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Background: Ginkgo biloba extract (GBE), a complementary and alternative medicine, has been widely used for disorders such as brain infarction, dementia, and coronary heart disease, in recent decades. Given its widespread clinical use, GBE has always been a vital research topic. However, there are no bibliometric analyses on this topic; furthermore, published reviews of GBE focus only on a specific research field or lack scientific and systematic evaluation. This study combined bibliometrics with thematic reviews by visual analysis to identify the current status of GBE research and to better identify research hotspots and trends in the past 40 years to understand future developments in basic and clinical research. Methods: Articles and reviews on GBE were retrieved by topic from the Web of Science Core Collection from inception to 2022.12.01. Countries, institutions, authors, journals, references, and keywords in the field were visually analyzed using CiteSpace, Scimago Graphica, and VOSviewer software; then, these visualization results for references and keywords were clarified in detail by thematic reviews in subdivisions of the fields. Results: In total, 2015 publications were included. The GBE-related literature has high volumes of publications and citations. The majority of literature is from China, and the USA cooperates most closely with other countries. In GBE research, Christen Yves is the most cited author, Phytotherapy Research is the most prolific journal, and the Journal of Ethnopharmacology is the most co-cited journal. Through a comprehensive analysis of keywords, references, and reviews, the quality of the meta-analysis of randomized controlled clinical trials of GBE in treating dementia was evaluated by the Risk of Bias in Systematic Reviews scale (ROBIS). Current research on GBE focuses on its pharmacological mechanisms, and neuroprotective application in diseases such as Alzheimer's disease, and glaucoma. Randomized controlled trials are the current research hotspot. Conclusion: Research on GBE is flourishing; using bibliometric and thematic analysis, we identified its hotspots and trends. The pharmacological mechanisms and clinical applications of GBE are the focus of present and likely future research.

Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza’s unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%–35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%–6.5%) and the domestic community (1.6%–2.8%). In 2020–2021, the mask-wearing intervention alone could decline percent positivity by 13.3–19.8. The mobility change alone could reduce percent positivity by 5.2–14.0, of which 79.8%–98.2% were attributed to the deflected international travel. Only in 2019–2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4–14.4) and 10.2 (7.2–13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.

Abstract A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

Background: Critical questions remain regarding the need for intensity to continue NPIs as the public was vaccinated. We evaluated the association of intensity and duration of non-pharmaceutical interventions (NPIs) and vaccines with COVID-19 infection, death, and excess mortality in Europe. Methods: Data comes from Our Word in Data. We included 22 European countries from January 20, 2020, to May 30, 2021. The time-varying constrained distribution lag model was used in each country to estimate the impact of different intensities and duration of NPIs on COVID-19 control, considering vaccination coverage. Country-specific effects were pooled through meta-analysis. Results: This study found that high-intensity and long-duration of NPIs showed a positive main effect on reducing infection in the absence of vaccines, especially in the intensity above the 80th percentile and lasted for 7 days (RR = 0.93, 95% CI: 0.89–0.98). However, the adverse effect on excess mortality also increased with the duration and intensity. Specifically, it was associated with an increase of 44.16% (RR = 1.44, 95% CI: 1.27–1.64) in the excess mortality under the strict intervention (the intensity above the 80th percentile and lasted for 21 days). As the vaccine rollouts, the inhibition of the strict intervention on cases growth rate was increased (RR dropped from 0.95 to 0.87). Simultaneously, vaccination also alleviated the negative impact of the strict intervention on excess mortality (RR decreased from 1.44 to 1.25).Besides, maintaining the strict intervention appeared to more reduce the cases, as well as avoids more overall burden of death compared with weak intervention. Conclusions: Our study highlights the importance of continued high-intensity NPIs in low vaccine coverage. Lifting of NPIs in insufficient vaccination coverage may cause increased infections and death burden. Policymakers should coordinate the intensity and duration of NPIs and allocate medical resources reasonably with widespread vaccination.

