DEVELOPMENT AND VALIDATION OF UV SPECTROPHOTOMETRIC AND REVERSED PHASE‑HIGH PERFORMANCE LIQUID CHROMATOGRAPHY ‑ PDA METHODS FOR THE ESTIMATION OF ALOGLIPTIN BENZOATE

Objective: To develop and validate simple, rapid, precise, accurate, and economical UV spectrophotometric and reverse phase high performanceliquid chromatographic methods for the estimation of alogliptin benzoate (AGP).Methods: UV spectrophotometric method was performed using UV/Vis double beam spectrophotometer with a spectral bandwidth of 1 nm and1.0 cm matched quartz cells. The maximum absorbance of AGP was observed at 276 nm using methanol as solvent. Reversed phase-high performanceliquid chromatography (RP-HPLC) method was carried out on a Unisol reverse phase C18 column (150 mm × 4.6 mm, 3 μm) with a mobile phasecomposed of methanol and 10 mM ammonium acetate buffer (adjusted to pH 5.0 with glacial acetic acid) in the ratio of 80:20 v/v with a flow rate of0.8 ml/minutes.Results: The linearity of methods was found to be in the range of 5-35 µg/ml (UV) and 20-100 µg/ml (RP-HPLC) and the correlation coefficient was0.999 for both the methods. The regression equations were y = 0.028x + 0.023 (UV) and y = 28,58,942x - 4,33,647 (HPLC). The retention time of AGPwas 2.37 minutes.Conclusion: The proposed methods were validated in terms of linearity, precision, accuracy, specificity, robustness, limit of detection, and limit ofquantitation as per International Conference on Harmonization Q2 R1 guidelines. Thus, the proposed methods are novel, sensitive, and reliable andcan be successfully used for the quantitation of AGP.Keywords: Alogliptin benzoate, UV-visible spectrophotometer, Reversed phase-high performance liquid chromatography, International Conferenceon Harmonization guidelines

Interfacility transfer of pregnant women using publicly funded emergency call centre-based ambulance services: a cross-sectional analysis of service logs from five states in India.

OBJECTIVE: To estimate the proportion of interfacility transfers (IFTs) transported by '108' ambulances and to compare the characteristics of the IFTs and non-IFTs to understand the pattern of use of '108' services for pregnant women in India. DESIGN: A cross-sectional analysis of '108' ambulance records from five states for the period April 2013 to March 2014. Data were obtained from the call centre database for the pregnant women, who called '108'. MAIN OUTCOMES: Proportion of all pregnancies and institutional deliveries in the population who were transported by '108', both overall and for IFT. Characteristics of the women transported; obstetric emergencies, the distances travelled and the time taken for both IFT and non-IFT. RESULTS: The '108' ambulances transported 6 08 559 pregnant women, of whom 34 993 were IFTs (5.8%) in the five states. We estimated that '108' transferred 16.5% of all pregnancies and 20.8% of institutional deliveries. Only 1.2% of all institutional deliveries in the population were transported by '108' for IFTs-lowest 0.6% in Gujarat and highest 3.0% in Himachal Pradesh. Of all '108' IFTs, only 8.4% had any pregnancy complication. For all states combined, on adjusted analysis, IFTs were more likely than non-IFTs to be for older and younger women or from urban areas, and less likely to be for women from high-priority districts, from backward or scheduled castes, or women below the poverty line. Obstetric emergencies were more than twice as likely to be IFTs as pregnant women without obstetric emergencies (OR=2.18, 95% CI 2.09 to 2.27). There was considerable variation across states. CONCLUSION: Only 6% institutional deliveries made use of the '108' ambulance for IFTs in India. The vast majority did not have any complication or emergency. The '108' service may need to consider strategies to prioritise the transfer of women with obstetric emergency and those requiring IFT, over uncomplicated non-IFT

Population differentiation of Southern Indian male lineages correlates with agricultural expansions predating the caste system

Christina J. Adler, Alan Cooper, Clio S.I. Der Sarkissian and Wolfgang Haak are contributors to the Genographic ConsortiumPrevious studies that pooled Indian populations from a wide variety of geographical locations, have obtained contradictory conclusions about the processes of the establishment of the Varna caste system and its genetic impact on the origins and demographic histories of Indian populations. To further investigate these questions we took advantage that both Y chromosome and caste designation are paternally inherited, and genotyped 1,680 Y chromosomes representing 12 tribal and 19 non-tribal (caste) endogamous populations from the predominantly Dravidian-speaking Tamil Nadu state in the southernmost part of India. Tribes and castes were both characterized by an overwhelming proportion of putatively Indian autochthonous Y-chromosomal haplogroups (H-M69, F-M89, R1a1-M17, L1-M27, R2-M124, and C5-M356; 81% combined) with a shared genetic heritage dating back to the late Pleistocene (10–30 Kya), suggesting that more recent Holocene migrations from western Eurasia contributed, <20% of the male lineages. We found strong evidence for genetic structure, associated primarily with the current mode of subsistence. Coalescence analysis suggested that the social stratification was established 4–6 Kya and there was little admixture during the last 3 Kya, implying a minimal genetic impact of the Varna(caste) system from the historically-documented Brahmin migrations into the area. In contrast, the overall Y-chromosomal patterns, the time depth of population diversifications and the period of differentiation were best explained by the emergence of agricultural technology in South Asia. These results highlight the utility of detailed local genetic studies within India, without prior assumptions about the importance of Varna rank status for population grouping, to obtain new insights into the relative influences of past demographic events for the population structure of the whole of modern India.GaneshPrasad ArunKumar, David F. Soria-Hernanz, Valampuri John Kavitha, Varatharajan Santhakumari Arun, Adhikarla Syama, Kumaran Samy Ashokan, Kavandanpatti Thangaraj Gandhirajan, Koothapuli Vijayakumar, Muthuswamy Narayanan, Mariakuttikan Jayalakshmi, Janet S. Ziegle, Ajay K. Royyuru, Laxmi Parida, R. Spencer Wells, Colin Renfrew, Theodore G. Schurr, Chris Tyler Smith, Daniel E. Platt, Ramasamy Pitchappan, The Genographic Consortiu

