Clinical effects of robot-assisted esophageal hiatal hernia repair and laparoscopic esophageal hiatal hernia repair: a retrospective comparative study / 中华外科杂志

Objective: To analyze the short-term clinical effects of robot-assisted and laparoscopic repair of the hiatal hernia. Methods: The clinical data of 56 patients underwent minimally invasive hiatal hernia repair from January 2021 to January 2022 in the Department of Minimally Invasive Surgery, Hernias and Abdominal Wall Surgery, People's Hospital of Xinjiang Uygur Autonomous Region were retrospectively analyzed. There were 32 males and 24 females, aging (59.7±10.7) years (range: 28 to 75 years). All patients were divided into laparoscopy group (n=27) and robot group (n=29) according to surgical procedures. Perioperative conditions, hospital stay, and improvement in symptoms before and after surgery were compared between the two groups by the t test, Wilcoxon rank-sum test and χ2 test. Results: All surgical procedures were successfully completed, without conversion to laparotomy or change in operation mode. There were no serious complications related to the operation. The intraoperative blood loss of the robot group was less than that of the laparoscopic group (M (IQR)): (20 (110) ml vs. 40 (80) ml, Z=-4.098, P<0.01). The operation time ((111.7±33.6) minutes vs. (120.4±35.0) minutes, t=-0.943, P=0.350) and hospitalization time ((3.9±1.4) days vs. (4.7±1.9) days, t=-1.980, P=0.053) of the robot group and the laparoscopic group were similar. Follow-up for 12 months after the operation showed no postoperative complications and recurrence. The score of the health-related quality of life questionnaire for gastroesophageal reflux disease in the robot group decreased from 10.8±2.8 before the operation to 6.5±0.6 after the operation, and that in the laparoscopic group decreased from 10.6±2.1 before the operation to 6.3±0.6 after the operation. There was no difference in the influence of different surgical methods on the change in score (t=0.030,P=0.976). Conclusion: Compared with laparoscopic repair of the hiatal hernia, robot-assisted hiatal hernia repair has the advantages of less bleeding, rapid postoperative recovery and good short-term effect.

mRNA-1273 vaccination protects against SARS-CoV-2 elicited lung inflammation in non-human primates

Vaccine-elicited SARS-CoV-2 antibody responses are an established correlate of protection against viral infection in humans and non-human primates. However, it is less clear that vaccine-induced immunity is able to limit infection-elicited inflammation in the lower respiratory tract. To assess this, we collected bronchoalveolar lavage fluid samples post-SARS-CoV-2 strain USA-WA1/2020 challenge from rhesus macaques vaccinated with mRNA-1273 in a dose-reduction study. Single-cell transcriptomic profiling revealed a broad cellular landscape 48 hours post-challenge with distinct inflammatory signatures that correlated with viral RNA burden in the lower respiratory tract. These inflammatory signatures included phagocyte-restricted expression of chemokines such as CXCL10 (IP10) and CCL3 (MIP-1A) and the broad expression of interferon-induced genes such as MX1, ISG15, and IFIT1. Induction of these inflammatory profiles was suppressed by prior mRNA-1273 vaccination in a dose-dependent manner, and negatively correlated with pre-challenge serum and lung antibody titers against SARS-CoV-2 spike. These observations were replicated and validated in a second independent macaque challenge study using the B.1.351/beta-variant of SARS-CoV-2. These data support a model wherein vaccine-elicited antibody responses restrict viral replication following SARS-CoV-2 exposure, including limiting viral dissemination to the lower respiratory tract and infection-mediated inflammation and pathogenesis. One Sentence SummarySingle cell RNA sequencing analysis demonstrates that mRNA-1273 vaccination limits the development of lower respiratory tract inflammation in SARS-CoV-2 challenged rhesus macaques

National and subnational short-term forecasting of COVID-19 in Germany and Poland, early 2021

BackgroundDuring the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. MethodsWe evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess forecast calibration. The presented work is part of a pre-registered evaluation study and covers the period from January through April 2021. ResultsWe find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods (i.e., combinations of different available forecasts) show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (alpha) variant in March 2021, prove challenging to predict. ConclusionsMulti-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance. Plain language summaryThe goal of this study is to assess the quality of forecasts of weekly case and death numbers of COVID-19 in Germany and Poland during the period of January through April 2021. We focus on real-time forecasts at time horizons of one and two weeks ahead created by fourteen independent teams. Forecasts are systematically evaluated taking uncertainty ranges of predictions into account. We find that combining different forecasts into ensembles can improve the quality of predictions, but especially case numbers proved very challenging to predict beyond quite short time windows. Additional data sources, in particular genetic sequencing data, may help to improve forecasts in the future.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Social and racial inequalities in COVID-19 risk of hospitalisation and death across Sao Paulo state, Brazil

BackgroundLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. FindingsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1{middle dot}60, 95% CI: 1{middle dot}48 - 1{middle dot}74) and were more likely to be hospitalised between April and July, 2020 (OR: 1{middle dot}08, 95% CI: 1{middle dot}04 - 1{middle dot}12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1{middle dot}37, 95% CI: 1{middle dot}32 - 1{middle dot}41; OR: 1{middle dot}23, 95% CI: 1{middle dot}21 - 1{middle dot}25, respectively), and were more likely to die (OR: 1{middle dot}14, 95% CI: 1{middle dot}07 - 1{middle dot}21; 1{middle dot}09, 95% CI: 1{middle dot}05 - 1{middle dot}13, respectively). InterpretationLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities. FundingThis project was supported by a Medical Research Council-Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). This work received funding from the U.K. Medical Research Council under a concordat with the U.K. Department for International Development.

From predictions to prescriptions: A data-drivenresponse to COVID-19

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemics spread and inform social distancing policies. Fourth, we propose an optimization model to reallocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Controls pandemic forecast. Significance StatementIn the midst of the COVID-19 pandemic, healthcare providers and policy makers are wrestling with unprecedented challenges. How to treat COVID-19 patients with equipment shortages? How to allocate resources to combat the disease? How to plan for the next stages of the pandemic? We present a data-driven approach to tackle these challenges. We gather comprehensive data from various sources, including clinical studies, electronic medical records, and census reports. We develop algorithms to understand the disease, predict its mortality, forecast its spread, inform social distancing policies, and re-distribute critical equipment. These algorithms provide decision support tools that have been deployed on our publicly available website, and are actively used by hospitals, companies, and policy makers around the globe.

Forecasting COVID-19 and Analyzing the Effect of Government Interventions

One key question in the ongoing COVID-19 pandemic is understanding the impact of government interventions, and when society can return to normal. To this end, we develop DELPHI, a novel epidemiological model that captures the effect of under-detection and government intervention. We applied DELPHI across 167 geographical areas since early April, and recorded 6% and 11% two-week out-of-sample Median Absolute Percentage Error on cases and deaths respectively. Furthermore, DELPHI successfully predicted the large-scale epidemics in many areas months before, including US, UK and Russia. Using our flexible formulation of government intervention in DELPHI, we are able to understand how government interventions impacted the pandemics spread. In particular, DELPHI predicts that in absence of any interventions, over 14 million individuals would have perished by May 17th, while 280,000 current deaths could have been avoided if interventions around the world started one week earlier. Furthermore, we find mass gathering restrictions and school closings on average reduced infection rates the most, at 29.9 {+/-} 6.9% and 17.3 {+/-} 6.7%, respectively. The most stringent policy, stay-at-home, on average reduced the infection rate by 74.4 {+/-} 3.7% from baseline across countries that implemented it. We also illustrate how DELPHI can be extended to provide insights on reopening societies under different policies.

Seroprevalence and risk factors for Toxoplasma gondii in sheep and goats in Jinzhou, Northeastern China

In the present study, serum samples from 402 sheep and 216 goats were collected from 5 counties in Jinzhou from August to October 2012 and antibodies to Toxoplasma gondii were detected by modified agglutination test (MAT). Overall, 104 (16.8%) had antibodies to T. gondii with antibody titres of 1:25 to 1:800. Seropositive samples were distributed in all the 5 counties and seroprevalences of T. gondii varied significantly with flock size, age and rearing system, but not with breed, gender and farm location. The seroprevalences in small farms (18.3%, 95/518, 95% confidence interval [CI], 15.0-21.7%) were statistically higher than that in large farms (9%, 9/100, 95% CI, 3.4-14.6%) (P < 0.05), older animals were statistically higher than that in younger animals (P < 0.01). The prevalence in extensively and semiintensively raised samples was statistically higher than that in intensively raised animals (P < 0.01). Small flock size and extensive rearing system are the potential risk factors for the prevalence of Toxoplasma infection in sheep and goats in Jinzhou. This is the first report of T. gondii infection in sheep and goats in Jinzhou, northeastern China, and of an association of seropositivity to T. gondii and the risk factors.

[Budesonide attenuates airway remodeling and modules the expression of Janus protein tyrosine kinase 1, and signal transducers and activators of transcription 6 in asthma: an experiment with mice].

