GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India.

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

The single point insulin sensitivity estimator (SPISE) index as a predictor of metabolic syndrome in Korean adults.

PURPOSE: Recently, the single-point insulin sensitivity estimator (SPISE) has been developed as a simple surrogate of insulin resistance based on BMI, triglycerides (TG), and HDL-C. However, no studies have focused on the predictive power of the SPISE index for identifying metabolic syndrome (MetSyn) in Korean adults. Here, this study aimed to estimate the predictive power of the SPISE index for determining MetSyn and to compare its predictive power with other insulin sensitivity/resistance indices in South Korean adults. METHODS: A total of 7837 participants from the 2019 and 2020 Korean National Health and Nutrition Examination Surveys were analyzed in the present study. MetSyn was defined by the AHA/NCEP criteria. In addition, HOMA-IR, inverse insulin, TG/HDL, TyG index (triglyceride-glucose index), and SPISE index were calculated based on the previous literature. RESULTS: Predictive power of the SPISE index for determining MetSyn (ROC-AUC [95 % CI] = 0.90 [0.90-0.91], sensitivity = 83.4 %, specificity = 82.2 %, cut-off point = 6.14, p < .001) was higher than that of HOMA-IR (ROC-AUC: 0.81), inverse insulin (ROC-AUC: 0.76), TG/HDL-C (ROC-AUC: 0.87), and TyG index (ROC-AUC: 0.88), the P value for ROC-AUC comparison < .001. CONCLUSION: SPISE index has demonstrated superior predictive value for diagnosing MetSyn regardless of sex and is strongly correlated with blood pressure compared with other surrogate indices of insulin resistance, attesting to its utility as a reliable indicator of insulin resistance and MetSyn in Korean adults.

Mortality prediction among ICU inpatients based on MIMIC-III database results from the conditional medical generative adversarial network.

Background and aims: Improved mortality prediction among intensive care unit (ICU) inpatients is a valuable and challenging task. Limited clinical data, especially with appropriate labels, are an important element restricting accurate predictions. Generative adversarial networks (GANs) are excellent generative models and have shown great potential for data simulation. However, there have been no relevant studies using GANs to predict mortality among ICU inpatients. In this study, we aim to evaluate the predictive performance of a variant of GAN called conditional medical GAN (c-med GAN) compared with some baseline models, including simplified acute physiology score II (SAPS II), support vector machine (SVM), and multilayer perceptron (MLP). Methods: Data from a publicly available intensive care database, the Medical Information Mart for Intensive Care III (MIMIC-III) database (v1.4), were included in this study. The area under the precision-recall curve (PR-AUC), area under the receiver operating characteristic curve (ROC-AUC), and F1 score were used to evaluate the predictive performance. In addition, the size of the dataset was artificially reduced, and the performance of the c-med GAN was compared in different size datasets. Results: The results showed that c-med GAN achieves the best PR-AUC, ROC-AUC, and F1 score compared with SAPS II, SVM, and MLP when training in the full MIMIC-III dataset. When the size of the dataset was reduced, the prediction performances of both MLP and c-med GAN were affected. However, the c-med GAN still outperformed MLP on smaller datasets and had less degradation. Conclusion: The prediction of in-hospital mortality based on the c-med GAN for ICU patients showed better performance than the baseline models. Despite some inadequacies, this model may have a promising future in clinical applications which will be explored by further research.

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification.

Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard metric to evaluate binary classifications in most scientific fields. The ROC curve has true positive rate (also called sensitivity or recall) on the y axis and false positive rate on the x axis, and the ROC AUC can range from 0 (worst result) to 1 (perfect result). The ROC AUC, however, has several flaws and drawbacks. This score is generated including predictions that obtained insufficient sensitivity and specificity, and moreover it does not say anything about positive predictive value (also known as precision) nor negative predictive value (NPV) obtained by the classifier, therefore potentially generating inflated overoptimistic results. Since it is common to include ROC AUC alone without precision and negative predictive value, a researcher might erroneously conclude that their classification was successful. Furthermore, a given point in the ROC space does not identify a single confusion matrix nor a group of matrices sharing the same MCC value. Indeed, a given (sensitivity, specificity) pair can cover a broad MCC range, which casts doubts on the reliability of ROC AUC as a performance measure. In contrast, the Matthews correlation coefficient (MCC) generates a high score in its [Formula: see text] interval only if the classifier scored a high value for all the four basic rates of the confusion matrix: sensitivity, specificity, precision, and negative predictive value. A high MCC (for example, MCC [Formula: see text] 0.9), moreover, always corresponds to a high ROC AUC, and not vice versa. In this short study, we explain why the Matthews correlation coefficient should replace the ROC AUC as standard statistic in all the scientific studies involving a binary classification, in all scientific fields.

