Dental Abnormalities Caused by Novel Compound Heterozygous CTSK Mutations

Cathepsin K (CTSK) is an important protease responsible for degrading type I collagen, osteopontin, and other bone matrix proteins. The mutations in the CTSK gene can cause pycnodysostosis (OMIM 265800), a rare autosomal recessive bone dysplasia. Patients with pycnodysostosis have been reported to present specific dental abnormalities; however, whether these dental abnormalities are related to dysfunctional CTSK has never been reported. Here we investigated the histologic changes of cementum and alveolar bone in a pycnodysostosis patient, caused by novel compound heterozygous mutations in the CTSK gene (c.87 G>A p.W29X and c.848 A>G p.Y283C). The most impressive manifestations in tooth were extensive periradicular high-density clumps with unclear periodontal space by orthopantomography examination and micro-computed tomography scanning analysis. Hematoxylin/eosin and toluidine blue staining and atomic force microscopy analysis showed that the cementum became significantly thickened, softened, and full of cementocytes. The disorganized bone structure was the main character of alveolar bone. The p.W29X mutation may represent the loss-of-function allele with an earlier termination codon in the precursor CTSK polypeptide. Residue Y283 is highly conserved among papain-like cysteine proteases. Three-dimensional structure modeling analysis found that the loss of the hydroxybenzene residue in the Y283C mutation would interrupt the hydrogen network and possibly affect the self-cleavage of the CTSK enzyme. Furthermore, p.Y283C mutation did not affect the mRNA and protein levels of overexpressed CTSK in COS-7 system but did reduce CTSK enzyme activity. In conclusion, the histologic and ultrastructural changes of cementum and alveolar bone might be affected by CTSK mutation via reduction of its enzyme activity (clinical trial registration: ChiCTR-TNC-10000876).

Quantitative analysis of mouse pancreatic islet architecture by serial block-face SEM

We have applied serial block-face scanning electron microscopy (SBF-SEM) to measure parameters that describe the architecture of pancreatic islets of Langerhans, microscopic endocrine organs that secrete insulin and glucagon for control of blood glucose. By analyzing entire mouse islets, we show that it is possible to determine (1) the distributions of alpha and beta cells, (2) the organization of blood vessels and pericapillary spaces, and (3) the ultrastructure of the individual secretory cells. Our results show that the average volume of a beta cell is nearly twice that of an alpha cell, and the total mitochondrial volume is about four times larger. In contrast, nuclear volumes in the two cell types are found to be approximately equal. Although the cores of alpha and beta secretory granules have similar diameters, the beta granules have prominent halos resulting in overall diameters that are twice those of alpha granules. Visualization of the blood vessels revealed that every secretory cell in the islet is in contact with the pericapillary space, with an average contact area of 9±5% of the cell surface area. Our data show that consistent results can be obtained by analyzing small numbers of islets. Due to the complicated architecture of pancreatic islets, such precision cannot easily be achieved by using TEM of thin sections.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

BACKGROUND

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.

METHOD

Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.

RESULTS

Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.

CONCLUSIONS

Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Evaluating subject-level incremental values of new markers for risk classification rule

Suppose that we need to classify a population of subjects into several well-defined ordered risk categories for disease prevention or management with their "baseline" risk factors/markers. In this article, we present a systematic approach to identify subjects using their conventional risk factors/markers who would benefit from a new set of risk markers for more accurate classification. Specifically for each subgroup of individuals with the same conventional risk estimate, we present inference procedures for the reclassification and the corresponding correct re-categorization rates with the new markers. We then apply these new tools to analyze the data from the Cardiovascular Health Study sponsored by the US National Heart, Lung, and Blood Institute. We used Framingham risk factors plus the information of baseline anti-hypertensive drug usage to identify adult American women who may benefit from the measurement of a new blood biomarker, CRP, for better risk classification in order to intensify prevention of coronary heart disease for the subsequent 10 years.