Measurement of Turbulence and pitch of the airstream in the 5'* 7' tunnel of Indian Institute of Science

Abstract is not available

India Landslide Susceptibility Map ILSM (100m resolution)

&lt;p&gt;Landslide susceptibility represents the potential of slope failure for given geoenvironmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of one-side sampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001o (~100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73%, sensitivity of 97.08%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.&nbsp;&lt;/p&gt

Prevalence of Depression in Diabetes Mellitus: A hospital based Cross sectional study

Introduction: Diabetes is a metabolic disorder that has life-changing consequences for individuals affected by it. Diabetes may be diagnosed and treated, but the depression in these patients often goes unnoticed. Most of the time depression is not considered as an important factor and often ignored and left untreated. Depression is associated with poor health behaviors (i.e., smoking, physical inactivity, caloric intake) that increase risk of type 2 diabetes .Depression is also associated to central obesity and potentially to impaired glucose tolerance and may worsen diabetes.Objectives of the study: The primary aim is to study the prevalence of depression in type 2 diabetes mellitus patients.Materials &amp; Methods: A hospital based observational study was carried out on diabetic patients attending the general medicine OPD in a tertiary care hospital during the period June-October2018. Results: The prevalence of depression in diabetic patients is 56% in the current study which is in par with earlier studies. In the present study, among depressed patients 38.39% had mild depression, 34.82% has moderate symptoms, 19.64% had moderately severe depression, 7.14% had very severe symptoms. On evaluation of various parameters of diabetes, the parameters that are significantly associated with depression are glycated hemoglobin ( p=0.028),Fasting blood sugar(p=0.019),Body mass index(p=0.004),Duration of diabetes(p-0.002).Depression is mostly observed in patients with diabetes duration of 7-9 years. Among the parameters evaluated in the present study, number of complications and treatment regimen (p=0.000) have showed the most significant association with severity of depression.Conclusion: Higher glycated hemoglobin value, fasting blood sugar levels and duration of diabetes, higher BMI all are significantly associated with severity of depression. The number of complications and treatment regimen in diabetes are also significantly associated with depression. As the disease burden of depression increases in terms of duration of illness or poor control or complications, the severity of depression also seems to increase. Therefore adequate screening and intervention of depression is necessary for better outcome on both diabetes and depression especially in diabetic patients with higher morbidity and longstanding illness

Utility of prehospital call center ambulance dispatch data for COVID‐19 cluster surveillance: A retrospective analysis

IntroductionCluster surveillance, identification, and containment are primary outbreak management techniques; however, adapting these for low- and middle-income countries is an ongoing challenge. We aimed to evaluate the utility of prehospital call center ambulance dispatch (CCAD) data for surveillance by examining the correlation between influenza-like illness (ILI)-related dispatch calls and COVID-19 cases.MethodsWe performed a retrospective analysis of state-level CCAD and COVID-19 data recorded between January 1 and April 30, 2020, in Telangana, India. The primary outcome was a time series correlation between ILI calls in CCAD and COVID-19 case counts. Secondarily, we looked for a year-to-year correlation of ILI calls in the same period over 2018, 2019, and 2020.ResultsOn average, ILI calls comprised 12.9% (95% CI 11.7%-14.1%) of total daily calls in 2020, compared to 7.8% (95% CI 7.6%-8.0%) in 2018, and 7.7% (95% CI 7.5%-7.7%) in 2019. ILI call counts from 2018, 2019, and 2020 aligned closely until March 19, when 2020 ILI calls increased, representing 16% of all calls by March 23 and 27.5% by April 7. In contrast to the significant correlation observed between 2020 and previous years' January-February calls (2020 and 2019-Durbin-Watson test statistic [DW]&nbsp;=&nbsp;0.749, p &lt; 0.001; 2020 and 2018-DW&nbsp;=&nbsp;1.232, p &lt; 0.001), no correlation was observed for March-April calls (2020 and 2019-DW&nbsp;=&nbsp;2.012, p&nbsp;=&nbsp;0.476; 2020 and 2018-DW&nbsp;=&nbsp;1.820, p&nbsp;=&nbsp;0.208). In March-April 2020, the daily reported COVID-19 cases by time series significantly correlated with the ILI calls (DW&nbsp;=&nbsp;0.977, p &lt; 0.001). The ILI calls on a specific day significantly correlated with the COVID-19 cases reported 6 days prior and up to 14 days after (cross-correlation &gt; 0.251, the 95% upper confidence limit).ConclusionsThe statistically significant time series correlation between ILI calls and COVID-19 cases suggests prehospital CCAD can be part of early warning systems aiding outbreak cluster surveillance, identification, and containment