Improving dengue fever predictions in Taiwan based on feature selection and random forests.

BACKGROUND: Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan. RESULTS: This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results. CONCLUSIONS: Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.

Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History.

This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model's usage impacts patient outcomes.

Assessing Internet Search Models in Predicting Daily New COVID-19 Cases and Deaths in South Korea.

Search data were found to be useful variables for COVID-19 trend prediction. In this study, we aimed to investigate the performance of online search models in state space models (SSMs), linear regression (LR) models, and generalized linear models (GLMs) for South Korean data from January 20, 2020, to July 31, 2021. Principal component analysis (PCA) was run to construct the composite features which were later used in model development. Values of root mean squared error (RMSE), peak day error (PDE), and peak magnitude error (PME) were defined as loss functions. Results showed that integrating search data in the models for short- and long-term prediction resulted in a low level of RMSE values, particularly for SSMs. Findings indicated that type of model used highly impacts the performance of prediction and interpretability of the model. Furthermore, PDE and PME could be beneficial to be included in the evaluation of peaks.

Low- and high-level information analyses of transcriptome connecting endometrial-decidua-placental origin of preeclampsia subtypes: A preliminary study.

BACKGROUND: Existing proposed pathogenesis for preeclampsia (PE) was only applied for early onset subtype and did not consider pre-pregnancy and competing risks. We aimed to decipher PE subtypes by identifying related transcriptome that represents endometrial maturation and histologic chorioamnionitis. METHODS: We utilized eight arrays of mRNA expression for discovery (n=289), and other eight arrays for validation (n=352). Differentially expressed genes (DEGs) were overlapped between those of: (1) healthy samples from endometrium, decidua, and placenta, and placenta samples under histologic chorioamnionitis; and (2) placenta samples for each of the subtypes. They were all possible combinations based on four axes: (1) pregnancy-induced hypertension; (2) placental dysfunction-related diseases (e.g., fetal growth restriction [FGR]); (3) onset; and (4) severity. RESULTS: The DEGs of endometrium at late-secretory phase, but none of decidua, significantly overlapped with those of any subtypes with: (1) early onset (p-values ≤0.008); (2) severe hypertension and proteinuria (p-values ≤0.042); or (3) chronic hypertension and/or severe PE with FGR (p-values ≤0.042). Although sharing the same subtypes whose DEGs with which significantly overlap, the gene regulation was mostly counter-expressed in placenta under chorioamnionitis (n=13/18, 72.22%; odds ratio [OR] upper bounds ≤0.21) but co-expressed in late-secretory endometrium (n=3/9, 66.67%; OR lower bounds ≥1.17). Neither the placental DEGs at first-nor second-trimester under normotensive pregnancy significantly overlapped with those under late-onset, severe PE without FGR. CONCLUSIONS: We identified the transcriptome of endometrial maturation in placental dysfunction that distinguished early- and late-onset PE, and indicated chorioamnionitis as a PE competing risk. This study implied a feasibility to develop and validate the pathogenesis models that include pre-pregnancy and competing risks to decide if it is needed to collect prospective data for PE starting from pre-pregnancy including chorioamnionitis information.

Uteroplacental insufficiency decreases leptin expression and impairs lung development in growth-restricted newborn rats.

BACKGROUND: The study aimed to analyze the effect of uteroplacental insufficiency (UPI) on leptin expression and lung development of intrauterine growth restriction (IUGR) rats. METHODS: On day 17 of pregnancy, time-dated Sprague-Dawley rats were randomly divided into either an IUGR group or a control group. Uteroplacental insufficiency surgery (IUGR) and sham surgery (control) were conducted. Offspring rats were spontaneously delivered on day 22 of pregnancy. On postnatal days 0 and 7, rats' pups were selected at random from the control and IUGR groups. Blood was withdrawn from the heart to determine leptin levels. The right lung was obtained for leptin and leptin receptor levels, immunohistochemistry, proliferating cell nuclear antigen (PCNA), western blot, and metabolomic analyses. RESULTS: UPI-induced IUGR decreased leptin expression and impaired lung development, causing decreased surface area and volume in offspring. This results in lower body weight, decreased serum leptin levels, lung leptin and leptin receptor levels, alveolar space, PCNA, and increased alveolar wall volume fraction in IUGR offspring rats. The IUGR group found significant relationships between serum leptin, radial alveolar count, von Willebrand Factor, and metabolites. CONCLUSION: Leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment. IMPACT: The neonatal rats with intrauterine growth restriction (IUGR) caused by uteroplacental insufficiency (UPI) showed decreased leptin expression and impaired lung development. UPI-induced IUGR significantly decreased surface area and volume in lung offspring. This is a novel study that investigates leptin expression and lung development in neonatal rats with IUGR caused by UPI. If our findings translate to IUGR infants, leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment.

