Ribosomal quality control factors inhibit repeat-associated non-AUG translation from GC-rich repeats.

A GGGGCC (G4C2) hexanucleotide repeat expansion in C9ORF72 causes amyotrophic lateral sclerosis and frontotemporal dementia (C9ALS/FTD), while a CGG trinucleotide repeat expansion in FMR1 leads to the neurodegenerative disorder Fragile X-associated tremor/ataxia syndrome (FXTAS). These GC-rich repeats form RNA secondary structures that support repeat-associated non-AUG (RAN) translation of toxic proteins that contribute to disease pathogenesis. Here we assessed whether these same repeats might trigger stalling and interfere with translational elongation. We find that depletion of ribosome-associated quality control (RQC) factors NEMF, LTN1 and ANKZF1 markedly boost RAN translation product accumulation from both G4C2 and CGG repeats while overexpression of these factors reduces RAN production in both reporter assays and C9ALS/FTD patient iPSC-derived neurons. We also detected partially made products from both G4C2 and CGG repeats whose abundance increased with RQC factor depletion. Repeat RNA sequence, rather than amino acid content, is central to the impact of RQC factor depletion on RAN translation-suggesting a role for RNA secondary structure in these processes. Together, these findings suggest that ribosomal stalling and RQC pathway activation during RAN translation inhibits the generation of toxic RAN products. We propose augmenting RQC activity as a therapeutic strategy in GC-rich repeat expansion disorders.

MoCab: A Model Management System Based on FHIR for Clinical Decision Support.

MoCab is a framework that deploys high-accuracy medical models across various health information systems (HISs) using fast healthcare interoperability resources (FHIR). MoCab simplifies the process by importing and configuring stored models and retrieving data for prediction. Two case studies illustrate how MoCab can be used to support decision-making. The proposed framework increases model reusability across EHRs and improves the clinical decision-making process.

Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection.

OBJECTIVE: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. METHODS AND PROCEDURES: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. RESULTS: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ([Formula: see text]) compared to prior GLP processing. CONCLUSION: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. CLINICAL IMPACT: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.

Trend of HPV Molecular Epidemiology in the Post-Vaccine Era: A 10-Year Study.

Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade (2010-2020), where prophylactic HPV vaccination has been implemented since 2007. Analyzing data from 40,561 vaginal swab samples, with 42.0% testing positive for HPV, we reveal shifting trends in HPV genotype distribution and infection patterns. The 12 high-risk genotypes, in order of decreasing percentage, were HPV 52, 58, 16, 18, 51, 56, 39, 59, 33, 31, 45, and 35. The predominant genotypes were HPV 52, 58, and 16, accounting for over 70% of cases annually. The proportions of high-risk and non-high-risk HPV infections varied across age groups. High-risk infections predominated in sexually active individuals aged 30-50 and were mixed-type infections. The composition of high-risk HPV genotypes was generally stable over time; however, HPV31, 33, 39, and 51 significantly decreased over the decade. Of the strains, HPV31 and 33 are shielded by the nonavalent HPV vaccine. However, no reduction was noted for the other seven genotypes. This study offers valuable insights into the post-vaccine HPV epidemiology. Future investigations should delve into HPV vaccines' effects and their implications for cervical cancer prevention strategies. These findings underscore the need for continued surveillance and research to guide effective public health interventions targeting HPV-associated diseases.

Engaging a national-scale cohort of smart thermometer users in participatory surveillance.

Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6-2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4-2.5). Compared with participants aged 50-65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3-0.3). Participants in a household with both adults and children (OR = 1.6 [1.6-1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.

lab: an R package for generating analysis-ready data from laboratory records.

Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods: To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results: Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression. Conclusions: The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Risk Stratification for Herpes Simplex Virus Pneumonia Using Elastic Net Penalized Cox Proportional Hazard Algorithm with Enhanced Explainability.