OBJECTIVE: To investigate the effects of budesonide (BUD) on the airway remodeling and the expression of Janus protein tyrosine kinases 1 (JAK1) and signal transducer and activator of transcription 6 (STAT6) in asthma. METHODS: Thirty female Balb/c mice were randomly divided into 3 equal groups: control group; asthma group, sensitized on day 1, 8, and 15 and challenged from day 21 to 52 with periodically repeated intranasal drip of ovalbumin (OVA); and BUD treated group, undergoing intranasal drip of OVA as mentioned above and intranasal administration of BUD 2 hours before each OVA challenge. 24 h after the final OVA inhalation an invasive single-chamber whole body plethysmograph was used to assess the airway responsiveness. Then bronchoalveolar lavage fluid (BALF) was obtained and ELISA was used to measure the contents of interleukin (IL)-4 and IL-13. The mice were killed and their lungs taken out. HE staining and periodic acid Schiff (PAS) staining were used to observe the airway score of goblet cells. Peribronchiolar collagen deposition was imaged in Masson-stained lung sections. Biochemical assay was used to determine the total lung tissue level of collagen. Potass hydrolyse method was used to examine the content of hydroxyproline in the lung tissue. Western blotting was used to detect the protein expression of alpha-smooth muscle actin (SMA), JAK1, and STAT6. RT-PCR was used to detect the mRNA expression of alpha-SMA. RESULTS: The value of LogPC100 of the asthma group was 1.88 +/- 0.34, significantly higher than those of the BUD and control groups (1.79 +/- 0.18 and 0.82 +/- 0.78 respectively, both P = 0.000). The airway score of goblet cells of the asthma group was 3.05 +/- 0.23, significantly higher than those of the BUD and control groups (1.35 +/- 0.26 and 0.40 +/- 0.13 respectively, both P < 0.01). The hydroxyproline content of the asthma group was (459 +/- 47) microg/100 mg tissue, significantly higher than those of the BUD and control groups [(284 +/- 16) and (181 +/- 22) microg/100 mg tissue respectively, both P < 0.01]. The level of IL-4 of the asthma group was (14.4 +/- 1.12) ng/L, significantly higher than those of the BUD and control groups [(7.3 +/- 0.6) and (5.6 +/- 0.8) ng/L respectively, both P < 0.01]. The IL-13 level of the asthma group was (16.8 +/- 0.9) ng/L, significantly higher than those of the BUD and control groups [(10.6 +/- 0.9) and (5.6 +/- 0.8) ng/L respectively, both P < 0.01]. Treatment of BUD attenuated the allergen-induced airway hyperresponsiveness (AHR) and structural changes in airway, and decreased the values of the airway scores of goblet cells, and levels of hydroxyproline, IL-4, and IL-13 in comparison with the asthma group (all P < 0.01). Repeated OVA challenge resulted in an upregulation of the expression levels of alpha-SMA, JAK1 and STAT6 protein and alpha-SMA mRNA, while use of BUD suppressed these changes. The changes of JAK1 and STAT6 expression were correlated significantly with the changes in the airway score of goblet cells, hydroxyproline content, expression level of alpha-SMA, and levels of IL-4 and IL-13 in BALF (all P < 0.05). CONCLUSION: BUD ameliorates the progression of airway remodeling following prolonged allergen challenge via regulation of JAK1/STAT6 signal pathway.

Kinase-deficient CMVpp65 triggers a CMVpp65 specific T-cell immune response in HLA-A*0201.Kb transgenic mice after DNA immunization.

CMVpp65, a candidate component of human cytomegalovirus (CMV) vaccines, has phosphokinase (PK) activity that could affect vaccine safety. A mutated form of CMVpp65 substituting asparagine for lysine at the adenosine triphosphate (ATP)-binding site (CMVpp65mII) is kinase-deficient. Using DNA immunizations in a transgenic human leucocyte antigen (HLA)A*0201.Kb mouse model, the mutated CMVpp65 induced cytotoxic T lymphocytes (CTL) immunity similarly to native CMVpp65. Murine CTL lines generated from these immunizations killed human cells either after sensitization with CMVpp65-specific peptides or after infection with either CMV-Towne strain or rvac-pp65. It is proposed that CMVpp65mII be evaluated in candidate vaccines for CMV.

Changes and their relationship of aquaporin-9 mRNA expression and Ca~(2+) concentration after cerebral ischemic in rats / 临床神经病学杂志

Objective To investigate the changes and their relationship of aquaporin-9(AQP9) mRNA expression and Ca2+ concentration of brain tissue after cerebral ischemic.Methods The models of cerebral ischemic in the rats were made by occluding unilateral middle cerebral artery with the suture method.The expression level of AQP9 mRNA was assessed by RT-PCR at interval times of 6 h,1 d,2 d,3 d,5 d after cerebral ischemic,respectively.Fura-2/AM fluoremetry technique was used to determine the cellular Ca2+ concentration of brain tissue.The results were compared with control group.Results Compared with control group,the expression level of AQP9 mRNA and the concentrations of Ca2+ significantly increased at 6 h in ischemic edema tissue,and reached a peak at 2 d,3 d after cerebral ischemic(P