Symptom-based scoring technique by machine learning to predict COVID-19: a validation study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) surges, such as that which occurred when omicron variants emerged, may overwhelm healthcare systems. To function properly, such systems should balance detection and workloads by improving referrals using simple yet precise and sensitive diagnostic predictions. A symptom-based scoring system was developed using machine learning for the general population, but no validation has occurred in healthcare settings. We aimed to validate a COVID-19 scoring system using self-reported symptoms, including loss of smell and taste as major indicators. METHODS: A cross-sectional study was conducted to evaluate medical records of patients admitted to Dr. Sardjito Hospital, Yogyakarta, Indonesia, from March 2020 to December 2021. Outcomes were defined by a reverse-transcription polymerase chain reaction (RT-PCR). We compared the symptom-based scoring system, as the index test, with antigen tests, antibody tests, and clinical judgements by primary care physicians. To validate use of the index test to improve referral, we evaluated positive predictive value (PPV) and sensitivity. RESULTS: After clinical judgement with a PPV of 61% (n = 327/530, 95% confidence interval [CI]: 60% to 62%), confirmation with the index test resulted in the highest PPV of 85% (n = 30/35, 95% CI: 83% to 87%) but the lowest sensitivity (n = 30/180, 17%, 95% CI: 15% to 19%). If this confirmation was defined by either positive predictive scoring or antigen tests, the PPV was 92% (n = 55/60, 95% CI: 90% to 94%). Meanwhile, the sensitivity was 88% (n = 55/62, 95% CI: 87% to 89%), which was higher than that when using only antigen tests (n = 29/41, 71%, 95% CI: 69% to 73%). CONCLUSIONS: The symptom-based COVID-19 predictive score was validated in healthcare settings for its precision and sensitivity. However, an impact study is needed to confirm if this can balance detection and workload for the next COVID-19 surge.

Constructing a Learning Curve to Discuss the Medical Treatments and the Effect of Vaccination of COVID-19.

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities.

XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.

An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors-age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores-to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.

Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication.

BACKGROUND AND OBJECTIVE: Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database. METHODS: To guide modeling, we constructed and verified association diagrams respectively from literatures and electronic health records. Non-image data were transformed into meaningful images utilizing predictor-to-predictor similarities, harnessing the power of convolutional neural network mostly used for diagnostic imaging. The network architecture was also inferred from the similarities. RESULTS: This resulted the best model for prelabor rupture of membranes (n=883, 376) with the area under curves 0.73 (95% CI 0.72 to 0.75) and 0.70 (95% CI 0.69 to 0.71) respectively by internal and external validations, and outperformed previous models found by systematic review. It was explainable by knowledge-based diagrams and model representation. CONCLUSIONS: This allows prognostication with actionable insights for preventive medicine.

Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults.

The 15-item Geriatric Depression Scale (GDS-15) is widely used to screen for depressive symptoms among older populations. This study aimed to develop and validate a questionnaire-free, machine-learning model as an alternative triage test for the GDS-15 among community-dwelling older adults. The best models were the random forest (RF) and deep-insight visible neural network by internal validation, but both performances were undifferentiated by external validation. The AUROC of the RF model was 0.619 (95% CI 0.610 to 0.627) for the external validation set with a non-local ethnic group. Our triage test can allow healthcare professionals to preliminarily screen for depressive symptoms in older adults without using a questionnaire. If the model shows positive results, then the GDS-15 can be used for follow-up measures. This preliminary screening will save a lot of time and energy for healthcare providers and older adults, especially those persons who are illiterate.