Herpes simplex virus (HSV) pneumonia is a serious and often fatal respiratory tract infection that occurs in immunocompromised individuals. The early detection of accurate risk stratification is essential in identifying patients who are at high risk of mortality and may benefit from more aggressive treatment. In this study, we developed and validated a risk stratification model for HSV bronchopneumonia using an elastic net penalized Cox proportional hazard algorithm. We analyzed data from a cohort of 104 critically ill patients with HSV bronchopneumonia identified in Chang Gung Memorial Hospital, Linkou, Taiwan: one of the largest tertiary medical centers in the world. A total of 109 predictors, both clinical and laboratory, were identified in this process to develop a risk stratification model that could accurately predict mortality in patients with HSV bronchopneumonia. This model was able to differentiate the risk of death and predict mortality in patients with HSV bronchopneumonia compared to the APACHE II score in the early stage of ICU admissions. Both hazard ratio coefficient and selection frequency were used as the metrics to enhance the explainability of the informative predictors. Our findings suggest that the elastic net penalized Cox proportional hazard algorithm is a promising tool for risk stratification in patients with HSV bronchopneumonia and could be useful in identifying those at high risk of mortality.

Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis.

BACKGROUND: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. METHODS: We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. RESULTS: From 2014 to 2017, 1,483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). CONCLUSION: IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.

Ribosomal quality control factors inhibit repeat-associated non-AUG translation from GC-rich repeats.

A GGGGCC (G4C2) hexanucleotide repeat expansion in C9ORF72 causes amyotrophic lateral sclerosis and frontotemporal dementia (C9ALS/FTD), while a CGG trinucleotide repeat expansion in FMR1 leads to the neurodegenerative disorder Fragile X-associated tremor/ataxia syndrome (FXTAS). These GC-rich repeats form RNA secondary structures that support repeat-associated non-AUG (RAN) translation of toxic proteins that contribute to disease pathogenesis. Here we assessed whether these same repeats might trigger stalling and interfere with translational elongation. We find that depletion of ribosome-associated quality control (RQC) factors NEMF, LTN1, and ANKZF1 markedly boost RAN translation product accumulation from both G4C2 and CGG repeats while overexpression of these factors reduces RAN production in both reporter cell lines and C9ALS/FTD patient iPSC-derived neurons. We also detected partially made products from both G4C2 and CGG repeats whose abundance increased with RQC factor depletion. Repeat RNA sequence, rather than amino acid content, is central to the impact of RQC factor depletion on RAN translation - suggesting a role for RNA secondary structure in these processes. Together, these findings suggest that ribosomal stalling and RQC pathway activation during RAN translation elongation inhibits the generation of toxic RAN products. We propose augmenting RQC activity as a therapeutic strategy in GC-rich repeat expansion disorders.

Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic.

Importance: Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing. Objective: To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19. Design, Setting, and Participants: This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing. Main Outcomes and Measures: Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data. Results: A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years. Conclusions and Relevance: In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.

Dissecting the roles of EIF4G homologs reveals DAP5 as a modifier of CGG repeat-associated toxicity in a Drosophila model of FXTAS.

Neurodegeneration in Fragile X-associated tremor/ataxia syndrome (FXTAS) is caused by a CGG trinucleotide repeat expansion in the 5' UTR of FMR1. Expanded CGG repeat RNAs form stable secondary structures, which in turn support repeat-associated non-AUG (RAN) translation to produce toxic peptides. The parameters that impact RAN translation initiation efficiency are not well understood. Here we used a Drosophila melanogaster model of FXTAS to evaluate the role of the eIF4G family of eukaryotic translation initiation factors (EIF4G1, EIF4GII and EIF4G2/DAP5) in modulating RAN translation and CGG repeat-associated toxicity. DAP5 knockdown robustly suppressed CGG repeat-associated toxicity and inhibited RAN translation. Furthermore, knockdown of initiation factors that preferentially associate with DAP5 (such as EIF2ß, EIF3F and EIF3G) also selectively suppressed CGG repeat-induced eye degeneration. In mammalian cellular reporter assays, DAP5 knockdown exhibited modest and cell-type specific effects on RAN translation. Taken together, these data support a role for DAP5 in CGG repeat associated toxicity possibly through modulation of RAN translation.

