Risk factors for extra-pulmonary tuberculosis compared to pulmonary tuberculosis

BACKGROUND

Tuberculosis (TB) continues to be a major global health problem. Extra-pulmonary TB (EPTB) manifests with protean symptoms, and establishing a diagnosis is more difficult than pulmonary TB (PTB).

SETTING

A university-affiliated hospital in southern Taiwan.

OBJECTIVE

To analyse the risk factors for EPTB compared with PTB.

DESIGN

This retrospective study compared patients with EPTB and PTB in southern Taiwan by analysing their demographic data and clinical underlying diseases. Risk factors for EPTB were further analysed.

RESULTS

A total of 766 TB patients were enrolled in this study, with 102 (13.3%) EPTB and 664 (86.7%) PTB cases. Of the 766 patients, 3% of PTB patients had EPTB, while 19.6% of EPTB patients also had PTB. The most frequently involved EPTB site was the bone and joints (24.5%). The incidence of EPTB vs. PTB decreased significantly for each decade increase in patient age. Multivariate logistic regression analysis showed that being female, not being diabetic, having end-stage renal disease and not smoking were independent risk factors for EPTB.

CONCLUSION

This study defines the risk factors for EPTB compared with PTB. Awareness of these factors is essential for physicians to have a high index of suspicion for accurate and timely diagnosis.

Prediction of antibiotic resistance proteins from sequence-derived properties irrespective of sequence similarity

Increasing antibiotic resistance has become a worldwide challenge to the clinical treatment of infectious diseases. The identification of antibiotic resistance proteins (ARPs) would be helpful in the discovery of new therapeutic targets and the design of novel drugs to control the potential spread of antibiotic resistance. In this work, a support vector machine (SVM)-based ARP prediction system was developed using 1308 ARPs and 15587 non-ARPs. Its performance was evaluated using 313 ARPs and 7156 non-ARPs. The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs. A potential application of this method is the identification of ARPs non-homologous to proteins of known function. Further genome screening found that ca. 3.5% and 3.2% of proteins in Escherichia coli and Staphylococcus aureus, respectively, are potential ARPs. These results suggest the usefulness of SVMs for facilitating the identification of ARPs. The software can be accessed at SARPI (Server for Antibiotic Resistance Protein Identification).

Homology-free prediction of functional class of proteins and peptides by support vector machines

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

The effect of activation treatments on the development of reconstructed bovine oocytes

The purpose of this study was to investigate the effect of different activation treatments on the development of IVM-derived and cloned bovine embryos. The effect of oocyte age (20h versus 24h after IVM) on the blastocyst rate was also investigated. No differences in the percentage of blastocyst development were observed between the oocytes matured for 20 and 24h (15% versus 27%, p>0.05). Reconstructed oocytes activated 4h after fusion (fusion before activation, FBA) had a higher blastocyst rate than those oocytes activated immediately after electrofusion (fusion and activation simultaneously, FAS) (26% versus 5%, p<0.01). Blastocyst rates were significantly greater (p<0.01) for the reconstructed oocytes activated by calcium ionophore (A23187) combined with 6-dimethylaminopurine (6-DMAP) (51.6%) than for those activated with cycloheximide (CHX) plus cytochalasin B (CB) treatment (1h, 8.2%; 5h, 14.3%). However, the blastocyst rates were similar among reconstructed oocytes activated by electric pulses and A23187 (30.5% versus 42.2%) or by A23187 and ionomycin (36.7% versus 33.3%) combined with 6-DMAP, respectively. Blastocysts that developed from reconstructed oocytes activated by A23187 and 6-DMAP resulted in three pregnancies (3/9) and one live birth from 18 embryos transferred to recipient cows. Genotypic analysis of six bovine microsatellite markers by polymerase chain reaction confirmed that the cloned calf was genetically identical to the nuclear donor. In conclusion, reconstructed oocytes that derived from oocytes exposed to activation treatment 4h after electrofusion are more likely to develop to the blastocyst stage. The best treatment to activate reconstructed bovine oocytes in this study was A23187 combined with 6-DMAP.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Modulation spectroscopy study of the effects of growth interruptions on the interfaces of GaAsSb/GaAs multiple quantum wells

