Sodium Hypochlorite Concentration and Postendodontic Pain - Unveiling the Optimal Balance: A Systematic Review and Meta-Analysis.

INTRODUCTION: This study systematically reviewed literature regarding the effect of different concentrations of sodium hypochlorite (NaOCl) used during root canal treatment (RCT) on postendodontic pain (PEP) and rescue analgesia. METHODS: Following registration with PROSPERO (CRD42023388916), a search was conducted using PubMed, Scopus, Web of Science, and Embase databases. Randomized controlled trials of patients receiving RCT which assessed PEP at different time intervals were included. Following data extraction and Cochrane risk of bias assessment 2, meta-analyses were performed to evaluate PEP during the first 48 hours along with rescue analgesic intake. The certainty of the evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation approach. RESULTS: Five randomized controlled trials with 674 patients were included. One study exhibited a low risk of bias, while 4 raised some concerns. Patients treated with low concentrations of NaOCl (≤3%) were significantly less likely to report PEP at 24 hours (OR = 2.32; [95% CI, 1.63-3.31]; P < .05) and 48 hours (OR = 2.49; [95% CI, 1.73-3.59]; P < .05) as compared with high concentrations of NaOCl (≥5%). Furthermore, with low concentrations of NaOCl, significantly lesser moderate-severe PEP was reported at 24 hours (OR = 2.32; [95% CI, 1.47-3.62]; P < .05) and 48 hours (OR = 2.35; [95% CI, 1.32-4.16]; P < .05) and lesser analgesia was needed (OR = 2.43; [95% CI, 1.48-4.00]; P < .05). CONCLUSIONS: While PEP can be influenced by several factors, low certainty evidence suggests that when NaOCl is used as an irrigant during RCT, PEP may be less likely with lower concentrations of NaOCl. Moderate certainty evidence indicates that lesser analgesia may be required with lower concentrations of NaOCl. These results should be cautiously interpreted.

Identifying Gut Microbiome Features that Predict Responsiveness Toward a Prebiotic Capable of Increasing Calcium Absorption: A Pilot Study.

Previously, we demonstrated that prebiotics may provide a complementary strategy for increasing calcium (Ca) absorption in adolescents which may improve long-term bone health. However, not all children responded to prebiotic intervention. We determine if certain baseline characteristics of gut microbiome composition predict prebiotic responsiveness. In this secondary analysis, we compared differences in relative microbiota taxa abundance between responders (greater than or equal to 3% increase in Ca absorption) and non-responders (less than 3% increase). Dual stable isotope methodologies were used to assess fractional Ca absorption at the end of crossover treatments with placebo, 10, and 20 g/day of soluble corn fiber (SCF). Microbial DNA was obtained from stool samples collected before and after each intervention. Sequencing of the 16S rRNA gene was used to taxonomically characterize the gut microbiome. Machine learning techniques were used to build a predictive model for identifying responders based on baseline relative taxa abundances. Model output was used to infer which features contributed most to prediction accuracy. We identified 19 microbial features out of the 221 observed that predicted responsiveness with 96.0% average accuracy. The results suggest a simplified prescreening can be performed to determine if a subject's bone health may benefit from a prebiotic. Additionally, the findings provide insight and prompt further investigation into the metabolic and genetic underpinnings affecting calcium absorption during pubertal bone development.

The influence of laser-activated irrigation on post-operative pain following root canal treatment: A systematic review.

