Point-of-care testing device platform for the determination of creatinine on an enzyme@CS/PB/MXene@AuNP-based screen-printed carbon electrode.

Screen-printed carbon electrodes (SPCE) functionalized with MXene-based three-dimensional nanomaterials are reported for rapid determination of creatinine. Ti3C2TX MXene with in situ reduced AuNPs (MXene@AuNP) were used as a coreactant accelerator for efficient immobilization of enzymes. Creatinine could be oxidized by chitosan-embedded creatinine amidohydrolase, creatine amidinohydrolase, or sarcosine oxidase to generate H2O2, which could be electrochemically detected enhanced by Prussian blue (PB). The enzyme@CS/PB/MXene@AuNP/SPCE detected creatinine within the range 0.03-4.0 mM, with a limit of detection of 0.01 mM, with an average recovery of 96.8-103.7%. This indicates that the proposed biosensor is capable of detecting creatinine in a short amount of time (4 min) within a ± 5% percentage error, in contrast with the standard clinical colorimetric method. With this approach, reproducible and stable electrochemical responses could be achieved for determination of creatinine in serum, urine, or saliva. These results demonstrated its potential for deployment in resource-limited settings for early diagnosis and tracking the progression of chronic kidney disease (CKD).

Recovery of Cu, Co, and Fe from Pyrite Cinder Based on Mineral Phase Reconstruction.

Pyrite cinder (PyC) containing polymetallics is difficult to use due to the low grade of metals and complex mineral phase composition, the low reutilized rate of which causes the wastage of resources. In this paper, a novel approach based on mineral phase reconstruction was proposed to recover Cu, Co, and Fe from PyC. A feasible reduction roasting process was developed for mineral phase reconstruction, followed by leaching with sulfuric acid to recover Cu and Co; finally, the leaching residue was separated by a magnetic tube to recover Fe. The maximum copper and cobalt extract rates of 86.15 and 79.61% were achieved respectively under the optimized conditions of reduction roasting for 30 min at 550 °C and 30% CO/N2 volume fraction, followed by leaching for 4 h at a liquid-solid ratio of 4:1 (mL/g), a mass concentration of 160 g/L sulfuric acid, and a temperature of 70 °C. The iron concentrate can be obtained with 63.08% Fe grade and 98.91% recovery rate by magnetic separation at a magnetic field strength of 28.26 kA/m. The mechanism analysis results of mineral phase reconstruction revealed that the primary copper sulfide in PyC was transformed into free copper oxide, combined copper oxide, and secondary copper sulfide without changing the valence state of copper by reduction roasting, resulting in a higher extraction rate of copper. Meanwhile, cobaltosic oxide and cobalt ferrite in PyC were transformed into cobalt sulfate and cobalt sulfide with the reduction of Co(III) to Co(II), improving the extraction rate of cobalt.

Fair prediction of 2-year stroke risk in patients with atrial fibrillation.

OBJECTIVE: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups. MATERIALS AND METHODS: Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation. RESULTS: Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach. DISCUSSION: Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice. CONCLUSIONS: Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.

Reaction Mechanism of Rapid CO Electroreduction to Propylene and Cyclopropane (C3+) over Triple Atom Catalysts.

The carbon monoxide reduction reaction (CORR) toward C2+ and C3+ products such as propylene and cyclopropane can not only reduce anthropogenic emissions of CO and CO2 but also produce value-added organic chemicals for polymer and pharmaceutical industries. Here, we introduce the concept of triple atom catalysts (TACs) that have three intrinsically strained and active metal centers for reducing CO to C3+ products. We applied grand canonical potential kinetics (GCP-K) to screen 12 transition metals (M) supported by nitrogen-doped graphene denoted as M3N7, where M stands for Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, and Au. We sought catalysts with favorable CO binding, hydrogen binding, and C-C dimerization energetics, identifying Fe3N7 and Ir3N7 as the best candidates. We then studied the entire reaction mechanism from CO to C3H6 and C2H4 as a function of applied potential via, respectively, 12-electron and 8-electron transfer pathways on Fe3N7 and Ir3N7. Density functional theory (DFT) predicts an overpotential of 0.17 VRHE for Fe3N7 toward propylene and an overpotential of 0.42 VRHE toward cyclopropane at 298.15 K and pH = 7. Also, DFT predicts an overpotential of 0.15 VRHE for Ir3N7 toward ethylene. This work provides fundamental insights into the design of advanced catalysts for C2+ and C3+ synthesis at room temperature.

Household transmission of human metapneumovirus and seasonal coronavirus.

