Improvement in hemodynamics of amlodipine besylate combined with metoprolol in patients with hypertension complicated by heart failure.

To observe the effect of amlodipine besylate combined with metoprolol in treating hypertension and heart failure. Total number of patients with hypertension combined with HF admitted to our hospital was One hundred and fifty from May 2017 to May 2022 selected for the study and they were distributed into single drug group and combination group by the method of random number table, with the total number of 75 cases in every group. Metoprolol treatment was given to the single drug group and metoprolol combined with amlodipine besylate treatment was given to the combination group. Both groups' scientific outcomes were compared, including their ventricular function, inflammatory factors, hemodynamics and liver and kidney function. Adverse treatment-related side events for patients were also tallied. Compared to the single drug group, the combination group's overall treatment effectiveness was higher (P<0.05). The combined group had better ventricular function, improved hemodynamics and lower levels of inflammatory factors (P<0.05). The liver, kidney function and adverse effects outcomes were the same in both groups (P>0.05). Amlodipine besylate combined with metoprolol has a better clinical effect in treating hypertension combined with heart failure, which can more effectively improve patients' cardiac function, inflammation and hemodynamics.

Investigation and analysis of occupational physical injuries among healthcare staffs during allopatric medical aid for the fight against COVID-19.

BACKGROUND: Occupational health impairment of medical personnel manifested as a prominent problem in COVID-19. The aim of this study is to investigate the occupational physical injuries of front-line medical staffs in Hubei province during the fight against COVID-19. MATERIAL AND METHODS: questionnaire survey was conducted among 476 medical staffs from 3 regions of Hubei Province, including general characteristics and the physical discomfort/damage suffered in the isolation wards during working hours. RESULTS: A total of 457 valid questionnaires were collected. The common physical discomfort/damage included skin injuries (22.76%), conjunctivitis (15.10%), falls (9.19%), intolerant unwell symptoms (8.53%) and sharp injuries (6.13%). Logistic regression analysis showed that: lack of protective work experience (OR = 2.049, 95% CI: 1.071-3.921), continuous working for 4 h (OR = 3.771, 95% CI: 1.858-7.654), and working >4 h (OR = 7.076, 95% CI: 3.197-15.663) were high-risk factors for skin injuries. Working continuously for 4 h (OR = 3.248, 95% CI: 1.484-7.110) and working >4 h (OR = 3.096, 95% CI: 1.232-7.772) were high-risk factors for conjunctivitis. Lack of protective work experience was a high risk factor for falls (OR = 5.508, 95% CI: 1.299-23.354). The high risk factors for intolerant unwell symptoms were continuous working for 4 h (OR = 5.372, 95% CI: 1.239-23.301) and working >4 h (OR = 8.608, 95% CI: 1.843-40.217). Working in a COVID-19 critical care unit (OR = 3.249, 95% CI: 1.344-7.854) and implementation of nursing (OR = 9.766, 95% CI: 1.307-72.984) were high risk factors for sharp injuries. CONCLUSIONS: Occupational physical injuries are universal in the COVID-19 ward. Those who take up nursing, work in a critical care ward, with no experience in an isolation ward for infectious diseases, and work continuously for ≥4 h on the same day should get more attention. Med Pr. 2022;73(3):209-18.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo.

BACKGROUND: The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience's attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited. OBJECTIVE: This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics. METHODS: A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide. RESULTS: In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ21=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ21=28.9; P<.001), false representation (χ21=144.4; P<.001), weak and pathetic (χ21=20.4; P<.001), glorified and normalized (χ21=177.6; P<.001), and immoral (χ21=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85. CONCLUSIONS: The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.

Mind the gap: Exploring differences in suicide literacy between cybersuicide and offline suicide.

