Integrating, Harmonizing, and Curating Studies With High-Frequency and Hourly Physiological Data: Proof of Concept from Seven Traumatic Brain Injury Data Sets

Research in severe traumatic brain injury (TBI) has historically been limited by studies with relatively small sample sizes that result in low power to detect small, yet clinically meaningful outcomes. Data sharing and integration from existing sources hold promise to yield larger more robust sample sizes that improve the potential signal and generalizability of important research questions. However, curation and harmonization of data of different types and of disparate provenance is challenging. We report our approach and experience integrating multiple TBI data sets containing collected physiological data, including both expected and unexpected challenges encountered in the integration process. Our harmonized data set included data on 1536 patients from the Citicoline Brain Injury Treatment Trial (COBRIT), Effect of erythropoietin and transfusion threshold on neurological recovery after traumatic brain injury: a randomized clinical trial (EPO Severe TBI), BEST-TRIP, Progesterone for the Treatment of Traumatic Brain Injury III Clinical Trial (ProTECT III), Transforming Research and Clinical Knowledge in Traumatic brain Injury (TRACK-TBI), Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II (BOOST-2), and Ben Taub General Hospital (BTGH) Research Database studies. We conclude with process recommendations for data acquisition for future prospective studies to aid integration of these data with existing studies. These recommendations include using common data elements whenever possible, a standardized recording system for labeling and timing of high-frequency physiological data, and secondary use of studies in systems such as Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), to engage investigators who collected the original data.

Long-term immune response to SARS-CoV-2 infection and vaccination in children and adolescents

BACKGROUND

This analysis examined the durability of antibodies present after SARS-CoV-2 infection and vaccination in children and adolescents.

METHODS

Data were collected over 4 time points between October 2020-November 2022 as part of a prospective population-based cohort aged 5-to-19 years (N = 810). Results of the (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test); and (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test); and (3) self-reported antigen/PCR COVID-19 test results, vaccination and symptom status were analyzed.

RESULTS

N antibody levels reached a median of 84.10 U/ml (IQR: 20.2, 157.7) cutoff index (COI) ~ 6 months post-infection and increased slightly to a median of 85.25 (IQR: 28.0, 143.0) COI at 12 months post-infection. Peak S antibody levels were reached at a median of 2500 U/mL ~6 months post-vaccination and remained for ~12 months (mean 11.6 months, SD 1.20).

CONCLUSIONS

This analysis provides evidence of robust durability of nucleocapsid and spike antibodies in a large pediatric sample up to 12 months post-infection/vaccination. This information can inform pediatric SARS-CoV-2 vaccination schedules.

IMPACT

This study provided evidence of robust durability of both nucleocapsid and spike antibodies in a large pediatric sample up to 12 months after infection. Little is known about the long-term durability of natural and vaccine-induced SARS-CoV-2 antibodies in the pediatric population. Here, we determined the durability of anti-SARS-CoV-2 spike (S-test) and nucleocapsid protein (N-test) in children/adolescents after SARS-CoV-2 infection and/or vaccination lasts at least up to 12 months. This information can inform future SARS-CoV-2 vaccination schedules in this age group.

SARS-CoV-2 Serostatus and COVID-19 Illness Characteristics by Variant Time Period in Non-Hospitalized Children and Adolescents

OBJECTIVE

To describe COVID-19 illness characteristics, risk factors, and SARS-CoV-2 serostatus by variant time period in a large community-based pediatric sample.

DESIGN

Data were collected prospectively over four timepoints between October 2020 and November 2022 from a population-based cohort ages 5 to 19 years old.

SETTING

State of Texas, USA.

PARTICIPANTS

Participants ages 5 to 19 years were recruited from large pediatric healthcare systems, Federally Qualified Healthcare Centers, urban and rural clinical practices, health insurance providers, and a social media campaign.

EXPOSURE

SARS-CoV-2 infection.

MAIN OUTCOME(S) AND MEASURE(S)

SARS-CoV-2 antibody status was assessed by the Roche Elecsys^® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test). Self-reported antigen or PCR COVID-19 test results and symptom status were also collected.

