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.

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.

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.

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 .

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.

Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. METHODS: Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211-1230, haptoglobin (HPT)78-108, immunoglobulin (Ig) kappa chain C region (IGKC)2-19, and prothrombin (THRB)328-345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. RESULTS: Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgM anti-IGKC2-19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78-108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2-19 (OR 12.655, p < 0.001), and IgG anti-THRB328-345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. CONCLUSIONS: This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 may play heavy roles in RA development.

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.

Hiltonol Cocktail Kills Lung Cancer Cells by Activating Cancer-Suppressors, PKR/OAS, and Restraining the Tumor Microenvironment.

NSCLC (non-small cell lung cancer) is a leading cause of cancer-related deaths worldwide. Clinical trials showed that Hiltonol, a stable dsRNA representing an advanced form of polyI:C (polyinosinic-polycytidilic acid), is an adjuvant cancer-immunomodulator. However, its mechanisms of action and effect on lung cancer have not been explored pre-clinically. Here, we examined, for the first time, how a novel Hiltonol cocktail kills NSCLC cells. By retrospective analysis of NSCLC patient tissues obtained from the tumor biobank; pre-clinical studies with Hiltonol alone or Hiltonol+++ cocktail [Hiltonol+anti-IL6+AG490 (JAK2 inhibitor)+Stattic (STAT3 inhibitor)]; cytokine analysis; gene knockdown and gain/loss-of-function studies, we uncovered the mechanisms of action of Hiltonol+++. We demonstrated that Hiltonol+++ kills the cancer cells and suppresses the metastatic potential of NSCLC through: (i) upregulation of pro-apoptotic Caspase-9 and Caspase-3, (ii) induction of cytosolic cytochrome c, (iii) modulation of pro-inflammatory cytokines (GRO, MCP-1, IL-8, and IL-6) and anticancer IL-24 in NSCLC subtypes, and (iv) upregulation of tumor suppressors, PKR (protein kinase R) and OAS (2'5' oligoadenylate synthetase). In silico analysis showed that Lys296 of PKR and Lys66 of OAS interact with Hiltonol. These Lys residues are purportedly involved in the catalytic/signaling activity of the tumor suppressors. Furthermore, knockdown of PKR/OAS abrogated the anticancer action of Hiltonol, provoking survival of cancer cells. Ex vivo analysis of NSCLC patient tissues corroborated that loss of PKR and OAS is associated with cancer advancement. Altogether, our findings unraveled the significance of studying tumor biobank tissues, which suggests PKR and OAS as precision oncological suppressor candidates to be targeted by this novel Hiltonol+++ cocktail which represents a prospective drug for development into a potent and tailored therapy for NSCLC subtypes.

Ovatodiolide, isolated from Anisomeles indica, suppresses bladder carcinogenesis through suppression of mTOR/ß-catenin/CDK6 and exosomal miR-21 derived from M2 tumor-associated macrophages.

Bladder cancer (BCa) is the fourth leading cause of cancer deaths worldwide due to its aggressiveness and resistance against therapies. Intricate interactions between cancer cells and the tumor microenvironment (TME) are essential for both disease progression and regression. Thus, interrupting molecular communications within the TME could potentially provide improved therapeutic efficacies. M2-polarized tumor-associated macrophages (M2 TAMs) were shown to contribute to BCa progression and drug resistance. We attempted to provide evidence for ovatodiolide (OV) as a potential therapeutic agent that targets both TME and BCa cells. First, tumor-suppressing functions of OV were determined by cell viability, colony, and tumor-sphere formation assays using a coculture system composed of M2 TAMs/BCa cells. Subsequently, we demonstrated that extracellular vesicles (EVs) isolated from M2 TAMs containing oncomiR-21 and mRNAs, including Akt, STAT3, mTOR, and ß-catenin, promoted cisplatin (CDDP) resistance, migration, and tumor-sphere generation in BCa cells, through increasing CDK6, mTOR, STAT3, and ß-catenin expression. OV treatment also prevented M2 polarization and reduced EV cargos from M2 TAMs. Finally, in vivo data demonstrated that OV treatment overcame CDDP resistance. OV only and the OV + CDDP combination both resulted in significant reductions in mTOR, ß-catenin, CDK6, and miR-21 expression in tumor samples and EVs isolated from serum. Collectively, we demonstrated that M2 TAMs induced malignant properties in BCa cells, in part via oncogenic EVs. OV treatment prevented M2 TAM polarization, reduced EV cargos derived from M2 TAMs, and suppressed ß-catenin/mTOR/CDK6 signaling. These findings provide preclinical evidence for OV as a single or adjuvant agent for treating drug-resistant BCa.

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.

BACKGROUND: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. OBJECTIVE: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. METHODS: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19-related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. RESULTS: The numbers of COVID-19-related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test-related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19-related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case-based model and potentially be used to predict epidemic curves. CONCLUSIONS: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion.

BACKGROUND: Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms. RESULTS: By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models. CONCLUSIONS: The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.

BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. RESULTS: Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. CONCLUSIONS: Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future.

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.

Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such asage, gender, history of alcohol use, and violent behaviors, and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.

BACKGROUND: The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable. RESULTS: In this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance. CONCLUSIONS: Experiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.

ChemiRs: a web application for microRNAs and chemicals.

BACKGROUND: MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways. RESULTS: We develop a creditable online web server, ChemiRs, for predicting interactions and relations among miRNAs, chemicals and pathways. The database not only compares gene lists affected by chemicals and miRNAs, but also incorporates curated pathways to identify possible interactions. CONCLUSIONS: Here, we manually retrieved associations of miRNAs and chemicals from biomedical literature. We developed an online system, ChemiRs, which contains miRNAs, diseases, Medical Subject Heading (MeSH) terms, chemicals, genes, pathways and PubMed IDs. We connected each miRNA to miRBase, and every current gene symbol to HUGO Gene Nomenclature Committee (HGNC) for genome annotation. Human pathway information is also provided from KEGG and REACTOME databases. Information about Gene Ontology (GO) is queried from GO Online SQL Environment (GOOSE). With a user-friendly interface, the web application is easy to use. Multiple query results can be easily integrated and exported as report documents in PDF format. Association analysis of miRNAs and chemicals can help us understand the pathogenesis of chemical components. ChemiRs is freely available for public use at http://omics.biol.ntnu.edu.tw/ChemiRs .

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.

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.

Prediction of B-cell epitopes using evolutionary information and propensity scales.

BACKGROUND: Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists. METHODS: We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results. RESULTS: In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066. CONCLUSIONS: Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.