PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.

Using Low-Cost Sensors to Assess Fine Particulate Matter Infiltration (PM2.5) during a Wildfire Smoke Episode at a Large Inpatient Healthcare Facility.

Wildfire smoke exposure is associated with a range of acute health outcomes, which can be more severe in individuals with underlying health conditions. Currently, there is limited information on the susceptibility of healthcare facilities to smoke infiltration. As part of a larger study to address this gap, a rehabilitation facility in Vancouver, Canada was outfitted with one outdoor and seven indoor low-cost fine particulate matter (PM2.5) sensors in Air Quality Eggs (EGG) during the summer of 2020. Raw measurements were calibrated using temperature, relative humidity, and dew point derived from the EGG data. The infiltration coefficient was quantified using a distributed lag model. Indoor concentrations during the smoke episode were elevated throughout the building, though non-uniformly. After censoring indoor-only peaks, the average infiltration coefficient (range) during typical days was 0.32 (0.22-0.39), compared with 0.37 (0.31-0.47) during the smoke episode, a 19% increase on average. Indoor PM2.5 concentrations quickly reflected outdoor conditions during and after the smoke episode. It is unclear whether these results will be generalizable to other years due to COVID-related changes to building operations, but some of the safety protocols may offer valuable lessons for future wildfire seasons. For example, points of building entry and exit were reduced from eight to two during the pandemic, which likely helped to protect the building from wildfire smoke infiltration. Overall, these results demonstrate the utility of indoor low-cost sensors in understanding the impacts of extreme smoke events on facilities where highly susceptible individuals are present. Furthermore, they highlight the need to employ interventions that enhance indoor air quality in such facilities during smoke events.

Where Are We Headed? Diagnosing Abnormal Head Shapes Through Virtual Care Visits.

PURPOSE: Since the beginning of the coronavirus disease 2019 pandemic in early March, there has been a push to expand virtual patient care visits instead of in-person clinic visits. Studies have found that telemedicine can provide efficient triaging, reduction in emergency room visits, and conservation of health care resources and personnel. Although virtual patient care has been implicated in providing similar outcomes to traditional face-to-face care in patients affected with coronavirus disease 2019, there are a lack of studies on the effectiveness of virtual care visits (VCVs) for patients with craniosynostosis or deformational plagiocephaly. This study aims to develop an understanding of whether physicians can accurately diagnose pediatric patients with craniosynostosis or deformational plagiocephaly via VCVs, and whether they can determine if affected patients will benefit from helmet correction or if surgical treatment is required. METHODS: An Institutional Review Board-approved retrospective chart analysis over a 4-month period (March 1, 2020 to June 30, 2020) was performed analyzing all pediatric patients (<18 years old) who underwent virtual care calls for diagnosis and treatment of abnormal head shape. Patients were referred to UT Physicians Pediatric Surgery clinic for evaluation by a member of the Texas Cleft-Craniofacial Team (2 surgeons or 1 physician's assistant). Variables such as patient demographics, diagnosis, and need for confirmation were pulled and recorded from Allscripts Electronic Medical Records software. RESULTS: Thirty-five patients were identified who fit our search criteria. Out of these patients, eleven (31.43%) cases were diagnosed with craniosynostosis, twenty-two (62.86%) cases were diagnosed with deformational plagiocephaly, and 2 (5.71%) cases were diagnosed as being normocephalic. Median age at virtual care evaluation was 14.10 months (Interquartile Range [IQR] 5.729, 27.542) for patients diagnosed with craniosynostosis and 6.51 months (IQR 4.669, 7.068) for patients diagnosed with deformational plagiocephaly. All eleven (100%) patients diagnosed with craniosynostosis were referred for a confirmatory computed tomography scan before undergoing surgical intervention and saw an alleviation in head shape postoperatively. Eighteen (81.82%) of patients diagnosed with deformational plagiocephaly were recommended to undergo conservative treatment and the remaining 4 (18.18%) were recommended for helmet therapy. Two cases were unable to be diagnosed virtually. These patients needed a follow-up visit in person to establish a diagnosis and plan of treatment. CONCLUSIONS: Virtual care visits are increasing in frequency and this includes consultations for abnormal head shapes. Our experience demonstrates that the majority of patients can be evaluated safely in this modality, with only 5.71% requiring additional imaging or in-person visits to confirm the diagnosis. Our study underscores the feasibility of virtually diagnosing and recommending a plan for treatment in pediatric patients with abnormal head shapes. This information can be implemented to further our knowledge on the accuracy of diagnosis and treatment options for patients with craniosynostosis and deformational plagiocephaly. Further analyses are needed to quantify the financial and patient-reported outcomes of VCVs for these patients.