A reduction in sucrose preference is a key characteristic of depressive-like behaviors after spinal cord injury as judged by the sucrose preference test, the hypothalamic-pituitary-adrenal axis and adult hippocampal neurogenesis. Male rats were divided into three groups: control, sham and spinal cord injury groups. The spinal cord injury rats received a severe mid-thoracic contusion. The Basso, Beattie and Bresnahan score was used to assess motor function. The sucrose preference test and forced swim test were used to evaluate depressive-like behaviors. Serum corticosterone levels were examined by enzyme-linked immunosorbent assay and hippocampal glucocorticoid receptor levels were examined by Western blot to evaluate the function of the hypothalamic-pituitary-adrenal axis. Adult hippocampal neurogenesis was assessed by testing hippocampal brain-derived neurotrophic factor and tropomyosin receptor kinase B levels by Western blot and doublecortin levels by immunohistochemistry. Data showed that spinal cord injury impaired motor function. The spinal cord injury rats exhibited decreased sucrose preference on day six, which continued to decrease until day twelve, followed by a plateau phase. Additionally, the immobility time of the spinal cord injury rats was increased on day thirty-four. Moreover, serum corticosterone levels in the spinal cord injury group peaked on day seven, was decreased by day twenty-one and was increased again on day thirty-five. Serum corticosterone levels were significantly negatively correlated with sucrose preference and positively correlated with immobility time. Finally, hippocampal doublecortin levels on days twenty-one and thirty-five were lower in the spinal cord injury group than in the other groups. These results suggest that hyperactivation of the hypothalamic-pituitary-adrenal axis and the inhibition of adult hippocampal neurogenesis may be part of the underlying mechanism responsible for depressive-like behaviors after spinal cord injury.

In the context of limited supply, strategies for optimising allocation of COVID-19 vaccines are needed. Here, the authors explore time-varying strategies that adapt to the epidemiological situation and simultaneously optimise for multiple objectives including reducing numbers of infections, hospitalisations, and deaths.

Emerging studies have focused on ways to treat cancers by modulating T cell activation. However, whether B cell receptor signaling in the tumor microenvironment (TME) can be harnessed for immunotherapy is unclear. Here, we report that an Asia-specific variant of human IgG1 containing a Gly396 to Arg396 substitution (hIgG1-G396R) conferred improved survival of patients with colorectal cancer (CRC). Mice with knockin of the murine functional homolog mIgG2c-G400R recapitulated the alleviated tumorigenesis and progression in murine colon carcinoma models. Immune profiling of the TME revealed broad mobilizations of IgG1+ plasma cells, CD8+ T cells, CD103+ DCs, and active tertiary lymphoid structure formation, suggesting an effective antitumor microenvironment in hIgG1-G396R CRC patients. Mechanistically, this variant potentiated tumor-associated antigen–specific (TAA-specific) plasma cell differentiation and thus antibody production. These elevated TAA-specific IgG2c antibodies in turn efficiently boosted the antibody-dependent tumor cell phagocytosis and TAA presentation to effector CD8+ T cells. Notably, adoptive transfer of TAA-specific class-switched memory B cells harboring this variant exhibited therapeutic efficacy in murine tumor models, indicating their clinical potential. All these results prompted a prospective investigation of hIgG1-G396R in patients with CRC as a biomarker for clinical prognosis and demonstrated that manipulating the functionality of IgG1+ memory B cells in tumors could improve immunotherapy outcomes.

