On the feasibility of malaria hypothesis.

In 1954, Allison proposed that hemoglobin S (HbS) gene causes protection against fatal malaria. This would explain the high HbS gene frequency observed in certain regions hyperendemic for malaria, so-called "malaria hypothesis". This in silico study was conducted to examine the feasibility of the hypothesis under more realistic initial conditions, where a mutant gene with heterozygous advantage against malaria (e.g., HbS) was introduced in a group of Neolithic hunter-gatherers who decided to start agriculture nearby water where malaria killed a proportion of population. The tribe population size, number of children born to each woman in each generation, mortality from malaria and sickle cell disease, the protection factor provided by the gene carriers against malaria, the probability of mating between the members of the parent and offspring populations, population growth, and increased fertility in women heterozygous for HbS, were also considered. For effectively confer protection against malaria within the shortest possible period, the mutation needs to be happened in a small population. For a large population, the process would take around 100 generations (~ 2500 years) or more to provide an effective protection. Even then, the probability that the new gene could survive and propagate to future generations is about 35%. Conventional population genetics equations with differential or difference equations, give totally incorrect estimates of the gene frequency in small populations; discrete mathematics should be used, instead. After introduction of the advantageous mutation, the gene frequency increased until a steady state value. This value is far less than the gene frequency reported in certain tribes of Africa. It seems that the malaria hypothesis, per se, could not explain such a high observed gene frequency, unless HbS is associated with lower mortality from other causes too.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

On the use of receiver operating characteristic curve analysis to determine the most appropriate p value significance threshold.

BACKGROUND: p value is the most common statistic reported in scientific research articles. Choosing the conventional threshold of 0.05 commonly used for the p value in research articles, is unfounded. Many researchers have tried to provide a reasonable threshold for the p value; some proposed a lower threshold, eg, 0.005. However, none of the proposals has gained universal acceptance. Using the analogy between the diagnostic tests with continuous results and statistical inference tests of hypothesis, I wish to present a method to calculate the most appropriate p value significance threshold using the receiver operating characteristic curve (ROC) analysis. RESULTS: As with diagnostic tests where the most appropriate cut-off values are different depending on the situation, there is no unique cut-off for the p significance threshold. Unlike the previous proposals, which mostly suggest lowering the threshold to a fixed value (eg, from 0.05 to 0.005), the most appropriate p significance threshold proposed here, in most instances, is much less than the conventional cut-off of 0.05 and varies from study to study and from statistical test to test, even within a single study. The proposed method provides the minimum weighted sum of type I and type II errors. CONCLUSIONS: Given the perplexity involved in using the frequentist statistics in a correct way (dealing with different p significance thresholds, even in a single study), it seems that the p value is no longer a proper statistic to be used in our research; it should be replaced by alternative methods, eg, Bayesian methods.

Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on chatbots and generative artificial intelligence in relation to scholarly publications.

This statement revises our earlier "WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications" (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop.

Esta declaración revisa las anteriores "Recomendaciones de WAME sobre ChatGPT y Chatbots en Relation to Scholarly Publications" (20 de enero de 2023). La revisión refleja la proliferación de chatbots y su creciente uso en las publicaciones académicas en los últimos meses, así como la preocupación por la falta de autenticidad de los contenidos cuando se utilizan chatbots. Estas recomendaciones pretenden informar a los editores y ayudarles a desarrollar políticas para el uso de chatbots en los artículos sometidos en sus revistas. Su objetivo es ayudar a autores y revisores a entender cuál es la mejor manera de atribuir el uso de chatbots en su trabajo y a la necesidad de que todos los editores de revistas tengan acceso a herramientas de selección de manuscritos. En este campo en rápida evolución, seguiremos modificando estas recomendaciones a medida que se desarrollen el software y sus aplicaciones.

Plagiarism: A Bird's Eye View.

Plagiarism is among the prevalent misconducts reported in scientific writing and common causes of article retraction in scholarly journals. Plagiarism of idea is not acceptable by any means. However, plagiarism of text is a matter of debate from culture to culture. Herein, I wish to reflect on a bird's eye view of plagiarism, particularly plagiarism of text, in scientific writing. Text similarity score as a signal of text plagiarism is not an appropriate index and an expert should examine the similarity with enough scrutiny. Text recycling in certain instances might be acceptable in scientific writing provided that the authors could correctly construe the text piece they borrowed. With introduction of artificial intelligence-based units, which help authors to write their manuscripts, the incidence of text plagiarism might increase. However, after a while, when a universal artificial unit takes over, no one will need to worry about text plagiarism as the incentive to commit plagiarism will be abolished, I believe.

