Buccal mucosal graft ureteroplasty: The new normal in ureteric reconstructive surgery - Our initial experience with the laparoscopic and robotic approaches.

INTRODUCTION: Upper ureteric stricture is always a challenging case to treat for any urologist. Due to chronic inflammation and multiple interventions, it becomes a complex entity to treat. Buccal Mucosal Graft (BMG) Ureteroplasty is a reconstructive surgery used to treat upper ureteric stricture but the results and experience with this modality is less explored so far. We present here our study of 16 cases of BMG ureteroplasty and its outcomes done by the laparoscopic and robotic approaches. PATIENTS AND METHODS: We analysed 16 cases of BMG ureteroplasty, which were performed both laparoscopically and robotically. All these cases were long ureteric strictures, not amenable to excision or endoscopic intervention. We performed using an onlay BMG without complete mobilisation of the ureter. The omentum or nearby fat was used as a bed for onlay BMG. RESULTS: All 16 patients underwent onlay ureteroplasty. The reconstructed ureter was wrapped with omentum in nine of the cases, while in seven patients, nearby fat was used. The median stricture length was 5.28 cm, and the median operative time was 143.5 min. The mean operative time was 143.5 min. 15 of 16 (93.75%) cases were successfully clinically and radiologically on follow-up. CONCLUSION: Long-segment upper ureteric strictures are a difficult entity to operate on. BMG ureteroplasty is a safe and effective way of managing such strictures. Robot-assisted ureteroplasty provides the benefits of improved ergonomics, easy manoeuvrability and precision surgery to the patients. Our experience with both laparoscopic and robotic ureteroplasty would encourage urologists all over to use BMG ureteroplasty as an effective long-term procedure for ureteral reconstruction.

The underpinning factors affecting the classroom air quality, thermal comfort and ventilation in 30 classrooms of primary schools in London.

The health and academic performance of children are significantly impacted by air quality in classrooms. However, there is a lack of understanding of the relationship between classroom air pollutants and contextual factors such as physical characteristics of the classroom, ventilation and occupancy. We monitored concentrations of particulate matter (PM), CO2 and thermal comfort (relative humidity and temperature) across five schools in London. Results were compared between occupied and unoccupied hours to assess the impact of occupants and their activities, different floor coverings and the locations of the classrooms. In-classroom CO2 concentrations varied between 500 and 1500 ppm during occupancy; average CO2 (955 ± 365 ppm) during occupancy was ∼150% higher than non-occupancy. Average PM10 (23 ± 15 µgm-3), PM2.5 (10 ± 4 µgm-3) and PM1 (6 ± 3 µg m-3) during the occupancy were 230, 125 and 120% higher than non-occupancy. Average RH (29 ± 6%) was below the 40-60% comfort range in all classrooms. Average temperature (24 ± 2 °C) was >23 °C in 60% of classrooms. Reduction in PM10 concentration (50%) by dual ventilation (mechanical + natural) was higher than for PM2.5 (40%) and PM1 (33%) compared with natural ventilation (door + window). PM10 was higher in classrooms with wooden (33 ± 19 µg m-3) and vinyl (25 ± 20 µgm-3) floors compared with carpet (17 ± 12 µgm-3). Air change rate (ACH) and CO2 did not vary appreciably between the different floor levels and types. PM2.5/PM10 was influenced by different occupancy periods; highest value (∼0.87) was during non-occupancy compared with occupancy (∼0.56). Classrooms located on the ground floor had PM2.5/PM10 > 0.5, indicating an outdoor PM2.5 ingress compared with those located on the first and third floors (<0.5). The large-volume (>300 m3) classroom showed ∼33% lower ACH compared with small-volume (100-200 m3). These findings provide guidance for taking appropriate measures to improve classroom air quality.

Micro-characteristics of a naturally ventilated classroom air quality under varying air purifier placements.