OBJECTIVES: Synthesise evidence on post-endodontic pain (PEP) in adult teeth undergoing primary root canal treatment with the adjunctive use of laser-activated irrigation (LAI) as compared with conventional needle irrigation (CNI) during the first post-operative week. DATA: An electronic search was performed; no language constraints or restriction on the year of publication were applied. SOURCES: Medline, Scopus, Cochrane and PubMed on 04 June 2023 STUDY SELECTION: Randomised clinical trials (RaCTs) that evaluated PEP after LAI of endodontic irrgants were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used. PEP was analysed at various time intervals until 1 week after treatment, related to the type of LAI used and the need for analgesia. REULTS: Of the 793 articles identified through the electronic database search, 6 RaCTs were included. Qualitative review was favoured over meta-analysis due to substantial methodological heterogeneity between studies. Five studies were at high risk for bias determined by the Cochrane Risk-of-Bias 2 tool. Diode LAI demonstrated superior efficacy to needle irrigation in reducing pain 6-48 h post-treatment. The impact of LAI by photon-induced photoacoustic streaming (PIPS) was unclear and no difference was observed between PIPS and needle irrigation. However, PIPS mitigated PEP better than manual dynamic activation, sonic and ultrasonic activation. There was no difference in analgesia intake between LAI and needle irrigation groups. CONCLUSIONS: LAI may help reduce PEP in the first 48 h. Methodological standardisation of future RaCTs on LAI would be beneficial in allowing a more accurate review with the possibility of quantitative synthesis. CLINICAL SIGNIFICANCE: This unique synthesis used stringent criteria to reduce confounding factors and provided valuable evidence regarding PEP with different types of LAI. It helps clinicians choose an appropriate LAI technique as compared with CNI and predicts a time frame for reducing PEP.

VTDCE-Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data.

PURPOSE: To propose a robust time and space invariant deep learning (DL) method to directly estimate the pharmacokinetic/tracer kinetic (PK/TK) parameters from undersampled dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data. METHODS: DCE-MRI consists of 4D (3D-spatial + temporal) data and has been utilized to estimate 3D (spatial) tracer kinetic maps. Existing DL architecture for this task needs retraining for variation in temporal and/or spatial dimensions. This work proposes a DL algorithm that is invariant to training and testing in both temporal and spatial dimensions. The proposed network was based on a 2.5-dimensional Unet architecture, where the encoder consists of a 3D convolutional layer and the decoder consists of a 2D convolutional layer. The proposed VTDCE-Net was evaluated for solving the ill-posed inverse problem of directly estimating TK parameters from undersampled k - t $k-t$ space data of breast cancer patients, and the results were systematically compared with a total variation (TV) regularization based direct parameter estimation scheme. In the breast dataset, the training was performed on patients with 32 time samples, and testing was carried out on patients with 26 and 32 time samples. Translation of the proposed VTDCE-Net for brain dataset to show the generalizability was also carried out. Undersampling rates (R) of 8× , 12× , and 20× were utilized with PSNR and SSIM as the figures of merit in this evaluation. TK parameter maps estimated from fully sampled data were utilized as ground truth. RESULTS: Experiments carried out in this work demonstrate that the proposed VTDCE-Net outperforms the TV scheme on both breast and brain datasets across all undersampling rates. For K trans $\mathbf {K_{trans}}$ and V p $\mathbf {V_{p}}$ maps, the improvement over TV is as high as 2 and 5 dB, respectively, using the proposed VTDCE-Net. CONCLUSION: Temporal points invariant DL network that was proposed in this work to estimate the TK-parameters using DCE-MRI data has provided state-of-the-art performance compared to standard image reconstruction methods and is shown to work across all undersampling rates.

Case complexity of root canal treatments accepted for training in a secondary care setting assessed by three complexity grading systems: A service evaluation.

AIMS: To evaluate the complexity of root canal treatments accepted for treatment by postgraduate training grades at Cardiff University Dental Hospital (CUDH) using the English Commissioning Standard for Restorative Dentistry (ECS) in comparison with the American Association of Endodontists case complexity form (AAE) and the Restorative Index of Treatment Need (RIOTN). METHODOLOGY: Two hundred case records were evaluated using the AAE, RIOTN and ECS scoring systems. Each case received a score from minimal to high complexity (1-3). Examiners were calibrated and inter-examiner reliability calculated using the percentage agreement. Frequency of scores were then compared. RESULTS: Most cases were at level 3 and assessment varied amongst the criteria used (AAE: 99.5%, RIOTN: 65.5% and ECS: 55.5%). The AAE factor 'endodontic treatment history' was largely responsible for differing scores when compared with the RIOTN (78%) and ECS (64%). The RIOTN factor regarding post treatment disease ('endodontic retreatment') was responsible for increased complexity compared with ECS in most cases (74%). The ECS factor 'quality of root filling' was the most common reason (85%) for an increase in complexity compared with RIOTN. CONCLUSIONS: Within the limitations of this service evaluation, it was possible to conclude that a high proportion of cases treated by training grades at CUDH were of a high complexity level (level 3) using the three guidelines (ECS, AAE and RIOTN). These cases were appropriate for postgraduate training under various levels of supervision and substantiated by the findings reported here. The factors responsible for a large part of difference in allocation of scores amongst the systems were 'endodontic treatment history', 'root canal retreatment' and 'quality of root filling'.

