The Multidisciplinary Management of Cutaneous Squamous Cell Carcinoma: A Comprehensive Review and Clinical Recommendations by a Panel of Experts

Cutaneous squamous cell carcinomas (CSCC) account for about 20% of all keratinocyte carcinomas, which are the most common form of cancer. Heterogeneity of treatments and low mortality are a challenge in obtaining accurate incidence data and consistent registration in cancer registries. Indeed, CSCC mostly presents as an indolent, low-risk lesion, with five-year cure rates greater than 90% after surgical excision, and only few tumors are associated with a high-risk of local or distant relapse; therefore, it is particularly relevant to identify high-risk lesions among all other low-risk CSCCs for the proper diagnostic and therapeutic management. Chemotherapy achieves mostly short-lived responses that do not lead to a curative effect and are associated with severe toxicities. Due to an etiopathogenesis largely relying on chronic UV radiation exposure, CSCC is among the tumors with the highest rate of somatic mutations, which are associated with increased response rates to immunotherapy. Thanks to such strong pre-clinical rationale, clinical trials led to the approval of anti-PD-1 cemiplimab by the FDA (Food and Drug Administration) and EMA (European Medicines Agency), and anti-PD-1 pembrolizumab by the FDA only. Here, we provide a literature review and clinical recommendations by a panel of experts regarding the diagnosis, treatment, and follow-up of CSCC.

Incidence of myeloid neoplasms in Spain (2002–2013): a population-based study of the Spanish network of cancer registries

Comprehensive population-based data on myeloid neoplasms (MNs) are limited, mainly because some subtypes were not recognized as hematological cancers prior to the WHO publication in 2001, and others are too rare to allow robust estimates within regional studies. Herein, we provide incidence data of the whole spectrum of MNs in Spain during 2002–2013 using harmonized data from 13 population-based cancer registries. Cases (n = 17,522) were grouped following the HAEMACARE groupings and 2013-European standardized incidence rates (ASRE), incidence trends, and estimates for 2021 were calculated. ASRE per 100,000 inhabitants was 5.14 (95% CI: 5.00–5.27) for myeloproliferative neoplasms (MPN), 4.71 (95% CI: 4.59–4.84) for myelodysplastic syndromes (MDS), 3.91 (95% CI: 3.79–4.02) for acute myeloid leukemia, 0.83 (95% CI: 0.78–0.88) for MDS/MPN, 0.35 (95% CI: 0.32–0.39) for acute leukemia of ambiguous lineage, and 0.58 (95% CI: 0.53–0.62) for not-otherwise specified (NOS) cases. This study highlights some useful points for public health authorities, such as the remarkable variability in incidence rates among Spanish provinces, the increasing incidence of MPN, MDS, and MDS/MPN during the period of study, in contrast to a drop in NOS cases, and the number of cases expected in 2021 based on these data (8446 new MNs).

Examining Social Vulnerability and the Association With COVID-19 Incidence in Harris County, Texas

Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.

Potential application of Rapid Antigen Diagnostic Tests for the detection of infectious individuals attending mass gatherings: a simulation study

Rapid Antigen Diagnostic Tests (RADTs) for the detection of SARS-CoV-2 offer advantages in that they are cheaper and faster than currently used PCR tests but have reduced sensitivity and specificity. One potential application of RADTs is to facilitate gatherings of individuals, through testing of attendees at the point of, or immediately prior to entry at a venue. Understanding the baseline risk in the tested population is of particular importance when evaluating the utility of applying diagnostic tests for screening purposes. We used incidence data to estimate the prevalence of infectious individuals in the community at a particular time point and simulated mass gatherings by sampling from a series of age cohorts. Nine different illustrative scenarios were simulated, small (n=100), medium (n=1000) and large (n=10,000) gatherings each with 3 possible age constructs: mostly younger, mostly older or a gathering with equal numbers from each age cohort. For each scenario, we estimated the prevalence of infectious attendees, then simulated the likely number of positive and negative test results, the proportion of cases detected and the corresponding positive and negative predictive values, and the cost per case identified. Our findings suggest that for each detected individual on a given day, there are likely to be 13.8 additional infectious individuals also present in the community. Prevalence of infectious individuals at events was highest with mostly younger attendees (1.00%), followed by homogenous age gatherings (0.55%) and lowest with mostly older events (0.26%). For small events (100 attendees) the expected number of infectious attendees was less than 1 across all age constructs of attendees. For large events (10,000 attendees) the expected number of infectious attendees ranged from 26 (95% confidence intervals 12 to 45) for mostly older events, to almost 100 (95% confidence intervals 46 to 174) infectious attendees for mostly younger attendees. Given rapid changes in SARS-CoV-2 incidence over time, we developed an RShiny app to allow users to run updated simulations for specific events.

Defining Causality in Covid-19 and Google Search Trends in Java, Indonesia Cases: A Retrospective Analysis

The Coronavirus disease 2019 (Covid-19) has led all countries around the world to the unpredicted situation. It is such a crucial to investigate novel approaches in predicting the future behaviour of the outbreak. In this paper, Google trend analysis will be employed to analyse the seek pattern of Covid-19 cases. The first method to investigate the seek information behaviour related to Covid-19 outbreak is using lag-correlation between two time series data per regional data. The second method is used to encounter the cause-effect relation between time series data. We apply statistical methods for causal inference in epidemics. Our focus is on predicting the causal-effect relationship between information-seeking patterns and Google search in the Covid-19 pandemic. We propose the using of Granger Causality method to analyse the causal relation between incidence data and Google Trend Data.