GPTZero Performance in Identifying Artificial Intelligence-Generated Medical Texts: A Preliminary Study.

BACKGROUND: With emergence of chatbots to help authors with scientific writings, editors should have tools to identify artificial intelligence-generated texts. GPTZero is among the first websites that has sought media attention claiming to differentiate machine-generated from human-written texts. METHODS: Using 20 text pieces generated by ChatGPT in response to arbitrary questions on various topics in medicine and 30 pieces chosen from previously published medical articles, the performance of GPTZero was assessed. RESULTS: GPTZero had a sensitivity of 0.65 (95% confidence interval, 0.41-0.85); specificity, 0.90 (0.73-0.98); accuracy, 0.80 (0.66-0.90); and positive and negative likelihood ratios, 6.5 (2.1-19.9) and 0.4 (0.2-0.7), respectively. CONCLUSION: GPTZero has a low false-positive (classifying a human-written text as machine-generated) and a high false-negative rate (classifying a machine-generated text as human-written).

Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on chatbots and generative artificial intelligence in relation to scholarly publications

This statement revises our earlier "WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications" (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop.

Esta declaración revisa las anteriores "Recomendaciones de WAME sobre ChatGPT y Chatbots en Relation to Scholarly Publications" (20 de enero de 2023). La revisión refleja la proliferación de chatbots y su creciente uso en las publicaciones académicas en los últimos meses, así como la preocupación por la falta de autenticidad de los contenidos cuando se utilizan chatbots. Estas recomendaciones pretenden informar a los editores y ayudarles a desarrollar políticas para el uso de chatbots en los artículos sometidos en sus revistas. Su objetivo es ayudar a autores y revisores a entender cuál es la mejor manera de atribuir el uso de chatbots en su trabajo y a la necesidad de que todos los editores de revistas tengan acceso a herramientas de selección de manuscritos. En este campo en rápida evolución, seguiremos modificando estas recomendaciones a medida que se desarrollen el software y sus aplicaciones.

The Acceptable Text Similarity Level in Manuscripts Submitted to Scientific Journals.

Plagiarism is among commonly identified scientific misconducts in submitted manuscripts. Some journals routinely check the level of text similarity in the submitted manuscripts at the time of submission and reject the submission on the fly if the text similarity score exceeds a set cut-off value (e.g., 20%). Herein, I present a manuscript with 32% text similarity, yet without any instances of text plagiarism. This underlines the fact that text similarity is not necessarily tantamount to text plagiarism. Every instance of text similarity should be examined with scrutiny by a trained person in the editorial office. A high text similarity score does not always imply plagiarism; a low score, on the other hand, does not guarantee absence of plagiarism. There is no cut-off for text similarity to imply text plagiarism.

Correction of vaccine effectiveness derived from test-negative case-control studies.

BACKGROUND: Determining the vaccine effectiveness (VE) is an important part of studying every new vaccine. Test-negative case-control (TNCC) studies have recently been used to determine the VE. However, the estimated VE derived from a TNCC design depends on the test sensitivity and specificity. Herein, a method for correction of the value of VE derived from a TNCC study is presented. METHODS: An analytical method is presented to compute the corrected VE based on the sensitivity and specificity of the diagnostic test utilized. To show the application of the method proposed, a hypothetical TNCC study is presented. In this in silico study, 100 000 individuals referring to a healthcare system for COVID-19-like illness were tested with diagnostic tests with sensitivities of 0.6, 0.8, and 1.0, and specificities ranging from 0.85 to 1.00. A vaccination coverage of 60%, an attack rate of 0.05 for COVID-19 in unvaccinated group, and a true VE of 0.70, were assumed. In this simulation, a COVID-19-like illness with an attack rate of 0.30 could also affect all the studied population regardless of their vaccination status. RESULTS: The observed VE ranged from 0.11 (computed for a test sensitivity of 0.60 and specificity of 0.85) to 0.71 (computed for a test sensitivity and specificity of 1.0). The mean computed corrected VE derived from the proposed method was 0.71 (the standard deviation of 0.02). CONCLUSIONS: The observed VE derived from TNCC studies can be corrected easily. An acceptable estimate for VE can be computed regardless of the diagnostic test sensitivity and specificity used in the study.

When to Attend a Webinar?