Co-regulation of intragenic microRNA miR-153 and its host gene Ia-2 β: identification of miR-153 target genes with functions related to IA-2β in pancreas and brain

AIMS/HYPOTHESIS

We analysed the genomic organisation of miR-153, a microRNA embedded in genes that encode two of the major type 1 diabetes autoantigens, islet-associated protein (IA)-2 and IA-2β. We also identified miR-153 target genes that correlated with IA-2β localisation and function.

METHODS

A bioinformatics approach was used to identify miR-153's genomic organisation. To analyse the co-regulation of miR-153 and IA-2β, quantitative PCR analysis of miR-153 and Ia-2β (also known as Ptprn2) was performed after a glucose stimulation assay in MIN6B cells and isolated murine pancreatic islets, and also in wild-type Ia-2 (also known as Ptprn), Ia-2β single knockout and Ia-2/Ia-2β double knockout mouse brain and pancreatic islets. Bioinformatics identification of miR-153 target genes and validation via luciferase reporter assays, western blotting and quantitative PCR were also carried out.

RESULTS

Two copies of miR-153, miR-153-1 and miR-153-2, are localised in intron 19 of Ia-2 and Ia-2β, respectively. In rodents, only miR-153-2 is conserved. We demonstrated that expression of miR-153-2 and Ia-2β in rodents is partially co-regulated as demonstrated by a strong reduction of miR-153 expression levels in Ia-2β knockout and Ia-2/Ia-2β double knockout mice. miR-153 levels were unaffected in Ia-2 knockout mice. In addition, glucose stimulation, which increases Ia-2 and Ia-2β expression, also significantly increased expression of miR-153. Several predicted targets of miR-153 were reduced after glucose stimulation in vitro, correlating with the increase in miR-153 levels.

CONCLUSIONS/INTERPRETATION

This study suggests the involvement of miR-153, IA-2β and miR-153 target genes in a regulatory network, which is potentially relevant to insulin and neurotransmitter release.

TCF3, a novel positive regulator of osteogenesis, plays a crucial role in miR-17 modulating the diverse effect of canonical Wnt signaling in different microenvironments

Wnt signaling pathways are a highly conserved pathway, which plays an important role from the embryonic development to bone formation. The effect of Wnt pathway on osteogenesis relies on their cellular environment and the expression of target genes. However, the molecular mechanism of that remains unclear. On the basis of the preliminary results, we observed the contrary effect of canonical Wnt signaling on osteogenic differentiation of periodontal ligament stem cells (PDLSCs) in the different culture environment. Furthermore, we found that the expression level of miR-17 was also varied with the change in the culture environment. Therefore, we hypothesized that miR-17 and canonical Wnt signaling may have potential interactions, particularly the inner regulation relationship in different microenvironments. In this paper, we observed that canonical Wnt signaling promoted osteogenesis of PDLSCs in the fully culture medium, while inhibited it in the osteogenic differentiation medium. Interestingly, alteration in the expression level of endogenous miR-17 could partially reverse the different effect of canonical Wnt signaling. Furthermore, the role of miR-17 was because of its target gene TCF3 (transcription factor 3), a key transcription factor of canonical Wnt pathway. Overexpression of TCF3 attenuated the effect of miR-17 on modulating canonical Wnt signaling. Finally, we elucidated that TCF3 enhanced osteogenesis both in vitro and in vivo. In brief, the different level of miR-17 was the main cause of the different effect of canonical Wnt signaling, and TCF3 was the crucial node of miR-17–canonial Wnt signaling regulation loop. This understanding of microRNAs regulating signaling pathways in different microenvironments may pave the way for fine-tuning the process of osteogenesis in bone-related disorders.

Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

BACKGROUND

Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.

METHOD

Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.

RESULTS

Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85-0.88 v. 0.54-0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).

