Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

Selenosugars targeting the infective stage of Trypanosoma brucei with high selectivity

Earlier evidences showed that diglycosyl diselenides are active against the infective stage of African trypanosomes (top hits IC50 0.5 and 1.5 μM) but poorly selective (selectivity index <10). Here we extended the study to 33 new seleno-glycoconjugates with the aim to improve potency and selectivity. Three selenoglycosides and three glycosyl selenenylsulfides displayed IC50 against bloodstream Trypanosoma brucei in the sub-μM range (IC50 0.35-0.77 μM) and four of them showed an improved selectivity (selectivity index >38-folds vs. murine and human macrohages). For the glycosyl selenylsulfides, the anti-trypanosomal activity was not significantly influenced by the nature of the moiety attached to the sulfur atom. Except for a quinoline-, and to a minor extent a nitro-derivative, the most selective hits induced a rapid (within 60 min) and marked perturbation of the LMWT-redox homeostasis. The formation of selenenylsulfide glycoconjugates with free thiols has been identified as a potential mechanism involved in this process.

Saturation Transfer Difference NMR and Molecular Docking Interaction Study of Aralkyl-Thiodigalactosides as Potential Inhibitors of the Human-Galectin-3 Protein

Human Galectin-3 (hGal-3) is a protein that selectively binds to β-galactosides and holds diverse roles in both normal and pathological circumstances. Therefore, targeting hGal-3 has become a vibrant area of research in the pharmaceutical chemistry. As a step towards the development of novel hGal-3 inhibitors, we synthesized and investigated derivatives of thiodigalactoside (TDG) modified with different aromatic substituents. Specifically, we describe a high-yielding synthetic route of thiodigalactoside (TDG); an optimized procedure for the synthesis of the novel 3,3'-di-O-(quinoline-2-yl)methyl)-TDG and three other known, symmetric 3,3'-di-O-TDG derivatives ((naphthalene-2yl)methyl, benzyl, (7-methoxy-2H-1-benzopyran-2-on-4-yl)methyl). In the present study, using competition Saturation Transfer Difference (STD) NMR spectroscopy, we determined the dissociation constant (Kd) of the former three TDG derivatives produced to characterize the strength of the interaction with the target protein (hGal-3). Based on the Kd values determined, the (naphthalen-2-yl)methyl, the (quinolin-2-yl)methyl and the benzyl derivatives bind to hGal-3 94, 30 and 24 times more strongly than TDG. Then, we studied the binding modes of the derivatives in silico by molecular docking calculations. Docking poses similar to the canonical binding modes of well-known hGal-3 inhibitors have been found. However, additional binding forces, cation-π interactions between the arginine residues in the binding pocket of the protein and the aromatic groups of the ligands, have been established as significant features. Our results offer a molecular-level understanding of the varying affinities observed among the synthesized thiodigalactoside derivatives, which can be a key aspect in the future development of more effective ligands of hGal-3.

AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets

BACKGROUND

Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes' physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina.

MATERIALS AND METHODS

IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation.

RESULTS

The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes' physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance.

CONCLUSIONS

In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model's efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

BACKGROUND

One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers.

METHODS

This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances.

RESULTS

The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97-99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%.

CONCLUSIONS

The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate-the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.

77Se-Enriched Selenoglycoside Enables Significant Enhancement in NMR Spectroscopic Monitoring of Glycan-Protein Interactions

Detailed investigation of ligand-protein interactions is essential for better understanding of biological processes at the molecular level. Among these binding interactions, the recognition of glycans by lectins is of particular importance in several diseases, such as cancer; therefore, inhibition of glycan-lectin/galectin interactions represents a promising perspective towards developing therapeutics controlling cancer development. The recent introduction of 77Se NMR spectroscopy for monitoring the binding of a selenoglycoside to galectins prompted interest to optimize the sensitivity by increasing the 77Se content from the natural 7.63% abundance to 99%. Here, we report a convenient synthesis of 77Se-enriched selenodigalactoside (SeDG), which is a potent ligand of the medically relevant human galectin-3 protein, and proof of the expected sensitivity gain in 2D 1H, 77Se correlation NMR experiments. Our work opens perspectives for adding isotopically enriched selenoglycans for rapid monitoring of lectin-binding of selenated as well as non-selenated ligands and for ligand screening in competition experiments.