Background: As COVID-19 ravages continuously around the world, more information on the epidemiological characteristics and factors associated with time interval between critical events is needed to contain the pandemic and to assess the effectiveness of interventions. Methods: Individual information on confirmed cases from January 21 to March 2 was collected from provincial or municipal health commissions. We identified the difference between imported and local cases in the epidemiological characteristics. Two models were established to estimate the factors associated with time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS) respectively. Results: Among 7,042 cases, 3392 (48.17%) were local cases and 3304 (46.92%) were imported cases. Since the first intervention was adopted in Hubei on January 23, the daily reported imported cases reached a peak on January 28 and gradually decreased since then. Imported cases were on average younger (41 vs. 48), and had more male (58.66% vs. 47.53%) compared to local cases. Furthermore, imported cases had more contacts with other confirmed cases (2.80 ± 2.33 vs. 2.17 ± 2.10), which were mainly within family members (2.26 ± 2.18 vs. 1.57 ± 2.06). The TOH and LOS were 2.67 ± 3.69 and 18.96 ± 7.63 days respectively, and a longer TOH was observed in elderly living in the provincial capital cities that were higher migration intensity with Hubei. Conclusions: Measures to restrict traffic can effectively reduce imported spread. However, household transmission is still not controlled, particularly for the infection of imported cases to elderly women. It is still essential to surveil and educate patients about the early admission or isolation.

Abstract The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.

In estrogen receptor (ER)-positive breast cancer, changes in biomarker expression after neoadjuvant therapy indicate the therapeutic response and are prognostic. However, there is limited information about the biomarker alteration caused by neoadjuvant endocrine therapy in ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. We recruited ER-positive/HER2-negative breast cancer patients who received neoadjuvant chemotherapy (NCT), neoadjuvant endocrine therapy (NET), or sequential neoadjuvant endocrine-chemotherapy (NECT) at Peking University Cancer Hospital from 2015 to 2021. A total of 579 patients had paired immunohistochemistry information in both diagnostic biopsy samples and post-neoadjuvant therapy surgical samples. Through a paired comparison of the immunohistochemical information in pre-treatment and post-treatment samples, we found that progesterone receptor (PR) expression reductions were more frequent than ER expression reductions (70.8% vs. 35.2%) after neoadjuvant therapy. The percentage of patients who had a decreased Ki-67 index in the post-operative samples was similar in the three groups (79.8% vs. 79.7% vs. 78.4%). Moreover, PR losses caused by NET were related to low baseline PR expression (p = 0.001), while we did not find a significant association between PR losses and Ki-67 reductions (p = 0.428) or ER losses (p = 0.274). All three types of neoadjuvant therapies caused a reduction in ER, PR, and Ki-67 expression. In conclusion, we found that PR loss after NET was only significantly related to low baseline PR expression, and there is no significant difference in the extent of prognostic factor change including Ki-67 and ER between the PR loss and non-loss groups.

Abstract. Background:. We aimed to describe and analyze the pre-hospital emergency medical service (EMS) in Beijing and provide information for the government and medical institutions to optimize EMS. Methods:. We collected all pre-hospital emergency data in Beijing from 2008 to 2017. The chief complaint in each case was classified according to the Medical Priority Dispatch System (MPDS). The sites’ administrative districts were determined through geo-encoding of addresses and then classified into four functional regions. We analyzed the demand for EMS, emergency response times (ERT), and disease spectrum for Beijing as a whole, and for each functional region. Results:. A total of 4,192,870 pre-hospital EMS cases met the inclusion criteria, with a significant increase (P

Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10 940 confirmed cases outside Hubei province. Methods In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan. Results We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [CI]: 3139–3321) and 51 273 (95% CI: 49 844–52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5–1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0–40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4–73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8–100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019. Conclusions In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

Human height is a polygenic trait, influenced by a large number of genomic loci. In the pre-genomic era, height prediction was based largely on parental height. More recent predictions of human height have made great strides by integrating genotypic data from large biobanks with improved statistical techniques. Nevertheless, recent studies have not leveraged parental height, an added feature that we hypothesized would offer complementary predictive value. In this study, we assessed the predictive power of polygenic risk scores (PRS) combined with the traditional parental height predictors. Our study analyzed genotypic data and parental height from 1,071 trios from the United Kingdom Biobank and 444 trios from the Framingham Heart Study. We explored a series of statistical models to fully evaluate the performance of several PRS constructed together with parental information and proposed a model we call PRS++ that includes gender, parental height, and PRSs of parents and proband. Our estimate of height with an R2 of ∼0.82 is, to our knowledge, the most accurate estimate yet achieved for predicting human adult height. Without parental information, the R2 from the best PRS-driven model is ∼0.73. In summary, using adult height prediction as an example, we demonstrated that traditional predictors still play important roles and merit integration into the current trends of intensive PRS approaches.

Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using structural nested mean models to estimate the population-level effectiveness of the COVID-19 vaccination program against infection with the original strain. We found that in the US, every 1% increase of vaccination coverage rate reduced the weekly growth rate of COVID-19 confirmed cases by 1.02% (95% CI: 0.26%, 1.69%), and the estimated population-level effectiveness of the COVID-19 program was 63.9% (95% CI: 18.0%, 87.5%). In comparison to a no-vaccination scenario, the COVID-19 vaccination campaign averted 8.05 million infections through the study period. Scenario analyses show that a vaccination program with doubled vaccination speed or with more rapid vaccination speed at the early stages of the campaign would avert more infections and increase vaccine effectiveness. The COVID-19 vaccination program demonstrated a high population-level effectiveness and significantly reduced the disease burden in the US. Accelerating vaccine rollout, especially at an early stage of the campaign, is crucial for reducing COVID-19 infections.

Omicron, the latest SARS-CoV-2 Variant of Concern (VOC), first appeared in Africa in November 2021. At present, the question of whether a new VOC will out-compete the currently predominant variant is important for governments seeking to determine if current surveillance strategies and responses are appropriate and reasonable. Based on both virus genomes and daily-confirmed cases, we compare the additive differences in growth rates and reproductive numbers (R0) between VOCs and their predominant variants through a Bayesian framework and phylo-dynamics analysis. Faced with different variants, we evaluate the effects of current policies and vaccinations against VOCs and predominant variants. The model also predicts the date on which a VOC may become dominant based on simulation and real data in the early stage. The results suggest that the overall additive difference in growth rates of B.1.617.2 and predominant variants was 0.44 (95% confidence interval, 95% CI: −0.38, 1.25) in February 2021, and that the VOC had a relatively high R0. The additive difference in the growth rate of BA.1 in the United Kingdom was 6.82 times the difference between Delta and Alpha, and the model successfully predicted the dominating process of Alpha, Delta and Omicron. Current vaccination strategies remain similarly effective against Delta compared to the previous variants. Our model proposes a reliable Bayesian framework to predict the spread trends of VOCs based on early-stage data, and evaluates the effects of public health policies, which may help us better prepare for the upcoming Omicron variant, which is now spreading at an unprecedented speed.

IntroductionPrevious studies have provided equivocal evidence of antidepressant use on subsequent cognitive impairment; this could be due to inconsistent modeling approaches. Our goals are methodological and clinical. We evaluate the impact of statistical modeling approaches on the associations between antidepressant use and risk of Mild Cognitive Impairment (MCI) in older adults with depression.Methods716 participants were enrolled. Our primary analysis employed a time-dependent Cox proportional hazards model. We also implemented two fixed-covariate proportional hazards models-one based on having ever used antidepressants during follow-up, and the other restricted to baseline use only.ResultsTreating antidepressant use as a time-varying covariate, we found no significant association with incident MCI (HR = 0.92, 95% CI: 0.70, 1.20). In contrast, when antidepressant use was treated as a fixed covariate, we observed a significant association between having ever used antidepressants and lower risk of MCI (HR = 0.40, 95% CI: 0.28, 0.56). However, in the baseline-use only model, the association was non-significant (HR = 0.84, 95% CI: 0.60, 1.17).DiscussionOur results were dependent upon statistical models and suggest that antidepressant use should be modeled as a time-varying covariate. Using a robust time-dependent analysis, antidepressant use was not significantly associated with incident MCI among cognitively normal persons with depression.