Effects of elevated arsenic and nitrate concentrations on groundwater resources in deltaic region of Sundarban Ramsar site, Indo-Bangladesh region.

An attempt has been adopted to predict the As and NO3- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO3- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO3- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO3. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB.

Iranian plateau is a seismically active region. Within this region, northwestern Iran is of high importance. Before proper planning for mitigating the earthquake-induced hazards can be achieved, it is necessary to identify high-risk areas in terms of susceptibility to earthquakes. In this study, landslide susceptibility in Miandoab Country was modeled using the so-called random forest algorithm (RFA) in MATLAB based on records acquired at 67 earthquake hotspots considering 9 factors affecting the earthquake occurrence (i.e., height, slope, direction, distance from fault, distance from river, distance from road, land use, geology, and precipitation). Predictive power of the model and validity of its results were evaluated using relative operating characteristic (ROC) curve and area under the curve (AUC). The assessment results showed very good accuracy of the model (0.97). It was further found that digital height layer, geology, and distance from fault impose the largest contributions into earthquake potential. The results also showed that 53%, 8.3%, and 38.4% of the study area were classified as being at low risk, moderate risk, and high risk of earthquake. Among other climatic elements, the precipitation exhibits the largest fluctuations; we proceeded to evaluate precipitation trends in the study area for a statistical period of 30 years. This was practiced by implementing Mann-Kendall nonparametric test in MATLAB. This subject-matter is especially important in Iran where the annual precipitation level is as low as 250 mm. The results showed that the precipitation follows an increasing trend in the region.

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.

Effective use of sequence information to predict CRISPR-Cas9 off-target.

The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP.

Value of adjunctive evidence from MII-pH monitoring and high-resolution manometry in inconclusive GERD patients with AET 4-6.

OBJECTIVES: Gastro-esophageal reflux disease (GERD) is a common disease in gastroenterology outpatients. However, some patients with typical reflux symptoms does not satisfy diagnostic criteria. This study was to explore the value of adjunctive evidence from multichannel intraluminal impedance-pH (MII-pH) monitoring and esophageal high-resolution manometry (HRM) in inconclusive GERD patients with acid exposure time (AET) 4-6%. METHODS: Endoscopy, MII-pH monitoring and esophageal HRM were retrospectively analyzed from consecutive patients with typical reflux symptoms in a tertiary hospital from 2013 to 2019. Patients were categorized as conclusive or inconclusive GERD according to AET. Adjunctive evidence for GERD diagnosis from Lyon Consensus were collected and analyzed. RESULTS: Among 147 patients with typical reflux symptoms, conclusive GERD was found in only 31.97% of patients (N = 47). The remaining 100 patients (68.03%) were inconclusive GERD, of whom 28% (N = 28) had AET 4-6%. These patients suffered similar reflux burden and impaired esophageal movement. Inconclusive GERD patients with AET 4-6% had lots of positive adjunctive evidence from HRM and MII-pH monitoring. In receiver operating characteristic analysis, mean nocturnal baseline impedance (MNBI) and post-reflux swallow-induced peristaltic wave index (PSPWI) had an area under the curve (AUC) of 0.839 (CI: 0.765-0.913, p < 0.001) and 0.897 (CI: 0.841-0.953, p < 0.001), respectively, better than total reflux episode (AUC of 0.55, p = 0.33). When MNBI was combined with PSPWI, the AUC was elevated to 0.910 (CI: 0.857-0.963, p < 0.001). CONCLUSIONS: Inconclusive GERD patients with AET 4-6% have similar acid burden and esophagus motility dysfunction to GERD patients. MNBI and PSPWI are pivotal adjunctive evidence for diagnosing GERD when AET is borderline.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.