RESULTS

Over half (57.2%) of the sample (N = 3911) was antibody positive. Symptomatic infection increased over time from 47.09% during the pre-Delta variant time period, to 76.95% during Delta, to 84.73% during Omicron, and to 94.79% during the Omicron BA.2. Those who were not vaccinated were more likely (OR 1.71, 95% CI 1.47, 2.00) to be infected versus those fully vaccinated.

CONCLUSIONS

Results show an increase in symptomatic COVID-19 infection among non-hospitalized children with each progressive variant over the past two years. Findings here support the public health guidance that eligible children should remain up to date with COVID-19 vaccinations.

Incidence and predictors of breakthrough and severe breakthrough infections of SARS-CoV-2 after primary series vaccination in adults: A population-based survey of 22,575 participants

BACKGROUND

Breakthrough infections of SARS-CoV-2 are well-documented. The current study estimates breakthrough incidence across pandemic waves, and evaluates predictors of breakthrough and severe breakthrough infections (defined as those requiring hospitalization).

METHODS

89,762 participants underwent longitudinal antibody surveillance. Incidence rates were calculated using total person-days contributed. Bias-corrected and age-adjusted logistic regression determined multivariable predictors of breakthrough and severe breakthrough infection, respectively.

RESULTS

The incidence was 0.45 (0.38, 0.50) during pre-Delta, 2.80 (2.25, 3.14) during Delta, and 11.2 (8.80, 12.95) during Omicron, per 10,000 person-days. Factors associated with elevated odds of breakthrough included Hispanic ethnicity (vs non-Hispanic White, OR=1.243[1.073, 1.441]), larger household size (OR=1.251 [1.048, 1.494] for 3-5 vs. 1 and OR=1.726 [1.317, 2.262] for more than 5 vs. 1 person), rural vs urban living (OR=1.383 [1.122, 1.704]), receiving Pfizer or Johnson&Johnson vs. Moderna, and multiple comorbidities. Of the 1,700 breakthrough infections, 1,665 reported on severity; 112 (6.73%) were severe. Higher BMI, Hispanic ethnicity, vaccine type, asthma, and hypertension predicted severe breakthroughs.

CONCLUSION

Breakthrough infection was 4-25 times more common during the Omicron-dominant wave versus earlier waves. Higher burden of severe breakthrough infections was identified in subgroups.

A Recommender for Research Collaborators Using Graph Neural Networks

As most great discoveries and advancements in science and technology invariably involve the cooperation of a group of researchers, effective collaboration is the key factor. Nevertheless, finding suitable scholars and researchers to work with is challenging and, mostly, time-consuming for many. A recommender who is capable of finding and recommending collaborators would prove helpful. In this work, we utilized a life science and biomedical research database, i.e., MEDLINE, to develop a collaboration recommendation system based on novel graph neural networks, i.e., GraphSAGE and Temporal Graph Network, which can capture intrinsic, complex, and changing dependencies among researchers, including temporal user–user interactions. The baseline methods based on LightGCN and gradient boosting trees were also developed in this work for comparison. Internal automatic evaluations and external evaluations through end-users' ratings were conducted, and the results revealed that our graph neural networks recommender exhibits consistently encouraging results.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

ImmuneData: an integrated data discovery system for immunology data repositories

To meet the increasing demand for data sharing, data reuse and meta-analysis in the immunology research community, we have developed the data discovery system ImmuneData. The system provides integrated access to five immunology data repositories funded by the National Institute of Allergy and Infectious Diseases, Division of Allergy, Immunology and Transplantation, including ImmPort, ImmuneSpace, ITN TrialShare, ImmGen and IEDB. ImmuneData restructures the data repositories' metadata into a uniform schema using domain experts' knowledge and state-of-the-art Natural Language Processing (NLP) technologies. It comes with a user-friendly web interface, accessible at http://www.immunedata.org/, and a Google-like search engine for biological researchers to find and access data easily. The vast quantity of synonyms used in biomedical research increase the likelihood of incomplete search results. Thus, our search engine converts queries submitted by users into ontology terms, which are then expended by NLP technologies to ensure that the search results will include all synonyms for a particular concept. The system also includes an advanced search function to build customized queries to meet higher-level users' needs. ImmuneData ensures the FAIR principle (Findability, Accessibility, Interoperability and Reusability) of the five data repositories to benefit data reuse in the immunology research community. The data pipeline constructing our system can be extended to other data repositories to build a more comprehensive biological data discovery system.