Evaluation of Infective Endocarditis in Children: A 19-Year Retrospective Study in Taiwan.

BACKGROUND: Infective endocarditis (IE) is an important cause of morbidity and mortality in pediatric patients with heart disease. Little literature has explored differences in the presentation of endocarditis in children with and without heart disease. This study aimed to compare the clinical outcomes and determine the risk of in-hospital death in the study population. METHODS: Data were retrospectively collected from 2001 to 2019 from the Chang Gung Research Database (CGRD), which is the largest collection of multi-institutional electronic medical records in Taiwan. Children aged 0-20 years with IE were enrolled. We extracted and analyzed the demographic and clinical features, complications, microbiological information, and outcomes of each patient. RESULTS: Of the 208 patients with IE, 114 had heart disease and 94 did not. Compared to those without heart disease, more streptococcal infections (19.3% vs. 2.1%, p < 0.001) and cardiac complications (29.8% vs. 6.4%, p < 0.001) were observed in patients with heart disease. Although patients with heart disease underwent valve surgery more frequently (43.9% vs. 8.5%, p < 0.001) and had longer hospital stays (28.5 vs. 12.5, p = 0.021), their mortality was lower than that of those without heart disease (3.5% vs. 10.6%, p = 0.041). Thrombocytopenia was independent risk factor for in-hospital mortality in pediatric patients with IE (OR = 6.56, 95% CI: 1.43-40.37). CONCLUSION: Among pediatric patients diagnosed with IE, microbiological and clinical features differed between those with and without heart disease. Platelet counts can be used as a risk factor for in-hospital mortality in pediatric patients with IE.

Evaluating Resource Utilization with Free or Pedicled Perforator Flaps in Distal Leg Reconstruction.

BACKGROUND: The decision between local and free tissue coverage for distal lower leg defects has long been dictated by the location and size of defects. Recent reports of distal defects treated successfully with pedicled perforator flaps demonstrate equivalent clinical outcomes; however, the complication rate can be high. The goal of this study was to evaluate the cost equivalence of free versus pedicled perforator flap to assist decision-making and guide clinical care. METHODS: The institutional database was searched for patients with acute injury over the distal lower extremity requiring free or pedicled perforator flap. Demographic, clinical, and total resource cost was gathered. Patients were matched to Gustilo-Anderson or Arbeitsgemeinschaft fur Osteosynthesefragen classification as well as size of defect and outcomes, and cost compared. RESULTS: We have included 108 free flaps and 22 pedicled perforator flaps in the study. There was no difference in complication rate between groups. Free flaps had significantly more reoperations, required longer operative time, and had longer intensive care unit (ICU) care with higher cost of surgery and overall cost than pedicled flaps. When controlling for size of defect, surgical cost remained significantly different between groups (p = 0.013), but overall cost did not. Multivariable regression analysis indicated flap type to be the primary driver of cost of surgery, while body mass index elevated the total cost. CONCLUSION: Pedicled perforator flap coverage for small to medium-sized defects (< 70 cm2) is a viable and cost-effective option for distal lower leg soft tissue reconstruction after acute traumatic injury with similar clinical outcomes and shorter operative duration and ICU stay.

Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza-like illness surveillance study.

BACKGROUND: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study. METHODS: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. RESULTS: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups. CONCLUSIONS: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.

Development and validation of machine learning-based risk prediction models of oral squamous cell carcinoma using salivary autoantibody biomarkers.

