A highly sensitive ratiometric fluorescence probe for sensing and imaging sulfite in food samples and living cells.

In this work, a ratiometric and chromogenic fluorescent probe 1 was synthesized for the detection of SO32-. The probe 1 at PBS (10 mM, pH = 7.4) presented a marked emission band at 661 nm. Upon addition of SO32- ions, a highly emissive adduct with a marked fluorescence at 471 nm were obtained through a Michael addition. The probe 1 displayed a noticeable fluorescence ratiometric response with a large shift (190 nm) in emission wavelength. The probe can quantitatively detect SO32- with high specificity, fast response (within 130 s) as well as low detection limit (13 nM), and a large Stokes shift (139 nm). Fluorescence imaging of HeLa cells indicated that 1 could be used for monitoring the intrinsically generated intracellular SO32- in living cells by ratiometric fluorescence imaging. Furthermore, 1 could be application in real water and sugar samples with high sensitivity and good recoveries.

Randomized clinical trial on D2 lymphadenectomy versus D2 lymphadenectomy plus complete mesogastric excision in patients undergoing gastrectomy for cancer (DCGC01 study).

BACKGROUND: It is unknown whether D2 lymphadenectomy + complete mesogastric excision for gastric cancer improves survival compared with just D2 lymphadenectomy. METHODS: Between September 2014 and June 2018, patients with advanced gastric cancer were randomly assigned (1 : 1) to laparoscopic D2 lymphadenectomy or D2 lymphadenectomy + complete mesogastric excision gastrectomy. The modified intention-to-treat population was defined as patients who had pathologically confirmed gastric adenocarcinoma (pT1 N1-3 M0 and pT2-4 N0-3 M0). The primary endpoint was 3-year disease-free survival. Secondary endpoints were the recurrence pattern and overall survival. RESULTS: The median follow-up of patients in the D2 lymphadenectomy group (169 patients) and patients in the D2 lymphadenectomy +complete mesogastric excision group (169 patients) was 55 (interquartile range 37-60) months and 51 (interquartile range 40-60) months respectively. Recurrence occurred in 50 patients in the D2 lymphadenectomy group (29.6%) versus 33 patients in the D2 lymphadenectomy + complete mesogastric excision group (19.5%) (P = 0.032). The 3-year disease-free survival was 75.5% (95% c.i. 68.3% to 81.3%) in the D2 lymphadenectomy group versus 85.0% (95% c.i. 78.7% to 89.6%) in the D2 lymphadenectomy + complete mesogastric excision group (log rank P = 0.042). The HR for recurrence in the D2 lymphadenectomy + complete mesogastric excision group versus the D2 lymphadenectomy group was 0.64 (95% c.i. 0.41 to 0.99) by Cox regression (P = 0.045). The 3-year overall survival rate was 77.5% (95% c.i. 70.4% to 83.1%) in the D2 lymphadenectomy group versus 85.8% (95% c.i. 79.6% to 90.2%) in the D2 lymphadenectomy + complete mesogastric excision group (log rank P = 0.058). The HR for death in the D2 lymphadenectomy + complete mesogastric excision group versus the D2 lymphadenectomy group was 0.64 (95% c.i. 0.41 to 1.02) (P = 0.058). CONCLUSION: Compared with conventional D2 dissection, D2 lymphadenectomy + complete mesogastric excision is associated with better disease-free survival, but there is no statistically significant difference in overall survival. REGISTRATION NUMBER: NCT01978444 (http://www.clinicaltrials.gov).

Determination of fifteen illegal colorants in traditional Chinese medicines by two hydrophobic DES-based microextraction methods coupled with an HPLC-DAD.