We analyzed data from a community-based acute respiratory illness study involving K-12 students and their families in southcentral Wisconsin and assessed household transmission of two common seasonal respiratory viruses - human metapneumovirus (HMPV) and human coronaviruses OC43 and HKU1 (HCOV). We found secondary infection rates of 12.2% (95% CI: 8.1%-17.4%) and 19.2% (95% CI: 13.8%-25.7%) for HMPV and HCOV, respectively. We performed individual- and family-level regression models and found that HMPV transmission was positively associated age of the index case (individual model: p = .016; family model: p = .004) and HCOV transmission was positively associated with household density (family model: p = .048). We also found that the age of the non-index case was negatively associated with transmission of both HMPV (individual model: p = .049) and HCOV (individual model: p = .041), but we attributed this to selection bias from the original study design. Understanding household transmission of common respiratory viruses like HMPV and HCOV may help to broaden our understanding of the overall disease burden and establish methods to prevent the spread of disease from low- to high-risk populations.

A Network Meta-Analysis of the Systemic Therapies in Unresectable Head and Neck Squamous Cell Carcinoma.

The current standard treatment for locally advanced squamous cell carcinoma of the head and neck (LASCCHN) comprises concurrent radiotherapy (CRT) alongside platinum-based chemotherapy. However, innovative therapeutic alternatives are being evaluated in phase II/III randomized trials. This study employed a Bayesian network meta-analysis (NMA) using fixed effects to provide both direct and indirect comparisons of all existing treatment modalities for unresectable LASCCHN. METHODS: We referenced randomized controlled trials (RCTs) from January 2000 to July 2023 by extensively reviewing PubMed, EMBASE, and Web of Science databases, adhering to the Cochrane methodology. Relevant data, including summary estimates of overall survival (OS) and progression-free survival (PFS), were extracted from these selected studies and recorded in a predefined database sheet. Subsequently, we conducted a random effects network meta-analysis using a Bayesian framework. RESULTS: Based on the Surface Under the Cumulative Ranking (SUCRA) values, the league table organizes the various treatments for OS in the following order: IC + RT&MTT, MTT-CRT, IC + CRT&MTT, CRT, IC + CRT, MTT-RT, IC + MTT-RT, and RT. In a similar order, the treatments rank as follows according to the league table: IC + CRT&MTT, MTT-CRT, IC + CRT, IC + RT&MTT, CRT, IC + MTT-RT, MTT-RT, and RT. Notably, none of these treatments showed significant advantages over concurrent chemoradiotherapy. CONCLUSION: Despite concurrent chemoradiotherapy being the prevailing treatment for LASCCHN, our findings suggest the potential for improved outcomes when concurrent chemoradiotherapy is combined with targeted therapy or induction chemotherapy.

The current standard treatment for advanced head and neck cancer involves combining radiation therapy with chemotherapy. However, there are ongoing trials exploring alternative therapies. In this study, we conducted a comprehensive analysis of existing treatments using a statistical method called network meta-analysis. Our analysis included data from randomized controlled trials published between January 2000 and July 2023. We focused on overall survival and progression-free survival as key outcome measures. The results of our analysis showed that none of the alternative treatments demonstrated significant advantages over the standard concurrent chemoradiotherapy. Nevertheless, there is potential for improved outcomes when targeted therapy or induction chemotherapy is combined with concurrent chemoradiotherapy.

Descriptive Epidemiology of Pathogens Associated with Acute Respiratory Infection in a Community-Based Study of K-12 School Children (2015-2023).

