Molecular characterisation of neurotransmitter receptors in the CNS of the stargazer mutant mouse

The mutant mouse stargazer shows both ataxia and absence epilepsy from P14 onwards. A PGR amplification strategy was utilised to identify adult (i.e. > 3 months) +/stg from +/+ mice, which share the same phenotype, for use for breeding purposes. The same technique was employed to identify +/+, +/stg and stg/stg neonates (i.e. < 7 days old) for cell culture purposes, since the stargazer phenotype is not apparent at this age. GABA(_A) receptor α(_6) subunit expression levels were significantly decreased in adult stargazer (stg/stg) cerebella when compared to control (+/+ and +/stg) cerebella. Interestingly, autoradiography using [(^3)H] Ro15-4513 revealed an apparent upregulation in α(_4)γ-containing receptors in the adult stargazer dentate gyrus. No significant differences in the expression of NMDAR subunits were detected between adult control and stargazer brain membranes. A significant decrease was observed in AMPAR subunit expression within the adult stargazer cerebellum, particularly with the GIuR2 subunit, which was reduced by 73 %. This decrease was replicated in cerebellar granule cells cultured from stargazer neonates, which also expressed at the cell surface only 18 % of the total GluR2 found in control granule cells. Inmunohistochemistry analyses using mouse anti-stargazin antibodies revealed stargazin to be found throughout the adult control brain, with highest levels of expression being within the hippocampus and cerebellum. Stargazin protein, however, was not expressed in adult stargazer forebrain nor in adult stargazer cerebellar membranes. Finally, immunoaffinity columns using the anti-stargazin antibodies were prepared and demonstrated that stargazin could be purified from adult control mouse brain extracts. Moreover, AMPAR subunits co-immunoprecipitated, indicating an association in vivo

Adsorption behavior of activated charcoal and used battery cell carbon as composite for removal of cadmium ion from aqueous solution

To investigate the adsorption behavior of blend activated charcoal and used battery cell carbon for removal of cadmium metal ion, batch adsorption study was used. Batch study for the expansion of parameters such as pH, initial metal ion concentration and adsorbent quantity were utilized. The characterization of the adsorbent samples was done by technique Fourier Infra Red Spectrum (FTIR) and Scanning Electron Microscope (SEM). Adsorption data were computed by Langmuir and Freundlich isotherms. Kinetic data was well explained by Pseudo second order equation, Intra-particle diffusion and Elovich first order equation. Maximum adsorption efficiency was observed to be 75% at 50:50 ratios of activated charcoal and used battery cell carbon. Purpose of blending activated charcoal and used battery cell carbon reduce the consumption of activated charcoal as it is very costly to use for commercial purposes

Influence of parameters and kinetic study of nickel (II) and cadmium (III) metals on Dalbergia derived adsorbent

Background and aims: From the past studies, it has been proved that consumption of heavy metals by humans may cause several chronicle problems like cancer, kidney and liver damage, high blood pressure and low blood pressure problems and etc. So, it has become very crucial to remove these heavy metals from industrial wastewater. The aim of this study was to find out a low cost and easy available adsorbent for adsorption of heavy metals. Methods: Batch removal study of nickel and cadmium metal ions from their salt solutions was used. Preparation of adsorbent was done by following chemical treatment method and finally dried product obtained was characterized through FT-IR and morphological study. Influence of parameters initial metal ion concentration, pH and adsorbent dosage were done by varying one factor remaining others fixed. Equilibrium study at different temperatures while concentration fixed and kinetic study to know efficiency of adsorbent were done. Results: Influence of parameters gives optimum range for adsorption as pH gives 6.8 and 0.2 g for adsorbent dose. Adsorption isotherm well explained by Temkin isotherm as it gives positive value for calculating constants and high correlation coefficient 0.99. Kinetic behavior well followed by Pseudo second order, Intraparticle diffusion and Elovich second order kinetic models. Further, several thermodynamics parameters like ΔS°, ΔH° and ΔG° were predicted. The value of ΔG° predicted at different temperatures, highlighted the spontaneity. Conclusion: Dalbergia proved as a low cost and efficient adsorbent

