernel. The basic reproduction ratio $ \mathcal{R}_{0} $ is defined, and threshold-type results on global dynamics are investigated in terms of $ \mathcal{R}_{0} $. In certain cases, the monotonicity of $ \mathcal{R}_{0} $ with respect to the heterogeneous diffusion coefficients is established, but this is not true in all cases. Finally, when the diffusion rate of susceptible individuals approaches zero, the long-term behavior of the endemic equilibrium is explored. In contrast to most prior studies, which focused primarily on the mobility of susceptible and symptomatic infected individuals, our findings indicate the significance of the mobility of exposed and recovered persons in disease dynamics.

Abstract Background Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels. Methods Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC–MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. Results Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort. Conclusion Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R0). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R0 estimation and surveillance-based R0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Objectives To explore the prevalence and associated factors of COVID-19 anxiety in patients with late-life depression (LLD) during the adjustment of epidemic prevention policies in China.Design Cross-sectional study.Setting The data analysed in this study were collected from seven regions in China between November 2022 and January 2023.Participants A total of 1205 patients with LLD (aged 60–78 years) participated in the survey. They completed a social demographic assessment and the Chinese version of the five-point Coronavirus Anxiety Scale (CAS).Primary outcome measures The primary outcome was the anxiety level of the participants. Patients were categorised into two groups based on their anxiety levels, one with anxiety and one without, according to CAS scores.Results The prevalence of COVID-19 anxiety in depressed older adults was 47.3%. Regression analysis revealed that the average COVID-19 anxiety score was significantly higher among females (AOR: 2.177, 95% CI 1.201 to 3.947), widowed individuals (AOR: 3.015, 95% CI 1.379 to 6.591), patients residing at a distance from healthcare facilities (AOR: 3.765, 95% CI 1.906 to 7.438), and those who frequently experienced worry (AOR: 1.984, 95% CI 1.111 to 3.543). Conversely, the anxiety score was significantly lower among divorced individuals (AOR: 0.491, 95% CI 0.245 to 0.988), those aged 70 years and above (AOR: 0.117, 95% CI 0.064 to 0.213), patients without difficulty obtaining medication (AOR: 0.027, 95% CI 0.007 to 0.097), those living with family members (AOR: 0.080, 95% CI 0.022 to 0.282) or in nursing homes compared with those living alone (AOR: 0.019, 95% CI 0.004 to 0.087).Conclusion Women with LLD who are widowed, live far from healthcare facilities, and are prone to excessive worry are more likely to experience anxiety. It is advisable to implement appropriate preventive measures and provide psychosocial support programmes for this vulnerable group during the COVID-19 pandemic.

BackgroundSerotonin syndrome has been recognized as a serious adverse reaction to antidepressants and is characterized by sudden or severe autonomic nerve dysfunction and neuromuscular symptoms. Without an accurate diagnosis and prompt treatment, serotonin syndrome progresses rapidly and can be life-threatening. It is usually related to the dose of 5-hydroxytryptamine drugs, and the dose is the basis for diagnosis. Therefore, serotonin syndrome induced by low-dose antidepressants rarely occurs, and clinicians are more likely to misdiagnose patients who take low-dose antidepressants with similar symptoms. Here, we present a case study of serotonin syndrome caused by a relatively low dose of escitalopram, which is not common in past references.Case summaryThe patient was a 74-year-old Asian woman with a 42-year history of schizophrenia. After 6 weeks of antidepressant treatment, our patient presented with characteristic myoclonus in the lower limbs and closed eyes with fluttering. Initially, she was misdiagnosed with neuroleptic malignant syndrome (NMS) due to antipsychotic medication and was treated accordingly, even with discontinuation of clozapine. However, her symptoms persisted, and then therapeutic drug monitoring was initiated with the involvement of a clinical pharmacist. Eventually, she was diagnosed with serotonin syndrome due to escitalopram levels reaching the warning level. Subsequently, the patient’s treatment was modified, and her clinical outcome was satisfactory without any other serious adverse reactions. Gene detection was also performed, and a cytochrome P450 enzyme (CYP) 2C19-mediated interaction between low-dose escitalopram and clopidogrel seems to be a possible mechanism.ConclusionData on this is extremely scarce, and to the best of our knowledge, serotonin syndrome caused by low-dose antidepressants has not yet been discussed to any great extent in the literature. Our case provides more clinical experience in the treatment of serotonin syndrome.