A naturally-ventilated operational classroom was instrumented at 18 locations to assess spatial variations of classroom air pollution (CRAP), thermal comfort and ventilation indicators under 10 different scenarios (base scenario without air purifier (AP); three single AP scenarios; three scenarios with two APs at same locations; three scenarios with two APs at different locations). Unlike PM2.5, monitored PM10 and CO2 concentrations followed the diurnal occupancy profile. Highest vertical variation (38%) in CO2 was at the classroom entry zone at 40-300 cm height. CO2 increased until 225 cm before stratifying further. PM10 increased to highest levels at children sitting height (100 cm) before decreasing to adult breathing height (150 cm). Highest horizontal variations in CO2 (PM10) were 29% (22%) at 40 cm height between the entry and occupied zones. Teachers' exposure to CO2 (PM10) in breathing zone varied by up to 6% (3%); the corresponding variations across monitored locations were up to 14% (19%). Teachers' exposure to CO2 was up to 13% higher than that of children and 18% lower for PM10. Traffic emissions (PM2.5 and NOx), secondary pollutants (VOCs and O3), thermal comfort parameters and noise level in the classroom varied insignificantly among scenarios. PM10 reduction was not doubled by using two air purifiers, which were most effective when placed within the highest PM concentration zone. Cross-comparisons of scenarios showed: use of AP reduced classroom's spatial average PM10 up to 14%; PM10 was reduced by increasing the AP's filtration capacity; and AP had insignificant impact on spatial average CO2. PM10 showed a maximum reduction of 46% (teacher zone), 62% (occupied zone) and 50% (entry zone) at children's breathing height, depending on usage scenario. This study produced high-resolution data for validating the detailed numerical models for classrooms and informing decision-making on AP's placement to minimise children's exposure to CRAP and re-breathed CO2.

Machine learning-based approach for prediction of ion channels and their subclasses.

Ion channels are ion-permeable protein pores that are found in all cell lipid membranes. Distinct ion channels play multiple roles in biological processes. Proteomic data is fast accumulating as a result of the fast growth of mass spectrometry and giving us the chance to comprehensively explore ion channel classes along with their subclasses. This paper proposes an eXtreme Gradient Boosting (XGBoost)-based method to estimate the ion channel classes and their subclasses. Here, 12 feature vectors are applied to better characterize protein sequences like amino acid composition, pseudo-amino acid composition, normalized moreau-broto autocorrelation, amphiphilic pseudo-amino acid composition, dipeptide composition, Geary autocorrelation, tripeptide composition, sequence-order-coupling number, composition/transition/distribution, conjoint triad, moran autocorrelation, quasi-sequence-order descriptors. Here, a total of 9920 features are extracted from the protein sequence. The principal component analysis is applied to determine the optimal number of features to optimize the performance. In 10-fold cross-validation the proposed XGBoost based approach with optimal 50 features achieved accuracy of 100%, 98.70%, 98.77%, 97.26%, 87.40%, 97.39%, 98.03%, 96.42%, and F1-Score of 100%, 99%, 99%, 97%, 87%, 97%, 98%, 97%, for prediction of ion channel and nonion channel, voltage-gated and ligand-gated ion channels, subclasses of voltage-gated ion channels (VGICs), subclasses of ligand-gated ion channels (LGICs), subclasses of voltage-gated calcium channels (VGCCs), subclasses of voltage-gated potassium channels (VGKCs), subclasses of voltage-gated sodium channels (VGSCs), and subclasses of voltage-gated chloride channels, respectively. Here the proposed approach also compares with the other approaches such as support vector machine, k-nearest neighbor, Gaussian Naïve Bayes, and random forest and also compares with existing methods such as support vector machine (SVM) with maximum relevance maximum distance with an accuracy of 86.6%, 83.7%, and 85.1%, for ion channels, non-ion channels and overall respectively and SVM with radial basis function kernel-based method with an accuracy of 100%, 97% and 99.9% for ion channels, nonion channels, and overall accuracy, respectively.

An evaluation of the risk of airborne transmission of COVID-19 on an inter-city train carriage.

Experiments were conducted in an UK inter-city train carriage with the aim of evaluating the risk of infection to the SARS-CoV-2 virus via airborne transmission. The experiments included in-service CO2 measurements and the measurement of salt aerosol concentrations released within the carriage. Computational fluid dynamics simulations of the carriage airflow were also used to visualise the airflow patterns, and the efficacy of the HVAC filter material was tested in a laboratory. Assuming an infectious person is present, the risk of infection for a 1-h train journey was estimated to be 6 times lower than for a full day in a well-ventilated office, or 10-12 times lower than a full day in a poorly ventilated office. While the absolute risk for a typical journey is likely low, in the case where a particularly infectious individual is on-board, there is the potential for a number of secondary infections to occur during a 1-h journey. Every effort should therefore be made to minimize the risk of airborne infection within these carriages. Recommendations are also given for the use of CO2 sensors for the evaluation of the risk of airborne transmission on train carriages.

Quantification of green infrastructure effects on airborne nanoparticles dispersion at an urban scale.

Many dispersion models are available to simulate the mass concentrations of particulate matter in an urban environment. Still, fewer are capable of simulating the effect of green infrastructure (GI) on the airborne nanoparticles represented by total particle number concentration (ToNC). We developed an integrated approach capable of simulating the dispersion of airborne nanoparticles under the various scenarios of green infrastructure. We demonstrated the usefulness of this approach by simulating a high-resolution spatial (250 × 250 m) concentration of traffic-emitted airborne nanoparticles at an urban scale under eight GI urban planning scenarios: the base year 2015 (2015-Rl-GI); business-as-usual for 2039 (2039-BAU-GI); three hypothetical future scenarios with maximum possible coniferous (2039-HMax-Con), deciduous (2039-HMax-Dec) trees, and grassland (2039-HMax-Grl) over the available land; and three alternative future scenarios by considering coniferous (2039-HNR-Con), deciduous (2039-HNR-Dec) trees, and grassland (2039-HNR-Grl) around traffic lanes. We assessed both the parametric and structural uncertainties due to particle transformation processes (nucleation, coagulation and deposition) and uncertainty in particle number emission factors (PNEFs) on ToNC, respectively. We also simulated the combined impact of deposition and aerodynamic dispersion of GI on ToNC reduction. The annual average ToN emission (ToNE) reduced from 5.36 × 1022 (2015) to 2.84 × 1021 (2039) particles due to the UK's air quality plan in future. Parametric uncertainty due to variable PNEFs might cause variation in annual ToNC from -57% to +60%. However, structural uncertainties in ToNC, due to particle transformation processes were up to -12%, -11% and +0.14% for deposition, coagulation, and nucleation, respectively. The annual ToN deposition (ToND) and concentration were 28-4800 × 1019 particles and 3.94-19.10 × 103 # cm-3, respectively, depending on the percentage share of GI type and annual traffic emissions. Planting maximum coniferous trees (2039-HMax-Con) simulated maximum reduction in annual ToNC. Coniferous trees near traffic lanes (2039-HNR-Con) also found to be more effective to reduce annual ToNC.

The nexus between in-car aerosol concentrations, ventilation and the risk of respiratory infection.

We examined the trade-offs between in-car aerosol concentrations, ventilation and respiratory infection transmission under three ventilation settings: windows open (WO); windows closed with air-conditioning on ambient air mode (WC-AA); and windows closed with air-conditioning on recirculation (WC-RC). Forty-five runs, covering a total of 324 km distance on a 7.2-km looped route, were carried out three times a day (morning, afternoon, evening) to monitor aerosols (PM2.5; particulate matter < 2.5 µm and PNC; particle number concentration), CO2 and environmental conditions (temperature and relative humidity). Ideally, higher ventilation rates would give lower in-car pollutant concentrations due to dilution from outdoor air. However, in-car aerosol concentrations increased with ventilation (WO > WC-AA > WC-RC) due to the ingress of polluted outdoor air on urban routes. A clear trade-off, therefore, exists for the in-car air quality (icAQ) versus ventilation; for example, WC-RC showed the least aerosol concentrations (i.e. four-times lower compared with WO), but corresponded to elevated CO2 levels (i.e. five-times higher compared with WO) in 20 mins. We considered COVID-19 as an example of respiratory infection transmission. The probability of its transmission from an infected occupant in a five-seater car was estimated during different quanta generation rates (2-60.5 quanta hr-1) using the Wells-Riley model. In WO, the probability with 50%-efficient and without facemasks under normal speaking (9.4 quanta hr-1) varied only by upto 0.5%. It increased by 2-fold in WC-AA (<1.1%) and 10-fold in WC-RC (<5.2%) during a 20 mins trip. Therefore, a wise selection of ventilation settings is needed to balance in-car exposure in urban areas affected by outdoor air pollution and that by COVID-19 transmission. We also successfully developed and assessed the feasibility of using sensor units in static and dynamic environments to monitor icAQ and potentially infer COVID-19 transmission. Further research is required to develop automatic-alarm systems to help reduce both pollutant exposure and infection from respiratory COVID-19 transmission.

The impacts of existing and hypothetical green infrastructure scenarios on urban heat island formation.

Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200 h; local time) showed a higher temperature increase, up to 1.315 °C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563 °C under HGI-No scenario, followed by HG-Max (0.400 °C), BGI (0.343 °C), HGR-Max (0.326 °C) and HT-Max (0.277 °C). Furthermore, the central urban area experienced a 0.371 °C and 0.401 °C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered.

Temporary reduction in fine particulate matter due to 'anthropogenic emissions switch-off' during COVID-19 lockdown in Indian cities.

The COVID-19 pandemic elicited a global response to limit associated mortality, with social distancing and lockdowns being imposed. In India, human activities were restricted from late March 2020. This 'anthropogenic emissions switch-off' presented an opportunity to investigate impacts of COVID-19 mitigation measures on ambient air quality in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020. For each year, we isolated, analysed and compared fine particulate matter (PM2.5) concentration data from 25 March to 11 May, to elucidate the effects of the lockdown. Like other global cities, we observed substantial reductions in PM2.5 concentrations, from 19 to 43% (Chennai), 41-53% (Delhi), 26-54% (Hyderabad), 24-36% (Kolkata), and 10-39% (Mumbai). Generally, cities with larger traffic volumes showed greater reductions. Aerosol loading decreased by 29% (Chennai), 11% (Delhi), 4% (Kolkata), and 1% (Mumbai) against 2019 data. Health and related economic impact assessments indicated 630 prevented premature deaths during lockdown across all five cities, valued at 0.69 billion USD. Improvements in air quality may be considered a temporary lockdown benefit as revitalising the economy could reverse this trend. Regulatory bodies must closely monitor air quality levels, which currently offer a baseline for future mitigation plans.

Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale.

Green infrastructure (GI) can reduce air pollutants concentrations via coupled effects of surface deposition and aerodynamic dispersion, yet their magnitudes and relative effectiveness in reducing pollutant concentration are less studied at the urban scale. Here, we develop and apply an integrated GI assessment approach to simulate the individual effects of GI along with their combined impact on pollutant concentration reduction under eight GI scenarios. These include current for year 2015 (2015-Base); business-as-usual for year 2039 (2039-BAU); three alternative future scenarios with maximum possible coniferous (2039-Max-Con), deciduous (2039-Max-Dec) trees, and grassland (2039-Max-Grl) over the available land; and another three alternative future scenarios by considering coniferous (2039-NR-Con), deciduous (2039-NR-Dec) trees, and grassland (2039-NR-Grl) around traffic lanes. A typical UK town, Guildford, is chosen as study area where we estimated current and future traffic emissions (NOx, PM10 and PM2.5), annual deposited amount and pollutants concentration reductions and percentage shared by dispersion and deposition effect in concentration reduction under above scenarios. The annual pollutant deposition was found to vary from 0.27-2.77 t·yr-1·km-2 for NOx, 0.46-1.03 t·yr-1·km-2 for PM10 and 0.08-0.23 t·yr-1·km-2 for PM2.5, depending on the percentage share of GI type and traffic emissions. The 2039-Max-Dec showed the aerodynamic effect of GI can reduce the annual pollutant concentration levels up to ~10% in NOx, ~1% in PM10 and ~0.8% in PM2.5. Furthermore, the total reductions can be achieved, via GI's coupled effects of surface deposition and aerodynamic dispersion, up to ~35% in NOx, ~21% in PM10 and ~8% in PM2.5 with ~75% GI cover in modelled domain under 2015-Base scenario. Coniferous trees (2039-Max-Con) were found to promote enhanced turbulence flow and offer more surface for deposition. Moreover, planting coniferous trees near traffic lanes (2039-NR-Con) was found to be a more effective solution to reduce annual pollutant concentration.

The nexus between air pollution, green infrastructure and human health.

