Distribution and associated factors of optic disc diameter and cup-to-disc ratio in an elderly Chinese population

AbstractBackgroundGlaucoma is the second leading cause of blindness worldwide and East Asian people account for almost half of those affected. Vertical elongation of the optic cup is a characteristic feature of glaucoma. However, there is a significant overlap in the vertical cup-to-disc ratio (VCDR) between normal eyes and eyes affected by glaucoma. The purpose of this study was to determine the distribution of VCDR and vertical disc diameter (VDD) and their predictive factors in a population of elderly Chinese residents in Taiwan.MethodsFour hundred and sixty elderly Chinese residents aged 72 years and older in the Shihpai district, Taipei, Taiwan participated in this study. Slit lamp biomicroscopic measurement of the VCDR and VDD after pupil dilation with a 78 diopter lens was performed by one glaucoma specialist. Multiple linear regression analyses were used to fit the best model for independent variables.ResultsThe VCDR was recorded for 438 right eyes and 430 left eyes. After excluding participants with glaucoma, the mean ± SD VCDR was 0.44 ± 0.17 for both eyes, and the 97.5th percentile was 0.8. A greater VCDR was associated with a longer axial length [VCDR = −0.47 + 0.04(axial length)] under multiple regression analysis. The VDD was obtained for 420 right eyes and 406 left eyes. The mean ± SD VDD for all participants was 1.77 ± 0.22 mm for the right eye and 1.79 ± 0.22 mm for the left eye. A higher body mass index (BMI) and a longer axial length were significantly associated with a larger VDD under multiple regression analysis. [VDD = −0.05 + 0.07 (axial length) + 0.06 (obesity); if BMI <24, then obesity = 0; if BMI ≥24, then obesity = 1]. A larger VDD was associated with a larger VCDR (p < 0.001) and the VCDR could be predicted by the equation VCDR = −0.07 + 0.3VDD.ConclusionA greater VCDR was related to a longer axial length. A greater VDD was related to a higher BMI and a longer axial length

Effectiveness of mechanical chest compression for out-of-hospital cardiac arrest patients in an emergency department

AbstractBackgroundTo increase the chance of restoring spontaneous circulation, cardiopulmonary resuscitation (CPR) with high-quality chest compressions is needed. We hypothesized that, in a municipal hospital emergency department, the outcome in nontraumatic out-of-hospital cardiac arrest patients treated with standard CPR followed by mechanical chest compression (MeCC) was not inferior to that followed by manual chest compression (MaCC). The purposes of the study were to test our hypothesis and investigate whether the use of MeCC decreased human power demands for CPR.MethodsA total of 455 consecutive out-of-hospital cardiac arrest patients of presumed cardiac etiology were divided into two groups according to the chest compressions they received (MaCC or MeCC) in this retrospective review study. Human power demand for CPR was described according to the Basic Life Support/Advanced Cardiovascular Life Support guidelines and the device handbook. The primary endpoint was recovery of spontaneous circulation during resuscitation, and the secondary endpoints were survival to hospital admission and medical human power demands.ResultsIn this study, recovery of spontaneous circulation was achieved in 33.3% of patients in the MeCC group and in 27.1% in the MaCC group (p = 0.154), and the percentages of patients who survived hospitalization were 22.2% and 17.6%, respectively (p = 0.229). A ratio of 2:4 for the human power demand for CPR between the groups was found. Independent predictors of survival to hospitalization were ventricular fibrillation/pulseless ventricular tachycardia as initial rhythm and recovery of spontaneous circulation.ConclusionNo difference was found in early survival between standard CPR performed with MeCC and that performed with MaCC. However, the use of the MeCC device appears to promote staff availability without waiving patient care in the human power-demanding emergency departments of Taiwan hospitals