DATABASE URL

http://www.immunedata.org/.

Determination of hormoligosis of organophosphate insecticides against Phenacoccus solenopsis

Abstract Cotton mealybug is a highly invasive pest of agricultural crops worldwide. Major agriculturists most rely on the use of insecticides for the control of pesticides. So, the indiscriminate use of insecticides leads to resistance development in recent years. For this purpose, an experiment was conducted using different concentrations of the three insecticides (profenfos chlorpyrifos and triazophos) to check the hormoligosis effects against cotton mealybug (CMB) in laboratory conditions. Investigation of variations for % mortality of adults of CMB after three days revealed that all treatments had statistically significant (P ˂ 0.05). The highest mortality was observed at the highest concentrations of profenofos 2.4% (38.55%). After 7 days, all the treatments were significant with difference in means (P ˂ 0.05). The highest mortality was recorded at the highest dilution of pesticide profenofos 2.4% (77.11%). The values of fecundity and longevity exposed a valid difference among treatments (P ˂ 0.05). Maximum fecundity was observed at the concentration 2.4% (181.41%) and longevity showed (38.46%). The highest mortality was observed at a concentration of triazophos 4% (27.98%). For chlorpyriphos the highest mortality was examined at concentration 4% (24.79%). The fecundity showed a statistically significant difference for different concentrations of triazophos and chlorpyriphos (P ˂ 0.05). The results of the recent study provide valuable information regarding the selection of insecticides and hormoligosis effects. The study can be helpful in the implications of integrated pest management of P. solenopsis.

Strategies to Estimate Prevalence of SARS-CoV-2 Antibodies in a Texas Vulnerable Population: Results From Phase I of the Texas Coronavirus Antibody Response Survey

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and immunity remains uncertain in populations. The state of Texas ranks 2nd in infection with over 2.71 million cases and has seen a disproportionate rate of death across the state. The Texas CARES project was funded by the state of Texas to estimate the prevalence of SARS-CoV-2 antibody status in children and adults. Identifying strategies to understand natural as well as vaccine induced antibody response to COVID-19 is critical.

Materials and Methods: The Texas CARES (Texas Coronavirus Antibody Response Survey) is an ongoing prospective population-based convenience sample from the Texas general population that commenced in October 2020. Volunteer participants are recruited across the state to participate in a 3-time point data collection Texas CARES to assess antibody response over time. We use the Roche Elecsys® Anti-SARS-CoV-2 Immunoassay to determine SARS-CoV-2 antibody status.

Results: The crude antibody positivity prevalence in Phase I was 26.1% (80/307). The fully adjusted seroprevalence of the sample was 31.5%. Specifically, 41.1% of males and 21.9% of females were seropositive. For age categories, 33.5% of those 18–34; 24.4% of those 35–44; 33.2% of those 45–54; and 32.8% of those 55+ were seropositive. In this sample, 42.2% (89/211) of those negative for the antibody test reported having had a COVID-19 test.

Conclusions: In this survey we enrolled and analyzed data for 307 participants, demonstrating a high survey and antibody test completion rate, and ability to implement a questionnaire and SARS-CoV-2 antibody testing within clinical settings. We were also able to determine our capability to estimate the cross-sectional seroprevalence within Texas's federally qualified community centers (FQHCs). The crude positivity prevalence for SARS-CoV-2 antibodies in this sample was 26.1% indicating potentially high exposure to COVID-19 for clinic employees and patients. Data will also allow us to understand sex, age and chronic illness variation in seroprevalence by natural and vaccine induced. These methods are being used to guide the completion of a large longitudinal survey in the state of Texas with implications for practice and population health.

Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning

PURPOSE

Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles.