OBJECTIVE: To determine the most appropriate delay to start a webinar. METHODS: This cross-sectional study was conducted on weekly general staff scientific webinars held by the Institute of Human Virology (IHV), University of Maryland School of Medicine, Baltimore, USA. 35 observations were made at arbitrary chosen times of three consecutive IHV webinars. After standardizing the number of participants, a 4th-degree polynomial fit was applied to the data. A cost function was defined as the sum of the time wasted for those who attended the webinar early and the lost for those who attend with delay. The cost function was minimized to compute the most appropriate delay to start the webinar. RESULTS: The model could explain almost 95% of the observed variance in the number of participants. Normally, half of the participants attended the meeting at the webinar set starting time. The cost was a minimum if the webinar was delayed for about 3 minutes. CONCLUSION: It seems that the most appropriate time for starting the IHV general staff meetings is around 3 minutes after the webinar set starting time.

Malaria and the incidence of COVID-19 in Africa: an ecological study.

BACKGROUND: It has been shown that stimulation of innate immunity may provide temporary protection against a variety of infectious diseases. Malaria has been shown to induce a robust innate immune response. This study was conducted to test the hypothesis that if the cumulative number of cases diagnosed with COVID-19 per 100,000 population was correlated with the prevalence of malaria in African countries where both malaria and COVID-19 were prevalent. METHODS: In this ecological study, the cumulative incidence of COVID-19 and the prevalence of malaria were compared in 53 African countries. A negative binomial regression analysis with the cumulative incidence of COVID-19 as the dependent variable, and the human development index (HDI) and the prevalence of malaria, as independent variables, were used. RESULTS: The mean cumulative incidence of COVID-19 was 522 cases per 100,000. Each 0.1 unit increase in HDI was associated with 2.4-fold (95% confidence interval 1.8-3.1) increase in the cumulative incidence of COVID-19. Prevalence of malaria was also independently associated with the cumulative incidence; each 10% increase in the prevalence was associated with 28% (10-41%) decrease in the cumulative incidence of COVID-19. CONCLUSIONS: Malaria might protect people against SARS-CoV-2 through the stimulation of innate immunity. Stimulation of the innate immune system could be the first line of defense in the pandemic preparedness arsenal.

On determining the sensitivity and specificity of a new diagnostic test through comparing its results against a non-gold-standard test.

Diagnostic tests are important clinical tools. To assess the sensitivity and specificity of a new test, its results should be compared against a gold standard. However, the gold-standard test is not always available. Herein, I show that we can compare the new test against a well-established diagnostic test (not a gold-standard test, but with known sensitivity and specificity) and compute the sensitivity and specificity of the new test if we would have compared it against the gold-standard test. The technique presented is useful for situations where the gold standard is not readily available.

No need for a gold-standard test: on the mining of diagnostic test performance indices merely based on the distribution of the test value.

BACKGROUND: Diagnostic tests are important in clinical medicine. To determine the test performance indices - test sensitivity, specificity, likelihood ratio, predictive values, etc. - the test results should be compared against a gold-standard test. Herein, a technique is presented through which the aforementioned indices can be computed merely based on the shape of the probability distribution of the test results, presuming an educated guess. METHODS: We present the application of the technique to the probability distribution of hepatitis B surface antigen measured in a group of people in Shiraz, southern Iran. We assumed that the distribution had two latent subpopulations - one for those without the disease, and another for those with the disease. We used a nonlinear curve fitting technique to figure out the parameters of these two latent populations based on which we calculated the performance indices. RESULTS: The model could explain > 99% of the variance observed. The results were in good agreement with those obtained from other studies. CONCLUSION: We concluded that if we have an appropriate educated guess about the distributions of test results in the population with and without the disease, we may harvest the test performance indices merely based on the probability distribution of the test value without need for a gold standard. The method is particularly suitable for conditions where there is no gold standard or the gold standard is not readily available.

On Measuring Vaccine Effectiveness with Observational Study Designs.

Herein, we present a bird's eye view of common observational study designs utilized for measurement of vaccine effectiveness. Assessing vaccines effectiveness is an integral part of vaccine research, particularly for the newly developed vaccines. A cohort study is prospective, directing from an exposure to one or more outcomes. The design is the best method to ascertain the attack rate of an infectious disease. A traditional case-control study is retrospective, directing from a given outcome to one or more exposures. The design cannot provide the relative risk, but it can provide the odds ratio, which is a good estimation of the relative risk when the attack rate is low. Critically depending on laboratory test results and performance, the test-negative case-control study design is another type of observational study commonly used nowadays for the evaluation of the vaccine effectiveness. Comparing to cohort and traditional case-control designs, conducting a test-negative case-control study is relatively cheaper and faster. Herein, we describe each of the above-mentioned study designs through examples generated by a Monte-Carlo simulation program assuming real-world conditions. CONCLUSION: The simulation shows that regardless of the study design employed, the diagnostic test specificity is of utmost importance in providing a valid estimate of the vaccine effectiveness.

Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution.

Introduction: Coronavirus disease 2019 (COVID-19) is known to induce robust antibody response in most of the affected individuals. The objective of the study was to determine if we can harvest the test sensitivity and specificity of a commercial serologic immunoassay merely based on the frequency distribution of the SARS-CoV-2 immunoglobulin (Ig) G concentrations measured in a population-based seroprevalence study. Materials and methods: The current study was conducted on a subset of a previously published dataset from the canton of Geneva. Data were taken from two non-consecutive weeks (774 samples from May 4-9, and 658 from June 1-6, 2020). Assuming that the frequency distribution of the measured SARS-CoV-2 IgG is binormal (an educated guess), using a non-linear regression, we decomposed the distribution into its two Gaussian components. Based on the obtained regression coefficients, we calculated the prevalence of SARS-CoV-2 infection, the sensitivity and specificity, and the most appropriate cut-off value for the test. The obtained results were compared with those obtained from a validity study and a seroprevalence population-based study. Results: The model could predict more than 90% of the variance observed in the SARS-CoV-2 IgG distribution. The results derived from our model were in good agreement with the results obtained from the seroprevalence and validity studies. Altogether 138 of 1432 people had SARS-CoV-2 IgG ≥ 0.90, the cut-off value which maximized the Youden's index. This translates into a true prevalence of 7.0% (95% confidence interval 5.4% to 8.6%), which is in keeping with the estimated prevalence of 7.7% derived from our model. Our model can provide the true prevalence. Conclusions: Having an educated guess about the distribution of test results, the test performance indices can be derived with acceptable accuracy merely based on the test results frequency distribution without the need for conducting a validity study and comparing the test results against a gold-standard test.

The apparent prevalence, the true prevalence.

Serologic tests are important for conducting seroepidemiologic and prevalence studies. However, the tests used are typically imperfect and produce false-positive and false-negative results. This is why the seropositive rate (apparent prevalence) does not typically reflect the true prevalence of the disease or condition of interest. Herein, we discuss the way the true prevalence could be derived from the apparent prevalence and test sensitivity and specificity. A computer simulation based on the Monte-Carlo algorithm was also used to further examine a situation where the measured test sensitivity and specificity are also uncertain. We then complete our review with a real example. The apparent prevalence observed in many prevalence studies published in medical literature is a biased estimation and cannot be interpreted correctly unless we correct the value.

Use of Oral Polio Vaccine and the Global Incidence of Mother-to-Child Human Immunodeficiency Virus Transmission.

Background: Mother-to-child transmission (MTCT) of human immunodeficiency virus (HIV) is an important global health issue. We hypothesized that the live attenuated poliovirus existing in oral polio vaccine (OPV) may protect uninfected neonates born to HIV-positive mothers through the stimulation of innate immune system. Objective: To test the hypothesis that countries using OPV have a lower MTCT rate (due to postnatal protection provided by the vaccine) compared with those using only inactivated polio vaccine (IPV). Methods: In an ecological study, the incidence of HIV/AIDS in children aged <1 year (IncHIV1), considered a surrogate index for MTCT rate, was compared between countries using OPV vs. IPV. The aggregated population data were retrieved for 204 countries from the Global Burden of Disease (GBD 2019) Collaborative Network website, "Our World in Data" website, the World Bank website, and the WHO Global Polio Eradication Initiative (GPEI). We used a negative binomial regression model with IncHIV1 as the dependent variable and the prevalence of HIV/AIDS in women aged 15-49 years (PrevHIV), antiretroviral therapy (ART) coverage, human development index (HDI), and the type of vaccine used in each country as independent variables. Multivariate imputation by chained equations was used to treat missing values. Analyses were performed for both the original dataset (with missing values) and the five imputed datasets. Results: IncHIV1 and PrevHIV were available for all 204 countries; vaccine type, 194 countries; HDI, 182 countries; and ART coverage, 133 countries. One-hundred and twenty-nine countries in the original dataset had complete data for all the above-mentioned variables; the imputed datasets had complete data for all 204 countries. The results obtained from the analysis of the original dataset had no overall difference with the pooled results obtained from the analysis of the five imputed datasets. Countries with higher HDI mainly use IPV; those with lower HDI commonly use OPV. PrevHIV, HDI, and the type of vaccine were independent predictors of IncHIV1. Use of OPV compared to IPV, was independently associated with an average decrease of 17% in IncHIV1 at the median HDI of 0.75. The protection provided by OPV increased in countries with lower HDI. Conclusions: Use of OPV compared with IPV, was independently associated with lower MTCT rate.