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries.

Abstract The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster’s severity, progression and whether it can be defined as a hot-spot.

Based on the lung adenocarcinoma (LUAD) gene expression data from the cancer genome atlas (TCGA) database, the Stromal score, Immune score and Estimate score in tumor microenvironment (TME) were computed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. And gene modules significantly related to the three scores were identified by weighted gene co-expression network analysis (WGCNA). Based on the correlation coefficients and P values, 899 key genes affecting tumor microenvironment were obtained by selecting the two most correlated modules. It was suggested through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis that these key genes were significantly involved in immune-related or cancer-related terms. Through univariate cox regression and elastic network analysis, genes associated with prognosis of the LUAD patients were screened out and their prognostic values were further verified by the survival analysis and the University of ALabama at Birmingham CANcer (UALCAN) database. The results indicated that eight genes were significantly related to the overall survival of LUAD. Among them, six genes were found differentially expressed between tumor and control samples. And immune infiltration analysis further verified that all the six genes were significantly related to tumor purity and immune cells. Therefore, these genes were used eventually for constructing a Naive Bayes projection model of LUAD. The model was verified by the receiver operating characteristic (ROC) curve where the area under curve (AUC) reached 92.03%, which suggested that the model could discriminate the tumor samples from the normal accurately. Our study provided an effective model for LUAD projection which improved the clinical diagnosis and cure of LUAD. The result also confirmed that the six genes in the model construction could be the potential prognostic biomarkers of LUAD.

In this study, we determine and compare the incubation duration, serial interval, pre-symptomatic transmission, and case fatality rate of MERS-CoV and COVID-19 in Saudi Arabia based on contact tracing data we acquired in Saudi Arabia. The date of infection and infector-infectee pairings are deduced from travel history to Saudi Arabia or exposure to confirmed cases. The incubation times and serial intervals are estimated using parametric models accounting for exposure interval censoring. Our estimations show that MERS-CoV has a mean incubation time of 7.21 (95% CI: 6.59–7.85) days, whereas COVID-19 (for the circulating strain in the study period) has a mean incubation period of 5.43(95% CI: 4.81–6.11) days. MERS-CoV has an estimated serial interval of 14.13(95% CI: 13.9–14.7) days, while COVID-19 has an estimated serial interval of 5.1(95% CI: 5.0–5.5) days. The COVID-19 serial interval is found to be shorter than the incubation time, indicating that pre-symptomatic transmission may occur in a significant fraction of transmission events. We conclude that during the COVID-19 wave studied, at least 75% of transmission happened prior to the onset of symptoms. The CFR for MERS-CoV is estimated to be 38.1% (95% CI: 36.8–39.5), while the CFR for COVID-19 1.67% (95% CI: 1.63–1.71). This work is expected to help design future surveillance and intervention program targeted at specific respiratory virus outbreaks, and have implications for contingency planning for future coronavirus outbreaks.

Abstract Background Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. Methods We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. Results The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. Conclusions This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.

In the early stages of the pandemic, Saudi Arabia and other countries in the Arab Gulf region relied on non-pharmaceutical therapies to limit the effect of the pandemic, much like other nations across the world. In comparison to other nations in the area or globally, these interventions were successful at lowering the healthcare burden. This was accomplished via the deterioration of the economy, education, and a variety of other societal activities. By the end of 2020, the promise of effective vaccinations against SARS-CoV-2 have been realized, and vaccination programs have begun in developed countries, followed by the rest of the world. Despite this, there is still a long way to go in the fight against the disease. In order to explore disease transmission, vaccine rollout and prioritisation, as well as behavioural dynamics, we relied on an age-structured compartmental model. We examine how individual and social behaviour changes in response to the initiation of vaccination campaigns and the relaxation of non-pharmacological treatments. Overall, vaccination remains the most effective method of containing the disease and resuming normal life. Additionally, we evaluate several vaccination prioritisation schemes based on age group, behavioural responses, vaccine effectiveness, and vaccination rollout speed. We applied our model to four Arab Gulf nations (Saudi Arabia, Bahrain, the United Arab Emirates, and Oman), which were chosen for their low mortality rate compared to other countries in the region or worldwide, as well as their demographic and economic settings. We fitted the model using actual pandemic data in these countries. Our results suggest that vaccinations focused on the elderly and rapid vaccine distribution are critical for reducing disease resurgence. Our result also reinforces the cautious note that early relaxation of safety measures may compromise the vaccine's short-term advantages.

