Assessing Age-Specific Vaccination Strategies and Post-vaccination Reopening Policies for COVID-19 Control Using SEIR Modeling Approach

As the availability of COVID-19 vaccines, it is badly needed to develop vaccination guidelines to prioritize the vaccination delivery in order to effectively stop COVID-19 epidemic and minimize the loss. We evaluated the effect of age-specific vaccination strategies on the number of infections and deaths using an SEIR model, considering the age structure and social contact patterns for different age groups for each of different countries. In general, the vaccination priority should be given to those younger people who are active in social contacts to minimize the number of infections, while the vaccination priority should be given to the elderly to minimize the number of deaths. But this principle may not always apply when the interaction of age structure and age-specific social contact patterns is complicated. Partially reopening schools, workplaces or households, the vaccination priority may need to be adjusted accordingly. Prematurely reopening social contacts could initiate a new outbreak or even a new pandemic out of control if the vaccination rate and the detection rate are not high enough. Our result suggests that it requires at least nine months of vaccination (with a high vaccination rate \u3e 0.1%) for Italy and India before fully reopening social contacts in order to avoid a new pandemic

Controlling Multiple COVID-19 Epidemic Waves: An Insight from a Multi-scale Model Linking the Behaviour Change Dynamics to the Disease Transmission Dynamics

COVID-19 epidemics exhibited multiple waves regionally and globally since 2020. It is important to understand the insight and underlying mechanisms of the multiple waves of COVID-19 epidemics in order to design more efficient non-pharmaceutical interventions (NPIs) and vaccination strategies to prevent future waves. We propose a multi-scale model by linking the behaviour change dynamics to the disease transmission dynamics to investigate the effect of behaviour dynamics on COVID-19 epidemics using game theory. The proposed multi-scale models are calibrated and key parameters related to disease transmission dynamics and behavioural dynamics with/without vaccination are estimated based on COVID-19 epidemic data (daily reported cases and cumulative deaths) and vaccination data. Our modeling results demonstrate that the feedback loop between behaviour changes and COVID-19 transmission dynamics plays an essential role in inducing multiple epidemic waves. We find that the long period of high-prevalence or persistent deterioration of COVID-19 epidemics could drive almost all of the population to change their behaviours and maintain the altered behaviours. However, the effect of behaviour changes fades out gradually along the progress of epidemics. This suggests that it is essential to have not only persistent, but also effective behaviour changes in order to avoid subsequent epidemic waves. In addition, our model also suggests the importance to maintain the effective altered behaviours during the initial stage of vaccination, and to counteract relaxation of NPIs, it requires quick and massive vaccination to avoid future epidemic waves

MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.Comment: 15 page

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure

2-(3,3,4,4-Tetra­fluoro­pyrrolidin-1-yl)aniline

In the title fluorinated pyrrolidine derivative, C10H10F4N2, the dihedral angle between the best planes of the benzene and pyrrolidine rings is 62.6 (1)°. The crystal packing features inter­molecular N—H⋯F hydrogen bonds

Biological Correlates of the Effects of Auricular Point Acupressure on Pain

BACKGROUND: To identify candidate inflammatory biomarkers for the underlying mechanism of auricular point acupressure (APA) on pain relief and examine the correlations among pain intensity, interference, and inflammatory biomarkers. DESIGN: This is a secondary data analysis. METHODS: Data on inflammatory biomarkers collected via blood samples and patient self-reported pain intensity and interference from three pilot studies (chronic low back pain, n = 61; arthralgia related to aromatase inhibitors, n = 20; and chemotherapy-induced neuropathy, n = 15) were integrated and analyzed. This paper reports the results based on within-subject treatment effects (change in scores from pre- to post-APA intervention) for each study group (chronic low back pain, cancer pain), between-group differences (changes in scores from pre- to post-intervention between targeted-point APA [T-APA] and non-targeted-point APA [NT-APA]), and correlations among pain intensity, interference, and biomarkers. RESULTS: Within-group analysis (the change score from pre- to post-APA) revealed statistically significant changes in three biomarkers: TNF-α (cancer pain in the APA group, p = .03), β-endorphin (back pain in the APA group, p = .04), and IL-2 (back pain in the NT-APA group, p = .002). Based on between-group analysis in patients with chronic low back pain (T-APA vs NT-APA), IL-4 had the largest effect size (0.35), followed by TNF-α (0.29). A strong positive monotonic relationship between IL-1β and IL-2 was detected. CONCLUSIONS: The current findings further support the potential role of inflammatory biomarkers in the analgesic effects of APA. More work is needed to gain a comprehensive understanding of the underlying mechanisms of APA on chronic pain. Because it is simple, inexpensive, and has no negative side effects, APA can be widely disseminated as an alternative to opioids