Abstract To avoid possible confusions to the readers, we provide further explanations for the eq. (3) in the research article “Estimating the daily trend in the size of the COVID-19 infected population in Wuhan” published in the Infectious Diseases of Poverty.

Abstract Background Framingham Stroke Risk Score (FSRS) is the most well-regarded risk appraisal tools for evaluating an individual’s absolute risk on stroke onset. However, several widely accepted risk factors for stroke were not included in the original Framingham model. This study proposed a new model which combines an existing risk models with new risk factors using synthesis analysis, and applied it to the longitudinal Atherosclerosis Risk in Communities (ARIC) data set. Methods Risk factors in original prediction models and new risk factors in proposed model had been discussed. Three measures, like discrimination, calibration and reclassification, were used to evaluate the performance of the original Framingham model and new risk prediction model. Results Modified C-statistics, Hosmer-Lemeshow Test and classless NRI, class NRI were the statistical indices which, respectively, denoted the performance of discrimination, calibration and reclassification for evaluating the newly developed risk prediction model on stroke onset. It showed that the NEW-STROKE (new stroke risk score prediction model) model had higher modified C-statistics, smaller Hosmer-Lemeshow chi-square values after recalibration than original FSRS model, and the classless NRI and class NRI of the NEW-STROKE model over the original FSRS model were all significantly positive in overall group. Conclusion The NEW-STROKE integrated with seven literature-derived risk factors outperformed the original FSRS model in predicting the risk score of stroke. It illustrated that seven literature-derived risk factors contributed significantly to stroke risk prediction.

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

While the experimental Alzheimer's drugs recently developed by pharmaceutical companies failed to stop the progression of Alzheimer's disease, clinicians strive to seek clues on how the patients would be when they visit back next year, based upon the patients' current clinical and neuropathologic diagnosis results. This is related to how to precisely identify the transitional patterns of Alzheimer's disease. Due to the complexities of the diagnosis of Alzheimer's disease, the condition of the disease is usually characterized by multiple clinical and neuropathologic measurements, including Clinical Dementia Rating (CDRGLOB), Mini-Mental State Examination (MMSE), a score derived from the clinician judgement on neuropsychological tests (COGSTAT), and Functional Activities Questionnaire (FAQ). In this research article, we investigate a class of novel joint random-effects transition models that are used to simultaneously analyze the transitional patterns of multiple primary measurements of Alzheimer's disease and, at the same time, account for the association between the measurements. The proposed methodology can avoid the bias introduced by ignoring the correlation between primary measurements and can predict subject-specific transitional patterns.

The clinical relevance of human papillomavirus type 16 (HPV16) DNA methylation has not been well documented, although its role in modulation of viral transcription is recognized.Study subjects were 211 women attending Planned Parenthood clinics in Western Washington for routine Papanicolaou screening who were HPV16 positive at the screening and/or subsequent colposcopy visit. Methylation of 11 CpG dinucleotides in the 3' end of the long control region of the HPV16 genome was examined by sequencing the cloned polymerase chain reaction products. The association between risk of CIN2/3 and degree of CpG methylation was estimated using a logistic regression model.CIN2/3 was histologically confirmed in 94 (44.5%) of 211 HPV16 positive women. The likelihood of being diagnosed as CIN2/3 increased significantly with decreasing numbers of methylated CpGs (meCpGs) in the 3' end of the long control region (P(for trend) = 0.003). After adjusting for HPV16 variants, number of HPV16-positive visits, current smoking status and lifetime number of male sex partners, the odds ratio for the association of CIN2/3 with ≥4 meCpGs was 0.31 (95% confidence interval, 0.12-0.79). The proportion of ≥4 meCpGs decreased appreciably as the severity of the cervical lesion increased (P(for trend) = 0.001). The inverse association remained similar when CIN3 was used as the clinical endpoint. Although not statistically significant, the ≥4 meCpGs-related risk reduction was more substantial among current, as compared to noncurrent, smokers.Results suggest that degree of the viral genome methylation is related to the outcome of an HPV16 cervical infection.