Influence of perioperative oral nutritional supplementation on short-term efficacy of laparoscopic sleeve gastrectomy for obesity patients: a prospective study

Objective: To investigate the effect of perioperative oral nutritional supplementation on the short-term curative effect of obese patients after laparoscopic sleeve gastrectomy (LSG). Methods: A prospective research method was adopted. The clinical data of 218 obese patients who underwent LSG in Ningxia Medical University General Hospital from January 2018 to December 2021 were selected. The patients who received oral nutritional supplement therapy during the perioperative period were set as the experimental group, and those who received conventional treatment were set as the control group. Observation indicators: (1) Grouping of enrolled patients. (2) Postoperative and follow-up situation. (3) Nutrition-related indicators. (4) Diet compliance. (5) Status of weight loss-related indicators. Follow-up visits were conducted by telephone, We Chat and outpatient visits. The patients were followed up once 30 days after discharge, including albumin (Alb), hemoglobin (Hb), dietary compliance and weight loss-related indicators. The follow-up time will end in February 2022. The measurement data with normal distribution were expressed as x+or-s, and the comparison between groups was performed by independent sample t test. The measurement data is represented by M (range), and the comparison between groups is performed by Mann?Whitney U test. Enumeration data were expressed as absolute numbers or percentages, and the X2 test was used for comparison between groups. Repeated measures data were analyzed by repeated measures analysis of variance. The rank sum test was used to compare the rank data. Results (1) Grouping of the enrolled patients. Screened 218 eligible patients;42 males and 176 females;age (32+or-9) years;body mass index (BMI) (39+or-7) kg/m2. Among the 218 patients, there were 109 cases in the test group and 109 cases in the control group. Gender (male, female), age, BMI, preoperative Alb, and preoperative Hb of patients in the test group were 17 and 92 cases, (33+or-9) years old, (39+or-7) kg/m2, (40.6+or-4.8) g /L, (141.7+or-13.9) g/L;the above indicators in the control group were 25 and 84 cases, (31+or-8) years old, (39+or-8) kg/m2, (40.9+or-4.2) g/L, (142.9+or-9.7) g/L;there was no significant difference in the above (X2=1.89, t=-1.52, 0.51, 0.40, 0.71, P > 0.05). (2) Postoperative and follow-up situation. The first hospitalization time and first hospitalization expenses of the patients in the experimental group were (9.1+or-2.9) d and (3.6+or-0.5) ten thousand yuan respectively;the above indicators of the patients in the control group were (4.9+or-1.0) ten thousand yuan respectively;There were statistically significant differences in the above indicators between the two groups (t=5.58, 12.38, P < 0.05). Among the 218 patients, 119 were followed up, including 62 in the experimental group and 57 in the control group. The 119 patients were followed up for 31.0 (25.0-38.0) days. Among the 218 patients, 14 cases had postoperative complications and led to rehospitalization, including 2 cases in the experimental group, 1 case of nausea and vomiting and 1 case of intestinal obstruction;12 cases in the control group, 10 cases of nausea and vomiting, gastric fistula 2 cases;there was a statistically significant difference between the two groups in hospital readmission (X2=7.63, P < 0.05). The time interval between re-admission and first discharge of 14 patients was (22.0+or-6.7) days. (3) Nutrition-related indicators. The Alb and Hb levels of 62 patients in the experimental group who were followed up before operation, before the first discharge, and 1 month after operation were (40.4+or-5.5) g/L, (35.9+or-3.8) g/L, (45.4+or-2.9) g/L, respectively and (140.8+or-13.9) g/L, (130.5+or-16.9) g/L, (147.8+or-17.2) g/L;the above indicators of 57 patients in the control group were (41.2+or-3.9) g/L, (34.2 +or-3.9) g/L, (42.7+or-5.3) g/L and (143.0+or-9.7) g/L, (122.9+or-12.8) g/L, (139.0+or-11.4) g/L;There was a statistically significant difference between the Alb and Hb groups from preoperative to postoperative 1 mont

DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness (preprint)

Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the COVID-19 outbreak, the seasonal transmission pattern of RSV has been discontinued in 2020 and then shifted months ahead in 2021 in the northern hemisphere. It is critical to understand how COVID-19 impacts RSV and build predictive algorithms to forecast the timing and intensity of RSV reemergence in post-COVID-19 seasons. In this paper, we propose a deep coupled tensor factorization machine, dubbed as DeCom, for post COVID-19 RSV prediction. DeCom leverages tensor factorization and residual modeling. It enables us to learn the disrupted RSV transmission reliably under COVID-19 by taking both the regular seasonal RSV transmission pattern and the NPI into consideration. Experimental results on a real RSV dataset show that DeCom is more accurate than the state-of-the-art RSV prediction algorithms and achieves up to 46% lower root mean square error and 49% lower mean absolute error for country-level prediction compared to the baselines.

The Influence Mechanism and Measurement of Tourists' Authenticity Perception on the Sustainable Development of Rural Tourism - A Study Based on the 10 Most Popular Rural Tourism Destinations in China

Short-distance rural tourism has become a major form of tourism in China in recent years, as problems caused by urbanization emerge and because of the strict restrictions on the flow of people during the COVID-19 pandemic. This study takes the ten most popular rural tourism destinations in China from 2011 to 2021 as the research object. First, the grounded theory is used to construct the impact model of tourists' authenticity perception on the sustainable development of rural tourism. The results show that tourists' perception of rural tourism authenticity includes four dimensions, namely, visual perception, embodied perception, using perception, and interactive perception. With local attachment as the intermediary, authentic perception has a positive effect on the sustainable development of rural tourism, including economic sustainability, ecological sustainability, and cultural sustainability. In the early stage of tourism development, tourists mainly focus on visual authenticity. As tourists are deeply involved in rural tourism, they will pay more attention to interactive authenticity. Then, based on AHP, the measurement index was constructed and a questionnaire survey was conducted among ten villages to verify the effectiveness and universality of the model.

Extensive neutralization against SARS-CoV-2 variants elicited by Omicron-specific subunit vaccine as a heterologous booster

To overcome the increased risk of SARS-CoV-2 reinfection or post-vaccination infection caused by the Omicron variant, Omicron-specific vaccines were considered a potential strategy. We reported the increased magnitude and breadth of antibody response against VOCs elicited by post-vaccination Delta and Omicron infection, compared to WT infection without vaccination. Then, in mouse models, three doses of Omicron-RBD immunization elicited comparable neutralizing antibody (NAb) titers with three doses of WT-RBD immunization, but the neutralizing activity was not cross-active. By contrast, a heterologous Omicron-RBD booster following two doses of WT-RBD immunization increased the NAb titers against Omicron by 9 folds than the homologous WT-RBD booster. Moreover, it retains neutralization against both WT and current VOCs. Results suggest that Omicron-specific subunit booster shows its advantages in the immune protection from both WT and current VOCs and that SARS-CoV-2 vaccines including two or more virus lineages might improve the NAb response. Graphical

MTC: Multiresolution Tensor Completion from Partial and Coarse Observations (preprint)

Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data are often more complex due to: (i) Partial observation: Only a small subset (e.g., 5%) of tensor elements are available. (ii) Coarse observation: Some tensor modes only present coarse and aggregated patterns (e.g., monthly summary instead of daily reports). In this paper, we are given a subset of the tensor and some aggregated/coarse observations (along one or more modes) and seek to recover the original fine-granular tensor with low-rank factorization. We formulate a coupled tensor completion problem and propose an efficient Multi-resolution Tensor Completion model (MTC) to solve the problem. Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares. MTC ensures low computational and space complexity. We evaluate our model on two COVID-19 related spatio-temporal tensors. The experiments show that MTC could provide 65.20% and 75.79% percentage of fitness (PoF) in tensor completion with only 5% fine granular observations, which is 27.96% relative improvement over the best baseline. To evaluate the learned low-rank factors, we also design a tensor prediction task for daily and cumulative disease case predictions, where MTC achieves 50% in PoF and 30% relative improvements over the best baseline.

