Stereotactic ablative radiotherapy for medically inoperable early stage lung cancer: early outcomes

Objective To evaluate the clinical outcome and safety of stereotactic ablative radiotherapy for medically inoperable stage I non- small-cell lung carcinoma. Design Retrospective case series. Setting Pamela Youde Nethersole Eastern Hospital, Hong Kong. Patients All patients with medically inoperable stage I non-small-cell lung carcinoma receiving stereotactic ablative radiotherapy since its establishment in 2008. Main outcome measures Disease control rate, overall survival, and treatment toxicities. Results Sixteen stage I non-small-cell lung carcinoma patients underwent the procedure from June 2008 to November 2011. The median patient age was 82 years and the majority (81%) had moderate-tosevere co-morbidity based on the Adult Comorbidity Evaluation 27 index. With a median follow-up of 22 months, the 2-year primary tumour control rate, disease-free survival and overall survival rates were 91%, 71% and 87%, respectively. No grade 3 (National Cancer Institute Common Terminology Criteria for Adverse Events) or higher treatment-related complications were reported. Conclusion Stereotactic ablative radiotherapy can achieve a high degree of local control safely in medically inoperable patients with early lung cancer.published_or_final_versio

Delayed presentation of symptomatic breast cancers in Hong Kong: Experience in a public cancer centre

Objective Delayed presentation is an important obstacle to improving cancer treatment outcomes. We aimed to study the magnitude of this problem in Hong Kong and the factors associated with delayed presentation of patients with symptomatic breast cancers. Design Retrospective study using self-administered questionnaires. Setting Clinical Oncology Department in a regional public hospital in Hong Kong. Patients A total of 158 Chinese women with breast cancer referred to our hospital between October 2006 and December 2007 consented to participate in this study. Among these, 59 (37%) patients were referred after having surgery in private sector. Results The mean total delay (from first symptom to treatment) was 22 weeks. The mean patient delay (from first symptom to first consultation) was 13 weeks, constituting the largest component (60%) of the total delay. After symptom onset, the delay exceeded 12 weeks for consulting a doctor in 29%, and for receipt of treatment in 52% of them. Low family income (<HK$5000 per month; P<0.001) and surgery in public hospitals (P=0.013) were both independent predictors of patient delay. Surgery in public hospitals (P=0.006) and low family income (P=0.005) were the only predictors of doctor/system delay and total delay, respectively. Conclusions Delayed presentation and treatment of symptomatic breast cancer remains an important issue in Hong Kong. Apart from socio-economic factors, limited access to public medical care was likely an important contributing factor in delays related to patients as well as to doctor/system.published_or_final_versio

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.

BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration

A systematic review and recommendations on the use of plasma EBV DNA for nasopharyngeal carcinoma

Introduction: Nasopharyngeal carcinoma (NPC) is an endemic malignancy in Southeast Asia, particularly Southern China. The classical non-keratinising cell type is almost unanimously associated with latent Epstein-Barr virus (EBV) infection. Circulating plasma EBV DNA can be a useful biomarker in various clinical aspects, but comprehensive recommendations and international guidelines are still lacking. We conducted a systematic review of all original articles on the clinical application of plasma EBV DNA for NPC; we further evaluated its strengths and limitations for consideration as standard recommendations. Methods: The search terms 'nasopharyngeal OR nasopharynx', and 'plasma EBV DNA OR cell-free EBV OR cfEBV' were used to identify full-length articles published up to December 2020 in the English literature. Three authors independently reviewed the article titles, removed duplicates and reviewed the remaining articles for eligibility. Results: A total of 81 articles met the eligibility criteria. Based on the levels of evidence and grades of recommendation assessed, it is worth considering the inclusion of plasma EBV DNA in screening, pre-treatment work-up for enhancing prognostication and tailoring of treatment strategy, monitoring during radical treatment, post-treatment surveillance for early detection of relapse, and monitoring during salvage treatment for recurrent or metastatic NPC. One major limitation is the methodology of measurement requiring harmonisation for consistent comparability. Conclusions: The current comprehensive review supports the inclusion of plasma EBV DNA in international guidelines in the clinical aspects listed, but methodological issues must be resolved before global application. 2021 Elsevier Ltd. All rights reserved

Pathophysiology of acute experimental pancreatitis: Lessons from genetically engineered animal models and new molecular approaches

The incidence of acute pancreatitis is growing and worldwide population-based studies report a doubling or tripling since the 1970s. 25% of acute pancreatitis are severe and associated with histological changes of necrotizing pancreatitis. There is still no specific medical treatment for acute pancreatitis. The average mortality resides around 10%. In order to develop new specific medical treatment strategies for acute pancreatitis, a better understanding of the pathophysiology during the onset of acute pancreatitis is necessary. Since it is difficult to study the early acinar events in human pancreatitis, several animal models of acute pancreatitis have been developed. By this, it is hoped that clues into human pathophysiology become possible. In the last decade, while employing molecular biology techniques, a major progress has been made. The genome of the mouse was recently sequenced. Various strategies are possible to prove a causal effect of a single gene or protein, using either gain-of-function (i.e., overexpression of the protein of interest) or loss-of-function studies (i.e., genetic deletion of the gene of interest). The availability of transgenic mouse models and gene deletion studies has clearly increased our knowledge about the pathophysiology of acute pancreatitis and enables us to study and confirm in vitro findings in animal models. In addition, transgenic models with specific genetic deletion or overexpression of genes help in understanding the role of one specific protein in a cascade of inflammatory processes such as pancreatitis where different proteins interact and co-react. This review summarizes the recent progress in this field. Copyright (c) 2005 S. Karger AG, Basel

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BACKGROUND: Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs. RESULTS: We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided. CONCLUSION: Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs

Induction of Cytotoxic T Lymphocyte Antigen 4 (Ctla-4) Restricts Clonal Expansion of Helper T Cells

Cytotoxic T lymphocyte antigen (CTLA)-4 plays an essential role in immunologic homeostasis. How this negative regulator of T cell activation executes its functions has remained controversial. We now provide evidence that CTLA-4 mediates a cell-intrinsic counterbalance to restrict the clonal expansion of proliferating CD4+ T cells. The regulation of CTLA-4 expression and function ensures that, after ∼3 cell divisions of expansion, most progeny will succumb to either proliferative arrest or death over the ensuing three cell divisions. The quantitative precision of the counterbalance hinges on the graded, time-independent induction of CTLA-4 expression during the first three cell divisions. In contrast to the limits imposed on unpolarized cells, T helper type 1 (Th1) and Th2 effector progeny may be rescued from proliferative arrest by interleukin (IL)-12 and IL-4 signaling, respectively, allowing appropriately stimulated progeny to proceed to the stage of tissue homing. These results suggest that the cell-autonomous regulation of CTLA-4 induction may be a central checkpoint of clonal expansion of CD4+ T cells, allowing temporally and spatially restricted growth of progeny to be dictated by the nature of the threat posed to the host