Introduction: The highly public nature of cybersuicide contradicts long-held beliefs of offline suicide, which may cause differences in the way people perceive and respond to both of them. However, knowledge of whether and how suicide literacy differs between cybersuicide and offline suicide is limited. Methods: By analyzing social media data, this paper focused on livestreamed suicide and aimed to compare suicide literacy between cybersuicide and offline suicide on three aspects, including false knowledge structure, extent of association with stigma, and linguistic expression pattern. 7,236 Sina Weibo posts with relevant keywords were downloaded and analyzed. First, a content analysis was performed by human coders to determine whether each post reflected suicide-related false knowledge and stigma. Second, a text analysis was conducted using the Simplified Chinese version of LIWC software to automatically extract psycholinguistic features from each post. Third, based on selected features, classification models were developed using machine learning techniques to differentiate false knowledge of cybersuicide from that of offline suicide. Results: Results showed that, first, cybersuicide-related posts generally reflected more false knowledge than offline suicide-related posts ( χ 1 2 = 255.13, p < 0.001). Significant differences were also observed in seven false knowledge types. Second, among posts reflecting false knowledge, cybersuicide-related posts generally carried more stigma than offline suicide-related posts ( χ 1 2 = 116.77, p < 0.001). Significant differences were also observed in three false knowledge types. Third, among established classification models, the highest F1 value reached 0.70. Discussion: The findings provide evidence of differences in suicide literacy between cybersuicide and offline suicide, and indicate the need for public awareness campaigns that specifically target cybersuicide.

The impact of family violence incidents on personality changes: An examination of social media users' messages in China.

Changes in personality tend to be intertwined with life events (e.g., family violence [FV]). This study aimed to examine the personality changes before and after an FV incident using Weibo data. Samples were selected from 1.16 million Weibo users in China who had posted their own FV experience as victims. We used Linguistic Inquiry and Word Count (LIWC) to extract the linguistic features of these unstructured texts as the scores of participants' personality. We built prediction models to measure and compare personality differences between the victim group and control group in Sample 1; and personality changes between the victim group and control group before and after an FV incident in Sample 2. Results showed that the victims' neuroticism increased and conscientiousness decreased after experiencing FV. At the same time, their agreeableness and openness levels were lower than those of the control group. Implications and limitations are also discussed.

Using Social Media to Explore the Consequences of Domestic Violence on Mental Health.

A great deal of research has focused on the negative consequences of domestic violence (DV) on mental health. However, current studies cannot provide direct and reliable evidence on the impacts of DV on mental health in a short term as it is not feasible to measure mental health shortly before and after an unpredictable event like DV. This study aims to explore the short-term outcomes of DV on individuals' mental health. We collected a sample of 232 victims (77% female) and 232 nonvictims (gender and location matched with 232 victims) on Sina Weibo. In both the victim and nonvictim groups, we measured their mental health status during the 4 weeks before the first DV incident and during the 4 weeks after the DV incident. We used our proposed Online Ecological Recognition (OER) system, which is based on several predictive models to identify individuals' mental health statuses. Mental health statuses were measured based on individuals' Weibo profiles and messages, which included "Depression," "Suicide Probability," and "Satisfaction With Life." The results showed that mental health in the victim group was impacted by DV while individuals in the nonvictim group were not. Furthermore, the victim group demonstrated an increase in depression symptoms, higher suicide risks, and decreased life satisfaction after their DV experience. In addition, the effect of DV on individuals' mental health could appear in the conditions of child abuse, intimate partner violence, and exposure to DV. These findings inform that DV significantly impacts individuals' mental health over the short term, as in 4 weeks. Our proposed new data collection and analyses approach, OER, has implications for employing "big data" from social networks to identify individuals' mental health.

A Comparison of the Psycholinguistic Styles of Schizophrenia-Related Stigma and Depression-Related Stigma on Social Media: Content Analysis.