Biological Correlates of the Effects of Auricular Point Acupressure on Pain

BACKGROUND: To identify candidate inflammatory biomarkers for the underlying mechanism of auricular point acupressure (APA) on pain relief and examine the correlations among pain intensity, interference, and inflammatory biomarkers. DESIGN: This is a secondary data analysis. METHODS: Data on inflammatory biomarkers collected via blood samples and patient self-reported pain intensity and interference from three pilot studies (chronic low back pain, n = 61; arthralgia related to aromatase inhibitors, n = 20; and chemotherapy-induced neuropathy, n = 15) were integrated and analyzed. This paper reports the results based on within-subject treatment effects (change in scores from pre- to post-APA intervention) for each study group (chronic low back pain, cancer pain), between-group differences (changes in scores from pre- to post-intervention between targeted-point APA [T-APA] and non-targeted-point APA [NT-APA]), and correlations among pain intensity, interference, and biomarkers. RESULTS: Within-group analysis (the change score from pre- to post-APA) revealed statistically significant changes in three biomarkers: TNF-α (cancer pain in the APA group, p = .03), β-endorphin (back pain in the APA group, p = .04), and IL-2 (back pain in the NT-APA group, p = .002). Based on between-group analysis in patients with chronic low back pain (T-APA vs NT-APA), IL-4 had the largest effect size (0.35), followed by TNF-α (0.29). A strong positive monotonic relationship between IL-1β and IL-2 was detected. CONCLUSIONS: The current findings further support the potential role of inflammatory biomarkers in the analgesic effects of APA. More work is needed to gain a comprehensive understanding of the underlying mechanisms of APA on chronic pain. Because it is simple, inexpensive, and has no negative side effects, APA can be widely disseminated as an alternative to opioids

Performance study of the JadePix-3 telescope from a beam test

We present the results of a beam test conducted on a telescope using the JadePix-3 pixel sensor, developed with TowerJazz 180 nm CMOS imaging technology. The telescope is composed of five planes, each equipped with a JadePix-3 sensor with pitches of 26 um x 16 um and 23.11 um x 16 um. In addition, it features an FPGA-based synchronous readout system. The telescope underwent testing using an electron beam with energy ranging from 4 to 6 GeV. At an electron energy of 5.4 GeV, the telescope demonstrated superior spatial resolutions of 2.6 and 2.3 um in two dimensions. By designating the central plane as the device under test, we evaluated the JadePix-3 sensor's spatial resolutions as 5.2 and 4.6 um in two dimensions, achieving a detection efficiency of over 99%

Pickering emulsion-enhanced interfacial biocatalysis: tailored alginate microparticles act as particulate emulsifier and enzyme carrier

A robust Pickering emulsion stabilized by lipase-immobilized alginate gel microparticles with a coating of silanized titania nanoparticles is developed for biphasic biocatalysis. The good recyclability and high stability of the proposed interfacial catalysis system have been verified, retaining about 90% of relative enzyme activity in 10 catalytic cycles with operation for 240 h. Meanwhile the Pickering emulsions remain stable during a storage time of one year. The green system can be widely applied to construct powerful platforms for enzyme or microorganism-driven interfacial catalysis

Case Report: Primary cardiac synovial sarcoma invading the tricuspid valve in a pregnant woman

Primary cardiac synovial sarcoma (PCSS) is a rare and highly aggressive tumor with a significant mortality rate. Treatment guidelines have not been defined given the relative rarity of the condition, especially for pregnant women. Described herein is a 36-year-old pregnant woman at 29 weeks with gestation who was hospitalized due to chest tightness and nausea, and echocardiography found a mass involved in the right heart and the tricuspid valve. She had to undergo cardiac surgery because the mass almost blocked the opening of the tricuspid valve. She underwent a radical resection of the masses and tricuspid valve, followed by replacement of the tricuspid valve with a mechanical valve. She successfully delivered a healthy baby boy. The diagnosis of synovial sarcoma is confirmed by positive results indicating rearrangement of the SYT gene. The patient survived throughout the 30-month follow-up period. There are no reported cases of pregnant women diagnosed with cardiac synovial sarcoma and have undergone cardiac surgery and cesarean section. Our treatment plan not only maximizes patient survival but also ensures fetal survival. This situation is rare and needs documentation