Neuroimaging studies have demonstrated that irritable bowel syndrome (IBS)—a relapsing functional bowel disorder—presents with disrupted brain connections. However, little is known about the alterations of interhemispheric functional connectivity and underlying structural connectivity in IBS. This study combined resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) to investigate changes in interhemispheric coordination in IBS patients. Resting-state functional and structural magnetic resonance images were acquired from 65 IBS patients and 67 healthy controls . Interhemispheric voxel-mirrored homotopic connectivity (VMHC) was calculated and compared between groups. Homotopic regions showing abnormal VMHC in patients were targeted as regions of interest (ROIs) for analysis of DTI tractography. The fractional anisotropy (FA), fiber number and fiber length were compared between groups. Statistical analysis was also performed by including anxiety and depression as covariates to evaluate their effect. A Pearson correlation analysis between abnormal interhemispheric connectivity and clinical indices of IBS patients was performed. Compared to HCs, IBS patients had higher interhemispheric functional connectivity between bilateral thalami, cuneus, posterior cingulate cortices (PCC), lingual gyri and inferior occipital/cerebellum lobes, as well as lower interhemispheric functional connectivity between bilateral ventral anterior cingulate cortices (vACC) and inferior parietal lobules (IPL). The inclusion of anxiety and depression as covariates abolished VMHC difference in vACC. Microstructural features of white matter tracts connecting functionally abnormal regions did not reveal any differences between the groups. VMHC values in vACC negatively correlated with the quality of life (QOL) scores of patients. In conclusion, this study provides preliminary evidence of the disrupted functional coordination rather than anatomic coordination between interhemispheric regions within the cortex-thalamus circuit in IBS patients, which could partly account for the enhanced visceral information processing and impaired endogenous pain or emotion inhibition associated with IBS. Irritable bowel syndrome (IBS), a common functional gastrointestinal disorder with an estimated worldwide prevalence of 10%–20% . Two IBS patients and one healthy subject were excluded for excessive head motion, and one IBS patient was excluded for falling asleep in the MR scanner during scanning according to his self-reporting after scanning. The remaining 65 IBS patients  and 67 HCs  were included in the final analysis and were matched for age, sex and education level.Questionnaires were completed before MR scanning for all participants, including Mini-Mental State Examination  = 256 mm2 × 256 mm2, matrix size = 256 × 256, slice thickness = 1 mm, 191 slices in the sagittal orientation). Second, resting-state fMRI (rs-fMRI) data were obtained using a single-shot, gradient-recalled echo-planar imaging sequence . Then, diffusion tensor images were obtained using a spin echo-based echo planar imaging sequence in contiguous axial planes, including 20 volumes with diffusion gradients applied along 20 non-collinear directions (b = 1000 s/mm2) and one volume without diffusion weighting (b = 0 s/mm2). Each volume consisted of 30 contiguous axial slices covering the whole brain .Subjects were scanned using a 3 Tesla MR instrument . Foam padding was used to minimize head motion for all subjects, who were instructed to rest with their eyes closed, not think of anything in particular, and not fall asleep. First, high-resolution three dimensional T1 images were co-registered to the functional images, and then were segmented using the unified segmentation algorithm  of all subjects were averaged with their left–right mirrored version, producing a group-specific symmetrical template; second, the functional images were further normalized to this symmetrical template associated with T1 images; finally, the functional images were spatially smoothed with an 8 mm full width at half maximum isotropic Gaussian kernel.Functional images were preprocessed by using SPM8 softwarez transformed) and was used for group-level analysis.After smoothing, images were temporally filtered (bandpass: 0.01 Hz–0.08 Hz), and several sources of spurious variance were regressed out  and a filed map, and then co-registered to the B0 image with linear least-squares fitting method to minimize head movements. Diffusion-tensor models were estimated at each voxel. Whole-brain fiber tracking was performed in the DTI native space for each subject with a continuous tracking algorithm embedded in the Diffusion Toolkit. Path tracing proceeded until either the fractional anisotropy (FA) fell below 0.15, or the minimum angle between the current and the previous path segment was higher than 35°, as was done in our previous study  for DTI data analysis. Fiber bundles connecting symmetrical ROIs in each hemisphere were then extracted from the whole-brain fibers. This was done as follows: first, the ROIs were transformed from the normalized symmetric space to each individual’s native functional space; second, the mean functional image  was co-registered to the B0 image (native diffusion space) and this transformation was applied to all ROIs; third, the ROIs were dilated by one voxel into the white matter to ensure they were in contact with the fibers; finally, only those tracts that reached the symmetrical ROIs were picked from the whole-brain fiber tracking. This was accomplished using TrackVis softwaret test was performed with SPM8P < 0.005) and extent (P < 0.05) thresholds corrected at the whole-brain level. To assess differences of VMHC between groups, a random two-sample t test was then performed, while eliminating the effects of age, sex and educational level by regression. Significance thresholds were set at a corrected P < 0.05, with the joint expected probability distribution as done in the abovementioned one-sample t test. Path length, tract count and mean FA of the fiber connecting the bilateral ROIs were compared between groups by a two-sample t test using SPSS v16.0 , which was considered significant at P < 0.05. The comparison between the IBS and control groups was also performed by including anxiety and depression as covariates , they were taken together, rather than separately, as covariates to evaluate the psychosocial effect on brain connectivity as done in previous studies in IBS . Scores for MMSE (>26) and MoCA (≥26) for all subjects were in the normal range, while lower MMSE and higher anxiety and depression symptom scores were observed in IBS patients relative to HCs ; however, inter-group comparisons showed that IBS patients had lower VMHC in bilateral ventral anterior cingulate cortices (vACC) and inferior parietal lobules (IPL), as well as higher VMHC in bilateral thalami, cuneus, posterior cingulate cortices (PCC), lingual gyri and inferior occipital/cerebellum lobes relative to HCs (Table Figure Cs Table . InclusiCs Table .t test between groups did not show any significant differences in path length, tract count or FA of the two fibers (Figure Two commissural fibers connecting the left and right vACC and cuneus were detected in all HCs and patients. The commissural fibers that connected the other bilateral ROIs displaying abnormal VMHC were detected in less than half of the subjects. Therefore, we only extracted the anatomical parameters in commissural fibers connecting the bilateral vACC and cuneus. The result of a two-sample s Figure .r = −0.25, P = 0.04; Figure The VMHC value in vACC showed a slight negative correlation with the QOL scores of IBS patients (In this study, we investigated interhemispheric functional and anatomic coordination in IBS patients by combining rs-fMRI and DTI. We demonstrated specific disruptions of functional coordination rather than anatomic coordination in IBS patients compared with HCs. The VMHC abnormalities in IBS mainly localized in the cortex-thalamus circuit, with higher VMHC values in thalamus, cuneus, PCC, lingual gyrus and inferior occipital/cerebellum lobes, and lower values in vACC and IPL. Moreover, the inclusion of anxiety and depression as confounding variables led to a loss of inter-group VMHC differences in the vACC. On the contrary, anatomical connectivity showed no significant difference between groups.Our results showed that IBS patients had higher interhemispheric functional connectivity of bilateral thalami. The thalamus is a core region of the homeostatic afferent network, which encompasses the sensory input entering the thalamus from the brainstem, and then projects to the insular and anterior mid-cingulate cortex, mediating affective, motivational and motor aspects of the stimulus (Al Omran and Aziz, The cuneus and PCC also showed higher VMHC in IBS patients. The cuneus plays an important role in integrating somatosensory inputs with other sensory stimuli and with cognitive processes such as attention, learning and memory (Fulbright et al., We also found higher VMHC in the lingual gyrus and inferior occipital/cerebellum in IBS patients. Few studies have reported abnormalities in these regions in IBS patients. Ke et al.  showed iOur finding that decreased interhemispheric functional connectivity in the vACC was shown for IBS patients, but the inclusion of anxiety and depression as covariates abolished this difference. The ACC is a multifunctional structure situated in the medial frontal lobe that is highly interconnected with the insular, prefrontal, limbic and other subcortical structures, thus cognitive and affective factors may exert influence on pain transmission through the ACC (Bush et al., In addition to decreased VMHC in the vACC, lower VMHC in the IPL was also observed in IBS patients relative to controls. The IPL is also implicated in pain modulation (Schwedt and Chong, We performed an inter-group analysis for the commissural tracts connecting the bilateral ACC and cuneus, but did not observe significant differences between groups. Some DTI studies in IBS have demonstrated abnormalities of microstructural white matter