Effects of the Government Response and Community Mobility on the COVID-19 Pandemic in Southeast Asia.

Preventive policies and mobility restrictions are believed to work for inhibiting the growth rate of COVID-19 cases; however, their effects have rarely been assessed and quantified in Southeast Asia. We aimed to examine the effects of the government responses and community mobility on the COVID-19 pandemic in Southeast Asian countries. The study extracted data from Coronavirus Government Response Tracker, COVID-19 Community Mobility Report, and Our World in Data between 1 March and 31 December 2020. The government responses were measured by containment, health, and economic support index. The community mobility took data on movement trends at six locations. Partial least square structural equation modeling was used for bi-monthly analyses in each country. Results show that the community mobility generally followed government responses, especially the containment index. The path coefficients of government responses to community mobility ranged from -0.785 to -0.976 in March to April and -0.670 to -0.932 in May to June. The path coefficients of community mobility to the COVID-19 cases ranged from -0.058 to -0.937 in March to April and from -0.059 to -0.640 in September to October. It suggests that the first few months since the mobility restriction implemented is the optimal time to control the pandemic.

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection.

Background: A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection. Methods: The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information. Results: A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5. Conclusions: In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.

INTRODUCTION: Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF. MATERIALS AND METHODS: A total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy outcomes at Taipei Medical University Hospital. Data used included patient characteristics and treatment. We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots. RESULTS: Experimental results showed that the random forest algorithm outperforms logistic regression in terms of areas under the receiver operating characteristics curve. The ovarian stimulation protocol is the most important factor affecting pregnancy outcomes. Long and ultra-long protocols have shown positive effects on clinical pregnancy among all protocols. Furthermore, total frozen and transferred embryos are positive for a clinical pregnancy, but female age and duration of infertility have negative effects on clinical pregnancy. CONCLUSION: Our findings show the importance of variables and propensity of each variable by random forest algorithm for clinical pregnancy in the assisted reproductive technology cycle. This study provides a ranking of variables affecting clinical pregnancy and explores the effects of each treatment on successful pregnancy. Our study has the potential to help clinicians evaluate the success of IVF in patients.

Expression of the AHPND Toxins PirAvp and PirBvp Is Regulated by Components of the Vibrio parahaemolyticus Quorum Sensing (QS) System.

Acute hepatopancreatic necrosis disease (AHPND) in shrimp is caused by Vibrio strains that harbor a pVA1-like plasmid containing the pirA and pirB genes. It is also known that the production of the PirA and PirB proteins, which are the key factors that drive the observed symptoms of AHPND, can be influenced by environmental conditions and that this leads to changes in the virulence of the bacteria. However, to our knowledge, the mechanisms involved in regulating the expression of the pirA/pirB genes have not previously been investigated. In this study, we show that in the AHPND-causing Vibrio parahaemolyticus 3HP strain, the pirAvp and pirBvp genes are highly expressed in the early log phase of the growth curve. Subsequently, the expression of the PirAvp and PirBvp proteins continues throughout the log phase. When we compared mutant strains with a deletion or substitution in two of the quorum sensing (QS) master regulators, luxO and/or opaR (luxOD47E, ΔopaR, ΔluxO, and ΔopaRΔluxO), our results suggested that expression of the pirAvp and pirBvp genes was related to the QS system, with luxO acting as a negative regulator of pirAvp and pirBvp without any mediation by opaRvp. In the promoter region of the pirAvp/pirBvp operon, we also identified a putative consensus binding site for the QS transcriptional regulator AphB. Real-time PCR further showed that aphBvp was negatively controlled by LuxOvp, and that its expression paralleled the expression patterns of pirAvp and pirBvp. An electrophoretic mobility shift assay (EMSA) showed that AphBvp could bind to this predicted region, even though another QS transcriptional regulator, AphAvp, could not. Taken together, these findings suggest that the QS system may regulate pirAvp/pirBvp expression through AphBvp.

