TRADITIONAL USES, PHYTOCHEMISTRY AND PHARMACOLOGICAL ACTIVITIES OF PAPAVER SOMNIFERUM WITH SPECIAL REFERENCE OF UNANI MEDICINE AN UPDATED REVIEW

Papaver somniferum commonly known as Khashkhash /Afyon, belongs to family Papaveraceae. It is one of those traditional plants, which have a long history of usage as medicine. The opium poppy (Papaver somniferum) produces some of the most widely used medicinal alkaloids like morphine, codeine, thebain and porphyroxine which are the most important component of this plant. Apart from these alkaloids, opium poppy produces approximately eighty alkaloids belonging to various tetrahydrobenzylisoquinolinederived classes. It has been known for over a century that morphinan alkaloids accumulate in the latex of opium poppy. According to Unani literature, it possesses most important theurapeutic values as modern literature and research studies also prove its therapeutical importance. It is used as analgesic, narcotic, sedative, stimulant as well as nutritive, etc. It is also useful in headache, cough, insomnia, cardiac asthma, and biliary colic. In this paper we have provide a review on habitate, pharmacological actions, phytochemical with special refrence to Unani Medicine. In this review, an attempt is made to explore the complete information of Papaver somniferum including its  phytochemistry and pharmacology. Key words: Khashkhash, Biliary colic, Alkaloid, phytochemistry

Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up

Objectives: Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non–small-cell lung cancer (NSCLC). Methods: Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs—landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)—were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. Results: For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). Conclusions: We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up. Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes. Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data. Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting. Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup

Local Ferromagnetism in Microporous Carbon with the Structural Regularity of Zeolite Y

Magnetization M(H,T) measurements have been performed on microporous carbon (MC) with a three-dimensional nano-array structure corresponding to that of a zeolite Y supercage. The obtained results unambiguously demonstrate the occurrence of high-temperature ferromagnetism in MC, probably originating from a topological disorder associated with curved graphene sheets. The results provide evidence that the ferromagnetic behavior of MC is governed by isolated clusters in a broad temperature range, and suggest the occurrence of percolative-type transition with the temperature lowering. A comparative analysis of the results obtained on MC and related materials is given.Comment: To be published in Physical Review B (2003

Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network

In recent years, due to the high availability of omic data, data-driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omics approaches have been proposed in the literature, none of which takes into consideration the intrinsic topology of each omic, though. In this work, an unsupervised learning method based on a deep neural network is proposed. Foreach omic, a separate network is trained, whose outputs are fused into a single graph; at this purpose, an innovative loss function has been designed to better represent the data cluster manifolds. The graph adjacency matrix is exploited to determine similarities among samples. With this approach, omics having a different number of features are merged into a unique representation. Quantitative and qualitative analyses show that the proposed method has comparable results to the state of the art. The method has great intrinsic flexibility as it can be customized according to the complexity of the tasks and it has a lot of room for future improvements compared to more fine-tuned methods, opening the way for future research

Chemical Composition and Larvicidal Activities of the Himalayan Cedar, Cedrus deodara Essential Oil and Its Fractions Against the Diamondback Moth, Plutella xylostella

Plants and plant-derived materials play an extremely important role in pest management programs. Essential oil from wood chips of Himalayan Cedar, Cedrus deodara (Roxburgh) Don (Pinales: Pinaceae), was obtained by hydrodistillation and fractionated to pentane and acetonitrile from which himachalenes and atlantones enriched fractions were isolated. A total of forty compounds were identified from these fractions using GC and GC-MS analyses. Essential oils and fractions were evaluated for insecticidal activities against second instars of the diamondback moth, Plutella xylostella L. (Lepidoptera: Yponomeutidae), using a leaf dip method. All samples showed promising larvicidal activity against larvae of P. xylostella. The pentane fraction was the most toxic with a LC50 value of 287 µg/ml. The himachalenes enriched fraction was more toxic (LC50 = 362 µg/ml) than the atlantones enriched fraction (LC50 = 365 µg/ml). LC50 of crude oil was 425 µg/ml and acetonitrile fraction was LC50 = 815 µg/ml. The major constituents, himachalenes and atlantones, likely accounted for the insecticidal action. Present bioassay results revealed the potential for essential oil and different constituents of C. deodara as botanical larvicides for their use in pest management

On the probability of cost-effectiveness using data from randomized clinical trials

BACKGROUND: Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). METHODS: An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. RESULTS: Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. CONCLUSIONS: The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis

Study protocol of cost-effectiveness and cost-utility of a biopsychosocial multidisciplinary intervention in the evolution of non-specific sub-acute low back pain in the working population: cluster randomised trial.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Low back pain (LBP), with high incidence and prevalence rate, is one of the most common reasons to consult the health system and is responsible for a significant amount of sick leave, leading to high health and social costs. The objective of the study is to assess the cost-effectiveness and cost-utility analysis of a multidisciplinary biopsychosocial educational group intervention (MBEGI) of non-specific sub-acute LBP in comparison with the usual care in the working population recruited in primary healthcare centres. Methods/design: The study design is a cost-effectiveness and cost-utility analysis of a MBEGI in comparison with the usual care of non-specific sub-acute LBP.Measures on effectiveness and costs of both interventions will be obtained from a cluster randomised controlled clinical trial carried out in 38 Catalan primary health care centres, enrolling 932 patients between 18 and 65 years old with a diagnosis of non-specific sub-acute LBP. Effectiveness measures are: pharmaceutical treatments, work sick leave (% and duration in days), Roland Morris disability, McGill pain intensity, Fear Avoidance Beliefs (FAB) and Golberg Questionnaires. Utility measures will be calculated from the SF-12. The analysis will be performed from a social perspective. The temporal horizon is at 3 months (change to chronic LBP) and 12 months (evaluate the outcomes at long term. Assessment of outcomes will be blinded and will follow the intention-to-treat principle. Discussion: We hope to demonstrate the cost-effectiveness and cost-utility of MBEGI, see an improvement in the patients' quality of life, achieve a reduction in the duration of episodes and the chronicity of non-specific low back pain, and be able to report a decrease in the social costs. If the intervention is cost-effectiveness and cost-utility, it could be applied to Primary Health Care Centres. Trial registration: ISRCTN: ISRCTN5871969

Growth factor restriction impedes progression of wound healing following cataract surgery: identification of VEGF as a putative therapeutic target

Secondary visual loss occurs in millions of patients due to a wound-healing response, known as posterior capsule opacification (PCO), following cataract surgery. An intraocular lens (IOL) is implanted into residual lens tissue, known as the capsular bag, following cataract removal. Standard IOLs allow the anterior and posterior capsules to become physically connected. This places pressure on the IOL and improves contact with the underlying posterior capsule. New open bag IOL designs separate the anterior capsule and posterior capsules and further reduce PCO incidence. It is hypothesised that this results from reduced cytokine availability due to greater irrigation of the bag. We therefore explored the role of growth factor restriction on PCO using human lens cell and tissue culture models. We demonstrate that cytokine dilution, by increasing medium volume, significantly reduced cell coverage in both closed and open capsular bag models. This coincided with reduced cell density and myofibroblast formation. A screen of 27 cytokines identified nine candidates whose expression profile correlated with growth. In particular, VEGF was found to regulate cell survival, growth and myofibroblast formation. VEGF provides a therapeutic target to further manage PCO development and will yield best results when used in conjunction with open bag IOL designs

Lumican Expression in Diaphragm Induced by Mechanical Ventilation

Diaphragmatic dysfunction found in the patients with acute lung injury required prolonged mechanical ventilation. Mechanical ventilation can induce production of inflammatory cytokines and excess deposition of extracellular matrix proteins via up-regulation of transforming growth factor (TGF)-β1. Lumican is known to participate in TGF-β1 signaling during wound healing. The mechanisms regulating interactions between mechanical ventilation and diaphragmatic injury are unclear. We hypothesized that diaphragmatic damage by short duration of mechanical stretch caused up-regulation of lumican that modulated TGF-β1 signaling.Male C57BL/6 mice, either wild-type or lumican-null, aged 3 months, weighing between 25 and 30 g, were exposed to normal tidal volume (10 ml/kg) or high tidal volume (30 ml/kg) mechanical ventilation with room air for 2 to 8 hours. Nonventilated mice served as control groups.High tidal volume mechanical ventilation induced interfibrillar disassembly of diaphragmatic collagen fiber, lumican activation, type I and III procollagen, fibronectin, and α-smooth muscle actin (α-SMA) mRNA, production of free radical and TGF-β1 protein, and positive staining of lumican in diaphragmatic fiber. Mechanical ventilation of lumican deficient mice attenuated diaphragmatic injury, type I and III procollagen, fibronectin, and α-SMA mRNA, and production of free radical and TGF-β1 protein. No significant diaphragmatic injury was found in mice subjected to normal tidal volume mechanical ventilation.Our data showed that high tidal volume mechanical ventilation induced TGF-β1 production, TGF-β1-inducible genes, e.g., collagen, and diaphragmatic dysfunction through activation of the lumican