integrity (Chen et al., Taking into account that IBS patients showed abnormal interhemispheric functional connectivity but no significant changes in interhemispheric anatomic connectivity, we speculated that as for functional disorders such as chronic pain disorders including IBS, functional connectivity may be more sensitive than structural connectivity in detecting the exact mechanisms underlying the symptoms. In general, the functional connectivity which measures blood oxygenation level-dependent fluctuations is thought to be more flexible and sensitive; while the structural connectivity is relatively stable (Bullmore and Sporns, While this study has advanced our understanding of the changes in neural networking associated with IBS, it also had some limitations. First, our study cohort consisted of Chinese patients with diarrhea-predominant IBS, and as such, the results may not be generalizable to patients of other ethnicities or with other subtypes of IBS. Second, future studies will need to address the question of whether the observed results are altered by IBS treatment. Besides, further studies with more subjects with depression or anxious symptom and with different pain degrees are needed to investigate how the psychosocial factor and pain might modulate the observed differences. Third, as a newly developed approach, the exact physiological implication of VMHC and its relationship with anatomical connectivity warrant further examination.Our data provide preliminary evidence of the potential value of VMHC and DTI in detecting abnormalities of interhemispheric coordination in IBS patients. We demonstrated disrupted interhemispheric functional coordination rather than anatomical coordination in IBS patients, mainly localized within the cortex-thalamus circuit, which could partly explain the enhanced visceral information processing and impaired endogenous pain or emotion regulations associated with IBS.RQ was involved in literature review, experimental design, data analysis and writing of the manuscript. CL contributed to the data collection and the analysis of neuropsychological data. YW and LC contributed to the data collection. QX was involved in fMRI data analysis. FW contributed in the experimental design. LJZ contributed in the experimental design and revision of the manuscript. GML was involved in the experimental design and revision of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TM technology, makes them better candidates in comparison to contemporary adult stem cells. Following 30 replication cycles these cells have been observed to retain their stemness, with their phenotype and karyotype intact. However, their remarkable immunosuppressant properties, protecting self as well as co-transplanted allografts from rejection, are what truly define their transplantation potential. They have been successfully applied to many chronic conditions, using animal models, including type 1 diabetes, limbal stem cell deficiency, burn injuries, and wound healing, etc. with encouraging results.The human umbilical cord has been studied extensively in the past two decades. It is free of ethical dilemmas, non-tumorigenic, and less immunogenic and thus provides a significant advantage over other stem cell sources. The cord lining yields both mesenchymal and epithelial stem cells. The mesenchymal cells have been appraised at length by many researchers, which led to the current review focusing on the cord lining epithelial cells (CLECs). These cells have high proliferative capacity and their superior harvest and multiplication, using the revolutionary CellOptimaThis review first discusses some of the advantages afforded by CLECs over other stem cell lines and then delineates their potential use in various clinical applications. Clinical trials using CLECs are currently underway in the US in collaboration with CellResearch Corp. and their potential positive findings will help garner an FDA approval, likely leading to the eventual commercialization of this promising technology. Regenerative medicine focuses on the use of stem cells for the repair and replacement of damaged tissues and organs with the future prospect of curing chronic disorders. This involves the use of stem cells from different sources to generate specific cellular lineages through directed differentiation which can be applied to damaged environments for a speedy recovery . RegenerEmbryonic stem cells were first considered as a viable source for research . HoweverHematopoietic, epithelial, and mesenchymal cells have been isolated from umbilical cord blood, while both epithelial and mesenchymal cells have been collected from the Wharton’s jelly , amniotiThis review is directed at describing the various advantages the umbilical cord lining-derived epithelial cells have over other promising stem cell types and also explores their potential clinical applications.The richest source for umbilical cord-derived stem cells is the cord lining. Umbilical cords are collected from healthy women undergoing surgery and who have not been diagnosed with any infectious diseases like hepatitis, HIV, etc. The tissue is then cut into 2-cm segments, washed, disinfected with antibiotic mixture, and further cut into small squares of 0.5 cm for cell isolation by explant culture using specific media. The cord contains both mesenchymal and epithelial cells . The epiCertain embryonic and induced pluripotent stem cell properties have impeded their widespread clinical acceptance. The properties of umbilical cord lining cells are better in some regards compared to these pluripotent cell types  and they were successfully utilized in reconstructing an organotypic skin equivalent, including the stratified epithelium . They haThe limbus, a region lying between cornea and conjunctive, is considered as the reservoir of ocular stem cells. The preservation of ocular functionality and integrity is by virtue of the limbal stem cells located in the basal region of the limbus . A handfBoth mesenchymal and epithelial cells from cord lining have been investigated for their corneal regenerative capacity, with epithelial cells showing promising results. A study conducted by Reza et al.  tested t7 cells per mouse) was then intraperitoneally injected into a streptozotocin-treated diabetic mouse model. Both treatment and control groups had their blood glucose level tested once a day in the first week and twice a day for the next two weeks following which the mice were euthanized. An opaque cluster of transgene-expressing CLECs were found, proximal to the damaged pancreas, in the cell-treated group of mice. In this pilot study, the test subjects showed a statistically significant alteration in the hyperglycemic condition over 18 days post-stem cell therapy compared to the controls. These cells thus display therapeutic potential for treating diabetes mellitus proven through both in vitro and in vivo studies [Type 1 diabetes is a debilitating disorder which requires life-long exogenous insulin administration. Whole pancreatic allograft from cadaveric donors has succeeded in a number of clinical trials to restore the normal physiologic control of hyperglycemia, but this treatment has major shortcomings , 26. Fir studies .The majority of hemophilia cases are attributed to mutations in clotting factor VIII or factor IX . A key pSince hemophilia is a monogenic disorder, it is amenable to factor replacement as well as gene therapy. Several studies have reported immune responses with the currently most effective adenoviral vector-related therapy. After consideration of adult stem cells, autologous fibroblasts, platelets, and hematopoietic stem cells, the umbilical cord lining epithelial cells have also been recently considered.Kermani et al.  discusseA recent study also investigated AAVS1 locus-specific, programmable, non-viral zinc finger nuclease (ZFN)-directed FVIII transgene integration in primary human CLECs . Three dHepatic failure due to various disease conditions resulting in end-stage liver disease often requires hepatic transplantation. Grafting of hepatocytes rather than whole liver transplantation has proven to be a more effective treatment strategy. Since terminally differentiated hepatocytes are almost impossible to maintain in culture, stem cells are of critical importance in terms of clinical applications.Non-hepatic origin stem cells considered for grafting include bone marrow cells , umbilicCheong et al.  demonstrTakashima et al.  reportedIn a further assessment of CLECs, Zhou et al. evaluated the hepatoregenerative capacity of hepatic-like cloned cord lining epithelial cells in a porcine model. The treatment group received a tissue fleece lined with hepatic-like cloned CLECs and daily injections of CLECs for 7 days. The CLEC-treated livers showed up to 89% recovery of the original liver volume while the control subjects showed 67–75% liver regeneration, demonstrating their significant role in hepatic regeneration .In order to maintain cell pluripotency or stemness, certain feeder layers or conditioned medium have to be used for the culture of stem cells. The conventional use of mouse 3 T3 cells or mouse embryonic fibroblasts as feeder layers pose the risk of zoonotic infection . VariousThis review briefly introduces the currently available therapies for various chronic conditions followed by their treatment limitations and possible avenues for subsequent cell-based therapies which can be explored for their potential clinical applications. It delineates the various prospective stem cell sources examined thus far and makes a comparison with the CLEC-centered approach. This includes providing evidence-based implications for the future of CLECs in the treatment of ocular disorders, insulin-dependent diabetes, liver failure, wound regeneration, and various other conditions for which other stem cell sources have presented different limitations in their applicability. These cells are ethically acceptable, easily accessible, immunologically naive, and non-tumorigenic and a considerable amount of resources have been directed in many countries for the long-term storage of their source material.


Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765–0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis. Sepsis is characterized by deregulated host response to infection. Efficient therapies are still needed but a limitation for sepsis treatment is the heterogeneity in patients. Here Sweeney et al. generate prognostic models based on gene expression to improve risk stratification classification and prediction for 30-day mortality of patients. Although in-hospital sepsis outcomes have improved over the last decade with standardized sepsis care, mortality rates remain high (10–35%)4. Sepsis treatment still focuses on general management strategies including source control, antibiotics, and supportive care. Despite dozens of clinical trials, no treatment specific for sepsis has been successfully utilized in clinical practice5. Two consensus papers suggest that continued failure of proposed sepsis therapies is due to substantial patient heterogeneity in the sepsis syndrome and a lack of tools to accurately categorize sepsis at the molecular level6. Current tools for risk stratification include clinical severity scores such as APACHE or SOFA as well as blood lactate levels. While these measures assess overall illness severity, they do not adequately quantify the patient’s dysregulated response to the infection and therefore fail to achieve the personalization necessary to improve sepsis care7. Some peptide markers of sepsis severity have been validated (e.g. proadrenomedullin8 among others9), but these are not yet cleared for clinical use.Sepsis, recently defined as organ dysfunction caused by a dysregulated host response to infection6. Finally, as a direct quantitative measure of the dysregulation of the host response, molecular biomarkers could potentially help form a quantitative diagnosis of sepsis as distinct from non-septic acute infections11. Thus, overall, a quantitative test for sepsis could be a significant asset to clinicians if deployed as a rapid assay.A molecular definition of the severity of the host response in sepsis would provide several benefits. First, improved accuracy in sepsis prognosis would improve clinical care through appropriate matching of patients with resources: the very sick can be diverted to intensive care unit (ICU) for maximal intervention, while patients predicted to have a better outcome may be safely watched in the hospital ward or discharged early. Second, more-precise estimates of prognosis would allow for better discussions regarding patient preferences and the utility of aggressive interventions. Third, better molecular phenotyping of sepsis patients has the potential to improve clinical trials through both (1) patient selection and prognostic enrichment for drugs and interventions  and (2) better assessments of observed-to-expected ratios for mortality15. Important insights from these studies suggest that more-severe sepsis is accompanied by an overexpression of neutrophil proteases, adaptive immune exhaustion, and an overall profound immune dysregulation19. Quantitative evaluation of host response profiles based on these observations has been validated prospectively to show specific outcomes15, but none have yet been translated into clinical practice. Still, the availability of high-dimensional transcriptomic data from these accumulated studies has created unprecedented opportunities to address questions across heterogeneous representations of sepsis  that could not be answered by any individual cohort.Previously, a number of studies have used whole-blood transcriptomic (genome-wide expression) profiling to risk-stratify sepsis patients20. In contrast, community modeling approaches (where multiple groups create models using the same training data) can provide an opportunity to explicitly evaluate predictive performance across a diverse collection of prognostic models sampled from across a broad solution space25. Here, we systematically identified a large collection of both public and privately held gene expression data from clinical sepsis studies at the time of sepsis diagnosis. Three scientific groups were then invited to build models to predict 30-day mortality based on gene expression profiles. These three groups produced four different prognostic models, which were then validated in external validation cohorts composed of patients with either community-acquired sepsis or hospital-acquired infections (HAIs).Transcription-based modeling has been deployed across many diseases to improve prognostic accuracy. These are typically developed in a method-specific manner using data collected from single cohorts. As a result, prognostic models often lack the generalizability that is necessary to confer utility in clinical applications26 and random forests (RF)27.We used a community approach to build gene-expression-based models predictive of sepsis-induced mortality using all available gene expression datasets  composed primarily of patients with community-acquired sepsis. Performance was evaluated across two groups of heterogeneous validation datasets  analysis separately in the discovery, validation, and HAI cohorts. Boxplots of the AUROCs for each model are shown in Fig. 30. We found that that three of the four models show substantial improvement (average increase of roughly 0.1) compared to the prior models; this reached significance for the Duke and Stanford models t-tests vs. individual models all P = NS, Supplementary Table To assess whether the models contained complementary orthogonal information, we evaluated the prediction accuracy of an ensemble model based on the predictions of all four individual models (see Supplementary Methods). The prognostic power of the ensemble model was at an average AUROC of 0.81 across all five validation datasets 31 Fig. . The AUP31 Fig. , suggestWe examined the effects of clinical time course on the gene scores in the two validation datasets that tracked longitudinal data . However, we combined gene models and clinical severity scores into joint predictors, and each combination significantly outperformed clinical severity scores alone .We next assessed whether the performance of these gene expression-based predictors of mortality outperformed standard clinical severity scores. Notably, clinical measures of severity were only available in a subset of cohorts  index to quantify how well the model with gene scores reclassifies survivors over the model with clinical severity scores in each of these same datasets Table . In the Finally, we examined test characteristics at a high-sensitivity cutoff (95%) and a high-specificity cutoff (95%) for the gene scores in comparison to baseline error models , with summary AUROCs around 0.85 for predicting 30-day mortality. We also showed that combining the gene-expression-based models with clinical severity scores leads to significant improvement in the ability to predict 30-day mortality, indicating clinical utility.Sepsis is a heterogeneous disease, including a wide possible range of patient conditions, pre-existing comorbidities, severity levels, infection incubation times, and underlying immune states. Many investigators have hypothesized that molecular profiling of the host response may better predict sepsis outcomes. Here, we extensively assessed the predictive performance of whole-blood gene expression using a community-based modeling approach. This approach was designed to evaluate predictive capabilities in a manner that was independent of specific methodological preferences, and instead created robust prognostic models across a broad solution space. We developed four state-of-the-art data-driven prognostic models using a comprehensive survey of available data including 21 different sepsis cohorts , the Duke and Stanford scores showed large increases in specificity (13–21 percentage point absolute increase) compared with standard clinical severity scores alone. However, peptide assays have the significant advantage of potentially very rapid turnaround times. Moreover, a paucity of randomized data in application of existing biomarkers makes it unclear whether improved risk stratification will actually improve health and/or reduce costs9.Prediction of outcomes up to 30 days after the time of sampling represents a difficult task, given that the models must account for all interventions that occur as part of the disease course. An accuracy of 100% is likely not only unachievable but also undesirable, as it would suggest that mortality is pre-determined and independent of clinical care. Given this background, and since similar prognostic power was observed across all individual models and the ensemble model, our prognostic accuracy may represent an upper bound on transcriptomic-based prediction of sepsis outcomes. In addition, since prognostic accuracy was retained across broad clinical phenotypes  the models appear to have successfully incorporated the broad clinical heterogeneity of sepsis. The derived discriminatory power of the gene models (AUCs near 0.85) is at least similar to the AUC of proadrenomedullin (0.83) in a recent large prospective trial (TRIAGE study)1. Determining the SOFA score can help guide which organ systems are dysfunctional, but this fails to characterize the biological changes are driving the septic response. Molecular tools like the ones developed here provide an opportunity to provide a simple, informative prognosis for sepsis by improving patient risk stratification. Host-response profiles could also help to classify patients with sepsis as opposed to non-septic acute infections. Identifying such high-risk patients may also lead to greater success in clinical trials through improved enrichment strategies. This identification of subgroups or ‘endotypes’ of sepsis has already been successfully applied to both pediatric and adult sepsis populations15.Sepsis remains difficult to define. The most recent definition of sepsis (Sepsis-3) requires the presence organ dysfunction as measured by an increase in SOFA of two or more points over baseline32. Results of this study confirm this finding as we note increases in the neutrophil chemoattractant IL-8 as well as neutrophil-related antimicrobial proteins . These azurophilic granule proteases may indicate the presence of very immature neutrophils (metamyelocytes) in the blood33. Many of these genes have also been noted in the activation of neutrophil extracellular traps (NETs)35. NET activation leads to NETosis, a form of neutrophil cell death that can harm the host35. Whether these involved genes are themselves harmful or are markers of a broader pathway is unknown. Along with immune-related changes, there are changes in genes related to hypoxia and energy metabolism . Of particular interest is the increase in HIF1A, a hypoxia-induced transcription factor. This upregulation is corroborated by previous findings in patients with higher early mortality in the larger E-MTAB-4421.51 cohort13. This may be evidence of either a worsening cytopathic hypoxia in septic patients who progress to mortality, or of a shift away from oxidative metabolism (“pseudo-Warburg” effect), or both36. Modification of the Warburg effect due to sepsis has been implicated in immune activation37, trained immunity38, and immunoparalysis39.The goal of this study was to generate predictive models but not necessarily to define sepsis pathophysiology. However, our community approach identified a large number of genes associated with sepsis mortality that may point to underlying biology. The association with immature neutrophils and inflammation in sepsis has been previously shownN (1113), we were unable to perform robust subgroup analyses (such as infection site or pathogen type), although a broad range of clinical circumstances is included across the datasets. Finally, we note that some may find as a weakness the limited overlap in genes chosen by the four models. However, in the search for sparse models using highly collinear data such as gene expression, near-random selection of variables can occur40. The similar performance of the classifiers using disparate gene sets is thus further evidence that these models may be near an upper bound of discriminatory