BACKGROUND: Stigma related to schizophrenia is considered to be the primary focus of antistigma campaigns. Accurate and efficient detection of stigma toward schizophrenia in mass media is essential for the development of targeted antistigma interventions at the population level. OBJECTIVE: The purpose of this study was to examine the psycholinguistic characteristics of schizophrenia-related stigma on social media (ie, Sina Weibo, a Chinese microblogging website), and then to explore whether schizophrenia-related stigma can be distinguished from stigma toward other mental illnesses (ie, depression-related stigma) in terms of psycholinguistic style. METHODS: A total of 19,224 schizophrenia- and 15,879 depression-related Weibo posts were collected and analyzed. First, a human-based content analysis was performed on collected posts to determine whether they reflected stigma or not. Second, by using Linguistic Inquiry and Word Count software (Simplified Chinese version), a number of psycholinguistic features were automatically extracted from each post. Third, based on selected key features, four groups of classification models were established for different purposes: (a) differentiating schizophrenia-related stigma from nonstigma, (b) differentiating a certain subcategory of schizophrenia-related stigma from other subcategories, (c) differentiating schizophrenia-related stigma from depression-related stigma, and (d) differentiating a certain subcategory of schizophrenia-related stigma from the corresponding subcategory of depression-related stigma. RESULTS: In total, 26.22% of schizophrenia-related posts were labeled as stigmatizing posts. The proportion of posts indicating depression-related stigma was significantly lower than that indicating schizophrenia-related stigma (χ21=2484.64, P<.001). The classification performance of the models in the four groups ranged from .71 to .92 (F measure). CONCLUSIONS: The findings of this study have implications for the detection and reduction of stigma toward schizophrenia on social media.

Preparation of phosphatidylcholine nanovesicles containing bacteriocin CAMT2 and their anti-listerial activity.

A novel bacteriocin CAMT2, produced by Bacillus amyloliquefaciens ZJHD3-06, has potential as a natural biopreservative for the control of food-borne spoilage and pathogenic bacteria. To avoid interaction of CAMT2 with components of food that may adversely impact its antibacterial activity, CAMT2 was encapsulated into nanovesicles prepared from soybean phosphatidylcholine. The encapsulation of CAMT2 exhibited a limited impact on functional structure and crystallinity of bacteriocin CAMT2, but a high anti-listerial activity in agar, and increase its stability in food at refrigeration temperature (4 °C). The results also showed that both encapsulated and free CAMT2 had good anti-listerial effect in skim milk at refrigeration temperature. However, encapsulated CAMT2 performed better than free CAMT2 against Listeria in whole milk. These results showed that nano-encapsulation is an effective method of protecting bacteriocin from fat in milk and retaining its antimicrobial efficacy.

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media.

Live-stream suicide has become an emerging public health problem in many countries. Regular users are often the first to witness and respond to such suicides, emphasizing their impact on the success of crisis intervention. In order to reduce the likelihood of suicide deaths, this paper aims to use psycholinguistic analysis methods to facilitate automatic detection of negative expressions in responses to live-stream suicides on social media. In this paper, a total of 7212 comments posted on suicide-related messages were collected and analyzed. First, a content analysis was performed to investigate the nature of each comment (negative or not). Second, the simplified Chinese version of the LIWC software was used to extract 75 psycholinguistic features from each comment. Third, based on 19 selected key features, four classification models were established to differentiate between comments with and without negative expressions. Results showed that 19.55% of 7212 comments were recognized as "making negative responses". Among the four classification models, the highest values of Precision, Recall, F-Measure, and Screening Efficacy reached 69.8%, 85.9%, 72.9%, and 47.1%, respectively. This paper confirms the need for campaigns to reduce negative responses to live-stream suicides and support the use of psycholinguistic analysis methods to improve suicide prevention efforts.

Coherent phase transfer via fiber using heterodyne optical phase locking as optical amplification.

We demonstrate a coherent phase transfer via a 224 km cascaded fiber link comprising two 112 km links stabilized by two phase-locking loops, respectively. The optical signal is regenerated employing heterodyne optical phase locking (HOPL) after the first 112 km transfer. With a gain of more than 50 dB, the HOPL is capable of tracking the frequency of the incoming carrier with a fluctuation of 0.48 mHz and preserving the instability of the incoming laser to 6×10-20 at 1000 s. The phase noise cancellation of each span is investigated, and the out-loop transfer instability of the 224 km link reaches 7.7×10-19 at 10,000 s. The relation between the transfer instability of each span and that of the whole link is also deduced in the paper, in agreement with experimental results of the 224 km link.

Detecting depression stigma on social media: A linguistic analysis.