METHODS

A single-institution retrospective data of 746 patients with esophageal cancer treated with photons (n = 500) and protons (n = 246) was reviewed. The primary end point of our study was G4RIL. Clustering techniques were applied to identify patient subpopulations with similar pretreatment clinical and radiation dosimetric characteristics. XGBoost was built on a training set (n = 499) to predict G4RIL risks. Predictive performance was assessed on the remaining n = 247 patients. SHapley Additive exPlanations were used to rank the importance of individual predictors. Counterfactual analyses compared patients' risk profiles assuming that they had switched modalities.

RESULTS

Baseline absolute lymphocyte count and volumes of lung and spleen receiving ≥ 15 and ≥ 5 Gy, respectively, were the most important G4RIL risk determinants. The model achieved sensitivitytesting-set 0.798 and specificitytesting-set 0.667 with an area under the receiver operating characteristics curve (AUCtesting-set) of 0.783. The G4RIL risk for an average patient receiving protons increased by 19% had the patient switched to photons. Reductions in G4RIL risk were maximized with proton therapy for patients with older age, lower baseline absolute lymphocyte count, and higher lung and heart dose.

CONCLUSION

G4RIL risk varies for individual patients with esophageal cancer and is modulated by radiotherapy dosimetric parameters. The framework for machine learning presented can be applied broadly to study risk determinants of other adverse events, providing the basis for adapting treatment strategies for mitigation.

Recommender system of scholarly papers using public datasets

The exponential growth of public datasets in the era of Big Data demands new solutions for making these resources findable and reusable. Therefore, a scholarly recommender system for public datasets is an important tool in the field of information filtering. It will aid scholars in identifying prior and related literature to datasets, saving their time, as well as enhance the datasets reusability. In this work, we developed a scholarly recommendation system that recommends research-papers, from PubMed, relevant to public datasets, from Gene Expression Omnibus (GEO). Different techniques for representing textual data are employed and compared in this work. Our results show that term-frequency based methods (BM25 and TF-IDF) outperformed all others including popular Natural Language Processing embedding models such as doc2vec, ELMo and BERT.

Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis

OBJECTIVE

Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH.

METHODS

The authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000-2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined.

RESULTS

In total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70-2.81) and norepinephrine (OR 2.24, 95% CI 1.80-2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings.

CONCLUSIONS

In patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries

The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/.

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing

Aberrant activation of the Sonic Hedgehog (SHH) gene is observed in various cancers. Previous studies have shown a “cross-talk” effect between the canonical Hedgehog signaling pathway and the Epidermal Growth Factor (EGF) pathway when SHH is active in the presence of EGF. However, the precise mechanism of the cross-talk effect on the entire gene population has not been investigated. Here, we re-analyzed publicly available data to study how SHH and EGF cooperate to affect the dynamic activity of the gene population. We used genome dynamic analysis to explore the expression profiles under different conditions in a human medulloblastoma cell line. Ordinary differential equations, equipped with solid statistical and computational tools, were exploited to extract the information hidden in the dynamic behavior of the gene population. Our results revealed that EGF stimulation plays a dominant role, overshadowing most of the SHH effects. We also identified cross-talk genes that exhibited expression profiles dissimilar to that seen under SHH or EGF stimulation alone. These unique cross-talk patterns were validated in a cell culture model. These cross-talk genes identified here may serve as valuable markers to study or test for EGF co-stimulatory effects in an SHH+ environment. Furthermore, these cross-talk genes may play roles in cancer progression, thus they may be further explored as cancer treatment targets.

FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.

The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features

Background

Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models.

Methods

In this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead.

Results

After applying the template-based 8-state secondary structure prediction method, the 7-fold cross-validated Q8 accuracy is 78.85%. Even templates from structures with only 20%~30% sequence similarity can help improve the 8-state prediction accuracy. More importantly, when good templates are available, the prediction accuracy of less frequent secondary structures, such as 3-10 helices, turns, and bends, are highly improved, which are useful for practical applications.