The dyeing and adulteration of traditional Chinese medicines (TCMs) are continuously updated. Valuable analytical methods for the daily inspection of illegal colorant additives in TCMs and the preparations are in demand. Two deep eutectic solvent (DES)-based vortex-assisted liquid-liquid microextraction (VA-LLME) and ultrasonic-assisted solid-liquid microextraction (UA-SLME) were developed for the sample pretreatment of ten water-soluble colorants and five water-insoluble colorants, respectively, followed by an HPLC-DAD detection. Fifteen colorants were analyzed at four detection wavelengths within 40 min of gradient elution. The optimal DES of VA-LLME and UA-SLME were screened from 23 homemade DESs. The factors affecting the extraction efficiency of VA-LLME and UA-SLME were optimized systematically. Under the optimal conditions, ten water-soluble colorants analyzed by DES-based VA-LLME-HPLC-DAD showed good linearity (R ≥ 0.9995) within the optimal linear range. The LODs and LOQs were 0.2-1.0 µg g-1 and 0. 5-5.0 µg g-1, respectively. The recoveries of spiked samples were 80.2%-104.7 %, with RSDs ≤ 4.39 %. Five water-insoluble colorants of Sudan I‒IV and Sudan 7B analyzed by DES-based UA-SLME-HPLC-DAD showed good linearity (R ≥ 0.9995) within the optimal linear range. The LODs and LOQs were 0.8-8.0 µg g-1 and 4.0-40.0 µg g-1, respectively. The recoveries of spiked samples were 94.2%-103.1 %, with RSDs ≤ 4.81 %. The proposed DES-based VA-LLME-HPLC-DAD was successfully applied to analyze six water-soluble yellow colorants in Cuscutae Semen, salted Cuscutae Semen, and four water-soluble red colorants in Schisandrae Chinensis Fructus. The proposed DES-based UA-SLME-HPLC-DAD was successfully applied to analyze five water-insoluble red colorants in Dieda pills. The study provides analytical method options for routine tests of water-soluble, water-insoluble, or both water-soluble/-insoluble illegal colorant additives in herbal medical materials and preparations by the relevant proposed DES-based sample pretreatment method or a combination of the two proposed DES-based methods.

Changing epidemiology of COVID-19: potential future impact on vaccines and vaccination strategies.

INTRODUCTION: 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.

Viral infection dynamics with immune chemokines and CTL mobility modulated by the infected cell density.

We study a viral infection model incorporating both cell-to-cell infection and immune chemokines. Based on experimental results in the literature, we make a standing assumption that the cytotoxic T lymphocytes (CTL) will move toward the location with more infected cells, while the diffusion rate of CTL is a decreasing function of the density of infected cells. We first establish the global existence and ultimate boundedness of the solution via a priori energy estimates. We then define the basic reproduction number of viral infection R 0 and prove (by the uniform persistence theory, Lyapunov function technique and LaSalle invariance principle) that the infection-free steady state E 0 is globally asymptotically stable if R 0 < 1 . When R 0 > 1 , then E 0 becomes unstable, and another basic reproduction number of CTL response R 1 becomes the dynamic threshold in the sense that if R 1 < 1 , then the CTL-inactivated steady state E 1 is globally asymptotically stable; and if R 1 > 1 , then the immune response is uniform persistent and, under an additional technical condition the CTL-activated steady state E 2 is globally asymptotically stable. To establish the global stability results, we need to prove point dissipativity, obtain uniform persistence, construct suitable Lyapunov functions, and apply the LaSalle invariance principle.

A Survey of the Use of Modeling, Simulation, Visualization, and Mapping in Public Health Emergency Operations Centers during the COVID-19 Pandemic.

The COVID-19 pandemic has significantly changed life and work patterns and reshaped the healthcare industry and public health strategies. It posed considerable challenges to public health emergency operations centers (PHEOCs). In this period, digital technologies such as modeling, simulation, visualization, and mapping (MSVM) emerged as vital tools in these centers. Despite their perceived importance, the potential and adaptation of digital tools in PHEOCs remain underexplored. This study investigated the application of MSVM in the PHEOCs during the pandemic in Canada using a questionnaire survey. The results show that digital tools, particularly visualization and mapping, are frequently used in PHEOCs. However, critical gaps, including data management issues, technical and capacity issues, and limitations in the policy-making sphere, still hinder the effective use of these tools. Key areas identified in this study for future investigation include collaboration, interoperability, and various supports for information sharing and capacity building.

Photoswitchable Temperature Nanosensors Based on the Chemical Kinetics of Photochromic Naphthopyran for Live Cell Imaging.

Microscopic temperature imaging holds significant importance in various fields, particularly in the development of nanomaterials for photothermal therapy (PTT). In this study, we present an analytical method to probe cellular temperature based on chemical kinetics and additional luminescence quenching by photoswitchable naphthopyrans. Taking advantage of the rapid ring-closing reaction of naphthopyran, temperature sensing was realized with a linear relationship between the logarithmic decay time constant (ln τ) and the reciprocal temperature (T-1). To create luminescent temperature nanosensors, we harnessed the ability of ring-opened naphthopyran to quench the luminescence of a semiconducting polymer, resulting in a diverse array of probes. Structural modifications on the naphthopyran also provided a way to fine-tune the sensitivity and response window of the nanosensors. The method allowed cellular temperature imaging on a cost-effective fluorescence microscopic setup. As an application, the temperature increase induced by gold nanorods (AuNRs) in cell lysosomes was successfully monitored, laying the foundation for a new class of photoswitchable nanosensors with promising biological applications.

