Coccidioidomycosis is associated with a broad spectrum of illness severity, ranging from asymptomatic or self-limited pulmonary infection to life-threatening manifestations of disseminated disease. Serologic studies before the widespread availability of antifungals established current understanding of serologic kinetics and dynamics.

Disseminated coccidioidomycosis (DCM) is caused by Coccidioides, pathogenic fungi endemic to the Southwestern United States and Mexico. Illness occurs in approximately 30% of those infected, <1% of whom develop disseminated disease. To address why some individuals allow dissemination, we enrolled DCM patients and performed whole-exome sequencing. In an exploratory set of 67 DCM patients, two had haploinsufficient STAT3 mutations, while defects in b-glucan sensing and response were seen in 34/67 (50.7%) cases. Damaging CLEC7A (n=14) and PLCG2 (n=11) variants were associated with impaired production of b-glucan-stimulated TNF-a from peripheral blood mononuclear cells compared to healthy controls (P<0.005). Using ancestry-matched controls, damaging CLEC7A and PLCG2 variants were over-represented in DCM (P=0.0206, P=0.015, respectively) including CLEC7A Y238* (P=0.0105) and PLCG2 R268W (P=0.0025). A validation cohort of 111 DCM patients confirmed PLCG2 R268W (P=0.0276), CLEC7A I223S (P=0.044), and CLEC7A Y238* (P=0.0656). Stimulation with a DECTIN-1 agonist induced DUOX1/DUOXA1-derived H2O2 in transfected cells. Heterozygous DUOX1 or DUOXA1 variants which impaired H2O2 production were overrepresented in discovery and validation cohorts. Patients with DCM have impaired b-glucan sensing or response affecting TNF-a and H2O2 production. Impaired Coccidioides recognition and decreased cellular response are associated with disseminated coccidioidomycosis.

BackgroundHIV infection increases the risk of placental malaria, which is associated with poor maternal and infant outcomes. Recommendations in Uganda are for HIV-infected pregnant women to receive daily trimethoprim-sulphamethoxazole (TS) and HIV-uninfected women to receive intermittent sulphadoxine-pyrimethamine (SP). TS decreases the risk of malaria in HIV-infected adults and children but has not been evaluated among pregnant women.MethodsThis was a cross sectional study comparing the prevalence of placental malaria between HIV-infected women prescribed TS and HIV-uninfected women prescribed intermittent preventive therapy with sulphadoxine-pyrimethamine (IPT-SP) in a high malaria transmission area in Uganda. Placental blood was evaluated for malaria using smear and PCR.ResultsPlacentas were obtained from 150 HIV-infected women on TS and 336 HIV-uninfected women on IPT-SP. The proportion of HIV-infected and HIV-uninfected women with placental malaria was 19% vs. 26% for those positive by PCR and 6% vs. 9% for those positive by smear, respectively. Among all infants, smear+ placental malaria was most predictive of low birth weight (LBW). Primigravidae were at higher risk than multigravidae of having placental malaria among HIV-uninfected, but not HIV-infected, women. Adjusting for gravidity, age, and season at the time of delivery, HIV-infected women on TS were not at increased risk for placental malaria compared to HIV-uninfected women on IPT-SP, regardless of the definition used.ConclusionPrevalence of placental malaria was similar in HIV-infected women on TS and HIV-uninfected women on IPT-SP. Nonetheless, while nearly all of the women in this study were prescribed anti-folates, the overall risk of placental malaria and LBW was unacceptably high. The population attributable risk of placental malaria on LBW was substantial, suggesting that future interventions that further diminish the risk of placental malaria may have a considerable impact on the burden of LBW in this population.

ABSTRACT OBJECTIVE To describe the pattern of transmission of SARS-CoV-2 during 2 nosocomial outbreaks of COVID-19 with regard to the possibility of airborne transmission. DESIGN Contact investigations with active case finding were used to assess the pattern of spread from 2 COVID-19 index patients. SETTING A community hospital and university medical center in the United States, in February and March, 2020, early in the COVID-19 pandemic. PATIENTS Two index patients and 421 exposed health care workers. METHODS Exposed staff were identified by analyzing the EMR and conducting active case finding in combination with structured interviews. Staff were tested for COVID-19 by obtaining oropharyngeal/nasopharyngeal specimens, with RT-PCR testing to detect SARS-CoV-2. RESULTS Two separate index patients were admitted in February and March 2020, without initial suspicion for COVID-19 and without contact or droplet precautions in place; both patients underwent several aerosol generating procedures in this context. A total of 421 health care workers were exposed in total, and the results of the case contact investigations identified 8 secondary infections in health care workers. In all 8 cases, the staff had close contact with the index patients without sufficient personal protective equipment. Importantly, despite multiple aerosol generating procedures, there was no evidence of airborne transmission. CONCLUSION These observations suggest that, at least in a healthcare setting, a majority of SARS-CoV-2 transmission is likely to take place during close contact with infected patients through respiratory droplets, rather than by long-distance airborne transmission.

Background Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. Content In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. Summary The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of “data fusion” describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.