An assessment of the performance of different districts towards Sustainable Development targets in India

Sustainable development goals encapsulates 17 broad-based goals and 169 associated targets to reconcile among the three pillars of sustainability i.e. social, economic and environmental. In the present study, we sought to understand the performance of 641 districts ofIndiaon social, economic and environmental parameters and on the composite sustainability index as a whole. Our results suggest that there is a large regional variations in performance with districts of southern and western states outperforming northern and eastern states on sustainability index. There is a strong congruence between social and economic indicators. However, environment is still missing from the policy planning. Our results may provide a benchmark and help in micro-level planning at district level and may help in raising public and policy support, realignment and reorientation of the existing policy from the perspective of SDGs

Clinical analysis of gynecological diseases in postmenopausal women in tertiary care centre

ABSTRACTBackground: Menopause is a natural step in ageing process represents the period end of menstruation after last menstrual period in previous 12 months. Gynaecological disorder in older women differs from those who are younger. Disorders peculiar to ageing are pelvic organ prolapse, urinary incontinence, genital infections and malignancies. Present study is contemplated with a view to assess the magnitude of postmenopausal gynaecological morbidity. The goal of this study was to assess the age of onset of menopause and the spectrum of different gynaecological diseases, their incidence, diagnosis and treatment modality in postmenopausal females.Methods: A Prospective observational study of postmenopausal females attending Gynecology OPD or admitted in Sultania Zanana Hospital, Bhopal was carried out between July 2014 to June 2015. Total 401 postmenopausal females were included. Age of menopause and detail of all gynecological problems were recorded using predesigned proforma.Results: The study population was drawn from both rural (41.4%) and urban (58.8%) areas. Mean age of onset of menopause was 48.01 years in study population. In all, 28.4% had pelvic organ prolapse, 26.6% had genital malignancies, 25.5% had urogenital infections and 17.7% had benign disorder like senile endometritis, fibroid uterus etc.Conclusions: Menopausal health has been one of the neglected area in our country and needs timely vital attention as they are at risk of developing various genital malignancies. This emphasises the need for a screening programme for Indian women in our scenario

Optimizing Antibiotic Prescriptions and Infectious Disease Management in Hospitals using Neural Networks

This study introduces an innovative approach to antibiotic optimization and improved infectious disease management in healthcare facilities. Antibiotic stewardship and patient-specific outcomes are prioritized in the suggested strategy that uses neural networks to increase the precision and utility of antibiotic prescriptions. There are three primary algorithms at the heart of the technique. When it comes to identifying infectious illnesses from a picture, Algorithm 1 uses a Convolutional Neural Network (CNN). In order to provide educated antibiotic recommendations, Algorithm 2 uses a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) cells. The third algorithm integrates reinforcement learning to automatically modify therapies based on patient results and antibiotic use. The outcomes prove that the suggested strategy is better than the status quo. The F1 score, recall, and precision all increase dramatically, and the overall diagnostic accuracy is much higher. Antibiotic stewardship also improves noticeably, leading to fewer antibiotic prescriptions, more effective measures against antibiotic resistance, better health outcomes for patients, and lower overall healthcare expenditures. Addressing the difficulties of fluctuating patient states and changing disease patterns, the suggested methodology provides a comprehensive strategy for managing infectious diseases. Using this method, antibiotic prescriptions may be optimized while still meeting all legal and ethical requirements. The ethical use of AI in healthcare is further ensured by constant monitoring and flexibility

A Review on Design and Development of Pipelined Quaternary Adder for Fast Addition