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders.

INTRODUCTION: Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. METHODS AND MATERIALS: Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery. RESULTS: Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed. CONCLUSIONS: We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients. TRIAL REGISTRATION: Retrospectively registered.

Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study.

BACKGROUND: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. OBJECTIVE: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. METHODS: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. RESULTS: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for "thermometer" and "mask strap," showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. CONCLUSIONS: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound.

BACKGROUND AND OBJECTIVE: Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misdiagnoses with serious impacts. This study aimed to develop a machine learning system to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses. METHODS: Abdominal US images (> 89,000) were collected from 2386 patients in a hospital database. We constructed training sets for gallstones with or without cholecystitis (N = 10,971) and cholecystitis with or without gallstones (N = 7348) as positives. Validation sets were also constructed for gallstones (N = 2664) and cholecystitis (N = 1919). We applied a single-shot multibox detector (SSD) and a feature pyramid network (FPN) to classify and localize objects using image features extracted by ResNet-50 for gallstones, and MobileNet V2 to classify cholecystitis. The deep learning models were pretrained using the COCO-2017 and ILSVRC-2012 datasets. RESULTS: Using the validation sets, the SSD-FPN-ResNet-50 and MobileNet V2 achieved areas under the receiver operating characteristics curve of 0.92 and 0.94, respectively. The inference speeds were 21 (47.6 frames per second, fps) and 7 ms (142.9 fps). CONCLUSIONS: A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. This is the first study to develop this system for either gallstone or cholecystitis detection with absence or presence of each one. After clinical trials, this system may be used to assist EPs, including those in remote areas, for detecting these diseases.

An Examination of COVID-19 Mitigation Efficiency among 23 Countries.

The purpose of this paper was to compare the relative efficiency of COVID-19 transmission mitigation among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). The mitigation efficiency for each country was evaluated at each stage by using data envelopment analysis (DEA) tools and changes in mitigation efficiency were analyzed across stages. Pearson correlation tests were conducted between each change to examine the impact of efficiency ranks in the previous stage on subsequent stages. An indicator was developed to judge epidemic stability and was applied to practical cases involving lifting travel restrictions and restarting the economy in some countries. The results showed that Korea and Australia performed with the highest efficiency in preventing the diffusion of COVID-19 for the whole period covering 105 days since the first confirmed case, while the USA ranked at the bottom. China, Japan, Korea, and Australia were judged to have recovered from the attack of COVID-19 due to higher epidemic stability.

High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA.

OBJECTIVE: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA. METHODS: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for January-December 2020. RESULTS: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk. CONCLUSION: COVID-19 Google RSV model accuracy in the USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.

BACKGROUND: Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs perpetuate great potential that is not limited to antimicrobial activity. They are also crucial regulators of host immune responses that can modulate a wide range of activities, such as immune regulation, wound healing, and apoptosis. However, a microorganism's ability to adapt and to resist existing antibiotics triggered the scientific community to develop alternatives to conventional antibiotics. Therefore, to address this issue, we proposed Co-AMPpred, an in silico-aided AMP prediction method based on compositional features of amino acid residues to classify AMPs and non-AMPs. RESULTS: In our study, we developed a prediction method that incorporates composition-based sequence and physicochemical features into various machine-learning algorithms. Then, the boruta feature-selection algorithm was used to identify discriminative biological features. Furthermore, we only used discriminative biological features to develop our model. Additionally, we performed a stratified tenfold cross-validation technique to validate the predictive performance of our AMP prediction model and evaluated on the independent holdout test dataset. A benchmark dataset was collected from previous studies to evaluate the predictive performance of our model. CONCLUSIONS: Experimental results show that combining composition-based and physicochemical features outperformed existing methods on both the benchmark training dataset and a reduced training dataset. Finally, our proposed method achieved 80.8% accuracies and 0.871 area under the receiver operating characteristic curve by evaluating on independent test set. Our code and datasets are available at https://github.com/onkarS23/CoAMPpred .