The effects of growth interruption times combined with Sb exposure of GaAsSb/GaAs multiple quantum wells (MQWs) have been investigated by using phototransmittance (PT), contactless electroreflectance (CER) and wavelength modulated surface photovoltage spectroscopy (WMSPS). The features originated from different portions of the samples, including interband transitions of MQWs, interfaces and GaAs, are observed and identified through a detailed comparison of the obtained spectra and theoretical calculation. A red-shift of the interband transitions and a broader lineshape of the fundamental transition are observed from samples grown under Sb exposure compared to the reference sample grown without interruption. The results can be interpreted in terms of both increases in Sb content and mixing of Sb in the GaAs interface layers. An additional feature has been observed below the GaAs region in the samples with Sb treatment. The probable origin of this additional feature is discussed.

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties

Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .

The effect of electrical field strength on activation and development of cloned caprine embryos

The activation procedure used in nuclear transfer (NT) is one of the critical factors affecting the efficiency of animal cloning. The purpose of this study was to compare the effect of two electrical field strengths (EFS) for activation on the developmental competence of caprine NT embryos reconstructed from ear skin fibroblasts of adult Alpine does. The NT embryos were obtained by transfer of the quiescent fibroblasts at the fourth passage into the enucleated metaphase II (M II) oocytes. Four to five hours after electrical fusion, the NT-embryos were activated by EFS either at 1.67 or at 2.33 kV/cm and immediately incubated in 6-DMAP (2 mM) for 4 h. The cleavage rate of the NT-embryos activated with 2.33 kV/cm was greater than that activated with 1.67 kV/cm after in vitro culture for 18 h (65.6% versus 19.6%, p < 0.001). No pregnancy was found in 14 recipient does after transferring 51 NT embryos at 1-2 cell stages activated with 1.67 kV/cm. In contrast, two of the seven recipients were pregnant and gave birth to three kids after transferring 61 NT embryos at 1-2 cell stages activated by 2.33 kV/cm. The birth weights of three cloned kids were within the normal range of Alpine goats. However, one kid died 1h after birth while the remaining two are still healthy. DNA analysis by polymerase chain reaction (single-strand conformation polymorphism, SSCP) confirmed that the three kids were genetically identical to the nuclear donor.

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi).

PEARLS: Program for Energetic Analysis of Receptor−Ligand System

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.

Prediction of transporter family from protein sequence by support vector machine approach

Transporters play key roles in cellular transport and metabolic processes, and in facilitating drug delivery and excretion. These proteins are classified into families based on the transporter classification (TC) system. Determination of the TC family of transporters facilitates the study of their cellular and pharmacological functions. Methods for predicting TC family without sequence alignments or clustering are particularly useful for studying novel transporters whose function cannot be determined by sequence similarity. This work explores the use of a machine learning method, support vector machines (SVMs), for predicting the family of transporters from their sequence without the use of sequence similarity. A total of 10,636 transporters in 13 TC subclasses, 1914 transporters in eight TC families, and 168,341 nontransporter proteins are used to train and test the SVM prediction system. Testing results by using a separate set of 4351 transporters and 83,151 nontransporter proteins show that the overall accuracy for predicting members of these TC subclasses and families is 83.4% and 88.0%, respectively, and that of nonmembers is 99.3% and 96.6%, respectively. The accuracies for predicting members and nonmembers of individual TC subclasses are in the range of 70.7-96.1% and 97.6-99.9%, respectively, and those of individual TC families are in the range of 60.6-97.1% and 91.5-99.4%, respectively. A further test by using 26,139 transmembrane proteins outside each of the 13 TC subclasses shows that 90.4-99.6% of these are correctly predicted. Our study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity.