INTRODUCTION: The incidence of oral cavity squamous cell carcinoma (OSCC) continues to rise. OSCC is associated with a low average survival rate, and most patients have a poor disease prognosis because of delayed diagnosis. We used machine learning techniques to predict high-risk cases of OSCC by using salivary autoantibody levels and demographic and behavioral data. METHODS: We collected the salivary samples of patients recruited from a teaching hospital between September 2008 and December 2012. Ten salivary autoantibodies, sex, age, smoking, alcohol consumption, and betel nut chewing were used to build prediction models for identifying patients with a high risk of OSCC. The machine learning algorithms applied in the study were logistic regression, random forest, support vector machine with the radial basis function kernel, eXtreme Gradient Boosting (XGBoost), and a stacking model. We evaluated the performance of the models by using the area under the receiver operating characteristic curve (AUC), with simulations conducted 100 times. RESULTS: A total of 337 participants were enrolled in this study. The best predictive model was constructed using a stacking algorithm with original forms of age and logarithmic levels of autoantibodies (AUC = 0.795 ± 0.055). Adding autoantibody levels as a data source significantly improved the prediction capability (from 0.698 ± 0.06 to 0.795 ± 0.055, p < 0.001). CONCLUSIONS: We successfully established a prediction model for high-risk cases of OSCC. This model can be applied clinically through an online calculator to provide additional personalized information for OSCC diagnosis, thereby reducing the disease morbidity and mortality rates.

Multipeak Coercive Electric-Field-Based Multilevel Cell Nonvolatile Memory With Antiferroelectric-Ferroelectric Field-Effect Transistors (FETs).

An ultralow program/erase voltage ( |VP/E| = 4 V) is demonstrated by using an antiferroelectric-ferroelectric field-effect transistor (AFE-FE-FET) through a multipeak coercive E -field ( EC ) concept for a four-level stable state with outstanding endurance (>105 cycles) and data retention (>104 s at 65 °C). The mixture of ferroelectric (FE) and AFE domains can provide stable multistate and data storage with zero bias for multilevel cell (MLC) applications. HfZrO2 (HZO) with AFE-FE assembles an orthorhombic/tetragonal (o/t) phase composition and is achieved by [Zr] modulation in an HZO system. MLC characteristics not only improve high-density nonvolatile memory (NVM) but are also beneficial to neuromorphic device applications.

Clinical characteristics and outcomes of mixed virus or bacterial infection in children with laboratory-confirmed influenza infection.

BACKGROUND/PURPOSE: This study investigated the demographic characteristics and influenza complications of paediatric patients and explored the association of different influenza virus types and viral and bacterial coinfections with disease severity. METHODS: This retrospective cohort study used data collected in 2010-2016 from the Chang Gung Research Database (CGRD), the largest collection of multi-institutional electronic medical records in Taiwan. Data were retrieved for children aged 0-18 years with laboratory-confirmed influenza. We extracted and analysed the demographic characteristics and the data on clinical features, complications, microbiological information, and advanced therapies of each case. RESULTS: We identified 6193 children with laboratory-confirmed influenza, of whom 1964 (31.7%) were hospitalised. The age of patients with influenza A infection was lower than that of patients with influenza B (4.48 vs. 6.68, p < 0.001). Patients with influenza B infection had a higher incidence of myositis or rhabdomyolysis (4.4%, p < 0.001) and a higher need for advanced therapies (OR, 1.96; 95% CI, 1.32-2.9, p < 0.001). In addition to bacterial (OR, 9.07; 95% CI, 5.29-15.54, p < 0.001) and viral coinfection (OR, 7.73; 95% CI, 5.4-11.07, p < 0.001), dual influenza A and B infection was also a risk factor for influenza complications (OR, 2.13; 95% CI, 1.47-3.09, p < 0.001). CONCLUSION: Dual influenza A and B infection and bacterial coinfection can contribute to influenza complications. Early recognition of any influenza complication is critical for the timely initiation of organ-specific advanced therapies to improve influenza-associated outcomes.

Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance.

The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the "grey zone". We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.

BACKGROUND: The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. OBJECTIVE: The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms-logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms-when applied to clinical laboratory datasets. METHODS: We applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. RESULTS: The experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. CONCLUSIONS: The XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.