Conclusions

Our computational results show that the templates containing structural information are effective features to enhance 8-state secondary structure predictions. Our prediction algorithm is implemented on a web server named "C8-SCORPION" available at: http://hpcr.cs.odu.edu/c8scorpion.

Context-based features enhance protein secondary structure prediction accuracy

We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain secondary structure conformation within its amino acid environment. Encoding these context-based scores as important training and prediction features provides a way to address a long-standing difficulty in neural network-based secondary structure predictions of taking interdependency among secondary structures of neighboring residues into account. Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracy of secondary structure predictions. An overall 7-fold cross-validated Q3 accuracy of 82.74% and Segment Overlap Accuracy (SOV) accuracy of 86.25% are achieved on a set of more than 7987 protein chains with, at most, 25% sequence identity. The Q3 prediction accuracy on benchmarks of CB513, Manesh215, Carugo338, as well as CASP9 protein chains is higher than popularly used secondary structure prediction servers, including Psipred, Profphd, Jpred, Porter (ab initio), and Netsurf. More significant improvement is observed in the SOV accuracy, where more than 4% enhancement is observed, compared to the server with the best SOV accuracy. A Q8 accuracy of >70% (71.5%) is also found in eight-state secondary structure prediction. The majority of the Q3 accuracy improvement is contributed from correctly identifying β-sheets and α-helices. When the context-based scores are incorporated, there are 15.5% more residues predicted with >90% confidence. These high-confidence predictions usually have a rather high accuracy (averagely ~95%). The three- and eight-state prediction servers (SCORPION) implementing our methods are available online.

Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy

Background

Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.

Methods

In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction.

Results

The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%.

Conclusions

Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve.

Prevalence of Helicobacter pylori, gastric myoelectrical activity, gastric mucosal changes and dyspeptic symptoms before and after laparoscopic cholecystectomy

BACKGROUND/AIMS

Cholecystectomy may lead to anatomic and functional alterations which eventually induce reflux of duodenal contents with its sequlae. The aim of this study is to evaluate the prevalence of Helicobacter pylori (H. pylori), gastric myoelectrical activities and gastric mucosal changes before and after laparoscopic cholecystectomy.

METHODOLOGY

This prospective study has been carried out on 46 patients (20 M & 26 F) with mean age 41.7+/-0.2 years for whom laparoscopic cholecystectomy for gallstones was carried out. Prior to the operation and 1 year after, all patients were subjected to clinical assessment, upper gastrointestinal endoscopy, histopathology of antral mucosa, reflux gastritis score, detection of H. pylori and electrogastrography.

RESULTS

There was an increase in the postoperative suggestive symptoms of reflux gastritis compared to the preoperative: epigastric pain increased from 8 (17.4%) to 11 (23.39%) patients, nausea increased from 6 (13%) to 12 (26.1%) and bilious vomiting increased from 3 (6.5%) to 11 (23.9%) patients. Mild antral gastritis was detected endoscopically before laparoscopic cholecystectomy in 20 patients (43.5%) and increased to 27 patients (58.7%) after surgery. Meanwhile, severe antral gastritis and erosions were only detected after the operation in 10 (21.7%) patients, respectively. The histological results showed an increase of the histopathologic score of reflux gastritis after cholecystectomy from 4.28 (+/-1.56) to 9.28 (+/-1.99) (p<0.001). Active chronic superficial gastritis decreased from 23 (50%) to 13 (28.2%) patients while the inactive form increased from 15 (32.6%) to 23 (50%) patients. Also, chronic atrophic gastritis, intestinal metaplasia and dysplasia were detected postoperatively in 4 (8.6%) patients. The incidence of H. pylori infection was decreased from 32 (69.6%) to 19 (41.3%) patients (p<0.0001). Electrogastrography abnormal frequency decreased in fasting from 26.1% to 8.7% (p<0.001), and postprandial from 16.9% to 4.4% recording (p<0.002). On the other hand, there was an increase in the number of patients with decreased electrogastrography amplitude after a meal from 4.3% to 28.3% (p<0.0001).

CONCLUSIONS

Our study shows that dyspeptic symptoms, endoscopic and histologic gastric changes as well as electrogastrography abnormalities are present before and increase after cholecystectomy; meanwhile H. pylori colonization in gastric mucosa is decreased after cholecystectomy.