Communication Difficulties and Countermeasures of ＂Health Care＂ Short Government Affair Videos in Public Health Emergencies--Based on the Empirical Analysis of National 20 ''Health Care'' Government Affair TikTok

The outbreak of the COVID-19 epidemic in 2019 has made all sectors of society realize the importance of health communication again. The health commission at all levels throughout the country, which is responsible for the popularization of health science, news and information publishing work, what is the current status of health communication of short government affair videos in this public health emergency? The author takes the contents published by 20 ＂health care＂ government affair TikTok at central and provincial levels during the epidemic period as the research objects, and uses case analysis study the communication problem of ＂health care＂ short government affair videos at present. This paper finds that the current ＂health care＂ short government affairs video has weakened health communication function, and the content communication of government affair TikTok is difficult to meet the needs of the audience. In allusion to the problems above, ＂health care＂ government affair TikTok should clarify its position, can speak timely, communicate high-quality contents, and build internal and external matrixes, etc.

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization (preprint)

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations of a widely adopted epidemiological model. We use latent instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction.

A Survey of Psychological Responses During the Coronavirus Disease 2019 (COVID-19) Epidemic among Chinese Police Officers in Wuhu.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has presented serious threats to people's health and lives. Police officers are bravely fighting on the front lines of the epidemic. The main purpose of this study was to assess the prevalence and severity of psychological responses among police officers during the COVID-19 pandemic and find influencing factors in depression and anxiety. METHODS: A cross-sectional online questionnaire was administered to police officers in Wuhu through WeChat, and data were collected between March 10 and 26, 2020. A total of 3,561 questionnaires were received, of which 3,517 were considered valid. The questionnaires included demographic information and a psychological survey. The depression scale of the Patient Health QuestionnaireQ9) and Generalized Anxiety Disorder scale were utilized to assess depression and anxiety, respectively. RESULTS: The mean depression score of participants was 4.10±4.87 (0-27), and 12.17%had moderate-severe depression. The mean anxiety score of participants was 3.59±4.228 (0-21), and 8.79% had moderate-severe anxiety. Older and married police officers were at higher risk of anxiety. Those with a bachelor's degree or above, living near the city center, and taking sleeping pills were at greater risk of depression and anxiety. Auxiliary police had lower depression and anxiety scores. Depression scores were strongly correlated withanxiety scores (r=0.863, p<0.001). CONCLUSION: Our findings identify factors associated with higher levels of depression and anxiety that can be utilized to develop psychological interventions to improve the mental health of vulnerable populations during the COVID-19 pandemic.

STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence (preprint)

Objective: The COVID-19 pandemic has created many challenges that need immediate attention. Various epidemiological and deep learning models have been developed to predict the COVID-19 outbreak, but all have limitations that affect the accuracy and robustness of the predictions. Our method aims at addressing these limitations and making earlier and more accurate pandemic outbreak predictions by (1) using patients' EHR data from different counties and states that encode local disease status and medical resource utilization condition; (2) considering demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into deep learning models. Materials and Methods: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses an attention-based graph convolutional network to capture geographical and temporal trends and predict the number of cases for a fixed number of days into the future. We also designed a physical law-based loss term for enhancing long-term prediction. STAN was tested using both massive real-world patient data and open source COVID-19 statistics provided by Johns Hopkins university across all U.S. counties. Results: STAN outperforms epidemiological modeling methods such as SIR and SEIR and deep learning models on both long-term and short-term predictions, achieving up to 87% lower mean squared error compared to the best baseline prediction model. Conclusions: By using information from real-world patient data and geographical data, STAN can better capture the disease status and medical resource utilization information and thus provides more accurate pandemic modeling. With pandemic transmission law based regularization, STAN also achieves good long-term prediction performance.

COVID-19 Pandemic with Human Mobility Across Countries

This study develops a holistic view of the novel coronavirus (COVID-19) spread worldwide through a spatial-temporal model with network dynamics. By using a unique human mobility dataset containing 547 166 flights with a total capacity of 101 455 913 passengers from January 22 to April 24, 2020, we analyze the epidemic correlations across 22 countries in six continents and particularly the changes in such correlations before and after implementing the international travel restriction policies targeting different countries. Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high infection rates. Instead, they should develop a new holistic view of global human mobility to impose the international movement restriction. The study further highlights potential correlations between international human mobility and focal countries' epidemic situations in the global network of COVID-19 pandemic.