Bibliographic record
Abstract
Historically, infectious diseases have taken a heavy toll on the human population.The past has repeatedly warned us that one fatal pathogen can kill millions of people.The Black Death pandemic in Eurasia took as many as 100 million lives in the fourteenth century (Cohn, 2008), and the 1918 Spanish flu killed more than 50 million individuals in less than 2 years (Taubenberger and Morens, 2019).This situation began to change in the twentieth century with the advent of two remarkable successes, antibiotics and vaccines, which have saved hundreds of millions of lives from otherwise deadly infections.It is unimaginable how many lives would have been lost if we have not had vaccines for smallpox, yellow fever, polio, and other lethal pathogens.It is incomprehensible what would happen in surgical wards if we do not have antibiotics.One pleasant coincidence is that the tools and technologies leading to these great successes are often provided by microbes themselves: antibiotics are produced by bacteria and fungi, vaccines are often attenuated or inactivated microbes.Equally fascinating is that microbes, including viruses and bacteria, have taught us the molecular language to comprehend the most fundamental processes of life, and have inspired us to develop powerful biotechnologies to prevent and treat various lifethreatening infections.One pillar of modern health science is DNA biology and recombinant DNA techniques.It is the bacteria and viruses which have taught us DNA is the genetic material and how gene expression from DNA is executed and regulated.More gratefully, we have also acquired from these microbes the molecular tools to decode DNA sequences and engineer DNA clones.Nowadays, next generation sequencing and metadata analysis have revolutionized the ways we manage infectious diseases at the levels of diagnosis, prevention, and treatment.Despite these ground-breaking achievements, infectious diseases still lay a grave burden on public health, causing 10 to 15 million deaths annually.Attesting to this heavy global impact, six out of the 10 threats to global health, announced by WHO (World Health Organization) in 2019, are related to infectious diseases (https://www.who.int/emergencies/ten-threats-toglobal-health-in-2019).These six threats include the global influenza pandemic, antimicrobial resistance, Ebola and other high-threat pathogens, vaccine hesitancy, Dengue, and HIV (human immunodeficiency virus).It is by no accident that these infectious pathogens and related issues are listed atop the global health challenges.Influenza epidemics have been frequently recorded in human history.We are simply unable to eradicate influenza viruses from the human population partially due to their sporadic transmission into humans from their natural reservoirs of birds and other animals (Olsen et al., 2006).It has already been a challenge to produce an effective seasonal flu vaccine, and it will be a far more difficult task, if not currently impossible, to forecast and prepare for an unpredictable and yet forthcoming flu pandemic.We have benefited from the use of antibiotics for decades.However, overuse of antibiotics and other ill medical practices have accelerated the emergence of resistance bacteria.Without a sustainable pipeline of new antibiotics, and without other effective treatments of bacterial infections, we may succumb to infections caused by multidrug-resistant pathogenic bacteria, otherwise known as superbugs.In the United States alone, 35,000 people die of antibiotic-resistant bacterial infections annually, as reported by the Centers for Disease Control and Prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".