The Translation Inhibitor Rocaglamide Targets a Bimolecular Cavity between eIF4A and Polypurine RNA.

A class of translation inhibitors, exemplified by the natural product rocaglamide A (RocA), isolated from Aglaia genus plants, exhibits antitumor activity by clamping eukaryotic translation initiation factor 4A (eIF4A) onto polypurine sequences in mRNAs. This unusual inhibitory mechanism raises the question of how the drug imposes sequence selectivity onto a general translation factor. Here, we determined the crystal structure of the human eIF4A1⋅ATP analog⋅RocA⋅polypurine RNA complex. RocA targets the "bi-molecular cavity" formed characteristically by eIF4A1 and a sharply bent pair of consecutive purines in the RNA. Natural amino acid substitutions found in Aglaia eIF4As changed the cavity shape, leading to RocA resistance. This study provides an example of an RNA-sequence-selective interfacial inhibitor fitting into the space shaped cooperatively by protein and RNA with specific sequences.

Association between postinduction hypotension and postoperative mortality: a single-centre retrospective cohort study.

PURPOSE: We aimed to elucidate whether postinduction hypotension (PIH), defined as hypotension between anesthesia induction and skin incision, and intraoperative hypotension (IOH) are associated with postoperative mortality. METHODS: We conducted a retrospective cohort study of adult patients with an ASA Physical Status I-IV who underwent noncardiac and nonobstetric surgery under general anesthesia between 2015 and 2021 at Nagoya City University Hospital. The primary and secondary outcomes were 30-day and 90-day postoperative mortality, respectively. We calculated four hypotensive indices (with time proportion of the area under the threshold being the primary exposure variable) to evaluate the association between hypotension (defined as a mean blood pressure < 65 mm Hg) and mortality using multivariable logistic regression models. We used propensity score matching and RUSBoost (random under-sampling and boosting), a machine-learning model for imbalanced data, for sensitivity analyses. RESULTS: Postinduction hypotension and IOH were observed in 82% and 84% of patients, respectively. The 30-day and 90-day postoperative mortality rates were 0.4% (52/14,210) and 1.0% (138/13,334), respectively. Postinduction hypotension was not associated with 30-day mortality (adjusted odds ratio [aOR], 1.03; 95% confidence interval [CI], 0.93 to 1.13; P = 0.60) and 90-day mortality (aOR, 1.01; 95% CI, 0.94 to 1.07; P = 0.82). Conversely, IOH was associated with 30-day mortality (aOR, 1.19; 95% CI, 1.12 to 1.27; P < 0.001) and 90-day mortality (aOR, 1.12; 95% CI, 1.06 to 1.19; P < 0.001). Sensitivity analyses supported the association of IOH but not PIH with postoperative mortality. CONCLUSION: Despite limitations, including power and residual confounding, postoperative mortality was associated with IOH but not with PIH.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer si l'hypotension post-induction (HPI), définie comme une hypotension entre l'induction de l'anesthésie et l'incision cutanée, et l'hypotension peropératoire (HPO) étaient associées à la mortalité postopératoire. MéTHODE: Nous avons mené une étude de cohorte rétrospective de patient·es adultes ayant un statut physique I-IV selon l'ASA et ayant bénéficié d'une chirurgie non cardiaque et non obstétricale sous anesthésie générale entre 2015 et 2021 à l'Hôpital universitaire de la ville de Nagoya. Les critères d'évaluation principal et secondaire étaient la mortalité postopératoire à 30 et 90 jours, respectivement. Nous avons calculé quatre indices d'hypotension (la proportion temporelle de la zone sous le seuil étant la principale variable d'exposition) pour évaluer l'association entre l'hypotension (définie comme une tension artérielle moyenne < 65 mm Hg) et la mortalité à l'aide de modèles de régression logistique multivariée. Nous avons utilisé l'appariement par score de propension et le RUSBoost (sous-échantillonnage et boosting aléatoire), un modèle d'apprentissage automatique pour les données déséquilibrées, pour les analyses de sensibilité. RéSULTATS: Une HPI et une HPO ont été observées chez 82 % et 84 % des patient·es, respectivement. Les taux de mortalité postopératoire à 30 et 90 jours étaient respectivement de 0,4 % (52/14 210) et de 1,0 % (138/13 334). L'hypotension post-induction n'était pas associée à la mortalité à 30 jours (rapport de cotes ajusté [RCa], 1,03; intervalle de confiance [IC] à 95 %, 0,93 à 1,13; P = 0,60) et à la mortalité à 90 jours (RCa, 1,01; IC 95 %, 0,94 à 1,07; P = 0,82). À l'inverse, l'HPO était associée à une mortalité à 30 jours (RCa, 1,19; IC 95 %, 1,12 à 1,27; P < 0,001) et à la mortalité à 90 jours (RCa, 1,12; IC 95 %, 1,06 à 1,19; P < 0,001). Les analyses de sensibilité ont confirmé l'association de l'HPO, mais pas de l'HPI, avec la mortalité postopératoire. CONCLUSION: Malgré les limitations, y compris la puissance et persistance de facteurs confondants, la mortalité postopératoire était associée à l'hypotension peropératoire mais pas à l'hypotension post-induction seule.

