New Opportunities to Meet the Grand Challenges in Infectious Diseases
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».