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.

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 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.

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.

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Comparison of quantifiable electrocardiographic changes associated with severe hyperkalemia.

BACKGROUND: Hyperkalemia (HK) is a life-threatening condition that is frequently evaluated by electrocardiogram (ECG). ECG changes in severe HK (≥ 6.3 mEq/L) are not well-characterized. This study sought to compare and correlate ECG metrics in severe HK to baseline normokalemic ECGs and serum potassium. METHODS: A retrospective analysis of 340 severe HK encounters with corresponding normokalemic ECGs was performed. RESULTS: Various ECG metrics were analyzed. P wave amplitude in lead II, QRS duration, T wave slope, ratio of T wave amplitude: duration, and ratios of T wave: QRS amplitudes were significantly different between normokalemic and HK ECGs. P wave amplitude attenuation in lead II correlated better with serum potassium than in V1. T wave metrics that incorporated both T wave and QRS amplitudes correlated better than metrics utilizing T wave metrics alone. CONCLUSION: Multiple statistically significant and quantifiable differences among ECG metrics were observed between normokalemic and HK ECGs and correlated with increasing degrees of serum potassium and along the continuum of serum potassium. When incorporated into a logistic regression model, the ability to distinguish HK versus normokalemia on ECG improved significantly. These findings could be integrated into an ECG acquisition system that can more accurately identify severe HK.

Optimal glycated hemoglobin A1c value for prediabetes and diabetes in patients with pancreatic diseases.

Background: Some articles suggest that using HbA1c alone for diabetes diagnosis is inappropriate. It requires considerable researches to explore the efficacy of HbA1c for diagnosing hyperglycemia in patients with pancreatic disease. Methods: This study analyzed 732 patients, comprising of 331 without pancreatic disease and 401 patients diagnosed with pancreatic diseases. All participants underwent the HbA1c assay and oral glucose tolerance test. Kappa coefficients were calculated to assess agreement between the HbA1c and glucose criteria. The receiver operating characteristic curve (ROC) was used to calculate the optimal HbA1c value. DeLong test was analyzed to compared the aera under curves (AUCs). Results: There were 203 (61.3%) patients with NGT, 78 (23.6%) with prediabetes, and 50 (15.1%) with diabetes in patients without pancreatic diseases. In patients with pancreatic disease, 106 participants were diagnosed with NGT (36.4%), 125 with prediabetes (31.2%), and 130 with diabetes (32.4%). Patients with pancreatic disease exhibited elevated levels of bilirubin, transaminase enzymes, aspartate transaminase, high density lipoprotein cholesterol and total bile acid. The sensitivity and specificity of the HbA1c (6.5%) for diagnosing pancreatic diabetes were 60.8% (95% CI 52.3, 69.3) and 92.6% (95% CI 89.5, 95.7). In prediabetes, the sensitivity and specificity of HbA1c (5.7%) is 53.2% (44.3, 62.0) and 59.6 (51.5, 67.6). The optimal HbA1c value for diagnosing diabetes was 6.0% (AUC = 0.876, 95% CI 0.839, 0.906), with the sensitivity of 83.8% and the specificity of 76.8%. The optimal HbA1c value for the diagnosis of prediabetes was 5.8% (AUC = 0.617, 95% CI: 0.556, 0.675), with the corresponding sensitivity and specificity of 48.0% and 72.6% respectively. The combined tests (HbA1c, 6.0% or FPG, 7.0mmol/L) presented the sensitivity of 85.7% (95% CI 79.1, 91.3)and the specificity of 92.6% (95% CI 87.6, 97.3) in pancreatic diabetes. Conclusion: From our results, the recommended HbA1c by ADA criterion may not be sufficiently sensitive to diagnose hyperglycemia in pancreatic disease. The optimal value of 5.8% and 6.0% improved the accuracy for diagnosing prediabetes and diabetes and should be considered to be applied. Besides, we advocate the combination of HbA1c and FPG test for the diagnosis of diabetes in patients with pancreatic diseases.

Mechanical transmission of dengue virus by Aedes aegypti may influence disease transmission dynamics during outbreaks.

BACKGROUND: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible. METHODS: We used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population. FINDINGS: Naïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggest that this supplemental transmission route could result in larger outbreaks which peak sooner. INTERPRETATION: The potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes. FUNDING: This study was funded by grants from the National Health Research Institutes, Taiwan (04D2-MMMOST02), the Human Frontier Science Program (RGP0033/2021), the National Institutes of Health (1R01AI143698-01A1, R01AI151004 and DP2AI152071) and the Ministry of Science and Technology, Taiwan (MOST104-2321-B-400-016).

MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma.

Objective: Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC). Methods: A total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram. Results: Platelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram. Conclusion: A clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy.

A Systematic Review and Pooled Analysis of Hypothesized versus Observed Effect Sizes in Early Phase Cancer Prevention Clinical Trials.

Early phase cancer prevention trials are designed to demonstrate safety, tolerability, feasibility, and signals of efficacy of preventive agents. Yet it is often observed that many trials fail to detect intervention effects. We conducted a systematic review and pooled analyses of recently completed early phase chemoprevention trials to gain in depth insight on the failure of detecting efficacy signals by comparing hypothesized effect sizes to the corresponding observed effect sizes.Single- or multi-arm efficacy chemoprevention trials conducted under the phase 0/I/II Cancer Prevention Clinical Trials Program of the Division of Cancer Prevention, NCI between 2003 and 2019 were evaluated. A total of 59 chemoprevention trials were reviewed. Twenty-four studies were efficacy or biomarker trials with complete information on hypothesized and observed effect sizes and included in this analysis. The majority of the trials (n = 18) were multi-arm randomized studies of which 15 trials were blinded. The pooled estimate of the observed to hypothesized effect size ratio was 0.57 (95% confidence interval: 0.42-0.73, P < 0.001) based on a random-effects model. There were no significant differences detected in the ratio of observed to hypothesized effect sizes when conducting various subgroup analyses.The results demonstrate that the majority of early phase cancer chemoprevention trials have substantially smaller observed effect sizes than hypothesized effect sizes. Sample size calculations for early phase chemoprevention trials need to balance the potential detectable effect sizes with realistic and cost-effective accrual of study populations, thereby, detecting only intervention effects large enough to justify subsequent large-scale confirmatory trials. PREVENTION RELEVANCE: The results of this systematic review and pooled analyses demonstrate that for early chemoprevention trials, there are substantial differences between hypothesized and observed effect sizes, regardless of study characteristics. The conduct of early phase chemoprevention trial requires careful planning of study design, primary endpoint, and sample size determination.