A comprehensive review on advancements in tissue engineering and microfluidics toward kidney-on-chip.

This review provides a detailed literature survey on microfluidics and its road map toward kidney-on-chip technology. The whole review has been tailored with a clear description of crucial milestones in regenerative medicine, such as bioengineering, tissue engineering, microfluidics, microfluidic applications in biomedical engineering, capabilities of microfluidics in biomimetics, organ-on-chip, kidney-on-chip for disease modeling, drug toxicity, and implantable devices. This paper also presents future scope for research in the bio-microfluidics domain and biomimetics domain.

Chemical properties of emissions from solid residential fuels used for energy in the rural sector of the southern region of India.

In the present study, we have estimated the emission factors (EFs) of particulate matter (PM), organic and elemental carbon (OC and EC), oxide of sulfur and nitrogen, and water-soluble ionic species emitted from residential fuels (fuelwood, crop residue, dung cake) used in the rural sector of five states (Kerala, Karnataka, Andhra Pradesh, Telangana, Tamil Nadu) of the southern region of India. Average EFs of PM, OC, and EC from fuelwood (FW), crop residues (CR), and dung cakes (DC) from southern region of India are estimated as follows: PM: 6.35 ± 5.64 g/kg (FW), 6.99 ± 5.46 g/kg (CR), 9.69 ± 3.73 g/kg (DC); OC: 1.60 ± 1.72 g/kg (FW), 1.50 ± 1.52 g/kg (CR), 3.54 ± 0.75 g/kg (DC); and EC: 0.46 ± 0.53 g/kg (FW), 0.29 ± 0.17 g/kg (CR), 0.21 ± 0.11 g/kg (DC), respectively. Similarly, the average EFs of SO2, NOx from FW, CR, and DC are determined to be as follows: SO2: 0.40 ± 0.37 g/kg (FW), 1.17 ± 0.25 g/kg (CR), and 0.18 ± 0.10 g/kg (DC); NOx: 1.11 ± 1.22 g/kg (FW), 0.69 ± 0.37 g/kg (CR), and 0.91 ± 0.54 g/kg (DC), respectively. PO43- shows the highest EF from FW (646.02 ± 576.35 mg/kg), CR (531.06 ± 678.29 mg/kg) among all anions followed by Cl- (FW: 512.91 ± 700.35 mg/kg, CR: 661.61 ± 865.46 mg/kg and DC: 104.16 ± 54.01 mg/kg); whereas, Na+ shows highest EF from FW (254.05 ± 298.50 mg/kg) and CR (249.36 ± 294.85 mg/kg) among all cations. The total emissions of trace gases, PM, and their chemical composition from FW, CR, and DC have been calculated using laboratory-generated EFs over the southern region of India. CR (1595.58 ± 14.24 Gg) contributes to higher emission of PM as compared to FW (218.78 ± 53.93 Gg), whereas the contribution from DC is negligible.

Design and Modeling of Bioreactor Utilizing Electrophoresis and Di-Electrophoresis Techniques for Regenerating Reabsorption Function of Human Kidney PCT in Microfluidics Environment.