ability using whole-blood gene expression data.The present study has several limitations. First, as a retrospective study of primarily publically available data, we are not able to control for demographics, infection, patient severity, or individual treatment. However, our successful representation of this heterogeneity likely contributed to the successful validation in external community-acquired and hospital-acquired sepsis cohorts. Second, despite a large amount of validation data, we do not present the results of any prospective clinical studies of these biomarkers. Prospective analysis will be paramount in translating the test to a clinically relevant assay. In addition, while some rapid PCR techniques could bring the potential turnaround time of a gene-expression-based assay to under 30 min, this will require a substantial engineering effort. Third, the genes identified here were specifically chosen for their performance as biomarkers, not based on known relevance to the underlying pathophysiology of mortality in sepsis. As such, the biological insights gained from these biomarkers will need to be confirmed and expanded on by studies focused on the entire perturbation of the transcriptome during sepsis and through targeted study of individual genes and pathways. Fourth, the use of 30-day mortality as our endpoint is a crude measure of severity, and may miss important intermediate endpoints such as prolonged ICU stay or poor functional recovery. While such intermediate outcomes were not available in the current data, the models’ abilities to predict these functional outcomes will need to be tested prospectively. Fifth, despite a seemingly large total 41. Meta-analysis of multiple studies leads to results that are more reproducible than from similarly powered individual cohorts42. The community approach used here has shown that aggregated transcriptomic data can be used to define novel prognostic models in sepsis. This collaboration of multidisciplinary teams of experts encompassed both analytical and statistical rigor along with deep understandings of both the transcriptomics data and clinical data. To advance beyond the work presented here, more data must be made available, including demographics, treatments, and clinical outcomes, as well as other data types like proteomics and metabolomics. Data-driven collaborative modeling approaches using these data can be effective in discovering new clinical tools.Researchers, clinicians, funding agencies, and the public are all advocating for improved platforms and policies that encourage sharing of clinical trial dataWe have shown comprehensively that patients with sepsis can be risk-stratified based on their gene expression profiles at the time of diagnosis. The overall performance of expression-based predictors paired with clinical severity scores was significantly higher than clinical scores alone in multiple cohorts with heterogeneous sepsis. These gene expression models reflect a patient’s underlying biological response state and could potentially serve as a valuable clinical assay for prognosis and for defining the host dysfunction responsible for sepsis. These results serve as a benchmark for future prognostic model development and as a rich source of information that can be mined for additional insights. Improved methods for risk stratification would allow for better resource allocation in hospitals and for prognostic enrichment in clinical trials of sepsis interventions (removing those patients who will likely survive regardless of intervention). Ultimately, prospective clinical trials will be needed to confirm and extend the findings presented here.Two public gene expression repositories  were searched for all clinical-gene expression microarray or next-generation sequencing (NGS/RNAseq) datasets that matched any of the following search terms: sepsis, SIRS, trauma, shock, surgery, infection, pneumonia, critical, ICU, inflammatory, nosocomial. Clinical studies of acute infection and/or sepsis using whole blood were retained. Datasets that utilized endotoxin or lipopolysaccharide infusion as a model for inflammation or sepsis were excluded. Datasets derived from sorted cells  were also excluded.52 and pediatric56 cohorts. In these cases, the gene expression data were publicly available. When mortality and severity phenotypes were unavailable in the public data, the data contributors were contacted for this information. This included datasets E-MTAB-1548 (refs. 57), GSE10474 (ref. 44), GSE21802 (ref. 50), GSE32707 (ref. 47), GSE33341 (ref. 51), GSE63042 (ref. 19), GSE63990 (ref. 52), GSE66099 (ref. 56), and GSE66890 (ref. 49). Furthermore, where longitudinal data were available for patients admitted with sepsis, we only included data derived from the first 48 h after admission. The E-MTAB-4421 and E-MTAB-4451 cohorts both came from the GAinS study15, used the same inclusion/exclusion criteria, and were processed on the same microarray type. Thus, after re-normalizing from raw data, we used ComBat normalization58 to co-normalize these two cohorts into a single cohort, which we refer to as E-MTAB-4421.51. For this study, data were included only for patients sampled on the day of hospital admission. In addition to the above 17 datasets, we identified four additional privately held datasets  was initially held out for validation given its large size and representative patient characteristics. After we had trained the models some outcomes data became newly available, so these were added as validation cohorts52. Additionally, given the known differences in sepsis pathophysiology and gene expression profiles as compared to patients with community-acquired sepsis59, the HAI datasets were set aside as a second validation cohort. The validation cohorts were not matched to the discovery cohort on any particular criteria but rather provide a validation opportunity across a heterogeneous range of clinical scenarios.We selected cohorts as either discovery or validation based on their availability. Studies for which outcome data were readily available were included as discovery cohorts. Only GSE54514 (ref. 60). Output from other array types were normal-exponential background corrected and then between-arrays quantile normalized (R package limma61). For all gene analyses, the mean of probes for common genes was set as the gene expression level. All probe-to-gene mappings were downloaded from GEO from the most current SOFT files.All Affymetrix datasets were downloaded as CEL files and re-normalized using the gcRMA method  and were normalized using a weighted linear regression model using the voom method62 (R package limma61). The estimated precision weights of each observation were then multiplied with the corresponding log2(CPM) to yield final gene expression values.Two of the cohorts, CAPSODPrediction models were built by comparing patients who died within 30 days of hospital admission with sepsis to patients who did not. In the CAPSOD dataset (which was used in model training) we excluded two patients with unclear mortality outcomes, and one patient who died in-hospital but after 30 days. Mortality was modeled as a binary variable as since time-to-event data were not available. Overall, a total of four prognostic models were built by three different academic groups . All four models started with the same gene expression data in the discovery phase. Each model was built in two phases: a feature selection phase based on statistical thresholds for differential gene expression across all discovery cohorts, and then a model construction phase optimizing classification power. Full descriptions of the four models can be found in the supplementary text and in Supplementary Figs. t-tests. We further computed cNRI index to quantify how well our joint model reclassifies over clinical severity scores alone63. The cNRI is the sum of two scores: the improvement in classification of a positive event  by the tested model, plus the improvement in classification of a negative event  by the tested model. Each improvement has a possible range of , so the full cNRI has a possible range of . A score of −2 would mean that every prediction is made worse by the addition of the tested model; a score of 2 means that every prediction is made more accurate by the addition of the tested model. Finally, we calculated test characteristics at both a high-sensitivity cutoff and a high-specificity cutoff, for both clinical scores and gene scores separately, and for the joint clinical-gene models. These are reported as mean ± standard deviation across datasets in summary tables.We compared the prognostic accuracy of the gene scores with the prognostic accuracy of clinical severity scores  where such information was available. No datasets had more than one clinical severity score type available. These clinical severity scores were not necessarily built to predict mortality in the specific populations in which they were used here, but nonetheless serve as important comparators for the gene expression models. To compare prognostic power in the datasets which included subject-level severity data, LR was performed to predict mortality using either the clinical severity score or the given gene model’s output score. We then tested a joint model  and measured the AUROC of the combined model. Comparisons were made between AUROCs with paired 64. We examined the ability of the models to predict non-survivors using precision–recall curves generated from the gene scores in each examined dataset. Precision–recall curves of the gene scores were constructed within datasets, and the AUPRC)was calculated using the trapezoidal method.We examined class discriminatory power for separating survivors from non-survivors using ROC curves of the gene scores within datasets. The area under the ROC curves (AUROC) was calculated using the trapezoidal method. Summary ROC curves were calculated via the method of Kester and Buntinx56 and an exploratory enrichment analysis that assessed a large number of functionally annotated gene sets.We conducted two analyses to evaluate the functional enrichment of the genes selected as predictors by the four models. This included a targeted enrichment analysis for cell types as previously describedIn a mixed tissue such as blood, shifts in gene expression can be caused by changes in cell-type distribution. To check for this effect, we used gene expression profiles derived from known sorted cell types to determine whether a given set of genes is enriched for genes represented in a specific cell type. In each curated cell-type vector, a ‘score’ is calculated by the geometric mean of the upregulated genes minus the geometric mean of the downregulated genes. A higher ‘score’ represents a greater presence of the given cell type in the differential gene expression signature.65, the Reactome database of pathways and reactions in human biology66, and the Kyoto Encyclopedia of Genes and Genomes (KEGG)67. Our 12 discovery cohorts had approximately 6000 genes in common, which formed a ‘background’ set of genes.  Genes that are present in the GO/Reactome/KEGG sets but not in the background sets were removed prior to enrichment. We then retained all GO/Reactome/KEGG gene sets containing at least 10% and at least three genes overlapping with the predictor genes. The remaining GO/Reactome/KEGG gene sets were removed to reduce the multiple testing burden. Exploratory enrichment in each of the curated reference gene sets was performed using two different methodologies: gene-based Fisher’s exact test (FET), and, using discovery datasets, expression-based gene set enrichment analysis (GSEA) using GSVA package from bioconductor68. Significantly enriched reference gene sets were discovered after adjusting the nominal P-values using the Benjamini–Hochberg method.For exploratory enrichment, we curated thousands of gene sets from three widely used databases: gene ontology (GO)P-values were set at 0.05 and analyses were two-tailed. Analysis source code, final sample scores for the four models along with other relevant analysis results are made available through Synapse, an open source collaborative research platform69.All computation and calculations were carried out in the R language for statistical computing (version 3.2.0) and Matlab R 2016a . Significance levels for 69. Readers may access these data for independent research provided they (i) register onto Synapse and (ii) agree to properly acknowledge both the data contributor(s) and the synapse portal as described on the Data Use Requirements page69.All the raw and normalized gene expression data, mortality and/or clinical outcomes data, results are made available through SynapseSupplementary InformationPeer Review File


Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments.We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions  regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines.vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine  or biologic/targeted therapies . In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy . Using ten-fold cross-validation, the best classifiers achieved areas under the receiver operating characteristic curve (AUC) of 0.940 for chemotherapy , 0.899 for the endocrine therapy , and 0.977 for trastuzumab therapy  respectively. Overall, bootstrap aggregated classifiers performed better among all evaluated machine learning models.Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.The online version of this article (doi:10.1186/s12885-016-2972-z) contains supplementary material, which is available to authorized users. Decision-making in modern cancer treatment is a complex process that requires coordinated expertise from surgeons, oncologists, radiologists, pathologists, and allied health professionals. Multidisciplinary team  meetings are now routinely held to integrate these diverse management inputs, and have led to significant improvements in evidence-based decision-making and care quality , 2. PatiMDTs augment clinical decision-making by reconciling multiple viewpoints of an individual patient’s problem . With reOne strategy to address the foregoing problems is to use data captured from routine MDTs to derive models that systematically predict the decisions made therein. If reliable data-driven models could be developed, this would facilitate dissemination of expertise, provide automatic decision support, and permit data audit in a health service context. Here we have hypothesised that the decisions made in a cancer MDT may be predicted by supervised machine learning methods. To test this hypothesis, we have sought to develop models that predict MDT recommendations about adjuvant systemic treatments in early breast cancer.in situ, DCIS, or lobular carcinoma in situ, LCIS). A case was also excluded if none of the oestrogen receptor (ER), progesterone receptor (PR), and human epithelial growth factor receptor 2 (HER2) statuses was recorded. Cases without at least one of the three adjuvant systemic therapy decisions  were also excluded from the analysis.We conducted a single-centre study at a tertiary cancer referral centre in Sydney, Australia. Clinicopathologic data from consecutive cases presented to a weekly breast cancer MDT from January 2007 through March 2015 were screened. The MDT discussion process took place by first examining the relevant clinical, histopathology, imaging, and surgical findings by a panel of experts  followed by an open discussion to reach the final recommendations about further investigations, additional surgery, or adjuvant treatments. Patients with a new diagnosis of early breast cancer who underwent a curative resection  were including in the analysis. Cases excluded from the analysis included those presented prior to the definitive surgical resection, with metastatic disease at the time of presentation, and those limited to benign or non-invasive histology type  not recommended, where the MDT consensus is against the administration of the treatment modality, or (3) for discussion, where the patient may or may not be considered for the treatment modality, depending in part on their reaction to a full discussion of possible risks and benefits of taking either a pro-active or observation-only treatment approach. To capture both potential extremes of recommendation, the three-way decision was further dichotomised into two binary strategies, viz., the aggressive strategy  vs. the conservative strategy .Decision outcomes from the MDT were discretised into three categories: (1) Supervised machine learning encompasses a wide range of computational methods that use historical data to train models for predicting the outcomes of new cases. To determine which model type best predicted MDT decisions, we systematically examined 10 supervised machine learning classifiers from distinct classes include naïve Bayesian classifier, support vector machines with polynomial and radial basis function kernels, multivariate logistic regression, nearest neighbours, ripple down rules, J48 and alternating decision trees. Bootstrap aggregation was applied (using 10 bootstrap steps) on eight of the ten models. The parameters used for model training are listed in Additional file For each case, final MDT decisions of all modalities were compared against the corresponding recommendations by the algorithms specified in the European Society for Medical Oncology (ESMO) and National Comprehensive Cancer Networks (NCCN) guidelines published in the immediate preceding year(s) using the same clinicopathological variables –16. A deFor the dichotomised groupings , we also evaluated the sensitivity and specificity of each guideline for predicting against the corresponding MDT outcome. Both statistics were compared with the corresponding best classifier for each modality-strategy combination. A “wrapper-based” approach was used for comparing the performance between the best classifier and the two guidelines Fig. : (1) TwoThis study conformed to local ethical guidelines, and was approved by the Human Research Ethics Committee at the primary study institution. Waikato Environment for Knowledge Analysis (WEKA) version 3.6.6 was used for classifier training and evaluation . The R sFrom 1,924 cases screened, 1,065 cases were eligible for inclusion in the predictive analysis  decision trees [multiclass alternating decision tree (ADTree) and J48 decision tree] proved superior to probabilistic models, support vector machines, and un-bagged models Fig. . The besA similar trend of classifier performance was observed for prediction of MDT decisions recommending against the administration of a particular treatment modality Fig. . The accWe then compared the machine learning approach with two international guidelines on the use of adjuvant systemic treatment for early breast cancer. The proportion of agreement between the MDT decision and the ESMO/NCCN guidelines is detailed in Table We further compared the predictive power of the machine learning models and guidelines for predicting adjuvant therapy decisions. In general, the machine learning-based approach predicted MDT decisions better than either ESMO or NCCN guidelines. At the default classifiers threshold, the positive likelihood ratios (LR+) for the best classifiers were 8.8 for chemotherapy (95% C.I.: 4.6 – 16.9), 6.5 for endocrine therapy (95% C.I.: 3.17 – 13.5), and 77.9 for trastuzumab therapy (95% C.I.: 7.1 – 858) for the aggressive grouping. Machine learning methods were non-inferior to guidelines in all treatment modality-strategy combinations  must be carefully weighed against the toxicity, cost, inconvenience and other detriments to patient quality of life. Although MDT opinions on whether a patient should undergo toxic treatment can be contentious between experienced clinicians, the benefits of a multidisciplinary approach clearly reduce breast cancer-specific mortality .The goal of our modelling differs from prognosis-based decision aids such as Adjuvant! and the PREDICT Tool , 20, wheWe found that the machine learning models were high discriminative of the outcome variables, with the predictive accuracy consistently achieved at a clinically useful level. The internal validity was demonstrated by thorough cross-validation evaluations. Further studies at an external centre would clarify its clinical utility. We expect our analytic approach could also predict MDT recommendations for other treatment modalities such as surgery and radiotherapy, as well as assist in decision-making for patients suffering metastatic disease.Our analysis is strengthened by a comprehensive survey of classifiers with distinct inference techniques; the comparative design has allowed determination of the best algorithm for each task. The alternating decision tree algorithm outperformed other classifiers for predicting MDT decisions about endocrine and trastuzumab therapies; on the other hand, the bootstrap-aggregated ripple-down-rules classifier was superior for predicting adjuvant chemotherapy decisions. We conclude from this that a tree-based approach resembles more closely how experts make actual decisions in a collaborative environment. Conversely, both generative and discriminative probabilistic methods (such as naïve Bayesian classifier and multivariate logistic regression) did not perform as well as tree-based classifiers; one explanation for this may be that these algorithms were compromised by strong co-linearity between certain variables. Aggressive feature selection may thus be required to optimise their performance.For decisions about adjuvant chemotherapy, significant discrepancies were apparent between MDT decisions and the two international guidelines. Guideline-driven individualisation of treatments may thus prove challenging; factors such as treatment toxicity, performance status, quality of life, psychological well-being, and patient's perception of treatment efficacy can strongly influence the treatment decision , 21–23, A potential use of our modelling approach is to allow estimation of decision consistency within a cancer MDT. Intuitively, the most accurate model also indicates how well an MDT outcome can be predicted using the same clinical and pathological characteristics. A comparative evaluation of multiple models hence provides an objective mean for which the auditing of decision quality can be conducted within and/or between cancer centres.Several applications are made possible by the machine learning approach described here. First, the most predictive classifier(s) can be packaged into a site-specific decision support system to help real-time decision making in a MDT, which has the potential to enhance the decision making process by considering local resource constraints compared with using an external guideline. The use of a computerised decision support can also improve uptake of evidence-based care . Second,It is important to acknowledge that our study has several limitations. First, our data did not fully record the sequencing of treatment modalities, investigations, or chemotherapy regimens, which would otherwise allow us to fine-tune the predicted recommendations. Second, final decisions after patient review by medical oncologists  were not always available to us; we expect that these final treatment outcomes are modified by additional elements of patient preference. Third, survival benefits were unable to be quantified from our non-randomised (retrospective) data, since early breast cancer patients have a relatively good prognosis; a very large sample size with lengthy follow up would be required to draw meaningful conclusions on survival benefit. Fourth, our data did not fully record all administrative confounders, such as absence of a specific expert(s) from the MDT, delays in assessment, or attendance of the meeting. It is known that the team, social, and information factors do influence decisions made in a MDT . A prospIn summary, the present study demonstrates that the machine learning approach is indeed a useful method for predicting MDT decisions about adjuvant systemic therapy in early breast cancer, with better accuracy than using accepted therapeutic guidelines. This approach has the potential to provide direct decision support and facilitate transfer of local expertise to more remote centres, and hence to improve patient quality of care and clinical cancer outcomes.