Racial and Geographical Disparities in Colorectal Cancer Incidence in Mississippi, 2003-2018

Purpose: The aim of this study is to investigate racial and geographical disparities of colorectal cancer incidence in Mississippi. Methods: Incidence data from 2003-2018 were obtained at the county-level from the Mississippi Cancer Registry. Incidence rate difference and 95% confidence intervals between age-adjusted colorectal cancer incidence for whites and blacks were calculated and mapped using ArcGIS. Results: The black incidence rate for colorectal cancer was 59.8 per 100,000 while the white incidence rate was 48.9 per 100,000. Blacks experienced significantly higher incidence rates than whites in 39 counties throughout much of Mississippi. These areas of higher racial disparities did not cluster in a specific region of the state. In the southern part of the state an 8-county cold-spot region was detected without racial disparities; incidence rate for blacks was 41.3 per 100,000 in this region. Conclusions: There are racial disparities throughout Mississippi except for an 8-county region towards the southern part of the state. Additional research should be conducted to identify what factors are responsible for the lower incidence rates among blacks in this region, and to implement effective interventions statewide to reduce racial disparities in colorectal cancer incidence.

Prediction of COVID-19 pandemic dynamics in Russia based on simple mathematical models of epidemics

Introduction: The COVID-19 pandemic which began in 2020 and has taken more than five million lives has become a threat to the very existence of mankind. Therefore, predicting the spread of COVID-19 in each individual country is a very urgent task. The complexity of its solution is due to the requirement for fast processing of large amounts of data and the fact that the data are mostly inaccurate and do not have the statistical properties necessary for the successful application of statistical methods. Therefore, it seems important to develop simple forecasting methods based on classical simple models of epidemiology which are only weakly sensitive to data inaccuracies. It is also important to demonstrate the feasibility of the approach in relation to the incidence data in Russia. Purpose: Obtaining forecast data based on classical simple models of epidemics, namely SIR and SEIR. Methods: For discrete versions of SIR and SEIR models, it is proposed to estimate the parameters of the models using a reduced version of the least squares method, and apply a scenario approach to the forecasting. The simplicity and a small number of parameters are the advantages of SIR and SEIR models, which is very important in the context of a lack of numerical input data and structural incompleteness of the models. Results: A forecast of the spread of COVID-19 in Russia has been built based on published data on the incidence from March 10 to April 20, 2020, and then, selectively, according to October 2020 data and October 2021 data. The results of the comparison between SIR and SEIR forecasts are presented. The same method was used to construct and present forecasts based on morbidity data in the fall of 2020 and in the fall of 2021 for Russia and for St. Petersburg. To set the parameters of the models which are difficult to determine from the official data, a scenario approach is used: the dynamics of the epidemic is analyzed for several possible values of the parameters. Practical relevance: The results obtained show that the proposed method predicts well the time of the onset of the peak incidence, despite the inaccuracy of the initial data.

COVID-19 reproduction number and non-pharmaceutical interventions in Lithuania

Currently the world is threatened by a global COVID-19 pandemic and it has induced crisis creating a lot of disruptions in the healthcare system, social life and economy. In this article we present the analysis of COVID-19 situation in Lithuania and it's municipalities taking into consideration the effect of non-pharmaceutical interventions on the reproduction number. We have analysed the period from 20/03/2020 to 20/06/2021 covering two quarantines applied in Lithuania. We calculated the reproduction number using the incidence data provided by State Data Governance Information System, while the information for applied non-pharmaceutical interventions was extracted from Oxford COVID-19 Government Response Tracker and the COVID-19 website of Government of the Republic of Lithuania. The positive effect of applied non-pharmaceutical interventions on reproduction number was observed when internal movement ban was applied in 16/12/2020 during the second quarantine in Lithuania.

Estimation of R0 for the spread of SARS-CoV-2 in Germany from Excess Mortality

Background For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of PCR testing capacity. Methods We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). Results The uncorrected R0 value for the spread of SARS-CoV-2 based on PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95%CI 1.96-2.10) for Covid-19-related deaths. However, because the number of PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Discussion Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.

Genome-wide Association Study of Kernel Black Point Resistance in Chinese Wheat Landraces

Black point (BP) disease of wheat has become a noticeable problem in China. The symptoms are brown to black in color around the wheat kernel embryo or in the endosperm, resulting in a significant reduction of wheat grain quality. Here, we evaluated 272 Chinese wheat landraces for BP reaction and performed a genome-wide association study (GWAS) to identify BP resistance quantitative trait loci (QTL) in five field environments without artificial inoculation. The BP incidence data showed continuous distributions and had low to moderate correlations between environments (r = 0.094 – 0.314). Among the 272 landraces, 11 had 0.1% to 4.9%, 144 had 5% to 14.9%, and 100 had 15% to 29.9%, and 17 had over 30% incidence. We found three resistant accessions: WH094 (3.33%), AS661463 (2.67%), and AS661231 (2.67%), which can be used in breeding programs to enhance BP resistance. We identified 11 QTL, which explained 8.22% to 10.99% phenotypic BP variation and mapped them to eight wheat chromosomes. Three of the QTL were novel. The molecular markers for the BP resistance could facilitate molecular breeding for developing BP resistant cultivars.