Design of the binary logic circuits is limited by the requirement of the interconnections. A possible solution could be arrived at by using a larger set of signals over the same chip area. Multiple-valued logic (MVL) designs are gaining importance from that perspective. This paper presents the design of a multiple-valued half adder and full adder circuits. In Quaternary adders the binary value is first converted into the Quaternary value and then the addition operation is performed with less number of gates and minimum depth of net. Sum and carry are processed in two separate blocks, controlled by code generator unit. Simple pass transistors are used for implementation. Area of the designed circuits is less than the corresponding binary circuits and quaternary adders because number of transistors used are less. We can implement this paper by using pipelining which help us to reduce the delay of operation and also help us to improve the throughput of the system, the designing of the paper is done by using VHDL. DOI: 10.17762/ijritcc2321-8169.15034

Thermodyamic, ecoomic analysis and desiging of heating system for the swimming pool present at NIT rourkela

The swimming pool at NIT Rourkela, presently, has no heating system to heat the pool water during winters. Therefore, it is rendered unused during winters. The focus of this project is on designing an efficient and economically viable system to heat the swimming pool. Temperatures of pool water and air above the surface of pool water were recorded for the month of November (2010). Using this data, losses associated with the pool were calculated and were found to agree completely with the literature data. Three systems were proposed based on: heat pumps, natural gas heaters and solar based water heating system. A design has been proposed for solar based water heating system. Many models of heat pumps were considered and a detailed cost analysis was made. For natural gas heaters, different models were considered and cost analysis was carried out. Annual operating cost of heat pumps and natural gas heaters were compared and natural gas heaters were found to be about 1.8-3 times costlier than heat pumps. A combination of solar heating and a conventional heater in equal proportion has been proposed for a perfect heating system. Thermodynamic and economic analysis and designing of different systems for pool heating has been carried out in this work

ANALYSIS OF GENITAL TRACT MALIGNANCIES IN POSTMENOPAUSAL FEMALES – A HOSPITAL-BASED STUDY

Introduction: Female genital tract malignancy is common carcinoma. In the developed countries, ovarian cancer is the most common cancer and in developing countries, carcinoma cervix is the most common malignancy. Menopause does not cause cancer, but the risk of developing cancer increases as women ages. Therefore, women who have been through natural menopause are more likely to develop cancer because they are older. A woman who experiences menopause after age 55 has increased risk of ovarian, breast, and uterine cancers. The risk is greater if women also began menstruating before age 12. This is because a woman who menstruates longer than normal during her lifetime is exposed to more estrogen and has more ovulation. Hence, aims and objectives of our study are: (1) To determine the incidence of different genital malignancies in postmenopausal females and (2) to analyzes diagnosis and treatment of genital malignancies in postmenopausal females. Methods: 1. Data were collected using predesigned proforma; consent was taken from every participant, 2. After collection of data, it was tabulated. Statistical calculation and subsequent analysis was made has been presented in the form of Tables and graphs. Results: A total of 401 cases reported to the institute during a period of 1 year. Out of which, 107 patients were that of genital carcinoma. Incidence of female genital tract carcinoma was 26.68%. Approximately 73.87% (82 patients) of cases were that of cervical cancer. Conclusion: Hence, from above study, the most of the patients were diagnosed in advanced stage of malignancy. Carcinoma cervix was the most common female genital tract cancer with ovarian cancer taking the second rank. This is unfortunate as cancer cervix is preventable to a large extent as it takes a decade or more to progress from pre-invasive to invasive lesion, there are various screening modalities to diagnosed the cervix in pre-invasive age, that is, when it still curable

E-Mail Data Analysis by Considering Auxiliary Information

The immense evolution of the technologies is directly proportional to the rise of mails in our email boxes. Emails are always considered as the best source of communication. To utilize the true potential of these emails (unstructured data) transformation should be done on it in order to extract needed information from it thus saving time. Data mining fulfills this need. Also the main information is carried by the documents attached to these mails, so extraction of this auxiliary data is very necessary. To access these emails effectively with the auxiliary data present in them as per user’s sentiments, this paper propose text analytics method to cluster the mails into different groups on the basis of emotions using various scalable machine learning techniques