After struggling with COVID-19 pandemic for two years, the world is finally recovering from this crisis. Nonetheless, another virus, Monkeypox, is quickly spreading throughout the world and in non-endemic regions and continents, threatening the world to a new pandemic. Twitter as a popular social media has successfully been used for predicting and controlling outbreaks. Much research previously has been done for building early warning systems, trend prediction, and misinformation and fake news detection. Since tweets are not accessible to all researchers, in this work, a publicly available dataset containing 2400202 tweets gathered from May first to December twenty-fifth, 2022 is presented. Twitter developers academic researcher API which returns all the tweets matching a given query was used to gather the dataset. To this end, the full archive search and keywords related to Monkeypox and its equivalents in other languages, i.e. Monkeypox or “monkey pox” or “viruela dei mono” or “variole du singe” or “variola do macoco” were used. The retweets were excluded using the negation operator, and the tweet ids and user ids were extracted and shared with public. Approximately, 1.79 percent (43047 number) of tweets were geotagged. To visualize the geotagged tweets, the longitude and latitude of the bounding box coordinates were averaged. This work will help researchers shed light on the news, patterns, and on-going discussions of Monkeypox on social media, identify hotspots, and help contain the Monkeypox virus.

The 2022–2023 Mpox multi-country outbreak, identified in over 110 WHO Member States, revealed a predominant impact on cisgender men, particularly those engaging in sex with men, while less frequently affecting women. This disparity prompted a focused investigation into the gender-specific characteristics of Mpox infections, particularly among women, to address a notable knowledge gap. This review systematically gathers and analyzes the scientific literature and case reports concerning Mpox infections in women, covering a broad geographical spectrum including regions such as Argentina, Brazil, Colombia, Nigeria, Europe, Vietnam, and the United States. The analysis delves into various aspects of Mpox in women, including clinical features, epidemiology, psychological impacts, preparedness strategies, and case studies, with particular attention to pregnant women and those with underlying health conditions. Empirical data from multiple studies underscore the unique epidemiological and clinical patterns of Mpox in women. In the United States, a small percentage of Mpox cases were reported among cisgender women, with a notable portion involving non-Hispanic Black or African American, non-Hispanic White, and Hispanic or Latino ethnicities. The primary transmission route was identified as sexual or close intimate contact, with the virus predominantly manifesting on the legs, arms, and genital areas. Further, a study in Spain highlighted significant disparities in diagnosis delays, transmission modes, and clinical manifestations between genders, indicating a different risk profile and disease progression in women. Additionally, a case from Vietnam, linked to a new Mpox sub-lineage in women, emphasized the role of women in the transmission dynamics and the importance of genomic monitoring. This review emphasizes the necessity for inclusive surveillance and research to fully understand Mpox dynamics across diverse population groups, including women. Highlighting gender and sexual orientation in public health responses is crucial for an effective approach to managing the spread and impact of this disease. The findings advocate for a gender-diverse assessment in health services and further research to explore the nuances of Mpox transmission, behavior, and progression among different groups, thereby enhancing the global response to Mpox and similar public health challenges.

A significant distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. However, with the recurrence of the epidemic, the conflict between epidemic prevention and production recovery has become increasingly prominent on social media. To help design effective communication strategies to guide public opinion, we propose a susceptible-forwarding-immune pseudo-environment (SFI-PE) dynamic model for understanding the environment with direct and indirect propagation behaviors. Then, we introduce a system with external interventions for direct and indirect propagation behaviors, termed the macro-controlled SFI-PE (M-SFI-PE) model. Based on the numerical analyses that were performed using actual data from the Chinese Sina microblogging platform, the data fitting results prove our models' effectiveness. The research grasps the law of the new information propagation paradigm, and our work bridges the gap between reality and theory in information interventions.