This paper reports the nonsurgical endodontic therapy using calcium hydroxideintracanal dressing and prosthetic treatment of 9 teeth with periapical lesions in a16-year-old female patient. The periodontal treatment plan included oral hygieneinstructions, mechanical debridement and gingivectomy in the maxillary incisors toimprove gingival contouring. Root canal treatment was indicated for teeth 11-13, 21,22, 42-45. After successive changes of a calcium hydroxide intracanal dressing during6 weeks, the size of the periapical radiolucencies decreased and lesion remissionoccurred after root canal obturation. The endodontically treated teeth received abondable polyethylene reinforcement fiber (Ribbond) in the prepared canal space andcrown buildup was done with composite resin. Prosthetic rehabilitation was plannedwith single-unit metal-ceramic crowns and fixed partial dentures. Clinical andradiographic evaluation after 6 months showed successful results. The outcomes ofthis case showed that chronic periapical lesions can respond favorably to nonsurgicalendodontic treatment in adolescent patients and that, with proper indication,polyethylene fibers can provide an effective conservative and esthetic option forreinforcing endodontically treated teeth undergoing prosthetic rehabilitation. Current approaches in the treatment of teeth withperiapical lesions include initial nonsurgical root canal treatment. When this treatmentis unsuccessful in resolving the periradicular pathology, additional treatment optionsshould be considered24. In some situations, nonsurgical treatment may beineffective or difficult, and these cases may be treated surgically4.Younger patients have better treatment outcomes than older patients12.A complication of deep caries is pulp necrosis. After necrosis develops, bacteriainvading the root canal play a decisive role in the development of a periapicallesion23. Calcium hydroxide is an effective intracanal antibacterialdressing, owing mainly to its high pH and its ability to destroy bacterial cell wallsand protein structures5. Treatment with calcium hydroxide as aninterim dressing in the presence of large, chronic periapical lesions can create a morefavorable environment to healing and can encourage bone repair9.Some authors believe that if the endodontic infection is eliminated, the immune systemcan promote lesion repair26. Ithas been suggested that endodontically treated teeth are more brittle and fracture moreeasily than vital teeth16. The placement of an intraradicular postin the root canal after endodontic treatment has been recommended to strengthen orreinforce the tooth. However, several studies have pointed out that posts do notstrengthen teeth and that post space preparation and post cementation can weaken theroot, possibly leading to its fracture25. Therefore, the evaluation of whether a post is neededis based on how much natural tooth substance remains to retain a core buildup andsupport the final restoration after caries removal and endodontic treatment have beencompleted. The ideal post and core material should have physical properties that aresimilar to those of dentin, including its modulus of elasticity, compressive strength,and coefficient of thermal expansion26.Generally, the successful treatment of teeth with pulpal disease depends not only ongood endodontic therapy, but also on good reconstruction of the teeth after theendodontic therapy is completed15. Ultra-highmolecular-weight polyethylene (UHMWPE) fiberreinforcement systems are gaining popularity and have various clinical applications.Being bondable reinforcement fibers, they can be used to build up endodontic posts andcores, as they adapt to the root canal walls without requiring additional enlargement ofthe root canal after endodontic treatment. These woven fibers have a modulus ofelasticity similar to that of dentin and are thought to create a monoblockdentin-post-core system, which produces a better distribution of forces along theroot8.Adhesive restorations allow clinicians to create minimally invasive preparations,thereby preserving sound tooth structure18. Inendodontically treated teeth with excessive structural loss, this condition must beconsidered when planning a complete crown. Tooth reduction for placement of anartificial crown is considerable, particularly when teeth are prepared for metal-ceramiccrowns3. Due to estheticreasons, this is usually the method of choice for crown coverage of premolars andanterior teeth29.Several prosthetic treatment modalities are possible after completing the root canaltherapy. Most of them involve either crowns or direct restorations such as resincomposites, amalgam, or cementsThis paper reports the nonsurgical endodontic therapy and prosthetic treatment ofmultiple teeth with periapical lesions in a young patient.A 16-year-old female patient, who lives in a village 180 Km from Diyarbakir inTurkey, was referred to the Department of Pediatric Dentistry of the Faculty ofDentistry, Dicle University, for treatment of severe pain in the region of tooth 43.The girl was the fifth child in a very poor family of nine children with noeducation. Written informed consent was obtained from the patient and her parents fortreatment and publication of the case.Clinical and radiographic examinations showed that she had deep caries, periapicalradiolucent areas, and periodontal pockets  and 2. TRoot canal treatment was planned for teeth 11-13, 21, 22, and 41-45. However, tooth41 had to be extracted because no healing was observed after treatment . At the In preparation for crown buildup, 3 to 4 mm of gutta-percha were removed from thecanal to expose the dentin. This procedure has been shown to increase microretentionwhen using enamel dentin adhesive systems. The necessary width of the bondablepolyethylene reinforcement fiber  wasdetermined to be 2 mm. The prepared dowel space was measured with a periodontalprobe, and the measurement was tripled to determine the length of fiber required. Twopieces of fiber were then cut with Ribbond shears , coated with adual-cured resin composite , and placed in alightproof container. The internal surfaces of the root canal and pulp chamber weretreated with primer for 30 s and dried under a gentle stream of air for 15 s. A dual-cured dentin bondingagent  was applied tothe internal surfaces of the canal and pulp chamber and was thinned with a brush.Then, a highly filled, dualcured hybrid resin cement  was injected into the canal space. One piece of the reinforcement fiber, whichhad been coated with bonding agent, was folded and packed into the canal space astightly as possible, using an endodontic plugger. The second piece was then packedinto the canal space perpendicular to the first. The excess resin was removed, andthe core buildup was completed using a light-cured hybrid resin composite  .Complete rehabilitation of the dentition was planned. It was decided that teeth 11,12, 21 and 22 would receive single-unit metal-ceramic crowns; teeth 33, 42 and 43would support a 6unit metal-ceramic fixed partial denture; teeth 44 and 45 and thepontic tooth 46 form a 3-unit metal-ceramic cantilever fixed partial denture; andteeth 34 and 37 would support a 4 four-unit metalceramic fixed partial denture.Prosthetic treatment of the edentulous areas in the posterior maxilla was delayeduntil teeth 18 and 28 erupted in the oral cavity because they were posterior abutmentteeth.15. All teeth wereprepared with a long chamfer, and all margins were placed at the gingival level.Occlusal reduction and the crown margins were prepared using a diamond bur , and a minimum of 1 mm of coronal dentine was left above the chamfer.Impressions were made with a polyether impression material  usinga custom tray. After the impression procedure, a temporary resin crown wasimmediately adapted and cemented on the prepared tooth with zinc oxide eugenolbasedtemporary cement . The casting try-in wasperformed 1 week after the impression was made. After the casting try-in, theporcelain was applied. The crowns were cemented 1 week after the casting try-in,using zinc oxide eugenol-based temporary cement . Two weeks afterthe temporary cementation, the crown was removed and definitive cementation wasperformed with zinc phosphate cement .The patient was recalled after 6 months for clinical and radiographic evaluation andgood treatment outcomes were observed.22 (1991) found that the use of calciumhydroxide dressings for 1 week efficiently eliminated bacteria in infected root canals.Calcium hydroxide is readily available, simple to prepare, resorbable, and more solublein glycerin than in water. To produce more available hydroxyl ions for the antibacterialeffect, coating the root canals with a glycerin/calcium hydroxide paste is superior towater4. According to previous histopathological results, better apicaland periapical tissue repair was observed in groups treated with calcium hydroxideintracanal dressings compared to those treated with immediate obturation without anydressing13. Recent studies haveshown that teeth that receive intracanal medication present a less intense inflammatoryperiapical reaction, which confirms the importance of dressing in the endodontictreatment of teeth with pulp necrosis and periapical lesions22.The treatment of teeth with excessive structural loss and pulp necrosis is considereddifficult. However, as shown in this case, the healing of periapical lesions is rapidafter multiple dressings with calcium hydroxide. Sjögren, et al.24. The rapid healingresponse observed in the present case might be attributed to the high healing potentialof young patients. In previous reports, treatment with calcium hydroxide resulted inhigh rates of periapical healing, and some lesions, especially in young patients, werereduced or had disappeared completely 1 to 3 months after treatment19.In the present case, healing of the periapical lesions was observed very soon afterapplying the calcium hydroxide dressings. Radiographic signs such as changes in thedensity of the lesions, trabecular reformation, and lamina dura reformation confirmedhealing, demonstrating that the teeth were symptom free and that the soft tissues werehealthyper se, but also on the placement of coronal restorations28. This treatment includes the decision ofwhether or not using intraradicular posts. Dentin loss may result in tooth fractureafter the final restoration, and thus intracoronal strengthening is important