School-based outbreaks often precede increased incidence of acute respiratory infections in the greater community. We conducted acute respiratory infection surveillance among children to elucidate commonly detected pathogens in school settings and their unique characteristics and epidemiological patterns. The ORegon CHild Absenteeism due to Respiratory Disease Study (ORCHARDS) is a longitudinal, laboratory-supported, school-based, acute respiratory illness (ARI) surveillance study designed to evaluate the utility of cause-specific student absenteeism monitoring for early detection of increased activity of influenza and other respiratory viruses in schools from kindergarten through 12th grade. Eligible participants with ARIs provided demographic, epidemiologic, and symptom data, along with a nasal swab or oropharyngeal specimen. Multipathogen testing using reverse-transcription polymerase chain reaction (RT-PCR) was performed on all specimens for 18 respiratory viruses and 2 atypical bacterial pathogens (Chlamydia pneumoniae and Mycoplasma pneumoniae). Between 5 January 2015 and 9 June 2023, 3498 children participated. Pathogens were detected in 2455 of 3498 (70%) specimens. Rhinovirus/enteroviruses (36%) and influenza viruses A/B (35%) were most commonly identified in positive specimens. Rhinovirus/enteroviruses and parainfluenza viruses occurred early in the academic year, followed by seasonal coronaviruses, RSV, influenza viruses A/B, and human metapneumovirus. Since its emergence in 2020, SARS-CoV-2 was detected year-round and had a higher median age than the other pathogens. A better understanding of the etiologies, presentations, and patterns of pediatric acute respiratory infections can help inform medical and public health system responses.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data. MATERIALS AND METHODS: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Temporal validation was undertaken to ensure the models' temporal generalizability. Additionally, analyses to assess the variable importance were conducted. RESULTS: Both models demonstrated impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of over 0.8. Additionally, they showed considerable accuracy, with macro-F1 scores exceeding or near 0.7, in assessing injuries in the areas of the chest and head. We showed in our variable importance analysis that the most important features in the model have strong face validity in determining clinically relevant trauma injuries. DISCUSSION: The CUI-based model achieves comparable performance, if not higher, compared to the free-text-based model, with reduced complexity. Furthermore, integrating structured EHR data improves performance, particularly when the text modalities are insufficiently indicative. CONCLUSIONS: Our multi-modal, multiclass models can provide accurate stratification of trauma injury severity and clinically relevant interpretations.

A High-Entropy Single-Atom Catalyst Toward Oxygen Reduction Reaction in Acidic and Alkaline Conditions.

The design of high-entropy single-atom catalysts (HESAC) with 5.2 times higher entropy compared to single-atom catalysts (SAC) is proposed, by using four different metals (FeCoNiRu-HESAC) for oxygen reduction reaction (ORR). Fe active sites with intermetallic distances of 6.1 Å exhibit a low ORR overpotential of 0.44 V, which originates from weakening the adsorption of OH intermediates. Based on density functional theory (DFT) findings, the FeCoNiRu-HESAC with a nitrogen-doped sample were synthesized. The atomic structures are confirmed with X-ray photoelectron spectroscopy (XPS), X-ray absorption (XAS), and scanning transmission electron microscopy (STEM). The predicted high catalytic activity is experimentally verified, finding that FeCoNiRu-HESAC has overpotentials of 0.41 and 0.37 V with Tafel slopes of 101 and 210 mVdec-1 at the current density of 1 mA cm-2 and the kinetic current densities of 8.2 and 5.3 mA cm-2, respectively, in acidic and alkaline electrolytes. These results are comparable with Pt/C. The FeCoNiRu-HESAC is used for Zinc-air battery applications with an open circuit potential of 1.39 V and power density of 0.16 W cm-2. Therefore, a strategy guided by DFT is provided for the rational design of HESAC which can be replaced with high-cost Pt catalysts toward ORR and beyond.

Prognostic significance of systemic immune-inflammation index in patients with nonfunction pancreatic neuroendocrine tumor undergoing surgical resection.

PURPOSE: The purpose of our study was to investigate the clinical significance and prognostic role of the systemic immune-inflammation index (SII) in patients who underwent surgical resection for nonfunctioning pancreatic neuroendocrine tumors (pNETs). METHODS: We conducted a retrospective analysis of 364 patients with nonfunctioning pNETs. The association between the SII level and clinical parameters was investigated. The receiver operating characteristic (ROC) curve was used to calculate the optimal SII value. Cox proportional hazard analysis was performed to evaluate the prognostic factors. RESULTS: Our study included 364 patients with nonfunctioning pNETs who underwent surgery. The median age was 51.0 (43.0, 59.3), and 164 (45.1%) were male. The optimal threshold of SII determined by ROC analysis was 523.95. Higher SII levels were significantly associated with older age (p = 0.001), sex (p = 0.011), tumor size (p = 0.032), and tumor grade (p = 0.002). Recurrence was observed in 70 (19.2%) patients following a median follow-up of 98 months. Univariate analysis showed that higher SII (p < 0.0001), tumor size >4 cm (p = 0.015), and G2/G3 grade (p = 0.002) were significantly associated with disease-free survival (DFS). Multivariate analysis revealed that higher SII (HR: 7.35; 95% CI: 3.44, 15.70; p < 0.0001) and G2/G3 grade (HR: 3.11; 95% CI: 1.42, 6.82; p = 0.005) remained significantly associated with tumor recurrence. Furthermore, 46 (12.6%) patients died during the follow-up. Higher SII (HR: 8.43; 95% CI: 3.19, 22.72; p < 0.0001) and G2/G3 grade (HR: 3.16; 95% CI: 1.01, 9.86; p = 0.048) were independent predictors of overall survival (OS) by multivariate analysis. CONCLUSION: In conclusion, our study revealed that a higher SII level was associated with tumor-related features (larger tumor size and advanced grade) and subsequent shorter DFS and OS in patients with nonfunctioning pNETs. These results indicated that the SII could serve as an efficient prognostic biomarker for nonfunctioning pNETs.