Cities are constantly evolving and so are the living conditions within and between them. Rapid urbanization and the ever-growing need for housing have turned large areas of many cities into concrete landscapes that lack greenery. Green infrastructure can support human health, provide socio-economic and environmental benefits, and bring color to an otherwise grey urban landscape. Sometimes, benefits come with downsides in relation to its impact on air quality and human health, requiring suitable data and guidelines to implement effective greening strategies. Air pollution and human health, as well as green infrastructure and human health, are often studied together. Linking green infrastructure with air quality and human health together is a unique aspect of this article. A holistic understanding of these links is key to enabling policymakers and urban planners to make informed decisions. By critically evaluating the link between green infrastructure and human health via air pollution mitigation, we also discuss if our existing understanding of such interventions is sufficient to inform their uptake in practice. Natural science and epidemiology approach the topic of green infrastructure and human health very differently. The pathways linking health benefits to pollution reduction by urban vegetation remain unclear and the mode of green infrastructure deployment is critical to avoid unintended consequences. Strategic deployment of green infrastructure may reduce downwind pollution exposure. However, the development of bespoke design guidelines is vital to promote and optimize greening benefits, and measuring green infrastructure's socio-economic and health benefits are key for their uptake. Greening cities to mitigate pollution effects is on the rise and these need to be matched by scientific evidence and appropriate guidelines. We conclude that urban vegetation can facilitate broad health benefits, but there is little empirical evidence linking these benefits to air pollution reduction by urban vegetation, and appreciable efforts are needed to establish the underlying policies, design and engineering guidelines governing its deployment.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models.

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou's general PseAAC.

BACKGROUND AND OBJECTIVE: The G-protein coupled receptors are the largest superfamilies of membrane proteins and important targets for the drug design. G-protein coupled receptors are responsible for many physiochemical processes such as smell, taste, vision, neurotransmission, metabolism, cellular growth and immune response. So it is necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors and their subfamilies. METHODS: In this paper, the protein samples are represented by amino acid composition, dipeptide composition, correlation features, composition, transition, distribution, sequence order descriptors and pseudo amino acid composition with total 1497 number of sequence derived features. To address the issue of efficient classification of G-protein coupled receptors and their subfamilies, we propose to use a weighted k-nearest neighbor classifier with UNION of best 50 features, selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy, and support vector machine based recursive elimination feature selection methods to exploit the advantages of these feature selection methods. RESULTS: The proposed method achieved an overall accuracy of 99.9%, 98.3%, 95.4%, MCC values of 1.00, 0.98, 0.95, ROC area values of 1.00, 0.998, 0.996 and precision of 99.9%, 98.3% and 95.5% using 10-fold cross-validation to predict the G-protein coupled receptors and non-G-protein coupled receptors, subfamilies of G-protein coupled receptors, and subfamilies of class A G-protein coupled receptors, respectively. CONCLUSIONS: The high accuracies, MCC, ROC area values, and precision values indicate that the proposed method is better for the prediction of G-protein coupled receptors families and their subfamilies.

An efficient approach for the prediction of ion channels and their subfamilies.

Ion channels are integral membrane proteins that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively.

Anti-hyperglycemic and anti-hyperlipidemic potential of a polyherbal preparation "Diabegon" in metabolic syndrome subject with type 2 diabetes.

BACKGROUND: In the present study, "Diabegon" a poly-herbal preparation, with hypoglycemic activity, was evaluated for its preventive effect in metabolic syndrome subjects with type 2 diabetes and also to reveal its side effects, on liver and kidney. MATERIALS AND METHODS: Type 2 diabetic subjects with metabolic syndrome (N=58) were categorized on the basis of age and fasting blood glucose. The grouping was as follows: Group I (35-50 yrs), Group II (51-65 yrs), Group III >65 yrs, Group IV FBS<145.9, Group V FBS>145. Each group was administered 4 gm of diabegon daily. Blood glucose levels, lipid profile, liver and kidney function of the subjects were regularly monitored within 3 months of interval to 18 months. RESULTS: The reduction in fasting blood glucose level ranged from 12.3% (P<0.05) to 42% (P<0.001) after 18 month of therapy whereas in postprandial blood glucose, the decrease ranged from 28% (P<0.05) to 32% (P<0.05) after 18 month of therapy. Overall reductions in the individual parameters of the metabolic syndrome subjects were significantly higher in Group I. Cholesterol level decreased from 11% to 27.2% (P<0.001), triglyceride levels decreased from 24% to 55%, VLDL and LDL levels reduced by 60% & 54% respectively after 18 months of therapy. The HDL-C level increased in all groups. Moreover, diabegon administration for 1.5 years exhibited no alteration in liver and kidney function tests, which indicate its non-toxicity. CONCLUSION: Our study suggests that diabegon could be included as a preventive treatment in metabolic syndrome subjects with type 2 diabetes especially for long term treatment as it efficiently shows anti-hyperglycemic and anti-lipidemic effects with no adverse impacts on the liver and kidney.