Differentiating among stages of cognitive impairment in aging: Version 3 of the Uniform Data Set (UDS) neuropsychological test battery and MoCA index scores.

INTRODUCTION: Federally funded Alzheimer's Disease Centers in the United States have been using a standardized neuropsychological test battery as part of the National Alzheimer's Coordinating Center Uniform Data Set (UDS) since 2005. Version 3 (V3) of the UDS replaced the previous version (V2) in 2015. We compared V2 and V3 neuropsychological tests with respect to their ability to distinguish among the Clinical Dementia Rating (CDR) global scores of 0, 0.5, and 1. METHODS: First, we matched participants receiving V2 tests (V2 cohort) and V3 tests (V3 cohort) in their cognitive functions using tests common to both versions. Then, we compared receiver-operating characteristic (ROC) area under the curve in differentiating CDRs for the remaining tests. RESULTS: Some V3 tests performed better than V2 tests in differentiating between CDR 0.5 and 0, but the improvement was limited to Caucasian participants. DISCUSSION: Further efforts to improve the ability for early identification of cognitive decline among diverse racial groups are required.

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning.

Defining genes that are essential for life has major implications for understanding critical biological processes and mechanisms. Although essential genes have been identified and characterised experimentally using functional genomic tools, it is challenging to predict with confidence such genes from molecular and phenomic data sets using computational methods. Using extensive data sets available for the model organism Caenorhabditis elegans, we constructed here a machine-learning (ML)-based workflow for the prediction of essential genes on a genome-wide scale. We identified strong predictors for such genes and showed that trained ML models consistently achieve highly-accurate classifications. Complementary analyses revealed an association between essential genes and chromosomal location. Our findings reveal that essential genes in C. elegans tend to be located in or near the centre of autosomal chromosomes; are positively correlated with low single nucleotide polymorphim (SNP) densities and epigenetic markers in promoter regions; are involved in protein and nucleotide processing; are transcribed in most cells; are enriched in reproductive tissues or are targets for small RNAs bound to the argonaut CSR-1. Based on these results, we hypothesise an interplay between epigenetic markers and small RNA pathways in the germline, with transcription-based memory; this hypothesis warrants testing. From a technical perspective, further work is needed to evaluate whether the present ML-based approach will be applicable to other metazoans (including Drosophila melanogaster) for which comprehensive data sets (i.e. genomic, transcriptomic, proteomic, variomic, epigenetic and phenomic) are available.

An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.

The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes, and then employed a systematic machine-learning approach, using protein sequence-derived features and selection procedures, to investigate essential gene predictions within and among species. We showed that the numbers of essential gene orthologs comprise small fractions when compared with the total number of orthologs among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trained with subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species. The present work provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes using machine learning approaches.

Between a ROC and a hard place: Teaching prevalence plots to understand real world biomarker performance in the clinic.

The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) of the ROC curve are widely used in discovery to compare the performance of diagnostic and prognostic assays. The ROC curve has the advantage that it is independent of disease prevalence. However, in this note, we remind scientists and clinicians that the performance of an assay upon translation to the clinic is critically dependent upon that very same prevalence. Without an understanding of prevalence in the test population, even robust bioassays with excellent ROC characteristics may perform poorly in the clinic. While the exact prevalence in the target population is not always known, simple plots of candidate assay performance as a function of prevalence rate give a better understanding of the likely real-world performance and a greater understanding of the likely impact of variation in that prevalence on translation to the clinic.

Double-crescent sign as a predictor of chronic subdural hematoma recurrence following burr-hole surgery.