inprotecting the teeth against fracture14.The prognosis of endodontically treated teeth depends not only on the treatment21. Since 1991, polyethylene fibers have been successfully used in avariety of clinical techniques21. The use of polyethylene fibers torestore endodontically treated teeth has gained popularity as an alternative to casts orprefabricated metal posts. This may be attributed to two important characteristics offiber posts: their modulus of elasticity, which is similar to that of dentin, and theirability to be cemented14. It is believed that monoblockdentin-post-core systems with dentinal bonding enhance the distribution of forces alongthe root. Therefore, if excessive loads were applied to the tooth, the post would beable to absorb the stress, reducing the possibility of root fracture8. It is postulated that polyethylenefibers have a stress-modifying effect along the restoration-dentine interface. Inaddition, because fiber-reinforced posts are metal free, they do not cause metalallergies or corrode, and good esthetics in visible areas of the mouth. Finally,fiber-reinforced posts can be removed easily in the case of failure requiringretreatment26.The industrial development of polymer-based composites and laminated structures hasprovided dental researchers with bondable, fractureresistant, esthetic, and extremelymanageable reinforcement materials with ultra-high tensile strength that have thepotential to be applied in dental restoration26.However, root-filledteeth with exaggerated structural loss must treated using a complete crown forstructural stability3. In a 3-yearclinical study, Mannocci, et al.3(2002) concluded that the clinical success rates of endodontically treated premolarswith limited loss of tooth structure restored with fiber-reinforced posts and directcomposite were equivalent to those restored with full-coverage metal-ceramic crowns.Endodontically treated anterior teeth with minimal loss of tooth structure may berestored conservatively with a bonded restoration in the access cavity26.Unless the majority of natural tooth substance remains after endodontic treatment, it isprobably safer to provide some kind of cuspal coverage in the final coronal restoration,because most teeth that require endodontic treatment are usually damaged severely as aresult of caries or fractureIn the patient presented in this paper, metalceramic restorations were preferred becausethe marginal ridges were lost due to extensive loss of tooth structure from caries andfracture and thus cuspal protection was necessary for long-term preservation of theremaining tooth structure. In conclusion, periapical lesions can respond favorably tononsurgical endodontic treatment in young patients. Morever, with proper indication,polyethylene fibers can provide an effective conservative and esthetic option forreinforcing endodontically treated teeth undergoing prosthetic rehabilitation. These newfiber-reinforced posts may offer impressive results, but long-term clinical evaluationis necessary.


Drosophila melanogaster is ideal for such investigations as multiple individual and high-throughput efforts have captured the spatiotemporal patterns of thousands of embryonic expressed genes in the form of in situ images. FlyExpress (www.flyexpress.net), a knowledgebase based on a massive and unique digital library of standardized images and a simple search engine to find coexpressed genes, was created to facilitate the analytical and visual mining of these patterns. Here, we introduce the next generation of FlyExpress resources to facilitate the integrative analysis of sequence data and spatiotemporal patterns of expression from images. FlyExpress 7 now includes over 100,000 standardized in situ images and implements a more efficient, user-defined search algorithm to identify coexpressed genes via Genomewide Expression Maps (GEMs). Shared motifs found in the upstream 5′ regions of any pair of coexpressed genes can be visualized in an interactive dotplot. Additional webtools and link-outs to assist in the downstream validation of candidate motifs are also provided. Together, FlyExpress 7 represents our largest effort yet to accelerate discovery via the development and dispersal of new webtools that allow researchers to perform data-driven analyses of coexpression (image) and genomic (sequence) data.Gene expression patterns assayed across development can offer key clues about a gene’s function and regulatory role.  Drosophila melanogaster, have established the successive dependence of gene expression of later developmental stages on earlier events the utilization of the same functional products in different ways at different times during development, and extensive coexpression patterns between genes  was developed to facilitate large-scale, data-driven (image-content) analyses of embryonic in situ expression patterns, and to accelerate the discovery of functional, genetic, and regulatory motifs that underlie interactions among genes and genetic elements. The basic FlyExpress platform now contains a unique digital library of standardized in situ images  and a search engine to discover gene coexpression . These enhancements allow researchers to easily visualize a temporal sequence of gene expression  for each gene, providing the necessary prerequisite for image comparisons, discovering coexpressed genes, and building informative global views of stage- and view-specific expression patterns. FlyExpress is integrated with, and complements, widely-used fly resources primarily containing textual information such as FlyBase  a new GEM-based algorithm that quickly and precisely identifies coexpressed genes defined by users at the temporal-cellular level; (iii) development and implementation of a unique “painting” tool permitting users to initiate a precisely controlled search of pixels across FlyExpress’ large standardized database of expression patterns; (iv) an interactive viewer of overrepresented shared DNA motifs that compares IGRs (intergenic regions) across user-defined pairs of genes; (v) link-outs to assist in the downstream validation of candidate motifs, including FlyBase, FlyMine, STRING, and Tomtom; (vi) an in situ image carousel allowing users to quickly compare hybridization images across developmental stages via an interactive animation feature for each gene (Animate); and (vii) a downloadable CSV gene list, providing users with summary results that can be immediately transferred to other databases. Together, these webtools enable researchers to better explore regulatory space and will ultimately lead to new hypotheses about developmental networks and their regulation.In this paper, we develop a new generation of analytical webtools in FlyExpress 7 that begins to integrate granular spatiotemporal expression patterns with genomic sequence data . FlyExprin situ expression patterns of > 4500 embryo-expressed D. melanogaster genes. The majority (∼75%) of images come from the high-throughput BDGP and Fly-FISH  manually pointing at and clicking on pixel(s) of interest or (2) by painting or highlighting regions of interest directly onto a GEM  was developed using custom code written in PHP (an HTML-embedded server-side scripting language) that renders as an SVG  markup to increase interactivity using native JavaScript Application Program Interfaces. From a queried gene, users can choose a coexpressed gene to compare upstream gene regions for structural genomic signatures of similarity. The dotplot function first queries FlyMine (s.kumar@temple.edu).All data are available on the flyexpress.net website and can be provided upon request  gene query image , and identifying genes that share the highest numbers of coexpressed pixels.To determine how GEM-based searching compares to pairwise searching [see twi example at 40% pixel coverage, pairwise searching yields 980 genes exhibiting at least 25% similarity in coexpression. Of these, 103 have > 50% pixel similarity. All 980 genes were identified using the GEM-based searching method. GEM-based searching returns 1123 results coexpressed at ≥ 25% similarity of search pixels. Of these, 902 show ≥ 50% coexpression similarity. Interestingly, when repeated at twice the pixel coverage for twi (80% coexpression threshold), only 25 additional genes with ≥ 50% coexpression similarity appear among the GEM-based results . Overall, these results demonstrate a significantly greater return in similarly coexpressed genes using our new GEM search algorithms, and in a much faster time. Other gene queries performed with images from different data sources, stage ranges, and anatomical orientations produce similar results .A comparison of both search methods reveals that pairwise results return a subset of GEM-based results . For exaFrom the query gene coexpression search results page, users can choose to view shared motifs between the query gene and a coexpressed gene of interest. Specifically, upstream IGR sequences belonging to both the query gene and a user-selected coexpressed gene, are compared via an interactive shared motif viewer . Users si.e., contiguous line) in the dotplot will bring up a “Dotplot Match” context menu displaying start and end coordinates for the match as well as the matching sequence . Overall, this next generation of FlyExpress represents a turning point to integrate the Drosophila community’s unprecedented image-based resources with the generative potential of sequence data. Overall, these new tools and site enhancements represent our latest efforts to grow and evolve in response to the ever-changing needs of the fly community.Here, we briefly showcase the features and functionality of these new webtools and demonstrate their usefulness in allowing researchers to simultaneously visualize embryonic expression patterns and integrate genomic sequence analytical tools. Major site enhancements include a faster GEM-based search algorithm to identify coexpressed genes, a dotplot viewer between intergenic regions of a pair of genes, and direct link-outs to resources aiding in the downstream validation of candidate motifs and gene lists. As a result, FlyExpress provides a unique platform for users to identify genes with common regulatory features from its extensive database of curated images, visualize shared www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.040345/-/DC1.Supplemental material is available online at Click here for additional data file.