BACKGROUND: Efficient detection of depression stigma in mass media is important for designing effective stigma reduction strategies. Using linguistic analysis methods, this paper aims to build computational models for detecting stigma expressions in Chinese social media posts (Sina Weibo). METHODS: A total of 15,879 Weibo posts with keywords were collected and analyzed. First, a content analysis was conducted on all 15,879 posts to determine whether each of them reflected depression stigma or not. Second, using four algorithms (Simple Logistic Regression, Multilayer Perceptron Neural Networks, Support Vector Machine, and Random Forest), two groups of classification models were built based on selected linguistic features; one for differentiating between posts with and without depression stigma, and one for differentiating among posts with three specific types of depression stigma. RESULTS: First, 967 of 15,879 posts (6.09%) indicated depression stigma. 39.30%, 15.82%, and 14.99% of them endorsed the stigmatizing view that "People with depression are unpredictable", "Depression is a sign of personal weakness", and "Depression is not a real medical illness", respectively. Second, the highest F-Measure value for differentiating between stigma and non-stigma reached 75.2%. The highest F-Measure value for differentiating among three specific types of stigma reached 86.2%. LIMITATIONS: Due to the limited and imbalanced dataset of Chinese Weibo posts, the findings of this study might have limited generalizability. CONCLUSIONS: This paper confirms that incorporating linguistic analysis methods into online detection of stigma can be beneficial to improve the performance of stigma reduction programs.

An analysis of stigma and suicide literacy in responses to suicides broadcast on social media.

INTRODUCTION: Broadcasting a suicide attempt on social media has become a public concern in China. Stigmatizing attitudes around such broadcast can limit help-seeking and increase the likelihood of death. To reduce stigmatizing attitudes, this paper aims to detect stigma expressions in social media posts through language use patterns and then identify suicide literacy in responses to such broadcast. METHODS: Firstly, to examine linguistic patterns of stigma expressions, 6632 Weibo posts with keywords were collected and analyzed. Using 102 linguistic features, 2 classification models were built: one for differentiating between stigmatizing and nonstigmatizing attitudes, and one for differentiating between specific types of stigmatizing attitudes. Secondly, to identify the levels of suicide literacy, a content analysis was conducted on 4969 Weibo posts related to social media suicide. RESULTS: Firstly, the model accuracy ranged from 66.15% to 72.79%. Secondly, a total of 11.67% of the Weibo posts (n = 580) contained misinformation about suicide. In the category of knowledge of signs, 27.93% and 18.10% of posts endorsed the stigmatizing views that "suicide happens without warning" and "people who want to attempt suicide cannot change their mind quickly," both of which were related to a stigmatizing belief that a suicide attempt on social media is not genuine. In the category of knowledge of treatments, 35.17% of posts endorsed the stigmatizing view that "people who have thoughts about suicide should not tell others about it." DISCUSSION: This paper presents an opportunity for the dissemination of targeted online campaigns to increase mental health literacy and help-seeking.

Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services.

The increasing need of automated analyzing web texts especially the short texts on Social Network Services (SNS) brings new demands of computerized text analysis instruments. The psychometric properties are the basis of the extensive use of these instruments such as the Linguistic Inquiry and Word Count (LIWC). For this study, Sina Weibo statuses were analyzed via rater coding and Simplified Chinese version of LIWC (SCLIWC), in order to evaluate the validity of SCLIWC in detecting psychological expressions in Weibo statuses (n = 60) and in identifying the psychological meaning of a single Weibo status (n = 11). Significant correlations between human ratings and SCLIWC scores and the high sensitivities of capturing single statuses with certain expressions identified by raters, proved the validity of SCLIWC in detecting psychological expressions. The results also suggested that, the efficiency of SCLIWC in detecting psychological expressions of SNS short texts could be higher if using status count scoring method, rather than the word count method as the common usage of LIWC. However, SCLIWC may not perform well in identifying the psychological meaning of a single piece of SNS short text because of its over-identification of target expressions. This study provided primary evidence of validity of SCLIWC, as well as the proper way of using it efficiently on SNS short texts.