Abstract Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Abstract Widespread vaccination is necessary to minimize or halt the effects of many infectious diseases, including COVID-19. Stagnating vaccine uptake can prolong pandemics, raising the question of how we might predict, prevent, and correct vaccine hesitancy and unwillingness. In a multinational sample (N = 4,452) recruited from 13 countries that varied in pandemic severity and vaccine uptake (July 2021), we examined whether short-sighted decision-making as exemplified by steep delay discounting—choosing smaller immediate rewards over larger delayed rewards—predicts COVID-19 vaccination status. Delay discounting was steeper in unvaccinated individuals and predicted vaccination status over and above demographics or mental health. The results suggest that delay discounting, a personal characteristic known to be modifiable through cognitive interventions, is a contributing cause of differences in vaccine compliance.

Abstract Background Mass immunization is a potentially effective approach to finally control the local outbreak and global spread of the COVID-19 pandemic. However, it can also lead to undesirable outcomes if mass vaccination results in increased transmission of effective contacts and relaxation of other public health interventions due to the perceived immunity from the vaccine. Methods We designed a mathematical model of COVID-19 transmission dynamics that takes into consideration the epidemiological status, public health intervention status (quarantined/isolated), immunity status of the population, and strain variations. Comparing the control reproduction numbers and the final epidemic sizes (attack rate) in the cases with and without vaccination, we quantified some key factors determining when vaccination in the population is beneficial for preventing and controlling future outbreaks. Results Our analyses predicted that there is a critical (minimal) vaccine efficacy rate (or a critical quarantine rate) below which the control reproduction number with vaccination is higher than that without vaccination, and the final attack rate in the population is also higher with the vaccination. We also predicted the worst case scenario occurs when a high vaccine coverage rate is achieved for a vaccine with a lower efficacy rate and when the vaccines increase the transmission efficient contacts. Conclusions The analyses show that an immunization program with a vaccine efficacy rate below the predicted critical values will not be as effective as simply investing in the contact tracing/quarantine/isolation implementation. We reached similar conclusions by considering the final epidemic size (or attack rates). This research then highlights the importance of monitoring the impact on transmissibility and vaccine efficacy of emerging strains.

BackgroundThe global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community. ObjectiveThe aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)–related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination. MethodsIn total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets’ main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets’ and Facebook posts’ languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries. ResultsThe number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P

Tuberculosis (TB) is a major infectious disease induced by Mycobacterium tuberculosis (M. tb) which causes the world’s dominant fatal bacterial contagious disease. Increasing studies have indicated that exosomes may be a novel option for the diagnosis and treatment of TB. Exosomes are nanovesicles (30-150 nm) containing lipids, proteins and non-coding RNAs (ncRNAs) released from various cells, and can transfer their cargos and communicate between cells. Furthermore, exosomal ncRNAs exhibit diagnosis potential in bacterial infections, including TB. Additionally, differential exosomal ncRNAs regulate the physiological and pathological functions of M. tb-infected cells and act as diagnostic markers for TB. This current review explored the potential biological roles and the diagnostic application prospects of exosomal ncRNAs, and included recent information on their pathogenic and therapeutic functions in TB.

Abstract Background Mass gatherings can not only trigger major outbreaks on-site but also facilitate global spread of infectious pathogens. Hajj is one of the largest mass gathering events worldwide where over two million pilgrims from all over the world gather annually creating intense congestion. Methods We developed a meta-population model to represent the transmission dynamics of Neisseria meningitidis and the impact of Hajj pilgrimage on the risk of invasive meningococcal disease (IMD) for pilgrims population, local population at the Hajj site and country of origin of Hajj pilgrims. This model was calibrated using data on IMD over 17 years (1995–2011) and further used to simulate potential changes in vaccine policy and endemic conditions. Results The effect of increased density of contacts during Hajj was estimated to generate a 78-fold increase in disease transmission that impacts not only pilgrims but also the local population. Quadrivalent ACWY vaccination was found to be very effective in reducing the risk of outbreak during Hajj. Hajj has more limited impact on IMD transmission and exportation in the pilgrim countries of origin, although not negligible given the size of the population considered. Conclusion The analysis performed highlighted the amplifying effect of mass gathering on N. meningitidis transmission and confirm vaccination as a very effective preventive measure to mitigate outbreak risks.

