Sulfhydryl-Functionalized Amino Group Polymer Microcomposites toward Efficient Adsorption of Aqueous Cr(VI), As(III), Cd(II), and Pb(II).

The development of efficient adsorbents for heavy metal pollution, especially five toxic heavy metals, has attracted great research interest. Polymer-based adsorbents have aroused research value for their abundant functional groups and high porosity to the ability to capture metal ions. We designed a sulfhydryl-functionalized polymer microcomposite to take up Cr(VI), As(III), Cd(II), and Pb(II). The adsorption capacity achieved was 64.2 mg g-1 for Cr(VI), 44.9 mg g-1 for As(III), 35.5 mg g-1 for Cd(II), and 18.2 mg g-1 for Pb(II). Langmuir and Sips isotherm model is dominant for As(III), Cd(II), and Pb(II) adsorption. Pseudo-second-order kinetic models can better describe the adsorption behavior of Cr(VI), implying that chemisorption is accompanied by Cr(VI) adsorption. Cr(VI) simultaneous reduction to Cr(III) through the benzenoid amine oxidate pathway was the dominant mechanism, precipitation for Cd(II) adsorption was convinced, and chelation between As(III)/Pb(II) and─SH group and complexation between Pb(II) and C═O or benzene hydroxyl were a plausible mechanism for As(III) and Pb(II) adsorption.

Yellow pumped Pr3+-doped ZBLAN fiber laser at 1015 nm.

To date, lasing in the visible to near-infrared wavelengths has been studied for praseodymium-doped fluoride fibers with the upper energy level of 3P0. In this Letter, a fiber laser operating at 1015 nm has been realized for the first time, to the best of our knowledge, which confirms a new mechanism where 1D2 can be the upper energy level. A maximum output power of 241 mW, with a slope efficiency of 30%, was achieved by using a 150-cm-long active fiber pumped at a maximum pump power of 823 mW. Furthermore, the broad emission spectra of Pr3+-doped fibers in the near-infrared band have been exploited as new, to the best of our knowledge, spectral sources.

Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study.

BACKGROUND: Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. OBJECTIVE: This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. METHODS: We used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. RESULTS: Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). CONCLUSIONS: COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020.

BACKGROUND: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE: The aim of this study is to measure the public's behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic. METHODS: Natural language processing (NLP) algorithms were used to identify COVID-19-related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States. RESULTS: The final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%). CONCLUSIONS: There was a decline in COVID-19-related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue.

Basic research and clinical progress of sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE), a major cerebral complication of sepsis, occurs in 70% of patients admitted to the intensive care unit (ICU). This condition can cause serious impairment of consciousness and is associated with a high mortality rate. Thus far, several experimental screenings and radiological techniques (e.g., electroencephalography) have been used for the non-invasive assessment of the structure and function of the brain in patients with SAE. Nevertheless, the pathogenesis of SAE is complicated and remains unclear. In the present article, we reviewed the currently available literature on the epidemiology, clinical manifestations, pathology, diagnosis, and management of SAE. However, currently, there is no ideal pharmacological treatment for SAE. Treatment targeting mitochondrial dysfunction may be useful in the management of SAE.

NLR-A Simple Indicator of Inflammation for the Diagnosis of Left Ventricular Hypertrophy in Patients with Hypertension.

We aimed to investigate the relationship between neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), brain natriuretic peptide (BNP), and left ventricular hypertrophy (LVH) in hypertension. METHODS: This study included 386 patients with hypertension. Mann-Whitney U test and multivariate binary logistic regression analysis were used to investigate the relationship between NLR, CRP, BNP, and LVH in patients with hypertension, as well as compare the levels of NLR, CRP, and BNP in the four configurations. Receiver operator characteristic (ROC) curve was used to compare the diagnostic efficacy of NLR, CRP, and BNP on LVH. RESULTS: The NLR and CRP and BNP levels of the LVH group were significantly higher than those of the non-LVH group. In the multivariate logistic regression analysis, NLR as well as age, BMI, and SBP were associated with LVH. In addition, in patients with eccentric and concentric hypertrophy, the NLR and CRP and BNP levels were higher than those of the normal left ventricular geometry and concentric remodeling groups. The cutoff values of NLR, CRP, and BNP obtained by ROC curve were 2.185, 2.205, and 283.45, respectively, for the prediction of LVH. CONCLUSIONS: NLR is independently associated with LVH in patients with hypertension, and this is consistent with the diagnostic efficacy of CRP and BNP, which may be a simple and convenient indicator for judging LVH.