Effect of planned school breaks on student absenteeism due to influenza-like illness in school aged children-Oregon School District, Wisconsin September 2014-June 2019.

Background: School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) that coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI). Methods: We compared a-ILI counts in the 2-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a "pseudo-break" of 9 days' duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact. Results: We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared with the period before break, and the greatest impact when break coincided with increased local influenza activity while accounting for possible temporal and community risk confounders. Conclusions: These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Mitochondria-Associated Transcriptome Profiling via Localizable Aggregation-Induced Emission Photosensitizers in Live Cells.

In recent decades, there has been increasing interest in studying mitochondria through transcriptomic research. Various exogenous fusion protein-based proximity labeling methods have been reported that focus on the site of one particular protein/peptide and might also influence the corresponding localization or interactome. To enable unbiased and high spatial-resolution profiling of mitochondria-associated transcriptomes in live cells, a flexible RNA proximity labeling approach was developed using aggregation-induced emission (AIE) type photosensitizers (PSs) that possess great mitochondria-targeting capabilities. Their accumulation in an enclosed mitochondrial environment tends to enhance the fluorescence emission and reactive oxygen species generation. By comparing the in vitro optical properties, photosensitization processes, as well as the in cellulo mitochondrial specificity and RNA labeling performance of four AIE PSs, high-throughput sequencing analysis was conducted using TFPy-mediated RNA proximity labeling in live HeLa cells. This approach successfully captured a comprehensive list of transcripts, including mitochondria-encoded RNAs, as well as some nuclear-derived RNAs located at the outer mitochondrial membrane and interacting organelles. This small molecule-based proximity labeling method bypasses complex genetic manipulation and transfection steps, making it readily applicable for diverse research purposes.

Detection of anti-premembrane antibody as a specific marker of four flavivirus serocomplexes and its application to serosurveillance in endemic regions.

In the past few decades, several emerging/re-emerging mosquito-borne flaviviruses have resulted in disease outbreaks of public health concern in the tropics and subtropics. Due to cross-reactivities of antibodies recognizing the envelope protein of different flaviviruses, serosurveillance remains a challenge. Previously we reported that anti-premembrane (prM) antibody can discriminate between three flavivirus infections by Western blot analysis. In this study, we aimed to develop a serological assay that can discriminate infection or exposure with flaviviruses from four serocomplexes, including dengue (DENV), Zika (ZIKV), West Nile (WNV) and yellow fever (YFV) viruses, and explore its application for serosurveillance in flavivirus-endemic countries. We employed Western blot analysis including antigens of six flaviviruses (DENV1, 2 and 4, WNV, ZIKV and YFV) from four serocomplexes. We tested serum samples from YF-17D vaccinees, and from DENV, ZIKV and WNV panels that had been confirmed by RT-PCR or by neutralization assays. The overall sensitivity/specificity of anti-prM antibodies for DENV, ZIKV, WNV, and YFV infections/exposure were 91.7%/96.4%, 91.7%/99.2%, 88.9%/98.3%, and 91.3%/92.5%, respectively. When testing 48 samples from Brazil, we identified multiple flavivirus infections/exposure including DENV and ZIKV, DENV and YFV, and DENV, ZIKV and YFV. When testing 50 samples from the Philippines, we detected DENV, ZIKV, and DENV and ZIKV infections with a ZIKV seroprevalence rate of 10%, which was consistent with reports of low-level circulation of ZIKV in Asia. Together, these findings suggest that anti-prM antibody is a flavivirus serocomplex-specific marker and can be employed to delineate four flavivirus infections/exposure in regions where multiple flaviviruses co-circulate.

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach.

Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than 200 samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen and ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients are 0.96, 0.81, and 0.67 for the predictions of emission wavelength, PL quantum yield and radiative decay rate constant, respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Detection of anti-premembrane antibody as a specific marker of four flavivirus serocomplexes and its application to serosurveillance in endemic regions.