Epidemiological and Clinical Characteristics of Mpox in Cisgender and Transgender Women and Non-Binary Individuals Assigned to the Female Sex at Birth: A Comprehensive, Critical Global Perspective.

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.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

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.

Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup.

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.

ID2 Promotes Lineage Transition of Prostate Cancer through FGFR and JAK-STAT Signaling.

The use of androgen receptor pathway inhibitors (ARPIs) has led to an increase in the proportion of AR-null prostate cancer, including neuroendocrine prostate cancer (NEPC) and double-negative prostate cancer (DNPC), but the mechanism underlying this lineage transition has not been elucidated. We found that ID2 expression was increased in AR-null prostate cancer. In vitro and in vivo studies confirmed that ID2 promotes PCa malignancy and can confer resistance to enzalutamide in PCa cells. We generated an ID2 UP50 signature, which is capable of determining resistance to enzalutamide and is valuable for predicting patient prognosis. Functional experiments showed that ID2 could activate stemness-associated JAK/STAT and FGFR signaling while inhibiting the AR signaling pathway. Our study indicates a potentially strong association between ID2 and the acquisition of a stem-like phenotype in adenocarcinoma cells, leading to resistance to androgen deprivation therapy (ADT) and next-generation ARPIs in prostate cancer.

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer.

AIM: Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS: A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n â€‹= â€‹159) and a validation set (n â€‹= â€‹40). The total incidence of TDs was 37.2 â€‹% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI â€‹+ â€‹ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS: The Rad-score (T2WI â€‹+ â€‹ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI â€‹+ â€‹ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 â€‹% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 â€‹%, 92.0 â€‹%, 83.3 â€‹%, and 82.1 â€‹%, respectively. CONCLUSION: The radiomics nomogram based on Rad-score (T2WI â€‹+ â€‹ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Clinical applications and utility of ctDNA in cervical cancer and its precursor lesions: from screening to predictive biomarker.

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.

Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development.

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.

COVID-19 in Systemic Lupus Erythematosus patients treated with belimumab: a retrospective clinical study.

BACKGROUND: Routine use of immunosuppressive agents in systemic lupus erythematosus (SLE) patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) potentially increases the risk of adverse outcomes. belimumab, a monoclonal antibody for the treatment of SLE, remains untested for its specific impact on coronavirus disease 2019 (COVID-19) symptoms in these patients. Here, this research investigated the effect of belimumab on COVID-19 symptoms in SLE patients infected with SARS-CoV-2. METHODS: This study enrolled SLE patients who underwent treatment with belimumab. After thorough screening based on the inclusion and exclusion criteria, data pertaining to COVID-19 for both the participants and their cohabitants were obtained through telephone follow-up. The potential impact of belimumab on COVID-19 was evaluated by comparing COVID-19 symptoms and medication use across various groups to investigate the association between belimumab treatment and COVID-19 in SLE. RESULTS: This study involved 123 SLE patients, of whom 89.4% tested positive for SARS-CoV-2. Among cohabitants of SLE patients, the SARS-CoV-2 positive rate was 87.2% (p = 0.543). Patients treated with belimumab exhibited a lower incidence of multiple COVID-19 symptoms than their cohabitating counterparts (p < 0.001). This protective effect was found to be partially related to the time of last belimumab administration. Among those with COVID-19, 30 patients opted to discontinue their anti-SLE drugs, and among them, 53% chose to discontinue belimumab. Discontinuing drugs did not increase the risk of hospitalization due to SARS-CoV-2 infection. CONCLUSION: This study concluded that treatment with belimumab did not increase susceptibility to COVID-19 and beneficially alleviated the symptoms of COVID-19.

m6A regulator-mediated methylation modification patterns and tumor microenvironment immune infiltration with prognostic analysis in esophageal cancer.

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.

Twitter-based gender recognition using transformers.

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.

Modeling and analyzing an opinion network dynamics considering the environmental factor.

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.

COVID-19 anxiety and related factors amid adjusted epidemic prevention policies: a cross-sectional study on patients with late-life depression in China.

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.