Prevalence of dyslipidemia and hypertension in Indian type 2 diabetic patients with metabolic syndrome and its clinical significance.

OBJECTIVES: The present study was designed to estimate the prevalence of dyslipidemia and hypertension based on the National Cholesterol Educational Programme Adult Treatment Panel III definition of metabolic syndrome (MetS). The study also focuses on prevalence for MetS with respect to the duration of disease in Gwalior-Chambal region of Madhya Pradesh, India. METHODS: Type 2 diabetic patients (n = 700) were selected from a cross-sectional study that is regularly being conducted in the School of Studies in Biochemistry, Jiwaji University Gwalior, India. The period of our study was from January 2007 to October 2009. Dyslipidemia and hypertension were determined in type 2 diabetic patients with MetS as per National Cholesterol Educational Programme Adult Treatment Panel III criteria. RESULTS: The mean age of the study population was 54 ± 9.3 years with 504 (72%) males and 196 (28%) females. The prevalence of MetS increased with increased duration of diabetes in females; however, almost constant prevalence was seen in the males. Notable increase in the dyslipidemia (64.1%) and hypertension (49%) in type 2 diabetic patients were seen. The steep increase in dyslipidemia and hypertension could be the reason for the growing prevalence of diabetes worldwide. The study also noted a close association between age and occurrence of MetS. CONCLUSION: Individual variable of MetS appears to be highly rampant in diabetic population. Despite treatment, almost half of patients still met the criteria for MetS. Effective treatment of MetS components is required to reduce cardiovascular risk in diabetes mellitus hence accurate and early diagnosis to induce effective treatment of MetS in Indian population will be pivotal in the prevention of cardiovascular disease and type 2 diabetes.

Real-time PCR carried out on DNA extracted from serum or blood sample is not a good method for surveillance of bovine brucellosis.

Bovine brucellosis is endemic in many parts of the world including India. The disease diagnosis and surveillance are usually carried out by serological tests, which however have drawbacks. This study was undertaken to evaluate the potential of real-time PCR (RT-PCR) targeting bcsp31 gene for surveillance of bovine brucellosis. A total of 461 samples, which included 408 stored serum and 53 prospective blood samples, were used. It was found that 33 (7.15 %) samples were positive by RT-PCR, whereas 149 (32.32 %) and 132 (28.63 %) were positive by Rose Bengal plate test (RBPT) or standard agglutination test (STAT), respectively. The results of this study suggest that RT-PCR targeting bcsp31 gene carried out on DNA extracted from serum or blood may not be a suitable method for surveillance of brucellosis in bovines.

A survey of computational intelligence techniques in protein function prediction.

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.

Evaluation of the recombinant 10-kilodalton immunodominant region of the BP26 protein of Brucella abortus for specific diagnosis of bovine brucellosis.

Brucellosis is a disease with worldwide distribution affecting animals and human beings. Brucella abortus is the causative agent of bovine brucellosis. The cross-reactions of currently available diagnostic procedures for B. abortus infection result in false-positive reactions, which make the procedures unreliable. These tests are also unable to differentiate Brucella-infected and -vaccinated animals. The present work is focused on the use of a nonlipopolysaccharide (LPS) diagnostic antigen, a recombinant 10-kDa (r10-kDa) protein of B. abortus, for specific diagnosis of brucellosis. The purified recombinant protein was used as a diagnostic antigen in plate enzyme-linked immunosorbent assay (p-ELISA) format to screen 408 bovine serum samples (70 presumptively negative, 308 random, and 30 vaccinated), and the results were compared with those of the Rose Bengal plate agglutination test (RBPT) and the standard tube agglutination test (STAT). Statistical analysis in presumptive negative samples revealed 100 and 98.41% specificity of p-ELISA with RBPT and STAT, and an agreement of 91.43% with the tests using Cohen's kappa statistics. In random samples, the agreement of p-ELISA was 77.92% and 80.52% with RBPT and STAT, respectively. p-ELISA investigation of vaccinated samples reported no false-positive results, whereas RBPT and STAT reported 30% and 96.6% false-positive results, respectively. The data suggest that p-ELISA with r10-kDa protein may be a useful method for diagnosis of bovine brucellosis. Furthermore, p-ELISA may also be used as a tool for differentiating Brucella-vaccinated and naturally infected animals.