Tick infestation and tick-borne disease spread in a region of multiple adjacent patches with different environmental conditions depend heavily on the host mobility and patch-specific suitability for tick growth. Here we introduce a two-patch model where environmental conditions differ in patches and yield different tick developmental delays, and where feeding adult ticks can be dispersed by the movement of larger mammal hosts. We obtain a coupled system of four delay differential equations with two delays, and we examine how the dynamical behaviours depend on patch-specific basic reproduction numbers and host mobility by using singular perturbation analyses and monotone dynamical systems theory. Our theoretical results and numerical simulations provide useful insights for tick population control strategies.

An optimization algorithm for phase-only hologram (POH) based on the point source method (PSM) with the holographic viewing-window (HVW) by using a convolutional neural network (CNN) is proposed. The network training adopts an unsupervised strategy that maps the target image to optimized constant phases (OCPs) corresponding to the wavefront of each point source instead of random constant phases (RCPs), and then the optimized POH can be obtained by wavefront superposition. The simulation and experimental results show that the speckle noise of the reconstruction is significantly suppressed compared with the initial random phase method (RPM), which demonstrates the feasibility of the proposed method and shows a strong generalization ability of the trained CNN. The HVW of a holographic near-eye display (NED) has a continuous observation range of about 6 mm in the horizontal direction without using additional optical expansion elements, and a complete high-quality image can be observed within this range.

Abstract In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement “smart” interventions, which are as efficacious as stringent ones.

Dengue fever is endemic in tropical and subtropical countries, and certain important features of the spread of dengue fever continue to pose challenges for mathematical modelling. Here we propose a system of integro-differential equations (IDE) to study the disease transmission dynamics that involve multi-serotypes and cross immunity. Our main objective is to incorporate and analyze the effect of a general time delay term describing acquired cross immunity protection and the effect of antibody-dependent enhancement (ADE), both characteristics of Dengue fever. We perform qualitative analysis of the model and obtain results to show the stability of the epidemiologically important steady solutions that are completely determined by the basic reproduction number and the invasion reproduction number. We establish the global dynamics by constructing a suitable Lyapunov functional. We also conduct some numerical experiments to illustrate bifurcation structures, indicating the occurrence of periodic oscillations for a specific range of values of a key parameter representing ADE.

The co-circulation of two respiratory infections with similar symptoms in a population can significantly overburden a healthcare system by slowing the testing and treatment. The persistent emergence of contagious variants of SARS-CoV-2, along with imperfect vaccines and their waning protections, have increased the likelihood of new COVID-19 outbreaks taking place during a typical flu season. Here, we developed a mathematical model for the co-circulation dynamics of COVID-19 and influenza, under different scenarios of influenza vaccine coverage, COVID-19 vaccine booster coverage and efficacy, and testing capacity. We investigated the required minimal and optimal coverage of COVID-19 booster (third) and fourth doses, in conjunction with the influenza vaccine, to avoid the coincidence of infection peaks for both diseases in a single season. We show that the testing delay brought on by the high number of influenza cases impacts the dynamics of influenza and COVID-19 transmission. The earlier the peak of the flu season and the greater the number of infections with flu-like symptoms, the greater the risk of flu transmission, which slows down COVID-19 testing, resulting in the delay of complete isolation of patients with COVID-19 who have not been isolated before the clinical presentation of symptoms and have been continuing their normal daily activities. Furthermore, our simulations stress the importance of vaccine uptake for preventing infection, severe illness, and hospitalization at the individual level and for disease outbreak control at the population level to avoid putting strain on already weak and overwhelmed healthcare systems. As such, ensuring optimal vaccine coverage for COVID-19 and influenza to reduce the burden of these infections is paramount. We showed that by keeping the influenza vaccine coverage about 35% and increasing the coverage of booster or fourth dose of COVID-19 not only reduces the infections with COVID-19 but also can delay its peak time. If the influenza vaccine coverage is increased to 55%, unexpectedly, it increases the peak size of influenza infections slightly, while it reduces the peak size of COVID-19 as well as significantly delays the peaks of both of these diseases. Mask-wearing coupled with a moderate increase in the vaccine uptake may mitigate COVID-19 and prevent an influenza outbreak.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.