Individual resolvin E family members work distinctly and in a coordinated manner in the resolution of inflammation.

Three main E-type resolvins (RvEs): RvE1, RvE2, and RvE3, have roles in the resolution of inflammation as anti-inflammatory activities. To investigate the roles of each RvE in the resolution of inflammation, timing of interleukin (IL)- 10 release and IL-10 receptor expressions, and phagocytosis evoked by each RvE in differentiated human monocytes, macrophage-like U937 cells were examined. Here, we show that RvEs enhance the expression of IL-10, and IL-10 receptor-mediated signaling pathways and IL-10-mediated-signaling-independent resolution of inflammatory effects by activating the phagocytotic function. Thus, RvE2 mainly evoked an IL-10-mediated anti-inflammatory function, whereas RvE3 principally activated phagocytotic activity of macrophages, which may be involved in tissue repair. On the other hand, RvE1 showed both functions, although not prominent but rather acting as a relief mediator that takes over the RvE2 function and passes over to the RvE3 function. Therefore, each RvE may act as an important role/stage-specific mediator in a coordinated manner with other RvEs in the processes of the resolution of inflammation.

Helix-forming aliphatic homo-δ-peptide foldamers based on the conformational restriction effects of cyclopropane.

Considerable effort has been directed toward developing artificial peptide-based foldamers. However, detailed structural analysis of δ-peptide foldamers consisting of only aliphatic δ-amino acids has not been reported. Herein, we rationally designed and stereoselectively synthesized aliphatic homo-δ-peptides forming a stable helical structure by using a chiral cyclopropane δ-amino acid as a monomer unit. Structural analysis of the homo-δ-peptides using circular dichroism, infrared, and NMR spectroscopy indicated that they form a stable 14-helical structure in solution. Furthermore, we successfully conducted X-ray crystallographic analysis of the homo-δ-peptides, demonstrating a right-handed 14-helical structure. This helical structure of the crystal was consistent with those predicted by theoretical calculations and those obtained based on NMR spectroscopy in solution. This stable helical structure is due to the effective restriction of the backbone conformation by the structural characteristics of cyclopropane. This work reports the first example of aliphatic homo-δ-peptide foldamers having a stable helical structure both in the solution and crystal states.

Wearable sensor device-based detection of decreased heart rate variability in Parkinson's disease.

The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR had the highest AUC (SDNN: AUC 0.90, 95% confidence interval [CI] 0.78-0.96; CVRR: AUC 0.90, CI 0.76-0.96) among the evaluation methods tested. The minimum values of SDNN and CVRR were significantly decreased in PD (SDNN: 9.5 ± 4.0 ms vs. 4.4 ± 2.0 ms, p < 0.0001; CVRR: 1.15 ± 0.33% vs. 0.65 ± 0.24%, p < 0.0001). We detected decreased HRV in PD using wearable sensors. Analyzing the minimum values of the HRV parameter in long-term recordings appears to be appropriate for detecting the decrease in HRV in PD.

Design and Synthesis of Cyclopropane Congeners of Resolvin E3, an Endogenous Pro-Resolving Lipid Mediator, as Its Stable Equivalents.

Resolvins are pro-resolving lipid mediators with highly potent anti-inflammatory effects. Because of their polyunsaturated structures, however, they are unstable to oxygen as a drug prototype. To address this issue, we designed and synthesized CP-RvE3 as oxidatively stable congeners of RvE3 by replacing the cis-olefin with a cis-cyclopropane to avoid the unstable bisallylic structure. Although the oxidative stabilities of CP-RvE3 were not improved, ß-CP-RvE3 was 3.7 times more metabolically stable than RvE3. Thus, we identified ß-CP-RvE3 as a metabolically stable equivalent.

Resolvin E1 Attenuates Chronic Pain-Induced Depression-Like Behavior in Mice: Possible Involvement of Chemerin Receptor ChemR23.