CONCLUSIONS

The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

Deletion of Ia-2 and/or Ia-2β in mice decreases insulin secretion by reducing the number of dense core vesicles

AIMS/HYPOTHESIS

Islet antigen 2 (IA-2) and IA-2β are dense core vesicle (DCV) transmembrane proteins and major autoantigens in type 1 diabetes. The present experiments were initiated to test the hypothesis that the knockout of the genes encoding these proteins impairs the secretion of insulin by reducing the number of DCV.

METHODS

Insulin secretion, content and DCV number were evaluated in islets from single knockout (Ia-2 [also known as Ptprn] KO, Ia-2β [also known as Ptprn2] KO) and double knockout (DKO) mice by a variety of techniques including electron and two-photon microscopy, membrane capacitance, Ca(2+) currents, DCV half-life, lysosome number and size and autophagy.

RESULTS

Islets from single and DKO mice all showed a significant decrease in insulin content, insulin secretion and the number and half-life of DCV (p < 0.05 to 0.001). Exocytosis as evaluated by two-photon microscopy, membrane capacitance and Ca(2+) currents supports these findings. Electron microscopy of islets from KO mice revealed a marked increase (p < 0.05 to 0.001) in the number and size of lysosomes and enzymatic studies showed an increase in cathepsin D activity (p < 0.01). LC3 protein, an indicator of autophagy, also was increased in islets of KO compared with wild-type mice (p < 0.05 to 0.01) suggesting that autophagy might be involved in the deletion of DCV.

CONCLUSIONS/INTERPRETATION

We conclude that the decrease in insulin content and secretion, resulting from the deletion of Ia-2 and/or Ia-2β, is due to a decrease in the number of DCV.

Bid mediates anti-apoptotic COX-2 induction through the IKKbeta/NFkappaB pathway due to 5-MCDE exposure

Although Bid has been considered as a cell apoptotic mediator, current studies suggest a possible role in cell survival in mouse embryonic fibroblasts (MEFs) response to low doses of anti-(+/-)-5- methylchrysene-1,2-diol-3,4-epoxide(<or=0.25 microM) (5-MCDE). We found that exposure of MEFs to 0.25 microM 5-MCDE resulted in a slight apoptotic induction, while this apoptotic response was substantially increased in the Bid knockout MEFs (Bid(-/-)), suggesting Bid-mediated anti-apoptotic function in this response. This notion was further supported by the findings that re-constitution expression of Bid into Bid(-/-) cells could inhibit the increased apoptosis. Further studies showed that Bid anti-apoptotic function was associated with its mediation of COX-2 expression, which was based on the results of the reduction of COX-2 expression in Bid(-/-) cells, restoration of low sensitivity to 5-MCDE apoptotic response by the introduction of Bid into Bid(-/-) cells and increased sensitivity of WT MEFs to 5-MCDE apoptosis by knockdown of COX-2 expression. Furthermore, Bid mediated COX-2 expression through the IKKbeta/NFkappaB pathway because the deficiency of Bid in Bid(-/-) MEFs resulted in blockade of IKK/NFkappaB activation and knockout of IKKbeta caused abrogation of COX-2 expression induced by 5-MCDE. Collectively, our results demonstrate that Bid is critical for COX-2 induction through the IKKbeta/NFkappaB pathway, which mediates its anti-apoptotic function, in cell response to low doses of 5-MCDE exposure.

Calibrating parametric subject-specific risk estimation

For modern evidence-based medicine, decisions on disease prevention or management strategies are often guided by a risk index system. For each individual, the system uses his/her baseline information to estimate the risk of experiencing a future disease-related clinical event. Such a risk scoring scheme is usually derived from an overly simplified parametric model. To validate a model-based procedure, one may perform a standard global evaluation via, for instance, a receiver operating characteristic analysis. In this article, we propose a method to calibrate the risk index system at a subject level. Specifically, we developed point and interval estimation procedures for t-year mortality rates conditional on the estimated parametric risk score. The proposals are illustrated with a dataset from a large clinical trial with post-myocardial infarction patients.