OBJECTIVE: Subdural hygroma has been reported as a causative factor in the development of a chronic subdural hematoma (CSDH) following a head trauma and/or neurosurgical procedure. In some CSDH cases, the presence of a 2-layered space delineated by the same or similar density of CSF surrounded by a superficial, residual hematoma is seen on CT imaging after evacuation of the hematoma. The aims of the present study were to test the hypothesis that the double-crescent sign (DCS), a unique imaging finding described here, is associated with the postoperative recurrence of CSDH, and to investigate other factors that are related to CSDH recurrence. METHODS: The authors retrospectively analyzed data from 278 consecutive patients who underwent single burr-hole surgery for CSDH between April 2012 and March 2017. The DCS was defined as a postoperative CT finding, characterized by the following 2 layers: a superficial layer demonstrating residual hematoma after evacuation of the CSDH, and a deep layer between the brain's surface and the residual hematoma, depicted as a low-density space. Correlation of the recurrence of CSDH with the DCS was evaluated by multivariate logistic regression modeling. The authors also investigated other classic predictive factors including age, sex, past history of head injury, hematoma laterality, anticoagulant and antiplatelet therapy administration, preoperative hematoma volume, postoperative residual hematoma volume, and postoperative brain reexpansion rate. RESULTS: A total of 277 patients (320 hemispheres) were reviewed. Fifty (18.1%) of the 277 patients experienced recurrence of CSDH within 3 months of surgery. CSDH recurred within 3 months of surgery in 32 of the 104 hemispheres with a positive DCS. Multivariate logistic analyses revealed that the presence of the DCS (OR 3.36, 95% CI 1.72-6.57, p < 0.001), large postoperative residual hematoma volume (OR 2.88, 95% CI 1.24-6.71, p = 0.014), anticoagulant therapy (OR 3.03, 95% CI 1.02-9.01, p = 0.046), and bilateral hematoma (OR 3.57, 95% CI 1.79-7.13, p < 0.001) were significant, independent predictors of CSDH recurrence. CONCLUSIONS: In this study, the authors report that detection of the DCS within 7 days of surgery is an independent predictive factor for CSDH recurrence. They therefore advocate that clinicians should carefully monitor patients for postoperative DCS and subsequent CSDH recurrence.

Eficacia diagnóstica de la impedancia basal nocturna media, un nuevo parámetro para el estudio de la enfermedad por reflujo gastroesofágico / Diagnostic efficacy of mean nocturnal basal impedance, a new parameter for the study of gastroesophageal reflux disease

Resumen Introducción y objetivos: el análisis de la impedancia basal nocturna media (IBNM) se ha propuesto para incrementar la precisión diagnóstica de enfermedad por reflujo erosiva (ERGE). Nuestro objetivo fue evaluar el rendimiento diagnóstico de esta prueba en un grupo de pacientes con ERGE conocida. Materiales y métodos: incluimos 123 individuos (58 con ERGE y 65 controles sanos) a quienes se les realizó pH-impedanciometría (pH-IMM) consecutiva entre enero de 2015 y junio de 2017. Todos los pacientes tenían endoscopia tomada en los 6 meses previos. El tiempo de exposición ácida (TEA) anormal (>4,2%) y la presencia de pirosis y/o regurgitación en los 6 meses previos fueron los criterios para el diagnóstico de ERGE. Se encontraron 58 pacientes con ERGE, 24 con enfermedad por reflujo erosiva (ERE) y 34 con enfermedad por reflujo no erosiva (ERNE). Los 65 restantes fueron controles sanos (CS) asintomáticos con EGD y pH-IMM normales. Todos los trazos de pH-IMM se reanalizaron para medir la IBNM por un segundo observador que desconocía los datos previos. El análisis estadístico incluyó pruebas múltiples de Bonferroni para comparar los grupos; regresión lineal para variables continuas; y análisis de curva ROC para buscar valor IBNM con mayor rendimiento. Para los diferentes parámetros de precisión diagnóstica se utilizó el punto de corte de la IBNM. Se usó significancia estadística con valor de p <0,01 e intervalos de confianza de 95% (IC 95%) para todos los cálculos. Resultados: los pacientes con ERE y ERNE presentaron valores de IBNM significativamente más bajos que el grupo control (p <0,01). Se observó una correlación negativa entre los valores de la IBNM y TEA (r = 0,59; p = <0,001), y también entre la IBNM y número de eventos de reflujo (r = 0,37; p = <0,001). En el análisis de curva ROC, el área bajo la curva de la IBNM fue de 0,941 (IC 95%: 0,894-0,987) y el punto de corte con mayor eficiencia 1102 ohms (sensibilidad 98,5%; especificidad 84,5%). Usando este valor (<1,102), la IBNM tuvo una sensibilidad para detectar ERGE de 91% (ERNE 86% y ERE 100%) y una especificidad de 98%. Conclusión: la IBNM tiene alta sensibilidad y especificidad para el diagnóstico de la ERGE. Adicionar esta prueba al análisis convencional de la pH-impedancia y a los métodos actuales de estudio de la ERGE puede mejorar significativamente nuestra capacidad para diagnosticar la enfermedad.

