Early Warning Diagnostics for Emerging Infectious Diseases in Developing into Late-Stage Pandemics.

The spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaks-SARS, MERS, swine flu, and COVID-19-has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from pre-human predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.

Real-world diagnostic accuracy of lipoarabinomannan in three non-sputum biospecimens for pulmonary tuberculosis disease.

BACKGROUND: Development of a non-sputum test using readily-obtainable biospecimens remains a global priority for tuberculosis (TB) control. We quantified lipoarabinomannan (LAM) concentrations, a pathogen biomarker for Mycobacterium tuberculosis, in urine, plasma and serum for real-world diagnostic accuracy of pulmonary TB among people living with and without HIV. METHODS: We conducted a prospective diagnostic study among adults with TB symptoms in South Africa. We measured LAM concentrations in time-matched urine, plasma and serum with an electrochemiluminescence immunoassay using two capture antibodies (FIND 28 and S4-20). From the completed cohort, we randomly selected 210 participants (2 cases: 1 control) based on sensitivity estimates, and we compared diagnostic accuracy of LAM measurements against the microbiological reference standard. FINDINGS: Urine and blood specimens from 210 of 684 adults enrolled were tested for LAM. Among 138 TB-positive adults (41% female), median urine LAM was 137 pg/mL and 52 pg/mL by FIND 28 and S4-20, respectively. Average LAM concentrations were highest in HIV-positive participants with CD4+ T cells <200 cells/mm3. Urine LAM by S4-20 achieved diagnostic sensitivity of 62% (95% CI: 53%-70%) and specificity of 99% (95% CI: 96%-100%). Plasma and serum LAM by FIND 28 showed similar sensitivity (70%, 95% CI: 62%-78%) and comparable specificities (90%, 95% CI: 82%-97%; 94%, 95% CI: 88%-99%). Diagnostic sensitivity of urine LAM by S4-20 was higher among participants without HIV (41%, 95% CI: 24%-61%) compared to HIV-positive participants with CD4 ≥200 cells/mm3 (20%, 95% CI: 8%-39%). INTERPRETATION: Detection of LAM was achievable in non-sputum specimens for pulmonary TB, but additional analyte concentration or signal amplification may be required to achieve diagnostic accuracy targets. FUNDING: Bill and Melinda Gates Foundation.

The Learning Curve for Magnetic Resonance Imaging/Ultrasound Fusion-guided Prostate Biopsy.

BACKGROUND: Magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) is more accurate at detecting clinically significant prostate cancer than conventional transrectal ultrasound-guided systematic biopsy. However, learning curves for attaining accuracy may limit the generalizability of published outcomes. OBJECTIVE: To delineate and quantify the learning curve for FBx by assessing the targeted biopsy accuracy and pathological quality of systematic biopsy over time. DESIGN, SETTING, AND PARTICIPANTS: We carried out a retrospective analysis of 173 consecutive men who underwent Artemis FBx with computer-template systematic sampling between July 2015 and May 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The accuracy of targeted biopsy was determined by calculating the distance between planned and actual core trajectories stored on Artemis. Systematic sampling proficiency was assessed via pathological analysis of fibromuscular tissue in all cores and then comparing pathology elements from individual cores from men in the first and last tertiles. Polynomial linear regression models, change-point analysis, and piecewise linear regression were used to quantify the learning curve. RESULTS AND LIMITATION: A significant improvement in targeted biopsy accuracy occurred up to 98 cases (p<0.01). There was a significant decrease in fibromuscular tissue in the systematic biopsy cores up to 84 cases (p<0.01) and an improvement in pathological quality when comparing systematic cores from the first and third tertiles. Use of a different fusion platform may limit the generalizability of our results. CONCLUSIONS: There is a significant learning curve for targeted and systemic biopsy using the Artemis platform. Improvements in accuracy of targeted biopsy and better sampling for systematic biopsy can be achieved with greater experience. PATIENT SUMMARY: We define the learning curve for magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) using targeted biopsy accuracy and systematic core sampling quality as measures. Our findings underscore the importance of overcoming learning curves inherent to FBx to minimize patient discomfort and biopsy risk and improve the quality of care for accurate risk stratification, active surveillance, and treatment selection.