The antidepressant effect of eicosapentaenoic acid-derived bioactive lipid, resolvin E1 (RvE1), was examined in a murine model of chronic pain-induced depression using a tail suspension test. Because RvE1 reportedly possesses agonistic activity on a chemerin receptor ChemR23, we also examined the antidepressant effect of chemerin. Two weeks after surgery for unilateral spared nerve injury to prepare neuropathic pain model mice, immobility time was measured in a tail suspension test. Chronic pain significantly increased immobility time, and this depression-like behavior was attenuated by intracerebroventricular injection of RvE1 (1 ng) or chemerin (500 ng). These results demonstrate that RvE1 exerts an antidepressant effect in a murine model of chronic pain-induced depression, which is likely to be via ChemR23. RvE1 and its receptor may be promising targets to develop novel antidepressants.

Screening of sleep apnea based on heart rate variability and long short-term memory.

PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed. METHODS: Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep. RESULTS: The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods. CONCLUSION: Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home.

Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis.

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles-premature ventricular contraction (PVC) and premature atrial contraction (PAC)-which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

Synthesis of Resolvin E3, a Proresolving Lipid Mediator, and Its Deoxy Derivatives: Identification of 18-Deoxy-resolvin E3 as a Potent Anti-Inflammatory Agent.

We synthesized RvE3 and its deoxy derivatives, 17-deoxy-RvE3 and 18-deoxy-RvE3, by a common route via Sonogashira coupling as a key step. The evaluation of their anti-inflammatory activities revealed that 18-deoxy-RvE3 was remarkably more potent than the parent RvE3 and significantly active at a 300 fg dose in mice; additionally, 17-deoxy-RvE3 was significantly less potent than the parent RvE3. For the first time, we found that the 17-hydroxy group of RvE3 is very important for anti-inflammatory activity.

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Total Syntheses of (+)- and (-)-Tetrapetalones A and C.

Described herein are syntheses of the naturally occurring polyketides (-)-tetrapetalones A and C and their respective enantiomers. The employed strategy involves initial assembly of a masked N-aryl tetramic acid which is advanced via a highly selective conjugate addition/intramolecular Friedel-Crafts acylation sequence to deliver a key azepine intermediate. Application of recently developed C-H activation chemistry and subsequent Heck cyclization delivers the aglycone framework in an overall 12 steps. Resolution of the aglycone via stereospecific glycosylation with an enantiopure glycosyl donor followed by separation of the derived diastereomers enables further advancement to either (+)- or (-)-tetrapetalones A and C.

Total Synthesis and Structure Determination of JBIR-108-A 2-Hydroxy-2-(1-hydroxyethyl)-2,3-dihydro-3(2H)-furanone Isolated from Streptomyces gramineus IR087Pi-4.

The planar and stereostructures of JBIR-108 isolated from Streptomyces gramineus IR087Pi-4 were determined partly by spectral analysis, and these structural assignments were confirmed and completed by the total synthesis of both 1-epimers. The key stereocenters in JBIR-108 were constructed via a Corey-Bakshi-Shibata (CBS) reduction (C-1), vinylogous Mukaiyama aldol reaction (C-7), and Brown crotylation (C-14 and C-15). Although it was difficult to determine the stereochemistries at the C-1 and C-7 positions in the natural product using the modified Mosher's method, the synthesis of two possible C-1 diastereomers enabled the identification of the configurations at the hitherto unknown stereocenters.

Predictive Modeling for Hospital Readmissions for Patients with Heart Disease: An updated review from 2012-2023.

Hospital readmissions are a major concern for healthcare leaders, policy makers, and patients, resulting in adverse health outcomes and imposing an increased burden on hospital resources. This review aims to synthesize existing literature on predictive models focused on patients diagnosed with heart disease, which is known for its high readmission rates. Seven databases (i.e., Web of Science, Scopus, PubMed, ProQuest, Ovid, Cochrane Library and EBSCO) were consulted resulting in the inclusion of 56 eligible studies. Among these, 44 focused on model development, 7 on model validation, 4 on model improvement, and 1 on model implementation. Data were extracted on readmission types, data sources, modeling methods, and predictors, while assessments were conducted to analyze the quality of the studies. Findings showed that readmission types were significantly influenced by policy decisions, data predominantly originated from hospitals, and the prevalent modeling methods used were regression and single-layer machine learning techniques. The most important clinical predictors were related to comorbidities and complications, while the key demographic predictors were age and race. The study found that, despite advancements during the last decade, several limitations exist in current research, particularly in addressing attrition bias and handling missing data. Future research should, therefore, focus on optimizing readmission types, enhancing model generalization, using interpretable models, and emphasizing model implementation.

Polysomnographic features prior to dream enactment behaviors in isolated rapid eye movement sleep behavior disorder.