Nannochloropsis oceanica as a model, an electroporation-mediated chloroplast transformation approach was established. Fluorescent microscopy and laser confocal scanning microscopy confirmed the expression of the green fluorescence protein, driven by the endogenous plastid promoter and terminator. Zeocin-resistance selection led to an acquisition of homoplasmic strains of which a stable and site-specific recombination within the chloroplast genome was revealed by sequencing and DNA gel blotting. This demonstration of electroporation-mediated chloroplast transformation opens many doors for plastid genome editing in industrial microalgae, particularly species of which the chloroplasts are recalcitrant to chemical and microparticle bombardment transformation.Plastid engineering offers an important tool to fill the gap between the technical and the enormous potential of microalgal photosynthetic cell factory. However, to date, few reports on plastid engineering in industrial microalgae have been documented. This is largely due to the small cell sizes and complex cell-wall structures which make these species intractable to current plastid transformation methods . Here, employing the industrial oleaginous microalga  Nannochloropsis spp. have attracted sustained interest from algal biofuels researchers owing to their rapid growth, high amounts of triacylglycerol (TAG) and high-value polyunsaturated fatty acid (FA) and their successful cultivation at large scale using natural sunlight by multiple institutes and companies  higBesides the manipulation of plastid genes (with a number of ~100)    , plastome genetic engineering tools have not yet been developed. Polyethylene glycol (PEG) treatment of protoplasts provides an alternative way for chloroplast transformation  using KpnI, XhoI and SacI, BamHI sites, respectively. The rbcL promoter and psbA terminator were subsequently ligated into the resulting vector between the XhoI, HindIII and EcoRV, EcoRI sites, respectively. The codon optimized gfp gene was synthesized by Sangon Biotech  and was ligated into the above vector with a HindIII restriction site at the 5′ end and an EcoRV site at the 3′ end of the coding region. All enzymes were commercially available from New England BioLabs . The obtained vector was nominated as pMEMc1. The zeosin resistance (BLE) gene was amplified from vector pSP124 using primers BLE-F and BLE-R  following a described procedure  at 25°C. Transformation was conducted as description with minor modification  and pulsed using GenePulse XcellTM (BioRad) apparatus with 12 kV cm−1 field strength, 50 μF capacitance, and 600 Ohm shunt resistance. The cells were immediately transferred into fresh f/2 medium and recovered under dim light for 48 h. For pMEMc1 transformants, GFP fluorescence was observed at indicated intervals while for pMEMc2 transformants, cells were plated on solid f/2 with 2.5 μg ml−1 zeosin  and colonies appeared after ~4 weeks.TM FV1000 system was used to obtain laser confocal scanning microscope (LSCM) images of pMEMc1 transformants. Excitation at 488 and 559 nm were used for GFP and chlorophyll autofluorescence, respectively.An Olympus BX51 microscope , fitted with epifluorescence and differential interference contrast (DIC) optics, was used to visualize pMEMc1 transformants grown in liquid. Images were generated by either DIC or epifluorescence  optics. An Olympus FluoViewchlL gene regions , respectively. By contrast, for cells harboring heterogeneous chloroplast genomes, PCR amplification should generate both of the two bands . All PCR fragments were purified  and sequenced . Integration events were further analyzed by DNA gel blotting using non-radioactive DIG-containing ble gene probes . The ble gene probes were labeled with DIG-dUTP using a pair of primer . The cell pellet was washed twice, and then genomic DNA was extracted. Genomic PCR was used to confirm the homoplasmic integration of the vector into the plastid genome of transgenic lines. With the primers crossing gfp) was used as a reporter  and terminated by an endogenous terminator   region of the N. oceanica chloroplast genome. Transforming cassette will insert into the chlL locus by homologous recombination in expected transformants  were cloned into the plasmid pBluescript SK(-) and nominated as pMEMc1. The coding sequence of GFP in the vector was substituted with that of BLE gene and the resulting vector was designated as pMEMc2 . Approximately eight transformed colonies appeared on the selective plates which translated to a transformation frequency of about 2 × 10−8 μg−1 DNA. Two transformants were selected and analyzed for integration and homoplasmicity after multiple rounds of streaking of single colonies under zeocin-resistance selection (no less than four rounds each of which took approximately a month). Genomic PCR and sequencing confirmed that homoplasmic strains  were obtained and the rbcL-ble-psbA constructs (~1.3 kb) were integrated into all chloroplast genomes through homologous recombination of chlL regions (Figure ble probe in either HindIII or PstI digested genomic DNA of transformants (Figure To validate the homologous recombination of the exogenous constructs into chloroplast genomes, pMEMc2 was transformed into the wild-type ructs ~1. kb were s Figure . AltogetN. oceanica (Supplementary Dataset C. reinhardtii (Zhang et al., Chloroplast transformation was generally achieved by the biolistic process and ocassionally by PEG-mediated method Maliga, . Howevergfp gene was delivered into and expressed properly in N. oceanica by electroporation. Moreover, the antibiotic construct harboring the ble gene was utilized to validate the chloroplast integration of transformants. Genotyping of the homoplasmic cells showed a site-specific recombination of the transforming cassette into chloroplast genomes.Therefore, the aim of this study is to demonstrate the capacity of electroporation-mediated method in the development of transplastomic technology for species with small cell size and unknown cell-wall components. Using a plastid gene encoding chlorophyll biosynthetic enzyme CHLL as knock-in sites, the N. oceanica where the transformation frequency remains to be improved. To ensure the frequency, a standard practice should be developed and more recombination sites and more selectable marker genes should be tested. With a streamlined practice, electroporation should facilitate plastid engineering of relevant species with relative small cell sizes or unknown cell structure of which the chlroroplast manipultion is intractable by using microparticle bombardment or PEG-mediated transformation methods. Given that FA biosynthesis and photosynthesis processes predominantly take place in chloroplasts, transplastomic technology can be utilized to created engineered microalgal strains with optimized oil production and robust photosynthetic efficiency. Moreover, the incorporation of transgenes into the plastid genome for containment and high-level expression of recombinant proteins holds great promise for pharmaceutical and industrial applications.Restrictively, herein presented proof-of principle represents a starting point of plastome engineering for YL: Design the research; QG, JJ, and XH: Conducted the experiments; YL and SW: Wrote the first version of the manuscript; YL: Contributed to the final writing and presentation of the data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.