Does cholecystectomy affect antral mucosa? Endoscopic, histopathologic and DNA flow cytometric study

BACKGROUND/AIMS

Although cholecystectomy is still the "gold standard" for treatment of gallstones, this operation may be followed by gastric disorders. The aim of this study is to detect the effects of cholecystectomy on gastric antral mucosa.

METHODOLOGY

This prospective study has been carried out on 46 patients (20 M & 26 F) with mean age 41.7 +/- 0.2 years for whom simple cholecystectomy for gallstones was decided. Prior to the operation and 1 year after, patients were subjected to the following: clinical assessment, upper gastrointestinal endoscopy, histopathology of antral mucosa, detection of H. pylori and DNA flow cytometry.

RESULTS

There was an increase in the number of patients presenting suggestive symptoms of reflux gastritis: patients experiencing epigastric pain increased from 8 (17.4%) to 11 (23.39%) patients, nausea increased from 6 (13%) to 12 (26.1%) patients and bilious vomiting increased from 3 (6.5%) to 11 (23.9%) patients. Mild antral gastritis increased from 20 (43.5%) to 27 (58.7%) patients. Antral gastritis and antral erosions were detected only after the operation in 8 (17.4%) and 2 (4.3%) patients, respectively. The incidence of active chronic superficial gastritis decreased from 23 (50%) to 13 (28.2%) patients while the inactive form increased from 15 (32.6%) to 23 (50%) patients. Chronic atrophic gastritis, intestinal metaplasia and dysplasia were only detected postoperatively in 2 (4.3%) patients each. There was a decrease in the incidence of H. pylori infection from 32 (69.6) to 19 (41.3%) patients. DNA aneuploid pattern increased from 1 (2.2%) to 4 (8.7%) patients and there was a significant increase of DNA index from 1.01 (+/- 0.03) to 1.03 (+/- 0.05) (P < 0.005).

CONCLUSIONS

Changes in clinical, endoscopic and histopathologic findings suggest that cholecystectomy may affect gastric antral mucosa due to duodenogastric reflux. Flow cytometry may be used as an objective method for detection and evaluation of postcholecystectomy reflux gastritis.

Effect of dynorphin on insulin and somatostatin secretion, calcium uptake, and c-AMP levels in isolated rat islets of Langerhans

Dynorphin-[1-13], at concentrations of 5.8 X 10(-12) to 5.8 X 10(-9) M, stimulated insulin secretion from isolated islets of Langerhans of the rat, in medium containing 6 mM glucose. Higher concentrations of dynorphin had no significant effect on secretion. Dynorphin (5.8 X 10(-9) M) was unable to initiate insulin release from islets in the presence of 2 mM glucose, or to increase insulin secretion further in the presence of 20 mM glucose or 6 and 12 mM glyceraldehyde. Dynorphin-induced insulin secretion from islets was blocked by verapamil (5 microM) or by chlorpropamide (72 microM), but not by a mu opiate receptor antagonist, naloxone (0.11 microM), or by ICI 154129, a specific antagonist for the delta receptor (0.25 microM). Dynorphin had no effect on islet somatostatin secretion, under conditions in which insulin secretion was greatly stimulated. Glucose (20 mM) and glyceraldehyde (6 and 12 mM) significantly increased both insulin and somatostatin secretion. Dynorphin (5.8 X 10(-9) M) increased 45Ca2+ uptake into islets, and also increased intracellular islet c-AMP levels. These changes persisted when higher concentrations of dynorphin were used. These results suggest that (1) dynorphin is a very potent stimulus for insulin secretion; (2) dynorphin does not affect somatostatin secretion in static incubations of islets, in the same way as does glucose and glyceraldehyde; (3) dynorphin's effects may involve increased calcium ion movement and can be blocked by verapamil; (4) dynorphin can also increase islet c-AMP, and could thereby modulate the responsiveness of other secretagogues; (5) the actions of dynorphin on insulin secretion are not mediated by delta or mu opiate receptors in islets.