OBJECTIVE: This study aimed to identify electroencephalogram correlates of dream enactment behaviors (DEBs) and elucidate their cortical dynamics in patients with isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS: This cross-sectional study included 15 patients with iRBD. Two REM sleep periods in routine polysomnography were compared: the 60 s preceding the DEBs ("pre-representative behavior" [preR]), and the 60 s with the least submental electromyogram activity ("background" [BG]). Six EEG frequency bands and electrooculogram were analyzed; power spectra, coherence and phase-locking values in four 15-s periods were examined to assess trends. These indices were also compared between preR and BG. RESULTS: Compared with BG, significantly higher delta power in the F3 channel and gamma power in the F4 and O2 channels were observed during preR. For functional connectivity, the widespread beta-band connectivity was significantly increased during preR than BG. CONCLUSION: Before notable REM sleep behaviors, uneven distributed higher EEG spectral power in both very low and high frequencies, and increased wide-range beta band functional connectivity, were observed over 60 s, suggesting cortical correlates to subsequent DEBs. SIGNIFICANCE: This study may shed light on the pathological mechanisms underlies RBD through the routine vPSG analysis, leading to detection of DEBs.

Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients.

Background: Persistent hypotension in the intensive care unit (ICU) is associated with increased mortality. Predicting acute hypotensive events can lead to timely intervention. We aimed to develop a prediction model of acute hypotensive events in patients admitted to the ICU. Methods: We included adult patients admitted to the Nagoya City University (NCU) Hospital ICU between January 2018 and December 2021 for model training and internal validation. The MIMIC-III database was used for external validation. A hypotensive event was defined as a mean arterial pressure < 60 mmHg for at least 5 min in 10 min. The input features were age, sex, and time-series data for vital signs. We compared the area under the receiver-operating characteristic curve (AUROC) of three machine-learning algorithms: logistic regression, the light gradient boosting machine (LightGBM), and long short-term memory (LSTM). Results: Acute hypotensive events were found in 1325/1777 (74.6%) and 2691/5266 (51.1%) of admissions in the NCU and MIMIC-III cohorts, respectively. In the internal validation, the LightGBM model had the highest AUROC (0.835), followed by the LSTM (AUROC 0.834) and logistic regression (AUROC 0.821) models. Applying only blood pressure-related features, the LSTM model achieved the highest AUROC (0.843) and consistently showed similar results in external and internal validation. Conclusions: The LSTM model using only blood pressure-related features had the highest AUROC with comparable performance in external validation.

Beacon-based sleep-wake monitoring in dogs.

The sleep-wake cycle represents a crucial physiological process essential for maintaining homeostasis and promoting individual growth. In dogs, alterations in sleep patterns associated with age and dog's correlation with temperament factors, such as nervousness, have been reported, and there is an increasing demand for precise monitoring of sleep and physical activity in dogs. The present study aims to develop an analysis method for measuring sleep-wake patterns and physical activity in dogs by utilizing an accelerometer and a smartphone. By analyzing time series data collected from the accelerometer attached to the dog's collar, a comprehensive sleep and activity analysis model was constructed. This model classified the activity level into seven classes and effectively highlighted the variations in sleep-activity patterns. Two classes with lower activity levels were considered as sleep, while other five levels were regarded as wake based on the rate of occurrence. This protocol of data acquisition and analysis provides a methodology that enables accurate and extended evaluation of both sleep and physical activity in dogs.

Prediction model of postoperative pain exacerbation using an intravenous patient-controlled analgesia device and a wearable electrocardiogram sensor.

There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. In this study, we developed a machine learning model which was trained from intravenous patient-controlled analgesia (IV-PCA) records and electrocardiogram (ECG) of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate.Clinical relevance- We proposed a novel method for evaluating postoperative pain in real time and demonstrated the possibility of predicting pain exacerbation. The proposed method would realize the automatic administration of analgesics and the optimization of opioid doses.

Learning curve of i-gel insertion in novices using a cumulative sum analysis.

The i-gel, a popular second-generation supraglottic airway device, has been used in a variety of airway management situations, including as an alternative to tracheal intubation for general anesthesia, rescue in difficult airway settings, and out-of-hospital cardiac arrest resuscitation. We aimed to investigate the number of experiences needed to achieve a rapid, highly successful first attempt i-gel insertion in novices with a cumulative sum analysis. We also looked at how learning affected success rates, insertion time, and bleeding and reflex (limb movement, frowning face, or coughing) incidences. This prospective observational study included 15 novice residents from March 2017 to February 2018 in a tertiary teaching hospital. Finally, 13 residents with 35 [30-42] (median [interquartile range]) cases of i-gel insertion were analyzed. The cumulative sum analysis showed that 11 of 13 participants had an acceptable failure rate after 15 [8-20] cases. With increasing experience, success rate (P = 0.004), insertion time (P < 0.001), and incidence of bleeding (P = 0.006) all improved. However, the incidence of reflex did not change (P = 0.43). Based on our results, we suggest that 20 cases are preferable for novices to develop skills in using the i-gel in airway management.

FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon.

Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision-recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.