The need for innovation in medical device technology is immense; especially to replace dialysis techniques the necessity is extremely high. Available techniques that promised to replace dialysis have not yet geared up to the marketization level. The utilization of live kidney cells makes these devices costly, delicate, and unreliable. This paper aims to design a bioreactor to mimic the reabsorption function of the kidney that is fully artificial and highly controllable, which can be one step forward to the emerging Kidney-on-Chip (KOC) technology. The additional benefit of the proposed design is that it utilizes size-dependent reabsorption along with charge-dependent reabsorption phenomena to make it more compatible with human kidney function. The electrophoresis (EP), and di-electrophoresis (DEP) techniques are utilized to mimic the reabsorption function in this report. The structure utilized in the present design exactly replicates the proximal convoluted tubule (PCT) dimensions and functions as well. The whole setup is implemented in the COMSOL Multiphysics FEM benchmark tool for simulation, and analysis with appropriate boundary conditions. The device when excited by an electric field, Electrophoresis has produced a maximum velocity of 1.07 m/s for DC excitation and di-electrophoresis has produced a maximum flow velocity of 1.23 m/s, where both the offset voltages are the same (0.7 V). The flow velocity obtained utilizing both EP and DEP produced a reabsorption rate of 50-58% depending on the voltage applied and dimensions considered which is close to 60% reabsorption rate of the normal human kidney PCT. In accordance with the outcomes produced, the di-electrophoresis technique proved to be more efficient in realizing bioreactor as compared to electrophoresis. The novelty of the present work lies in the creation of a simulation environment, rigorous analysis, and optimization of the bioreactor supported by compact mathematical model.

3D CNN to Estimate Reaction Time from Multi-Channel EEG.

The study of human reaction time (RT) is invaluable not only to understand the sensory-motor functions but also to translate brain signals into machine comprehensible commands that can facilitate augmentative and alternative communication using brain-computer interfaces (BCI). Recent developments in sensor technologies, hardware computational capabilities, and neural network models have significantly helped advance biomedical signal processing research. This study is an attempt to utilize state-of-the-art resources to explore the relationship between human behavioral responses during perceptual decision-making and corresponding brain signals in the form of electroencephalograms (EEG). In this paper, a generalized 3D convolutional neural network (CNN) architecture is introduced to estimate RT for a simple visual task using single-trial multi-channel EEG. Earlier comparable studies have also employed a number of machine learning and deep learning-based models, but none of them considered inter-channel relationships while estimating RT. On the contrary, the use of 3D convolutional layers enabled us to consider the spatial relationship among adjacent channels while simultaneously utilizing spectral information from individual channels. Our model can predict RT with a root mean square error of 91.5 ms and a correlation coefficient of 0.83. These results surpass all the previous results attained from different studies.Clinical relevance Novel approaches to decode brain signals can facilitate research on brain-computer interfaces (BCIs), psychology, and neuroscience, enabling people to utilize assistive devices by root-causing psychological or neuromuscular disorders.

Analyzing the relationship between productivity and human communication in an organizational setting.

Though it is often taken as a truism that communication contributes to organizational productivity, there are surprisingly few empirical studies documenting a relationship between observable interaction and productivity. This is because comprehensive, direct observation of communication in organizational settings is notoriously difficult. In this paper, we report a method for extracting network and speech characteristics data from audio recordings of participants talking with each other in real time. We use this method to analyze communication and productivity data from seventy-nine employees working within a software engineering organization who had their speech recorded during working hours for a period of approximately 3 years. From the speech data, we infer when any two individuals are talking to each other and use this information to construct a communication graph for the organization for each week. We use the spectral and temporal characteristics of the produced speech and the structure of the resultant communication graphs to predict the productivity of the group, as measured by the number of lines of code produced. The results indicate that the most important speech and network features for predicting productivity include those that measure the number of unique people interacting within the organization, the frequency of interactions, and the topology of the communication network.

Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion.

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

A Generalized Model to Estimate Reaction Time Corresponding to Visual Stimulus Using Single-Trial EEG.

The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.

Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG.

Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.

Characterization of a novel family VIII esterase EstM2 from soil metagenome capable of hydrolyzing estrogenic phthalates.