Abstract Introduction and Objectives: Analysis of nocturnal basal impedance (IBNM) has been proposed as a way to increase accuracy of GERD diagnosis. Our objective was to evaluate the diagnostic performance of this test in a group of patients known to have GERD. Materials and methods: We included 123 individuals: 58 with GERD and 65 healthy controls. They underwent consecutive pH-impedance monitoring between January 2015 and June 2017. All had undergone endoscopy in the 6 months prior to testing. Criteria used for diagnosis of GERD were abnormal acid exposure time (AET > 4.2%), pyrosis and/or regurgitation in the previous 6 months. We found 58 patients with GERD of whom 24 had erosive reflux disease (ERE) and 34 had non-erosive reflux disease (NERD). The remaining 65 were asymptomatic healthy controls with normal endoscopic results and pH impedance monitoring. A second observer who did not know the previous data measurements analyzed all pH impedance monitoring traces for IBMN. Statistical analysis included multiple Bonferroni tests for comparison between groups, linear regression for continuous variables, and receiver operating characteristic (ROC) curve analysis to find high performance IBNM values. The IBNM cutoff point was used for diagnostic precision parameters. Statistical significance was set at p <0.01, and 95% confidence intervals were used for all calculations. Results: IBNM measures were significantly lower for patients with ERE and NERD than for the control group (p <0.01). A negative correlation was observed between IBNM and acid exposure time values ​​(r = 0.59, p = <0.001) and also between IBNM and number of reflux events (r = 0.37, p = <0.001). ROC curve analysis found that the area under the curve for IBNM was 0.941 (95% CI: 0.894-0.987), and the cutoff point with the highest efficiency was 1,102 ohms (sensitivity 98.5%, specificity 84.5%). Using this value (<1.102), the IBNM had a sensitivity for detecting GERD of 91% (NERD 86% and ERE 100%) and a specificity of 98%. Conclusion: IBNM has high sensitivity and specificity for diagnosis of GERD. Addition of this test to conventional pH-impedance analysis and current methods for studying GERD can significantly improve our ability to diagnose this disease.

Ecological niche modeling re-examined: A case study with the Darwin's fox.

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E-space index I) and extent of extrapolation versus Jaccard similarity (E-space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E-space indices I and II) may complement current methods for ENM evaluation.

Predictive modeling of inpatient mortality in departments of internal medicine.

Despite overwhelming data on predictors of inpatient mortality, it is unclear which variables are the most instructive in predicting mortality of patients in departments of internal medicine. This study aims to identify the most informative predictors of inpatient mortality, and builds a prediction model on an individual level, given a constellation of patient characteristics. We use a penalized method for developing the prediction model by applying the least-absolute-shrinkage and selection-operator regression. We utilize a cohort of adult patients admitted to any of 5 departments of internal medicine during 3.5 years. We integrated data from electronic health records that included clinical, epidemiological, administrative, and laboratory variables. The prediction model was evaluated using the validation sample. Of 10,788 patients hospitalized during the study period, 874 (8.1%) died during admission. We find that the strongest predictors of inpatient mortality are prior admission within 3 months, malignant morbidity, serum creatinine levels, and hypoalbuminemia at hospital admission, and an admitting diagnosis of sepsis, pneumonia, malignant neoplastic disease, or cerebrovascular disease. The C-statistic of the risk prediction model is 89.4% (95% CI 88.4-90.4%). The predictive performance of this model is better than a multivariate stepwise logistic regression model. By utilizing the prediction model, the AUC for the independent (validation) data set is 85.7% (95% CI 84.1-87.3%). Using penalized regression, this prediction model identifies the most informative predictors of inpatient mortality. The model illustrates the potential value and feasibility of a tool that can aid physicians in decision-making.