Amino acid classification based spectrum kernel fusion for protein subnuclear localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models for protein subnuclear localization thus far, to the best of our knowledge. Two models were based on protein primary sequence only. The first model assumed homogeneous amino acid substitution pattern across all protein sequence residue sites and used BLOSUM62 to encode <it>k</it>-mer of protein sequence. Ensemble of SVM based on different <it>k</it>-mers drew the final conclusion, achieving 50% overall accuracy. The simplified assumption did not exploit protein sequence profile and ignored the fact of heterogeneous amino acid substitution patterns across sites. The second model derived the <it>PsePSSM </it>feature representation from protein sequence by simply averaging the profile PSSM and combined the <it>PseAA </it>feature representation to construct a kNN ensemble classifier <it>Nuc-PLoc</it>, achieving 67.4% overall accuracy. The two models based on protein primary sequence only both achieved relatively poor predictive performance. The third model required that GO annotations be available, thus restricting the model's applicability.</p> <p>Methods</p> <p>In this paper, we only use the amino acid information of protein sequence without any other information to design a widely-applicable model for protein subnuclear localization. We use <it>K</it>-spectrum kernel to exploit the contextual information around an amino acid and the conserved motif information. Besides expanding window size, we adopt various amino acid classification approaches to capture diverse aspects of amino acid physiochemical properties. Each amino acid classification generates a series of spectrum kernels based on different window size. Thus, (I) window expansion can capture more contextual information and cover size-varying motifs; (II) various amino acid classifications can exploit multi-aspect biological information from the protein sequence. Finally, we combine all the spectrum kernels by simple addition into one single kernel called <it>SpectrumKernel+ </it>for protein subnuclear localization.</p> <p>Results</p> <p>We conduct the performance evaluation experiments on two benchmark datasets: <it>Lei </it>and <it>Nuc-PLoc</it>. Experimental results show that <it>SpectrumKernel+ </it>achieves substantial performance improvement against the previous model <it>Nuc-PLoc</it>, with overall accuracy <it>83.47% </it>against <it>67.4%</it>; and <it>71.23% </it>against <it>50% </it>of <it>Lei SVM Ensemble</it>, against 66.50% of <it>Lei GO SVM Ensemble</it>.</p> <p>Conclusion</p> <p>The method <it>SpectrumKernel</it>+ can exploit rich amino acid information of protein sequence by embedding into implicit size-varying motifs the multi-aspect amino acid physiochemical properties captured by amino acid classification approaches. The kernels derived from diverse amino acid classification approaches and different sizes of <it>k</it>-mer are summed together for data integration. Experiments show that the method <it>SpectrumKernel</it>+ significantly outperforms the existing models for protein subnuclear localization.</p

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Methylphenidate (Ritalin)-associated Cataract and Glaucoma

Methylphenidate hydrochloride (Ritalin) is the drug of choice for attention deficit hyperactivity disorder (ADHD). However, an association of Ritalin with glaucoma has been reported. We report a case of Ritalin-associated cataract and glaucoma. A 10-year-old boy was diagnosed with ADHD and had received methylphenidate hydrochloride, 60 mg/day for 2 years. He presented with blurred vision. Best-corrected visual acuity was 6/60 in both eyes. Ocular examinations revealed intraocular pressure (IOP) of 30 mmHg under medication, dense posterior subcapsular opacity of lens, pale disc with advanced cupping, and marked constriction of visual field. Despite maximal anti-glaucomatous medication, IOP still could not be controlled. The patient then received combined cataract and glaucoma surgery. Visual acuity improved and IOP was within normal limits in both eyes postoperatively. Large dose of methylphenidate may cause cataract and glaucoma. The mechanism remains unclear. Doctors should be aware of the possible ocular side effects of methylphenidate

The association of visual impairment and 3-year mortality among the elderly in Taiwan: The Shihpai Eye Study

Background: The association between visual impairment and mortality has been controversial. Moreover, literature on the relationship was very limited in the Asian population. The purpose of this study was to investigate whether visual impairment increases the 3-year risk of mortality in a cohort of urban Chinese elderly individuals. Methods: Participants in the Shihpai Eye Study, who were aged ≥65 years, with a baseline examination conducted between July 1, 1999 and December 31, 2000, were recruited for the current study. The total number of possible participants identified was 4750. Of those, 3746 persons were eligible, and 2045 persons were randomly selected to be invited to participate in the study. Of those 2045 individuals, 1361 (66.6%) participated in both the questionnaire and eye examination. A follow-up of a fixed cohort was also conducted after 3 years. The death of any participants was confirmed through the household registration system. Results: Of the 1361 participants included at baseline, 54 (3.97%) died before the 3-year follow-up. Multiple logistic regression analysis showed that mortality was significantly associated with a fall history [relative risk (RR): 2.12; 95% confidence intervals (CI): 1.08–3.98] and a history of diabetes (RR: 2.06; 95% CI: 1.03–3.95). Visual impairment was not a significant predictor of mortality after adjustment for confounders. Conclusion: After adjustments were made for age, sex, education, marital status, lifestyle factors, depression symptoms, fall history, and history of systemic diseases, visual impairment was not a significant predictor of 3-year mortality in elderly persons