BACKGROUND: Microbes are rich sources of enzymes and esterases are one of the most important classes of enzymes because of their potential for application in the field of food, agriculture, pharmaceuticals and bioremediation. Due to limitations in their cultivation, only a small fraction of the complex microbial communities can be cultured from natural habitats. Thus to explore the catalytic potential of uncultured organisms, the metagenomic approach has turned out to be an effective alternative method for direct mining of enzymes of interest. Based on activity-based screening method, an esterase-positive clone was obtained from metagenomic libraries. RESULTS: Functional screening of a soil metagenomic fosmid library, followed by transposon mutagenesis led to the identification of a 1179 bp esterase gene, estM2, that encodes a 392 amino acids long protein (EstM2) with a translated molecular weight of 43.12 kDa. Overproduction, purification and biochemical characterization of the recombinant protein demonstrated carboxylesterase activity towards short-chain fatty acyl esters with optimal activity for p-nitrophenyl butyrate at pH 8.0 and 37 °C. Amino acid sequence analysis and subsequent phylogenetic analysis suggested that EstM2 belongs to the family VIII esterases that bear modest similarities to class C ß-lactamases. EstM2 possessed the conserved S-x-x-K motif of class C ß-lactamases but did not exhibit ß-lactamase activity. Guided by molecular docking analysis, EstM2 was shown to hydrolyze a wide range of di- and monoesters of alkyl-, aryl- and benzyl-substituted phthalates. Thus, EstM2 displays an atypical hydrolytic potential of biotechnological significance within family VIII esterases. CONCLUSIONS: This study has led to the discovery of a new member of family VIII esterases. To the best of our knowledge, this is the first phthalate hydrolase (EstM2), isolated from a soil metagenomic library that belongs to a family possessing ß-lactamase like catalytic triad. Based on its catalytic potential towards hydrolysis of both phthalate diesters and phthalate monoesters, this enzyme may find use to counter the growing pollution caused by phthalate-based plasticizers in diverse geological environment and in other aspects of biotechnological applications.

Recreating the size-dependent reabsorption function of proximal convoluted tubule towards artificial kidney applications: Structural analysis and computational study.

Human kidneys tend to be affected adversely and fail to function more often than any other organ in the body because of diet, heredity, and lifestyle of a person. Dialysis is the technique presently in use for replacing the failed kidney function but it is packed with painfulness, bulkiness, and is costly also. There is a growing need for development of an artificial kidney that eradicates the problems associated with dialysis. This article proposes a structure that mimics the most important aspect of the human kidney: the size-dependent reabsorption of endothelial cells in the proximal convoluted tubule (PCT). The proposed structure consists of transporting channels connecting blood tubules surrounded on both sides of a main tubule. Geometries of the channels are analyzed for optimum flow by varying angles with respect to the main tubule. The analytical formulae have been developed by considering proper boundary conditions governing the flow in the structure, which makes the model as robust, concise, and realistic as the actual PCT. The mathematical model is validated against the benchmark FEM tool COMSOL Multiphysics and the results seem to be satisfactory. This article concludes, that slant channels possess a considerably higher average flow velocity of 5.39 × 10-5  m/s (≈52% reabsorption rate) than straight channels with 4.77 × 10-5  m/s (≈46% reabsorption rate) which is closer to the actual PCT reabsorption rate of 60%. The proposed model is first of its kind in nature among the reported works which creates and exhibits simulation environment of PCT reabsorption function supported by mathematical formulation and also can be useful to study and develop artificial kidney in near future.

Anticipating Postoperative Delirium During Burst Suppression Using Electroencephalography.

OBJECTIVE: This study develops an electro-encephalography-based method for predicting postoperative delirium early during cardiac surgeries involving deep hypothermia circulatory arrest (DHCA), potentially providing an opportunity to intervene and minimize poor surgical outcome. DHCA is a surgical technique used during cardiac surgeries to facilitate repairs. Deep hypothermia is induced and supplemented by perfusion techniques to protect the brain during circulatory arrest, but concern for cerebral injury still remains. METHODS: This research studies whether or not monitoring burst suppression, an electrophysiological phenomenon observed during patient cooling and warming, helps in predicting postoperative delirium, a correlate of poor prognosis. A metric called the burst suppression duty cycle (BSDC), akin to burst suppression ratio, is formulated to characterize this electrophysiological activity. RESULTS: Assuming no complications occur prior to circulatory arrest, delirium diagnoses are correlated with the time elapsed until suppression activity ceases since resuming cardiopulmonary bypass. By comparing against a benchmark the times when BSDC reaches 100%, 15 of 16 cases can be correctly predicted. Similar accuracy can be achieved when querying BSDC progress earlier during warming. CONCLUSION: Our results show that our BSDC metric is a promising candidate for early detection of postoperative delirium, and motivates further analysis of the causal relationship between postoperative delirium and the procedure transitioning out of circulatory arrest. SIGNIFICANCE: The developed methodology anticipates incidences of postoperative delirium during rewarming, which potentially provides an opportunity to intervene and avert it.

Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers.

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.

Characterization of a topologically unique oxygenase from Sphingobium sp. PNB capable of catalyzing a broad spectrum of aromatics.

A Rieske non-heme iron ring-hydroxylating oxygenase (RHO) from Sphingobium sp. PNB involved in the initial oxidation of a wide range of low and high molecular weight polycyclic aromatic hydrocarbons (PAHs) was investigated. The RHO was shown to comprise of the gene products of distantly located ahdA1f-ahdA2f, ahdA3 and ahdA4 genes, which encoded the oxygenase α- and ß-subunits, ferredoxin and reductase, respectively. In silico structural analysis of AhdA1f revealed a very large substrate-binding pocket, satisfying the spatial requirements to accommodate high molecular weight substrates. In addition, an atypical substrate access channel was noticed from the topology analysis of the oxygenase. Guided by molecular docking studies, dioxygenation of several PAHs as well as alkyl- and aryl benzenes was examined with the recombinant AhdA1fA2f expressed in Escherichia coli. Chromatographic and mass spectrometric analyses confirmed that AhdA1fA2f displays broad substrate specificity towards a wide range of aromatic hydrocarbons including potential xenobiotics, demonstrating metabolic robustness of strain PNB.

Functional Characterization of a Novel Member of the Amidohydrolase 2 Protein Family, 2-Hydroxy-1-Naphthoic Acid Nonoxidative Decarboxylase from Burkholderia sp. Strain BC1.

UNLABELLED: The gene encoding a nonoxidative decarboxylase capable of catalyzing the transformation of 2-hydroxy-1-naphthoic acid (2H1NA) to 2-naphthol was identified, recombinantly expressed, and purified to homogeneity. The putative gene sequence of the decarboxylase (hndA) encodes a 316-amino-acid protein (HndA) with a predicted molecular mass of 34 kDa. HndA exhibited high identity with uncharacterized amidohydrolase 2 proteins of various Burkholderia species, whereas it showed a modest 27% identity with γ-resorcylate decarboxylase, a well-characterized nonoxidative decarboxylase belonging to the amidohydrolase superfamily. Biochemically characterized HndA demonstrated strict substrate specificity toward 2H1NA, whereas inhibition studies with HndA indicated the presence of zinc as the transition metal center, as confirmed by atomic absorption spectroscopy. A three-dimensional structural model of HndA, followed by docking analysis, identified the conserved metal-coordinating and substrate-binding residues, while their importance in catalysis was validated by site-directed mutagenesis. IMPORTANCE: Microbial nonoxidative decarboxylases play a crucial role in the metabolism of a large array of carboxy aromatic chemicals released into the environment from a variety of natural and anthropogenic sources. Among these, hydroxynaphthoic acids are usually encountered as pathway intermediates in the bacterial degradation of polycyclic aromatic hydrocarbons. The present study reveals biochemical and molecular characterization of a 2-hydroxy-1-naphthoic acid nonoxidative decarboxylase involved in an alternative metabolic pathway which can be classified as a member of the small repertoire of nonoxidative decarboxylases belonging to the amidohydrolase 2 family of proteins. The strict substrate specificity and sequence uniqueness make it a novel member of the metallo-dependent hydrolase superfamily.

Urethral duplication in a 15-year-old: case report with review of the literature.

Urethral duplication is a rare congenital malformation mainly affecting men and boys. Although a number of theories have been proposed to describe this condition, the actual mechanism of this disorder is still not clear. This article highlights a case of urethral duplication in a 15-year-old boy. The malformation was characterized by the presence of continent epispadic and normal apical urethra. Retrograde urethrogram through both urethral tracts simultaneously revealed the malformation as Effmann type IIA2. The patient was not offered surgical intervention as he was asymptomatic and had no problems except for a double stream of urine.