Introducing 77Se NMR Spectroscopy to Analyzing Galectin -Ligand Interaction

Their emerging nature as multifunctional effectors explains the large interest to monitor glycan binding to galectins and to define bound-state conformer(s) of their ligands in solution. Basically, NMR spectroscopy facilitates respective experiments. Towards developing new and even better approaches for these purposes, extending the range of exploitable isotopes beyond ¹H, ¹³C, and ¹⁵N offers promising perspectives. Having therefore prepared selenodigalactoside and revealed its bioactivity as galectin ligand, monitoring of its binding by ⁷⁷Se NMR spectroscopy at a practical level becomes possible by setting up a 2D ¹H, ⁷⁷Se CPMG-HSQBMC experiment including CPMG-INEPT long-range transfer. This first step into applying ⁷⁷Se as sensor for galectin binding substantiates its potential for screening relative to inhibitory potencies in compound mixtures and for achieving sophisticated epitope mapping. The documented strategic combination of synthetic carbohydrate chemistry and NMR spectroscopy prompts to envision to work with isotopically pure ⁷⁷Se-containing β-galactosides and to build on the gained experience with ⁷⁷Se by adding ¹⁹F as second sensor in doubly labeled glycosides.

Self-Tuning Possibilistic c-Means Clustering Models

The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is [Formula: see text]. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.

Selenoglycosides as Lectin Ligands: 77 Se-Edited CPMG-HSQMBC NMR Spectroscopy To Monitor Biomedically Relevant Interactions

The fundamental importance of protein-glycan recognition calls for specific and sensitive high-resolution techniques for their detailed analysis. After the introduction of 19 F NMR spectroscopy to study the recognition of fluorinated glycans, a new 77 Se NMR spectroscopy method is presented for complementary studies of selenoglycans with optimised resolution and sensitivity, in which direct NMR spectroscopy detection on 77 Se is replaced by its indirect observation in a 2D 1 H,77 Se HSQMBC spectrum. In contrast to OH/F substitution, O/Se exchange allows the glycosidic bond to be targeted. As an example, selenodigalactoside recognition by three human galectins and a plant toxin is readily indicated by signal attenuation and line broadening in the 2D 1 H,77 Se HSQMBC spectrum, in which CPMG-INEPT long-range transfer ensures maximal detection sensitivity, clean signal phases, and reliable ligand ranking. By monitoring competitive displacement of a selenated spy ligand, the selective 77 Se NMR spectroscopy approach may also be used to screen non-selenated compounds. Finally, 1 H,77 Se CPMG-INEPT transfer allows further NMR sensors of molecular interaction to be combined with the specificity and resolution of 77 Se NMR spectroscopy.

Bivalent glycoconjugates based on 1,5-diazabicyclo[3.3.0]octa-3,6-diene-2,8-dione ("bimane") as a central scaffold

The heteroaromatic fused diazabicyclic "bimane" ring system, discovered four decades ago, is endowed with remarkable chemical and photophysical properties. No carbohydrate derivatives of bimanes have, however, been described thus far. Here we report on the syntheses of a range of bimanes decorated with various glycosyl residues. Mono- and disaccharide residues were attached to syn- or anti-bimane central cores via thio-, disulfido- or selenoglycosidic linkages to obtain novel fluorescent or nonfluorescent glycoconjugates. Cu(I)-catalyzed cycloaddition of glycosyl azides to a bimane diethynyl derivative furnished further bivalent glycoconjugates with sugar residues linked to the central bimane core via 1,2,3-triazole rings. We have determined the crystal and molecular structures of several glycosylated and non-glycosylated bimanes and report fluorescence data for the new compounds.

Aralkyl selenoglycosides and related selenosugars in acetylated form activate protein phosphatase-1 and -2A

Aralkyl and aryl selenoglycosides as well as glycosyl selenocarboxylate derivatives were assayed on the activity of protein phosphatase-1 (PP1) and -2A (PP2A) catalytic subunits (PP1c and PP2Ac) in search of compounds for PP1c and PP2Ac effectors. The majority of tested selenoglycosides activated both PP1c and PP2Ac by ∼2-4-fold in a phosphatase assay with phosphorylated myosin light chain substrate when the hydroxyl groups of the glycosyl moiety were acetylated, but they were without any effects in the non-acetylated forms. A peptide from the myosin phosphatase target subunit-1 (MYPT1^23-38) that included an RVxF PP1c-binding motif attenuated activation of PP1c by 2-Trifluoromethylbenzyl 2,3,4,6-tetra-O-acetyl-1-seleno-β-d-glucopyranoside (TFM-BASG) and 4-Bromobenzyl 2,3,4,6-tetra-O-acetyl-1-seleno-β-d-glucopyranoside (Br-BASG). MYPT1^23-38 stimulated PP2Ac and contributed to PP2Ac activation exerted by either Br-BASG or TFM-BASG. Br-BASG and TFM-BASG suppressed partially binding of PP1c to MYPT1 in surface plasmon resonance based binding experiments. Molecular docking predicted that the hydrophobic binding surfaces in PP1c for interaction with either the RVxF residues of PP1c-interactors or selenoglycosides are partially overlapped. Br-BASG and TFM-BASG caused a moderate increase in the phosphatase activity of HeLa cells in 1 h, and suppressed cell viability in 24 h incubations. In conclusion, our present study identified selenoglycosides as novel activators of PP1 and PP2A as well as provided insights into the structural background of their interactions establishing a molecular model for future design of more efficient phosphatase activator molecules.

The Early Repolarization ECG Pattern – An Update

Early repolarization pattern (ERP), a form of J-wave syndromes, was considered long time a benign ECG phenomenon. However, recent data confirmed that certain phenotypes of ERP are related to an increased risk of sudden cardiac death (idiopathic ventricular fibrillation). Our paper gives a short and practical update regarding the main issues related to ERP: epidemiological data, molecular and electrophysiological background, clinical significance and risk stratification. At the end, the future directions of research and clinical management related to ERP are presented.

[Hand hygiene technique assessment using electronic equipment in 26 Hungarian healthcare institutions]

INTRODUCTION

Hand hygiene is probably the most effective tool of nosocomial infection prevention, however, proper feedback and control is needed to develop the individual hand hygiene practice.

AIM

Assessing the efficiency of modern education tools, and digital demonstration and verification equipment during their wide-range deployment.

METHOD

1269 healthcare workers took part in a training organized by our team. The training included the assessment of the participants' hand hygiene technique to identify the most often missed areas. The hand hygiene technique was examined by a digital device.

RESULTS

33% of the participants disinfected their hands incorrectly. The most often missed sites are the fingertips (33% on the left hand, 37% on the right hand) and the thumbs (42% on the left hand, 32% on the right hand).

CONCLUSION

The feedback has a fundamental role in the development of the hand hygiene technique. With the usage of electronic devices feedback can be provided efficiently and simply. Orv Hetil. 2017; 158(29): 1143-1148.

Diglycosyl diselenides alter redox homeostasis and glucose consumption of infective African trypanosomes

With the aim to develop compounds able to target multiple metabolic pathways and, thus, to lower the chances of drug resistance, we investigated the anti-trypanosomal activity and selectivity of a series of symmetric diglycosyl diselenides and disulfides. Of 18 compounds tested the fully acetylated forms of di-β-D-glucopyranosyl and di-β-D-galactopyranosyl diselenides (13 and 15, respectively) displayed strong growth inhibition against the bloodstream stage of African trypanosomes (EC₅₀ 0.54 μM for 13 and 1.49 μM for 15) although with rather low selectivity (SI < 10 assayed with murine macrophages). Nonacetylated versions of the same sugar diselenides proved to be, however, much less efficient or completely inactive to suppress trypanosome growth. Significantly, the galactosyl (15), and to a minor extent the glucosyl (13), derivative inhibited glucose catabolism but not its uptake. Both compounds induced redox unbalance in the pathogen. In vitro NMR analysis indicated that diglycosyl diselenides react with glutathione, under physiological conditions, via formation of selenenylsulfide bonds. Our results suggest that non-specific cellular targets as well as actors of the glucose and the redox metabolism of the parasite may be affected. These molecules are therefore promising leads for the development of novel multitarget antitrypanosomal agents.

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Acetylated diglycosyl diselenides inhibit the proliferation of infective Trypanosoma brucei.

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A galactosyl derivative impairs parasite' glucose consumption and redox homeostasis.

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Diglycosyl diselenides react covalently with glutathione under mild conditions..

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Acetylated diglycosyl diselenides represent multitarget antitrypanosomal candidates.

Towards objective hand hygiene technique assessment: validation of the ultraviolet-dye-based hand-rubbing quality assessment procedure

Ultraviolet spectrum markers are widely used for hand hygiene quality assessment, although their microbiological validation has not been established. A microbiology-based assessment of the procedure was conducted. Twenty-five artificial hand models underwent initial full contamination, then disinfection with UV-dyed hand-rub solution, digital imaging under UV-light, microbiological sampling and cultivation, and digital imaging of the cultivated flora were performed. Paired images of each hand model were registered by a software tool, then the UV-marked regions were compared with the pathogen-free sites pixel by pixel. Statistical evaluation revealed that the method indicates correctly disinfected areas with 95.05% sensitivity and 98.01% specificity.

Review. Automatic Segmentation Techniques of the Coronary Artery Using CT Images in Acute Coronary Syndromes

Coronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomography angiography (CCTA) is currently one of the most reliable methods used for assessing the coronary arteries; however, its use in emergency settings is sometimes limited due to time constraints. This paper presents the main characteristics of plaque vulnerability, the role of CCTA in the assessment of vulnerable plaques, and automatic segmentation techniques of the coronary artery tree based on CT angiography images. A detailed inventory of existing methods is given, representing the state-of-the-art of computational methods applied in vascular system segmentation, focusing on the current applications in acute coronary syndromes.

A modified two-stage Markov clustering algorithm for large and sparse networks

BACKGROUND

Graph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions.

OBJECTIVE

A fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer.

METHODS

Improvements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices. Clustering is performed in two stages: the initial connected network is processed in a sparse matrix until it breaks into isolated, small, and relatively dense subgraphs, which are then processed separately until convergence is obtained. An intelligent stopping criterion is also proposed to quit further processing of a subgraph that tends toward completeness with equal edge weights. The main advantage of this algorithm is that the necessary number of iterations is separately decided for each graph node.

RESULTS

The proposed algorithm was tested using the SCOP95 and large synthetic protein sequence data sets. The validation process revealed that the proposed method can reduce 3-6 times the processing time of huge sequence networks compared to previous Markov clustering solutions, without losing anything from the partition quality.

CONCLUSIONS

A one-million-node and one-billion-edge protein sequence network defined by a BLAST similarity matrix can be processed with an upper-class personal computer in 100 minutes. Further improvement in speed is possible via parallel data processing, while the extension toward several million nodes needs intermediary data storage, for example on solid state drives.

Education of hand rubbing technique to prospective medical staff, employing UV-based digital imaging technology

The aim of this study was to objectively assess the hand hygiene performance of medical students. Hand rubbing technique was evaluated by employing innovative UV-light-based imaging technology, identifying patterns and trends in missed areas after applying WHO's six-step protocol. This specially designed hand hygiene education and assessment program targeted 1,344 medical students at two distant sites in Central Europe. Students were introduced to a short video, presenting the basics of hand hygiene, and then received further demonstration from professional trainers, focusing on the correct execution of WHO's six-step technique. To verify the acquired skill, participants rubbed their hands with UV-marked alcohol-based solution. Digital images of the hands were recorded under UV light, followed by computer evaluation and assessment. Immediate objective visual feedback was given to the participants showing missed areas on their hands. The statistical analysis of missed spots was based on retrospective expert-driven manual evaluation. Significant difference in rubbing quality was found between female and male participants [35.3% (CI 95%: 33-38%) versus 29.0% (CI 95%: 27-31%), p < 0.001], dominant and non-dominant hands [43.4% (CI 95%: 39-48%) versus 34.9% (CI 95%: 32-38%), p = 0.002], and various zones of the hands' dorsal side. Based on the participants' feedback and the evaluation of the infection control specialists, it can be stated that the identification of typically missed patterns and the instant visual feedback have a vital role in improving the hand hygiene technique of prospective medical staff.

Ambulatory monitoring derived blood pressure variability and cardiovascular risk factors in elderly hypertensive patients

Hypertension in the elderly is characterized by isolated systolic hypertension and high variability, but its clinical significance is not yet fully understood. The goal of this paper was to assess circadian blood pressure variability (BPV) in elderly hypertensives, and to determine its relationship to cardiovascular risk factors. To achieve this goal, a number of 75 inefficiently treated hypertensive patients were studied, 45 elderly, aged over 60 years, 30 middle-aged, younger than 60 years. After 24-hour ambulatory blood pressure monitoring (ABPM), blood pressure (BP) values, pulse pressure (PP), morning surge were compared between the groups. BPV was calculated using average real variability (ARV). The relationships between BPV, pulse pressure, left ventricular mass index (LVMI), and cardiovascular risk factors were assessed in both groups. As a result, it was found that left ventricular mass (p=0.01), PP, morning surge, 24-hour systolic ARV were significantly higher in the elderly group (p<0.05). In both groups, higher 24-hour BPV was associated with an increase in LVMI. In the elderly population 24-hour BPV was positively correlated to increased PP, total cholesterol and triglyceride levels (p<0.05). Moreover, it was concluded that ABPM-derived BP variability index could be an early predictive marker of end-organ damage in hypertension. Its reduction might be an important objective of hypertension management in elderly.

Precise measurement of long-range heteronuclear coupling constants by a novel broadband proton-proton-decoupled CPMG-HSQMBC method

A broadband proton–proton-decoupled CPMG-HSQMBC method for the precise and direct measurement of long-range heteronuclear coupling constants is presented. The Zangger–Sterk-based homodecoupling scheme reported herein efficiently removes unwanted proton–proton splittings from the heteronuclear multiplets, so that the desired heteronuclear couplings can be determined simply by measuring frequency differences between singlet maxima in the resulting spectra. The proposed pseudo-1D/2D pulse sequences were tested on nucleotides, a metal complex incorporating P heterocycles, and diglycosyl (di)selenides, as well as on other carbohydrate derivatives, for the extraction of ⁿJ(¹H,³¹P), ⁿJ(¹H,⁷⁷Se), and ⁿJ(¹H,¹³C) values, respectively.

A Convenient Route to N-Acetyl-D-glucosamine-2-C-d and N-Acetyl-D-mannosamine-2-C-d

Title compounds were prepared through epimerisation of 2-acetamido-2-deoxy-D-glucose in NaOD. The isotopic purity of the products was determined by NMR and mass spectroscopy. The mechanism of epimerisation is briefly discussed.

A Simple Conversion of 2'-Benzyloxyflavanone to Pterocarpan

A new synthesis of cis-6aH,11aH-pterocarpan (1a) was achieved via its trans-isomer 1b starting from the readily available 2'-benzyloxyflavanone (2b).

A fast hierarchical clustering algorithm for large-scale protein sequence data sets

TRIBE-MCL is a Markov clustering algorithm that operates on a graph built from pairwise similarity information of the input data. Edge weights stored in the stochastic similarity matrix are alternately fed to the two main operations, inflation and expansion, and are normalized in each main loop to maintain the probabilistic constraint. In this paper we propose an efficient implementation of the TRIBE-MCL clustering algorithm, suitable for fast and accurate grouping of protein sequences. A modified sparse matrix structure is introduced that can efficiently handle most operations of the main loop. Taking advantage of the symmetry of the similarity matrix, a fast matrix squaring formula is also introduced to facilitate the time consuming expansion. The proposed algorithm was tested on protein sequence databases like SCOP95. In terms of efficiency, the proposed solution improves execution speed by two orders of magnitude, compared to recently published efficient solutions, reducing the total runtime well below 1min in the case of the 11,944proteins of SCOP95. This improvement in computation time is reached without losing anything from the partition quality. Convergence is generally reached in approximately 50 iterations. The efficient execution enabled us to perform a thorough evaluation of classification results and to formulate recommendations regarding the choice of the algorithm׳s parameter values.

A large-scale assessment of hand hygiene quality and the effectiveness of the "WHO 6-steps"

BACKGROUND

Hand hygiene compliance is generally assessed by observation of adherence to the "WHO five moments" using numbers of opportunities as the denominator. The quality of the activity is usually not monitored since there is no established methodology for the routine assessment of hand hygiene technique. The aim of this study was to objectively assess hand rub coverage of staff using a novel imaging technology and to look for patterns and trends in missed areas after the use of WHO's 6 Step technique.

METHODS

A hand hygiene education and assessment program targeted 5200 clinical staff over 7 days at the National University Hospital, Singapore. Participants in small groups were guided by professional trainers through 5 educational stations, which included technique-training and UV light assessment supported by digital photography of hands. Objective criteria for satisfactory hand hygiene quality were defined a priori. The database of images created during the assessment program was analyzed subsequently. Patterns of poor hand hygiene quality were identified and linked to staff demographic.

RESULTS

Despite the assessment taking place immediately after the training, only 72% of staff achieved satisfactory coverage. Failure to adequately clean the dorsal and palmar aspects of the hand occurred in 24% and 18% of the instances, respectively. Fingertips were missed by 3.5% of subjects. The analysis based on 4642 records showed that nurses performed best (77% pass), and women performed better than men (75% vs. 62%, p<0.001). Further risk indicators have been identified regarding age and occupation.

CONCLUSION

Ongoing education and training has a vital role in improving hand hygiene compliance and technique of clinical staff. Identification of typical sites of failure can help to develop improved training.

Enhanced in vitro refolding of soluble human glucocorticoid-induced TNF receptor-related ligand

The glucocorticoid-induced tumor necrosis factor receptor (GITR) is a member of the tumor necrosis factor receptor superfamily. Attachment of GITR to its ligand (GITRL) regulates diverse biological functions, including cell proliferation, differentiation, and survival. In this study, the extracellular region of human GITRL (hGITRL) was cloned, expressed, and purified. The coding sequence of the extracellular region of hGITRL was isolated from human brain cDNA and inserted in pET20b vector. The hGITRL was expressed in Escherichia coli BL21 (DE3) Star at 37 and 25 °C. The majority of the protein was found in inclusion bodies. We identified three important factors for efficient refolding of hGITRL: a ratio of GSH/GSSG, pH, and addition of polyethylene glycol. The renaturated protein was purified by Ni-NTA chromatography. The overall yield of the expression and refolding was higher than 50 mg/l E. coli culture grown at 37 °C. Size exclusion chromatography showed that hGITRL exists as mixture of various multimeric forms in solution. We tested the association of recombinant hGITRL with THP-1 and U937 cell lines and its activity to promote extracellular signal-regulated protein kinase phosphorylation. The results showed that the recombinant protein was biologically active.

Anopheles gambiae, Anoga-HrTH hormone, free and bound structure--a nuclear magnetic resonance experiment

The spread of malaria by the female mosquito, Anopheles gambiae, is dependent, amongst other things, on its ability to fly. This in turn, is dependent on the adipokinetic hormone, Anoga-HrTH (pGlu-Leu-Thr-Phe-Thr-Pro-Ala-Trp-NH2). No crystal structure of this important neuropeptide is available and hence NMR restrained molecular dynamics was used to investigate its conformational space in aqueous solution and when bound to a membrane surface. The results showed that Anoga-HrTH has an almost cyclic conformation that is stabilized by a hydrogen bond between the C-terminus and Thr3. Upon docking of the agonist to its receptor, this H-bond is broken and the molecule adopts a more extended structure. Preliminary AKHR docking calculations give the free energy of binding to be -47.30 kJ/mol. There is a close correspondence between the structure of the docked ligand and literature structure-activity studies. Information about the 3D structure and binding mode of Anoga-HrTH to its receptor is vital for the design of suitable mimetics which can act as insecticides.

Efficient Markov clustering algorithm for protein sequence grouping

In this paper we propose an efficient reformulation of a Markov clustering algorithm, suitable for fast and accurate grouping of protein sequences, based on pairwise similarity information. The proposed modification consists of optimal reordering of rows and columns in the similarity matrix after every iteration, transforming it into a matrix with several compact blocks along the diagonal, and zero similarities outside the blocks. These blocks are treated separately in later iterations, thus reducing the computational burden of the algorithm. The proposed algorithm was tested on protein sequence databases like SCOP95. In terms of efficiency, the proposed solution achieves a speed-up factor in the range 15-50 compared to the conventional Markov clustering, depending on input data size and parameter settings. This improvement in computation time is reached without losing anything from the partition accuracy. The convergence is usually reached in 40-50 iterations. Combining the proposed method with sparse matrix representation and parallel execution will certainly lead to a significantly more efficient solution in future.

Efficient inhomogeneity compensation using fuzzy c-means clustering models

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.

Convenient syntheses of 1,2-trans selenoglycosides using isoselenuronium salts as glycosylselenenyl transfer reagents

Se-glycosyl-isoselenuronium salts such as three and four which can be prepared in one high-yielding step from acetohalogeno sugars proved to be convenient starting materials for the syntheses of a variety of selenoglycosides. Reaction with (ar)alkyl halides proceeds under mild conditions, in short time, at room temperature to afford the corresponding selenoglycosides in good yields. Aryl halides react to appreciable extent only if bearing activating nitro groups on the aromatic ring. Reactions with acylating reagents such as acetic anhydride and benzoyl chlorides furnished anomeric selenoesters some of which were recently proposed as starting compounds for alternative selenoglycoside syntheses. Selenodisaccharides with two different monosaccharide units could also be prepared via reactions of glycosyl-isoselenuronium salts with monosaccharide derivatives bearing primary or secondary triflate groups.

Resonance assignments of diastereotopic CH(2) protons in the anomeric side chain of selenoglycosides by means of (2) J(Se,H) spin-spin coupling constants

Unambiguous resonance assignments of diastereotopic CH(2) protons in the anomeric side chain of nine alkyl- and aralkylselenoglycosides have been carried out on the basis of experimental CPMG-HSQMBC measurements and theoretical second order polarization propagator approach (SOPPA) calculations of geminal (77) Se-(1) H spin-spin coupling constants involving diastereotopic pro-R and pro-S protons. Theoretical conformational analyses have been performed at the MP2/6-311G** level. The conformational space of each of the selenoglycosides under study could be adequately described as a mixture of six interconverting conformers with the molar fractions depending on the nature of the side chain substituent at the selenium atom. The good agreement observed between measured and the weighted conformational averaged values of the calculated coupling constants provides a basis for reliable diastereotopic assignments in this type of carbohydrate structures.

An enhanced method for accessory pathway localization in case of Wolff-Parkinson-White syndrome

This paper presents an analysis of the Arruda accessory pathway localization method for patients suffering from Wolff-Parkinson-White syndrome, with modifications to increase the overall accuracy. The Arruda method was tested on a total of 79 cases, and 91.1% localization performance was reached. After a deeper analysis of each decision point of the Arruda localization method, we considered that the lead aVF was not as relevant as other leads (I, II, III, V1) used. The branch of the decision tree, which evaluates the left ventricle positions, was entirely replaced using different decision criteria based on the same biological parameters. The modified algorithm significantly improves the localization accuracy in the left ventricle, reaching 94.9%. An accurate localization performance of non-invasive methods is relevant because it can enlighten the necessary invasive interventions, and it also reduces the discomfort caused to the patient.

2-Phenyl-ethyl 1-thio-β-d-galactopyran-oside hemihydrate

The title compound, C₁₄H₂₀O₅S·0.5H₂O, crystallizes with two organic mol­ecules and a solvent water mol­ecule in the asymmetric unit. In both mol­ecules, the hexa­pyranosyl rings adopt a slightly distorted chair conformation (⁵C₂) with four substituents in equatorial positions and one substituent in an axial position. The main difference between the organic mol­ecules is the dihedral angle between the phenyl ring and the best plane defined by the O—C₁—C₂—C₃ atoms (r.m.s deviations = 0.003 and 0.043 Å) of the hexa­pyranosyl rings [47.4 (4) and 86.5 (4)°]. In the asymmetric unit, mol­ecules are linked by two strong O—H⋯O hydrogen bonds. In the crystal, the components are linked by a total of 10 distinct O—H⋯O hydrogen bonds, resulting in the formation of a two-dimensional network parallel to the ab plane.

Experimental and computational studies of nJ(77Se, 1H) selenium-proton couplings in selenoglycosides

Selenoglycosides are important starting materials in synthetic carbohydrate chemistry and play a role in biological interactions as well. Both aspects are influenced by the conformation around the glycosidic bond. Here, we present a combined experimental and computational approach to measure and evaluate (n)J((77)Se, (1)H) coupling constants for their use in conformational analysis. The measurements were carried out using a modified CPMG-HSQMBC pulse scheme which yields pure absorption antiphase multiplets to allow accurate determination of the (n)J(XH) values regardless of the size of the proton-proton couplings. Theoretical calculations were performed at the Second-Order Polarization Propagator Approach (SOPPA) level. Population-averaged values calculated for geminal and vicinal couplings are in a good agreement with experiment indicating an adequate theoretical level of the calculations. Experimental observations and computations alike have indicated that two-bond (77)Se-(1)H couplings, (2)J((77)Se, (1)H), in a H1-C1-Se-X moiety are very sensitive to the torsion angle around the C1-Se-bond and will, therefore, be useful for conformational studies.

A patient specific electro-mechanical model of the heart

This paper presents a patient specific deformable heart model that involves the known electrical and mechanical properties of the cardiac cells and tissue. The whole heart model comprises ten Tusscher's ventricular and Nygren's atrial cell models, the anatomical and electrophysiological model descriptions of the atria (introduced by Harrild et al.) and ventricle (given by Winslow et al.), and the mechanical model of the periodical cardiac contraction and resting phenomena proposed by Moireau et al. During the propagation of the depolarization wave, the kinetic, compositional and rotational anisotropy is handled by the tissue, organ and torso model. The applied patient specific parameters were determined by an evolutionary computation method. An intensive parameter reduction was performed using the abstract formulation of the searching space. This patient specific parameter representation enables the adjustment of deformable model parameters in real-time. The validation process was performed using simultaneously measured ECG and ultrasound image records that were compared with simulated signals and shapes using an abstract, parameterized evaluation criterion.

Development of a virtual reality guided diagnostic tool based on magnetic resonance imaging

Computed tomography (CT) and virtual reality (VR) made it possible to create internal views of the human body without actual penetration. During the last two decades, several endoscopic diagnosis procedures have received virtual counter candidates. This paper presents an own concept of a virtual reality guided diagnostic tool, based on magnetic resonance images representing parallel cross-sections of the investigated organ. A series of image processing methods are proposed for image quality enhancement, accurate segmentation in two dimensions, and three-dimensional reconstruction of detected surfaces. These techniques provide improved accuracy in image segmentation, and thus they represent excellent support for three dimensional imaging. The implemented software system allows interactive navigation within the investigated volume, and provides several facilities to quantify important physical properties including distances, areas, and volumes.