14-day prulifloxacin treatment of acute uncomplicated cystitis in women with recurrent urinary tract infections: a prospective, open-label, pilot trial with 6-month follow-up

Recurrent urinary tract infections (UTI) are very common in otherwise healthy young women, and can have a very negative social and economic impact. In order to evaluate the tolerability and efficacy of a 14-day course of prulifloxacin orally administered once daily, 51 young female patients, attending the same STD center between may and June 2007 for symptoms of cystitis, with a history of recurrent UTI and urine culture positive for uropathogens, were enrolled in this prospective study. Microbiological and clinical efficacy was tested over three follow-up visits at 1, 3 and 6 months. Quality of life (QoL) was measured and the impact of prulifloxacin in modifying the Lactobacillus vaginal flora was also evaluated. At baseline, the pathogens most commonly isolated were Enterococcus faecalis (43.2%) and Escherichia coli (27.5%). 41 of the 51 women, (80.3%) had Lactobacillus spp. in vaginal samples at baseline. microbiological results at follow-up examinations were as follows: after 1 month, 47 patients were recurrence-free and 4 had recurrence; after 3 months, 41 were recurrence-free, while 6 reported recurrence; finally, after 6 months, 36 were recurrence-free and 5 had recurrence. A statistically significant difference was reported between the QoL questionnaire mean scores at baseline (0.63), 1 (0.77), 3 (0.77) and 6 months (0.78) after treatment (all p<0.001). the vaginal swab cultures demonstrated that Lactobacillus spp. flora was maintained in 38 out of the 41 (92.6%) patients who had positive vaginal swab sample at baseline. in conclusion, a 14-day administration of prulifloxacin 600 mg is a safe, well tolerated and effective treatment for the management of UTI in young women.

Complete Duplication of Bladder, Urethra, Clitoris and Vagina Associated with Bilateral Ureteral Reflux in an Old Female Patient: Unique Case

Complete duplication of the bladder, urethra, vaginal and clitoris is an extremely rare congenital anomaly that is usually diagnosed at pediatric age. We report an unusual case of a complete bladder, vaginal, clitoris and urethra duplication occurred in an old female patient (81 years old). This case highlighted the fact that an anatomical anomaly should be considered in all patients with persistent urinary tract problems, especially in old women, due to patients’ reluctance to consult physicians for this kind of problem.

Regularized estimation for the accelerated failure time model

SUMMARY

In the presence of high-dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes. Further complications arise when the outcome of interest is an event time, which is often not fully observed due to censoring. In this article, we develop robust prediction models for event time outcomes by regularizing the Gehan's estimator for the accelerated failure time (AFT) model (Tsiatis, 1996, Annals of Statistics 18, 305-328) with least absolute shrinkage and selection operator (LASSO) penalty. Unlike existing methods based on the inverse probability weighting and the Buckley and James estimator (Buckley and James, 1979, Biometrika 66, 429-436), the proposed approach does not require additional assumptions about the censoring and always yields a solution that is convergent. Furthermore, the proposed estimator leads to a stable regression model for prediction even if the AFT model fails to hold. To facilitate the adaptive selection of the tuning parameter, we detail an efficient numerical algorithm for obtaining the entire regularization path. The proposed procedures are applied to a breast cancer dataset to derive a reliable regression model for predicting patient survival based on a set of clinical prognostic factors and gene signatures. Finite sample performances of the procedures are evaluated through a simulation study.

Identifying subjects who benefit from additional information for better prediction of the outcome variables

Suppose that we are interested in using new bio- or clinical markers, in addition to the conventional markers, to improve prediction or diagnosis of the patient's clinical outcome. The incremental value from the new markers is typically assessed by averaging across patients in the entire study population. However, when measuring the new markers is costly or invasive, an overall improvement does not justify measuring the new markers in all patients. A more practical strategy is to utilize the patient's conventional markers to decide whether the new markers are needed for improving prediction of his/her health outcomes. In this article, we propose inference procedures for the incremental values of new markers across various subgroups of patients classified by the conventional markers. The resulting point and interval estimates can be quite useful for medical decision makers seeking to balance the predictive or diagnostic value of new markers against their associated cost and risk. Our proposals are theoretically justified and illustrated empirically with two real examples.