In the past few decades, several emerging/re-emerging mosquito-borne flaviviruses have resulted in disease outbreaks of public health concern in the tropics and subtropics. Due to cross-reactivities of antibodies recognizing the envelope protein of different flaviviruses, serosurveillance remains a challenge. Previously we reported that anti-premembrane (prM) antibody can discriminate between three flavivirus infections by Western blot analysis. In this study, we aimed to develop a serological assay that can discriminate infection or exposure with flaviviruses from four serocomplexes, including dengue (DENV), Zika (ZIKV), West Nile (WNV) and yellow fever (YFV) viruses, and explore its application for serosurveillance in flavivirus-endemic countries. We employed Western blot analysis including antigens of six flaviviruses (DENV1, 2 and 4, WNV, ZIKV and YFV) from four serocomplexes. We tested serum samples from YF-17D vaccinees, and from DENV, ZIKV and WNV panels that had been confirmed by RT-PCR or by neutralization assays. The overall sensitivity/specificity of anti-prM antibodies for DENV, ZIKV, WNV, and YFV infections/exposure were 91.7%/96.4%, 91.7%/99.2%, 88.9%/98.3%, and 91.3%/92.5%, respectively. When testing 48 samples from Brazil, we identified multiple flavivirus infections/exposure including DENV and ZIKV, DENV and YFV, and DENV, ZIKV and YFV. When testing 50 samples from the Philippines, we detected DENV, ZIKV, and DENV and ZIKV infections with a ZIKV seroprevalence rate of 10%, which was consistent with reports of low-level circulation of ZIKV in Asia. Together, these findings suggest that anti-prM antibody is a flavivirus serocomplex-specific marker and can be employed to delineate four flavivirus infections/exposure in regions where multiple flaviviruses co-circulate.

A framework for future national pediatric pandemic respiratory disease severity triage: The HHS pediatric COVID-19 data challenge.

Introduction: With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist. Methods: HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions? Results: This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected. Conclusion: This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.

Construct exchange-correlation functional via machine learning.

Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems. Recently, the transferability problem has been addressed through the use of quasi-local density-based descriptors, which are rooted in the holographic electron density theorem. We also discuss recent developments using deep-learning techniques that target high-level ab initio molecular energy and electron density for training. These efforts can be unified under a general framework, which will also be discussed from this perspective. Additionally, we explore the use of auxiliary machine-learning models for van der Waals interactions.

Synergistic Effects of Rotavirus and Co-Infecting Viral Enteric Pathogens on Diarrheal Disease - Guangzhou City, Guangdong Province, China, 2019.

What is already known about this topic?: Diarrhea represents a substantial public health issue, contributing globally to a high number of pediatric medical consultations, hospital admissions, and mortality rates. What is added by this report?: An increase in diarrheal frequency serves as a critical benchmark for evaluating severity. The predominant pathogens associated with pediatric diarrhea are rotavirus and norovirus, with co-infections exerting a notable compounding effect that leads to more severe diarrhea. What are the implications for public health practice?: Implementing sensitive diagnostic techniques and comprehensive monitoring is paramount in identifying co-infections. Such strategies can provide physicians with critical insights into disease progression, thus considerably reducing the burden of diarrhea.

A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients.

OBJECTIVE: Anlotinib is a multitarget anti-angiogenic drug that combined with temozolomide (TMZ) can effectively prolongs the overall survival (OS) of recurrent malignant glioma(rMG),but some patients do not respond to anlotinib combined with TMZ. These patients were associated with a worse prognosis and lack effective identification methods. Therefore, it is necessary to differentiate patients who may have good response to anlotinb in combination with TMZ from those who are not, in order to provide personalized targeted therapies. METHODS: Fifty three rMG patients (42 in training cohort and 11 in testing cohort) receiving anlotinib combined with TMZ were enrolled. A total of 3668 radiomics features were extracted from the recurrent MRI images. Radiomics features are reduced and filtered by hypothesis testing and Least Absolute Shrinkage And Selection (LASSO) regression. Eight machine learning models construct the radiomics model, and then screen out the optimal model. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with validation. RESULTS: Fifty three patients with rMG were enrolled in our study. Thirty four patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median progression-free survival(PFS) was 8.53 months versus 5.33 months (p = 0.06) and the median OS was 19.9 months and 7.33 months (p = 0.029), respectively. Three radiomics features were incorporated into the model construction as final variables after LASSO regression analysis. In testing cohort, Logistic Regression (LR) model has the best performance with an Area Under the Curve (AUC) of 0.93 compared with other models, which can effectively predict the response of rMG patients to anlotinib in combination with TMZ. The calibration curve confirmed the agreement between the observed actual and prediction probability. Within the reasonable threshold probability range (0.38-0.88), the radiomics model shows good clinical utility. CONCLUSIONS: The above-described radiomics model performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients.