Scale-Up of COVID-19 Testing Services in NYC, 2020-2021: Lessons Learned to Maximize Reach, Equity and Timeliness.

During infectious disease epidemics, accurate diagnostic testing is key to rapidly identify and treat cases, and mitigate transmission. When a novel pathogen is involved, building testing capacity and scaling testing services at the local level can present major challenges to healthcare systems, public health agencies, and laboratories. This mixed methods study examined lessons learned from the scale-up of SARS-CoV-2 testing services in New York City (NYC), as a core part of NYC's Test & Trace program. Using quantitative and geospatial analyses, the authors assessed program success at maximizing reach, equity, and timeliness of SARS-CoV-2 diagnostic testing services across NYC neighborhoods. Qualitative analysis of key informant interviews elucidated key decisions, facilitators, and barriers involved in the scale-up of SARS-CoV-2 testing services. A major early facilitator was the ability to establish working relationships with private sector vendors and contractors to rapidly procure and manufacture necessary supplies locally. NYC residents were, on average, less than 25 min away from free SARS-CoV-2 diagnostic testing services by public transport, and services were successfully directed to most neighborhoods with the highest transmission rates, with only one notable exception. A key feature was to direct mobile testing vans and rapid antigen testing services to areas based on real-time neighborhood transmission data. Municipal leaders should prioritize fortifying supply chains, establish cross-sectoral partnerships to support and extend testing services, plan for continuous testing and validation of assays, ensure open communication feedback loops with CBO partners, and maintain infrastructure to support mobile services during infectious disease emergencies.

Utility of Skin Tone on Pulse Oximetry in Critically Ill Patients: A Prospective Cohort Study.

OBJECTIVE: Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects minority Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone which is believed to be the root cause of the disparity. Our objective was to determine the utility of skin tone in explaining pulse oximetry discrepancies. DESIGN: Prospective cohort study. SETTING: Patients were eligible if they had pulse oximetry recorded up to 5 minutes before arterial blood gas (ABG) measurements. Skin tone was measured using administered visual scales, reflectance colorimetry, and reflectance spectrophotometry. PARTICIPANTS: Admitted hospital patients at Duke University Hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sao2-Spo2 bias, variation of bias, and accuracy root mean square, comparing pulse oximetry, and ABG measurements. Linear mixed-effects models were fitted to estimate Sao2-Spo2 bias while accounting for clinical confounders.One hundred twenty-eight patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited. Skin tone data were prospectively collected using six measurement methods, generating eight measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared with self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: -2.40%; 95% CI, -4.32% to -0.48%; p = 0.01) when comparing patients with lighter and dark skin tones. CONCLUSIONS: We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.

Functional genomics and taxonomic insights into heavy metal tolerant novel bacterium Brevibacterium metallidurans sp. nov. NCCP-602T isolated from tannery effluent in Pakistan.

The strain designated NCCP-602T was isolated from tannery effluent, and displayed aerobic, gram-positive, rod-shaped cells that were characterized by oxidase negative, catalase positive, and non-motile features. The most favourable growth conditions were observed at a temperature of 30°C, pH 7.0, and NaCl concentration of 1% (w/v). It tolerated heavy metals at high concentrations of chromium (3600 ppm), copper (3300 ppm), cadmium (3000 ppm), arsenic (1200 ppm) and lead (1500 ppm). The results of phylogenetic analysis, derived from sequences of the 16S rRNA gene, indicated the position of strain NCCP-602T within genus Brevibacterium and showed that it was closely related to Brevibacterium ammoniilyticum JCM 17537T. Strain NCCP-602 T formed a robust branch that was clearly separate from closely related taxa. A comparison of 16S rRNA gene sequence similarity and dDDH values between the closely related type strains and strain NCCP-602T provided additional evidence supporting the classification of strain NCCP-602T as a distinct novel genospecies. The polar lipid profile included diphosphatidylglycerol, glycolipid, phospholipids and amino lipids. MK-7 and MK-8 were found as the respiratory quinones, while anteiso-C15:0, iso-C15:0, iso-C16:0, iso-C17:0, and anteiso-C17:0 were identified as the predominant cellular fatty acids (> 10%). Considering the convergence of phylogenetic, phenotypic, chemotaxonomic, and genotypic traits, it is suggested that strain NCCP-602 T be classified as a distinct species Brevibacterium metallidurans sp. nov. within genus Brevibacterium with type strain NCCP-602T (JCM 18882T = CGMCC1.62055T).

Evaluation of the New York City COVID-19 case investigation and contact tracing program: a cascade of care analysis.

BACKGROUND: New York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach. METHODS: This cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact. RESULTS: A total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts. CONCLUSIONS: T2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.

Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake.

Background. Fish and rice are the main dietary sources of methylmercury (MeHg); however, rice does not contain the same beneficial nutrients as fish, and these differences can impact the observed health effects of MeHg. Hence, it is important to validate a biomarker, which can distinguish among dietary MeHg sources. Methods. Mercury (Hg) stable isotopes were analyzed in hair samples from peripartum mothers in China (n = 265). Associations between mass dependent fractionation (MDF) (δ202Hg) and mass independent fractionation (MIF) (Δ199Hg) (dependent variables) and dietary MeHg intake (independent variable) were investigated using multivariable regression models. Results. In adjusted models, hair Δ199Hg was positively correlated with serum omega-3 fatty acids (a biomarker for fish consumption) and negatively correlated with maternal rice MeHg intake, indicating MIF recorded in hair can be used to distinguish MeHg intake predominantly from fish versus rice. Conversely, in adjusted models, hair δ202Hg was not correlated with measures of dietary measures of MeHg intake. Instead, hair δ202Hg was strongly, negatively correlated with hair Hg, which explained 27-29% of the variability in hair δ202Hg. Conclusions. Our results indicated that hair Δ199Hg can be used to distinguish MeHg intake from fish versus rice. Results also suggested that lighter isotopes were preferentially accumulated in hair, potentially reflecting Hg binding to thiols (i.e., cysteine); however, more research is needed to elucidate this hypothesis. Broader impacts include 1) validation of a non-invasive biomarker to distinguish MeHg intake from rice versus fish, and 2) the potential to use Hg isotopes to investigate Hg binding in tissues.

ENCoDE - a skin tone and clinical dataset from a prospective trial on acute care patients.

Background: Although hypothesized to be the root cause of the pulse oximetry disparities, skin tone and its use for improving medical therapies have yet to be extensively studied. Studies previously used self-reported race as a proxy variable for skin tone. However, this approach cannot account for skin tone variability within race groups and also risks the potential to be confounded by other non-biological factors when modeling data. Therefore, to better evaluate health disparities associated with pulse oximetry, this study aimed to create a unique baseline dataset that included skin tone and electronic health record (EHR) data. Methods: Patients admitted to Duke University Hospital were eligible if they had at least one pulse oximetry value recorded within 5 minutes before an arterial blood gas (ABG) value. We collected skin tone data at 16 different body locations using multiple devices, including administered visual scales, colorimetric, spectrophotometric, and photography via mobile phone cameras. All patients' data were linked in Duke's Protected Analytics Computational Environment (PACE), converted into a common data model, and then de-identified before publication in PhysioNet. Results: Skin tone data were collected from 128 patients. We assessed 167 features per skin location on each patient. We also collected over 2000 images from mobile phones measured in the same controlled environment. Skin tone data are linked with patients' EHR data, such as laboratory data, vital sign recordings, and demographic information. Conclusions: Measuring different aspects of skin tone for each of the sixteen body locations and linking them with patients' EHR data could assist in the development of a more equitable AI model to combat disparities in healthcare associated with skin tone. A common data model format enables easy data federation with similar data from other sources, facilitating multicenter research on skin tone in healthcare. Description: A prospectively collected EHR-linked skin tone measurements database in a common data model with emphasis on pulse oximetry disparities.

In-situ revealed inhibition of W2C to excessive oxidation of CoOOH for high-efficiency alkaline overall water splitting.

The design of low-cost, efficient, and stable multifunctional basic catalysts to replace the high-cost noble metal catalysts remains a challenge. In this work, we report a dual-component Co-W2C catalytic system which achieves excellent properties of hydrogen evolution reaction (HER, η10 = 63 mV), oxygen evolution reaction (OER, η10 = 259 mV) and overall water splitting (η10 = 1.53 V) by adjusting the interfacial electronic structure of the material. Further density functional theory (DFT) calculations indicate that the efficient electronic modulation at the W2C/Co interface leads to the generation of favorable hydroxyl and hydrogen species energetics on the hybrid surface. The results of the in-situ Raman spectra show that W2C can suppress the excessive oxidation of the active site during the OER process, and the existence of core-shell structure also protects the W2C substrate. The stable and efficient catalytic performance of Co-W2C is attributed to the common advantages of structural and interface manipulation.

Synergistic effects of interface and phase engineering on telluride toward alkaline/neutral hydrogen evolution reaction in freshwater/seawater.

The development of efficient, stable, and versatile hydrogen evolution electrocatalysts is of great meaning, but still faces challenging. Interface engineering and phase engineering have been immensely applied in the field of hydrogen evolution reaction (HER) because of their unique physicochemical properties. However, they are typically used separately, which limits their effectiveness. Herein, we propose an interface-engineered CoMo/CoTe electrocatalyst, consisting of an amorphous CoMo (a-CoMo) layer-encapsulated crystalline CoTe array, achieving the profound optimization of catalytic performance. The experimental results and density functional theory calculations show that the d-band center of the catalyst shifts further upward in contrast with its crystalline-crystalline counterpart, optimizing the electronic structure and the intermediate adsorption, thereby reducing the kinetic barrier of HER. The a-CoMo/CoTe with superhydrophilic/superaerophobic features shows excellent catalytic performance in alkaline, neutral, and simulated seawater environments.

Systematic Mendelian Randomization Exploring Druggable Genes for Hemorrhagic Strokes.

Patients with hemorrhagic stroke have high rates of morbidity and mortality, and drugs for prevention are very limited. Mendelian randomization (MR) analysis can increase the success rate of drug development by providing genetic evidence. Previous MR analyses only analyzed the role of individual drug target genes in hemorrhagic stroke; therefore, we used MR analysis to systematically explore the druggable genes for hemorrhagic stroke. We sequentially performed summary-data-based MR analysis and two-sample MR analysis to assess the associations of all genes within the database with intracranial aneurysm, intracerebral hemorrhage, and their subtypes. Validated genes were further analyzed by colocalization. Only genes that were positive in all three analyses and were druggable were considered desirable genes. We also explored the mediators of genes affecting hemorrhagic stroke incidence. Finally, the associations of druggable genes with other cardiovascular diseases were analyzed to assess potential side effects. We identified 56 genes that significantly affected hemorrhagic stroke incidence. Moreover, TNFSF12, SLC22A4, SPARC, KL, RELT, and ADORA3 were found to be druggable. The inhibition of TNFSF12, SLC22A4, and SPARC can reduce the risk of intracranial aneurysm, subarachnoid hemorrhage, and intracerebral hemorrhage. Gene-induced hypertension may be a potential mechanism by which these genes cause hemorrhagic stroke. We also found that blocking these genes may cause side effects, such as ischemic stroke and its subtypes. Our study revealed that six druggable genes were associated with hemorrhagic stroke, and the inhibition of TNFSF12, SLC22A4, and SPARC had preventive effects against hemorrhagic strokes.

The regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy.

Ferroptosis is an iron-dependent programmed cell death process that involves lipid oxidation via the Fenton reaction to produce lipid peroxides, causing disruption of the lipid bilayer, which is essential for cellular survival. Ferroptosis has been implicated in the occurrence and treatment response of various types of cancer, and targeting ferroptosis has emerged as a promising strategy for cancer therapy. However, cancer cells can escape cellular ferroptosis by activating or remodeling various signaling pathways, including oxidative stress pathways, thereby limiting the efficacy of ferroptosis-activating targeted therapy. The key anti-oxidative transcription factor, nuclear factor E2 related factor 2 (Nrf2 or NFE2L2), plays a dominant role in defense machinery by reprogramming the iron, intermediate, and glutathione peroxidase 4 (GPX4)-related network and the antioxidant system to attenuate ferroptosis. In this review, we summarize the recent advances in the regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy and explore the prospect of combining Nrf2 inhibitors and ferroptosis inducers as a promising cancer treatment strategy.

Integrating molecular pathway with genome-wide association data for causality identification in breast cancer.

OBJECTIVE: The study purpose was to explore the causal association between pyruvate metabolism and breast cancer (BC), as well as the molecular role of key metabolic genes, by using bioinformatics and Mendelian randomization (MR) analysis. METHODS: We retrieved and examined diverse datasets from the GEO database to ascertain differentially acting genes (DAGs) in BC via differential expression analysis. Following this, we performed functional and pathway enrichment analyses to ascertain noteworthy molecular functions and metabolic pathways in BC. Employing MR analysis, we established a causal association between pyruvate metabolism and the susceptibility to BC. Additionally, utilizing the DGIdb database, we identified potential targeted medications that act on genes implicated in the pyruvate metabolic pathway and formulated a competing endogenous RNA (ceRNA) regulatory network in BC. RESULTS: We collected the datasets GSE54002, GSE70947, and GSE22820, and identified a total of 1127 DEGs between the BC and NC groups. GO and KEGG enrichment analysis showed that the molecular functions of these DEGs mainly included mitotic nuclear division, extracellular matrix, signaling receptor activator activity, etc. Metabolic pathways were mainly concentrated in PI3K-Akt signaling pathway, Cytokine-cytokine receptor binding and Pyruvate, Tyrosine, Propanoate and Phenylalanine metabolism, etc. In addition, MR analysis demonstrated a causal relationship between pyruvate metabolism and BC risk. Finally, we constructed a regulatory network between pathway genes (ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C) and targeted drugs, as well as a ceRNA (lncRNA-miRNA-mRNA) regulatory network for BC, further revealing their interactions. CONCLUSIONS: Our research revealed a causal association between pyruvate metabolism and BC risk, found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C takes place an important part in the development of BC in the molecular mechanisms related to pyruvate metabolism, and identified some potential targeted small molecule drugs.

Thalassospira aquimaris sp. nov. and Winogradskyella marincola sp. nov. two marine bacteria isolated from an agar-degrading co-culture.

Two novel Gram-stain-negative, aerobic, and non-motile strains, designated FZY0004T and YYF002T, were isolated from an agar-degrading co-culture, which was obtained from seawater of the intertidal zone of Yancheng City, the Yellow Sea of China. Strain FZY0004T optimally grew at 28 °C, pH 7.0, and 2-6% NaCl, while strain YYF002T optimally grew at 28 °C, pH 7.5, and 2-4% NaCl. Strain FZY0004T possessed Q-9 as the major respiratory quinone, and its major fatty acids (> 10%) were summed feature 8 (C18:1 ω7c), C16:0, and summed feature 3 (C16:1 ω7c/C16:1 ω6c). The polar lipids identified in strain FZY0004T were phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and several unidentified phospholipids (PL) and lipids (L). On the other hand, strain YYF002T had MK-6 as the predominant respiratory quinone and its major fatty acids consisted of iso-C15:0, iso-C15:1 G, and iso-C15:0 3-OH. The polar lipids identified in strain YYF002T were aminolipid (AL), PE, and several unidentified lipids. Strain FZY0004T shared 99.5% 16S rRNA gene sequence similarity and 90.1% average nucleotide identity (ANI) with T. povalilytica Zumi 95T, and strain YYF002T shared 99.2% 16S rRNA gene sequence similarity and 88.2% ANI with W. poriferorum JCM 12885T. The genomic DNA G + C contents of strains FZY0004T and YYF002T were 54.5% and 33.5%, respectively. The phylogenetic, phenotypic, and physiological characteristics permitted the distinction of the two strains from their neighbors, and we thus propose the names Thalassospira aquimaris sp. nov. (type strain FZY0004T = JCM 35895T = MCCC 1K08380T) and Winogradskyella marincola sp. nov. (type strain YYF002T = JCM 35950T = MCCC 1K08382T).

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study.

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.

Synthesis of 1,2,4,5-tetra-substituted benzenes via copper-catalyzed dimerization of γ,δ-unsaturated ketones.

An efficient cyclization for the synthesis of 1,2,4,5-tetra-substituted benzenes via copper catalyzed dimerization of γ,δ-unsaturated ketones has been described. This one-pot procedure employs the γ,δ-unsaturated ketones as the sole substrate with multiple C-C bond formation. This protocol features broad substrate scope and provides a facile and robust method to construct polysubstituted benzene derivatives under mild conditions.

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov., isolated from seawater of the Yellow Sea.

Two novel Gram-stain-negative, aerobic, non-motile and rod-shaped bacteria, designated as WL0004T and XHP0148T, were isolated from seawater samples collected from the coastal areas of Nantong and Lianyungang, PR China, respectively. Both strains were found to grow at 10-42 °C (optimum, 37 °C) and with 2.0-5.0 % (w/v) NaCl (optimum, 3.0 %). Strain WL0004T grew at pH 6.0-9.0 (optimum, pH 7.0-8.0), while XHP0148T grew at pH 6.0-10.0 (optimum, pH 7.0-8.0). The major cellular fatty acids (>10 %) of both strains included summed feature 8 (C18 : 1 ω6c and/or C18 : 1 ω7c). In addition, strain WL0004T contained 11-methyl C18 : 1 ω7c and strain XHP0148T contained C12 : 0 3-OH. The respiratory quinone of both strains was ubiquinone-10. The G+C content of genomic DNA of strains WL0004T and XHP0148T were 62.5 and 63.0 mol%, respectively. Strains WL0004T and XHP0148T showed the highest 16S rRNA gene sequence similarity to Ruegeria pomeroyi DSS-3T (99.4 and 99.0 %, respectively), and the 16S rRNA gene-based phylogenetic analysis indicated that the two strains were closely related to members of the genus Ruegeria. The average nucleotide identity and digital DNA-DNA hybridization values among the two strains and type strains of the genus Ruegeria were all below 95 and 70 %, respectively, and the phylogenetic tree reconstructed from the bac120 gene set indicated that the two strains are distinct from each other and the members of the genus Ruegeria. Based on this phenotypic and genotypic characterization, strains WL0004T (=MCCC 1K07523T=JCM 35565T=GDMCC 1.3083T) and XHP0148T (=MCCC 1K07543T=JCM 35569T=GDMCC 1.3089T) should be recognized as representing two novel species of the genus Ruegeria and the names Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov. are proposed, respectively.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Utility of skin tone on pulse oximetry in critically ill patients: a prospective cohort study.

Importance: Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Although the root cause of these clinical performance discrepancies is believed to be skin tone, previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone. Objective: To determine the utility of objectively measured skin tone in explaining pulse oximetry discrepancies. Design Setting and Participants: Admitted hospital patients at Duke University Hospital were eligible for this prospective cohort study if they had pulse oximetry recorded up to 5 minutes prior to arterial blood gas (ABG) measurements. Skin tone was measured across sixteen body locations using administered visual scales (Fitzpatrick Skin Type, Monk Skin Tone, and Von Luschan), reflectance colorimetry (Delfin SkinColorCatch [L*, individual typology angle {ITA}, Melanin Index {MI}]), and reflectance spectrophotometry (Konica Minolta CM-700D [L*], Variable Spectro 1 [L*]). Main Outcomes and Measures: Mean directional bias, variability of bias, and accuracy root mean square (ARMS), comparing pulse oximetry and ABG measurements. Linear mixed-effects models were fitted to estimate mean directional bias while accounting for clinical confounders. Results: 128 patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited, none with hidden hypoxemia. Skin tone data was prospectively collected using 6 measurement methods, generating 8 measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared to self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: -2.40%; 95% CI: -4.32%, -0.48%; p=0.01) when comparing patients with lighter and dark skin tones. Conclusions and relevance: We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.