Establishment of reference CD4+ T cell values for adult Indian population

<p>Abstract</p> <p>Background</p> <p>CD4+ T lymphocyte counts are the most important indicator of disease progression and success of antiretroviral treatment in HIV infection in resource limited settings. The nationwide reference range of CD4+ T lymphocytes was not available in India. This study was conducted to determine reference values of absolute CD4+ T cell counts and percentages for adult Indian population.</p> <p>Methods</p> <p>A multicentric study was conducted involving eight sites across the country. A total of 1206 (approximately 150 per/centre) healthy participants were enrolled in the study. The ratio of male (N = 645) to female (N = 561) of 1.14:1. The healthy status of the participants was assessed by a pre-decided questionnaire. At all centers the CD4+ T cell count, percentages and absolute CD3+ T cell count and percentages were estimated using a single platform strategy and lyse no wash technique. The data was analyzed using the Statistical Package for the Social Scientist (SPSS), version 15) and Prism software version 5.</p> <p>Results</p> <p>The absolute CD4+ T cell counts and percentages in female participants were significantly higher than the values obtained in male participants indicating the true difference in the CD4+ T cell subsets. The reference range for absolute CD4 count for Indian male population was 381-1565 cells/μL and for female population was 447-1846 cells/μL. The reference range for CD4% was 25-49% for male and 27-54% for female population. The reference values for CD3 counts were 776-2785 cells/μL for Indian male population and 826-2997 cells/μL for female population.</p> <p>Conclusion</p> <p>The study used stringent procedures for controlling the technical variation in the CD4 counts across the sites and thus could establish the robust national reference ranges for CD4 counts and percentages. These ranges will be helpful in staging the disease progression and monitoring antiretroviral therapy in HIV infection in India.</p

Evaluation of Two Internalizing Carcinoembryonic Antigen Reporter Genes for Molecular Imaging

PurposeThe objective of this article is to develop internalizing positron emission tomography (PET) reporter genes for tracking genetically modified T cells in vivo.ProceduresThe transmembrane and cytoplasmic domains of the human transferrin receptor (TfR) and CD5 were each fused to the carcinoembryonic (CEA) minigene N-A3 and expressed in Jurkat T cells. Internalization was evaluated by confocal microscopy or by intracellular uptake of ¹²⁵I-labeled anti-CEA scFv-Fc. Reporter gene-transfected Jurkat xenografts in mice were analyzed by immunohistochemistry (IHC) and imaged by PET using ¹²⁴I- or ⁶⁴Cu-scFv-Fc as tracers.ResultsSurface expression of TR(1-99)-NA3 was lower than that of NA3-CD5. Both reporter genes were internalized following binding of the anti-CEA antibody fragment. IHC of tumors showed strong staining of NA3-CD5, whereas TR(1-99)-NA3 stained weakly. Specific targeting of TR(1-99)-NA3 or NA3-CD5 was shown by PET in xenografted mice.ConclusionsThe in vivo imaging studies suggest a potential application of the internalizing form of CEA (N-A3) as a PET reporter gene

The Third Fermi Large Area Telescope Catalog of Gamma-ray Pulsars

We present 294 pulsars found in GeV data from the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope. Another 33 millisecond pulsars (MSPs) discovered in deep radio searches of LAT sources will likely reveal pulsations once phase-connected rotation ephemerides are achieved. A further dozen optical and/or X-ray binary systems co-located with LAT sources also likely harbor gamma-ray MSPs. This catalog thus reports roughly 340 gamma-ray pulsars and candidates, 10% of all known pulsars, compared to $\leq 11$ known before Fermi. Half of the gamma-ray pulsars are young. Of these, the half that are undetected in radio have a broader Galactic latitude distribution than the young radio-loud pulsars. The others are MSPs, with 6 undetected in radio. Overall, >235 are bright enough above 50 MeV to fit the pulse profile, the energy spectrum, or both. For the common two-peaked profiles, the gamma-ray peak closest to the magnetic pole crossing generally has a softer spectrum. The spectral energy distributions tend to narrow as the spindown power $\dot E$ decreases to its observed minimum near $10^{33}$ erg s$^{-1}$, approaching the shape for synchrotron radiation from monoenergetic electrons. We calculate gamma-ray luminosities when distances are available. Our all-sky gamma-ray sensitivity map is useful for population syntheses. The electronic catalog version provides gamma-ray pulsar ephemerides, properties and fit results to guide and be compared with modeling results.Comment: 142 pages. Accepted by the Astrophysical Journal Supplemen

Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic

Abstract The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics

Study of phenol biodegradation by an indigenous mixed consortium of bacteria

227-233The potential of a mixed consortium of bacteria has been isolated from the soil of the East Calcutta Wetlands, the major waste treatment and recovery site of Kolkata, for degradation of phenol, a representative of phenolic compounds has been investigated. The mixed culture is first acclimatized to higher phenol concentrations in the mineral salt (MS) media and then its behaviour for degradation of phenol has been studied. The mixed culture successfully degrades phenol till 200 mg/L and then undergoes substrate inhibition at 400 mg/L. At still higher phenol concentration of 800 mg/L this mixed culture shows an anomalous behaviour by degrading phenol at a higher rate as compared to lesser phenol concentration by overcoming the substrate inhibition effect. The bacterial growth curve also follows the same pattern which indicates the observation. By the kinetic modeling of the substrate inhibition biokinetic constants are calculated which conform to experimentally observed values. For the phenol degradation and growth studies, Haldane model and Yano and Koga model are found to be the most efficient kinetic models respectively

Performance Study of Chromium (VI) Removal in Presence of Phenol in a Continuous Packed Bed Reactor by Escherichia coli Isolated from East Calcutta Wetlands

Organic pollutants, like phenol, along with heavy metals, like chromium, are present in various industrial effluents that pose serious health hazard to humans. The present study looked at removal of chromium (VI) in presence of phenol in a counter-current continuous packed bed reactor packed with E. coli cells immobilized on clay chips. The cells removed 85% of 500 mg/L of chromium (VI) from MS media containing glucose. Glucose was then replaced by 500 mg/L phenol. Temperature and pH of the MS media prior to addition of phenol were 30 ∘ C and 7, respectively. Hydraulic retention times of phenol-and chromium (VI)-containing synthetic media and air flow rates were varied to study the removal efficiency of the reactor system. Then temperature conditions of the reactor system were varied from 10 ∘ C to 50 ∘ C, the optimum being 30 ∘ C. The pH of the media was varied from pH 1 to pH 12, and the optimum pH was found to be 7. The maximum removal efficiency of 77.7% was achieved for synthetic media containing phenol and chromium (VI) in the continuous reactor system at optimized conditions, namely, hydraulic retention time at 4.44 hr, air flow rate at 2.5 lpm, temperature at 30 ∘ C, and pH at 7

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Not AvailableSoil lead (Pb) contamination by anthropogenic and industrial activities is a problemof global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIRDRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky–Golay (SG) first derivative spectra (RPD=3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost.Not Availabl