Human cytomegalovirus glycoprotein genotypes in the genital tract tissue of tubal pregnancy patients

This study investigated human cytomegalovirus (HCMV) glycoprotein genotypes in the genital tract tissue of 125 tubal pregnancy patients. The HCMV glycoprotein-B N-terminus (gBn, 54 - 485 NT), gB endoprotease cleavage site (gBclv, 1284 - 1600 NT) and glycoproteinH (gH, -58 - 213 NT) gene fragments were amplified by nested polymerase chain reaction and sequenced to identify gB and gH genotypes. Of 16 gBn-positive samples, four were gBn1, one was gBn2 and 11 were the gBn3 genotype. Of 13 positive gBclv samples, seven were gBclv1, two were gBclv2 and four were the gBclv3 genotype. Of 20 positive gH samples, 10 were gH1, six were gH2 and four were a combined gH1/gH2 genotype. In 10 of the samples that were positive for the gBn and gBclv genotypes, the gBn and gBclv genotypes were not consistent (four were gBclv1-gBn3). This study showed that: (i) HCMV infection with the gB1 - gB3 glycoprotein genotypes is present in tubal pregnancy; (ii) the gBclv and gBn genotypes are not strictly consistent; and (iii) intragenetic variability within the gB gene due to homologous recombination occurs frequently.

[Chronic prostatitis and biofilm]

Nowadays, inflammatory prostatic diseases represent one of the main social and economic problems that affect male gender in our country. The disease's prevalence is estimated at 11-14% in European countries, and males aged between 20 and 50 years are the most affected subjects. Bacterial prostatitis is documented at a very low prevalence (7-13% of prostatitis overall considered) due to either reduced utilization of viral and bacteriological tests (Meares test, urethral swab, bacterial and viral PCR) or to the possibility that individual patients show a scarce expression of bacterial disease in biological fluids, because of intraglandular biofilm presence, with subsequent spreading of the sole plankton microrganisms. Actually, the presence of a biofilm may possibly represent the reason for difficult diagnoses and ineffective antibacterial treatments.

A prediction model for type 2 diabetes risk among Chinese people

AIMS/HYPOTHESIS

A range of prediction rules for the onset of type 2 diabetes have been proposed. However, most studies have been conducted in white groups and it is not clear whether these models apply to Asian populations. The purpose of this study was to construct a simple points model for predicting incident diabetes among Chinese people.

METHODS

We estimated the 10 year risk of diabetes in a cohort study of middle-aged and elderly participants who were free from diabetes at baseline. Cox regression coefficients were used to construct the simple points model and the discriminatory ability of the resulting prediction rule was determined using AUC and net reclassification improvement and integrated discrimination improvement statistics. Fivefold random splitting was used to test the internal validity and obtain bootstrap estimates of the AUC.

RESULTS

Of the 2,960 participants without diabetes at the baseline examination, 548 developed type 2 diabetes during a median 10 year follow-up period. Age (four points), elevated fasting glucose (11 points), body mass index (eight points), triacylglycerol (five points), white blood cell count (four points) and a higher HDL-cholesterol (negative four points) were found to strongly predict diabetes incidence in a multivariate model. The estimated AUC for the model was 0.702 (95% CI 0.676-0.727). This model performed better than existing prediction models developed in other populations, including the Prospective Cardiovascular Münster, Cambridge, San Antonia and Framingham models for diabetes risk.

CONCLUSIONS/INTERPRETATION

We have constructed a model for predicting the 10 year incidence of diabetes in Chinese people that could be useful for identifying individuals at high risk of diabetes in the Chinese population.