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.

Comparison of the severity of illness scoring systems for critically ill cirrhotic patients with renal failure

BACKGROUND

Mortality rates of cirrhotic patients with renal failure admitted to the medical intensive care unit (ICU) are high. End-stage liver disease is frequently complicated by disturbances of renal function. This investigation is aimed to compare the predicting ability of acute physiology, age, chronic health evaluation II and III (APACHE II and III), sequential organ failure assessment (SOFA), and Child-Pugh scoring systems, obtained on the first day of ICU admission, for hospital mortality in critically ill cirrhotic patients with renal failure.

METHODS

Sixty-seven patients with liver cirrhosis and renal failure were admitted to ICU from April 2001-March 2002. Information considered necessary for computing the Child-Pugh, SOFA, APACHE II and APACHE III score on the first day of ICU admission was prospectively collected.

RESULTS

The overall hospital mortality rate was 86.6%. Liver disease was most commonly attributed to hepatitis B viral infection. The development of renal failure was associated with a history of gastrointestinal bleeding. Goodness-of-fit was good for SOFA, APACHE II and APACHE III scores. The APACHE III and SOFA models reported good areas under receiver operating characteristic curve (0.878 +/- 0.050 and 0.868 +/- 0.051, respectively).

CONCLUSION

Renal failure is common in critically ill patients with cirrhosis. The prognosis for cirrhotic patients with renal failure is poor. APACHE III and SOFA showed excellent discrimination power in this group of patients. They are superior to APACHE II and Child-Pugh scores in this homogenous group of patients.

Clinical manifestations of Kikuchi's disease in southern Taiwan

Histiocytic necrotizing lymphadenitis, or Kikuchi's disease (KD), is a self-limiting cervical lymphadenitis of unknown origin. The diagnosis of KD is problematic due to the lack of specific laboratory tests. This study reviewed the clinical characteristics of 58 patients with KD. Clinical manifestations were of considerable diversity. The mean age of patients was 24.88 +/- 7.44 years and there was a female predominance (1.76:1). The most frequent clinical findings were enlarged tender lymph nodes (50%), fever (43%), sore throat (21%), non-productive cough (12%), headache (10%), chills (9%) and rhinorrhea (9%). The most common initial laboratory abnormalities were leukopenia (29%), elevated erythrocyte sedimentation rate (14%), liver function impairment (14%), elevated C-reactive protein level (12%), and anemia (10%). Most patients had unilateral lymph node involvement (79%), which was usually located in the posterior triangle of the cervical lymph nodes (90%). Most patients had no comorbid disease (93%). No recurrence occurred. KD should be included in the differential diagnosis of fever with cervical lymphadenopathy.

Dyspeptic symptoms and water load test in patients with functional dyspepsia and reflux disease

OBJECTIVE

Hypersensitivity to gastric distension has been reported in functional dyspepsia (FD). The aim of this study was to assess the perception to gastric distension and its relationship to specific symptoms using the water load test (WLT) in FD and gastroesophageal reflux disease (GERD).

MATERIAL AND METHODS

A 5-min WLT was used to evaluate sensitivity of gastric distension in 47 FD and 61 GERD subjects, and 49 healthy controls (HC). A visual analogue scale (VAS) measuring symptom severity was obtained from all subjects and its relationship with the maximal ingested volume was determined. The maximal ingested volume was registered and the subjective symptoms were assessed at baseline and 30 min after the WLT.

RESULTS

The maximal ingested volume by HC was 597 +/- 33 ml, which was statistically greater than that of FD (422 +/- 22 ml, p < 0.001) and GERD (504 +/- 23 ml, p < 0.02) subjects. The maximal ingested volume did not differ significantly between FD and GERD. The maximal ingested volume correlated well with nausea (r = -0.49, p < 0.01) and fullness (r = -0.33, p < 0.05) in FD. Male subjects ingested more water than females in the HC (p < 0.01) and GERD groups (p < 0.05), but not in FD (p = NS).

CONCLUSIONS

Both FD and GERD subjects have altered perception to gastric fullness induced by the WLT compared to healthy controls. Good correlations have been observed between the WLT and dyspeptic symptoms such as early satiety and postprandial fullness, but not in GERD.

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

Transfer function analysis of heart rate variability in response to water intake: correlation with gastric myoelectrical activity

We utilized transfer function analysis of heart rate variability (HRV) and respiration to investigate the effect of water intake on gastric myoelectrical activity and its relationship to vagal activity. The electrogastrography (EGG) and HRV were recorded simultaneously before and after drinking 500 ml of water in 10 healthy subjects. We observed good linearity between lung volumes and HRV signals at a ventilatory rate between 0.2 and 0.4 Hz before and after water intake. The EGG power of 3 cycles/min increased remarkably after the water intake. We found that there was a significant increase in the magnitude of the respiration-HRV transfer function after water intake (P < 0.05). The EGG 3 cycles/min power was positively correlated with the transfer magnitude throughout the study (r = 0.54, P = 0.01). These results confirm that transfer function analysis of HRV sensitively identifies subtle changes in the respiratory sinus arrhythmia that occurs with water intake. The present findings suggest that transfer function analysis of HRV and respiration after water intake can be used to evaluate vagal nervous activity in the human gut.

Randomization of heavily damaged regions in annealed low energy Ge+-implanted (0 0 1)Si

Apparent growth of amorphous layers during low temperature annealing was observed in low energy Ge(+)-implanted (001)Si by high-resolution transmission electron microscopy. The occurrence of abnormal growth is due to the randomization of heavily damaged regions beneath the original amorphous/crystalline interfaces. The randomization process is attributed to the strain, incurred by the presence of a high density of large Ge atoms in the heavily damaged Si substrate, relaxation to lower the free energy of the systems. The randomization upon annealing may be fruitfully applied to minimize the transient enhanced diffusion in shallow junction formation.

Hydrogen peroxide increases the activity of rat sympathetic preganglionic neurons in vivo and in vitro

Reactive oxygen species (ROS) have been shown to modulate neuronal synaptic transmission and have also been implicated in cardiovascular diseases such as hypertension. The hypothesis that H(2)O(2) acting on sympathetic preganglionic neurons (SPNs) affects spinal sympathetic outflow was tested in the present study. H(2)O(2) was applied intrathecally via an implanted cannula to the T7-T9 segments of urethane-anesthetized rats. Blood pressure and heart rate were used as indices to evaluate the spinal sympathetic effects of H(2)O(2) in vivo. Intrathecal H(2)O(2) (100-1000 nmol) dose-dependently increased both the mean arterial pressure and heart rate. Reproducible pressor effects of H(2)O(2) (1000 nmol) applied consecutively at intervals of 30 min were observed. The pressor effects of intrathecal H(2)O(2) (1000 nmol) were attenuated by pretreatment with intrathecal administration of catalase (500 units), or N-acetyl-cysteine (1000 nmol). The pressor effects of intrathecal H(2)O(2) (1000 nmol) were also antagonized dose-dependently by prior intrathecal injection of AP-5 (DL-2-amino-5- phosphonovaleric acid, 10 and 30 nmol), or 6-cyano-7- nitroquinoxaline-2,3-dione, 10 and 30 nmol. In vitro electrophysiological study in spinal cord slices showed that superfusion of 1 mM H(2)O(2) for 3 min, which had no effect on membrane potential, caused an increase in amplitude of excitatory postsynaptic potentials in SPNs, but had little effect on that of inhibitory postsynaptic potentials. Taken together, these results demonstrated that oxidative stress in spinal cord may cause an increase in spinal sympathetic tone by acting on SPNs, which may contribute to ROS-induced cardiovascular dysfunction.