Information

Salbutamol's Pathways of Interaction and Classification

Salbutamol's Pathways of Interaction and Classification


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Salbutamol is a very commonly used direct-acting β2-agonist. This suggests me that it is sympatholytic. However, it has sympathomimetic pathways, see PubChem for Sympathomimetic.

I am trying to think the group of Salbutamol. Sympathomimetic or sympatholytic.

How can you classify Salbutamol into a group?


An agonist works with the receptor: a substance that initiates a physiological response when combined with a receptor; in the case of salbutamol (or albutarol in the US) they activate the beta-2 receptor on the muscles surrounding the airways. They are also known as sympathomimetics, as they imitate β2 adrenergics.

A blocker (or antagonist) is one that that blocks the action of endogenous catecholamines; for that reason, they are also called sympatholytic: antagonistic to or inhibiting the transmission of nerve impulses in the sympathetic nervous system.

It appears you have experienced a simple confusion in terms. Salbutamol/albuterol is definitely a sympathomimetic, used extensively on asthma and COPD.

Pharmacology and Therapeutics of Bronchodilators


Linking the biological underpinnings of depression: Role of mitochondria interactions with melatonin, inflammation, sirtuins, tryptophan catabolites, DNA repair and oxidative and nitrosative stress, with consequences for classification and cognition

The pathophysiological underpinnings of neuroprogressive processes in recurrent major depressive disorder (rMDD) are reviewed. A wide array of biochemical processes underlie MDD presentations and their shift to a recurrent, neuroprogressive course, including: increased immune-inflammation, tryptophan catabolites (TRYCATs), mitochondrial dysfunction, aryl hydrocarbonn receptor activation, and oxidative and nitrosative stress (O&NS), as well as decreased sirtuins and melatonergic pathway activity. These biochemical changes may have their roots in central, systemic and/or peripheral sites, including in the gut, as well as in developmental processes, such as prenatal stressors and breastfeeding consequences. Consequently, conceptualizations of MDD have dramatically moved from simple psychological and central biochemical models, such as lowered brain serotonin, to a conceptualization that incorporates whole body processes over a lifespan developmental timescale. However, important hubs are proposed, including the gut-brain axis, and mitochondrial functioning, which may provide achievable common treatment targets despite considerable inter-individual variability in biochemical changes. This provides a more realistic model of the complexity of MDD and the pathophysiological processes that underpin the shift to rMDD and consequent cognitive deficits. Such accumulating data on the pathophysiological processes underpinning MDD highlights the need in psychiatry to shift to a classification system that is based on biochemical processes, rather than subjective phenomenology.

Keywords: Aryl hydrocarbon Classification Cognition Immune-inflammation Kynurenine Major depressive disorder Melatonin Mitochondria Oxidative and nitrosative stress Pathophysiology Treatment.


The mechanism of action of aspirin

The therapy of rheumatism began thousands of years ago with the use of decoctions or extracts of herbs or plants such as willow bark or leaves, most of which turned out to contain salicylates. Following the advent of synthetic salicylate, Felix Hoffman, working at the Bayer company in Germany, made the acetylated form of salicylic acid in 1897. This drug was named "Aspirin" and became the most widely used medicine of all time. In 1971, Vane discovered the mechanism by which aspirin exerts its anti-inflammatory, analgesic and antipyretic actions. He proved that aspirin and other non-steroid anti-inflammatory drugs (NSAIDs) inhibit the activity of the enzyme now called cyclooxygenase (COX) which leads to the formation of prostaglandins (PGs) that cause inflammation, swelling, pain and fever. However, by inhibiting this key enzyme in PG synthesis, the aspirin-like drugs also prevented the production of physiologically important PGs which protect the stomach mucosa from damage by hydrochloric acid, maintain kidney function and aggregate platelets when required. This conclusion provided a unifying explanation for the therapeutic actions and shared side effects of the aspirin-like drugs. Twenty years later, with the discovery of a second COX gene, it became clear that there are two isoforms of the COX enzyme. The constitutive isoform, COX-1, supports the beneficial homeostatic functions, whereas the inducible isoform, COX-2, becomes upregulated by inflammatory mediators and its products cause many of the symptoms of inflammatory diseases such as rheumatoid and osteoarthritis.


A central serotonergic mechanism

A central mechanism of action for paracetamol has been proposed ( 13, 14 ). Paracetamol concentrations in the cerebrospinal fluid mirror response to fever ( 15 ) and pain ( 16 ) to a greater extent than plasma concentrations. Paracetamol is effective in rat pain models after central administration ( 17 ). Animal data supports the contention that spinal 5-hydroxytryptamine type 3 (5-HT3) receptors are be involved in the antinociceptive effect of paracetamol ( 18, 19 ) and that paracetamol interferes with serotonergic descending pain pathways. Support for these data in humans comes from the demonstration that co-administration of tropisetron or granisetron (5-HT3 receptor antagonists) with paracetamol completely blocked the analgesic effect of acetaminophen in volunteers (rapid metabolizers of tropisetron, n = 26) when assessed by pain induced from electrical stimulation of the median nerve. Volunteers given granisetron, a more specific antagonist, had greater pain (measured as area under the time-pain curve) than those given tropisetron ( 20 ). It is believed that paracetamol reinforces descending inhibitory pain pathways ( 5 ).

Data supporting the central effect of paracetamol through activation of descending serotonergic pathways do not refute arguments that its primary site of action may still be inhibition of PG synthesis, as for the NSAIDs ( 8 ). For example, the expression of a PGE2 receptor (EP3) by most of the serotonergic, noradrenergic, and adrenergic cell groups suggests that PGE2 modulates many physiologic processes. It may modulate nociceptive and autonomic processes by affecting the descending serotonergic pathway from the raphe magnus nucleus to the spinal cord ( 21 ). Serotonergic cell bodies in the raphe magnus nucleus provide dense projections to the dorsal horn of the spinal cord, and this descending pathway has been shown to mediate the antinociceptive action of morphine ( 22, 23 ).


Metabolism or Biotransformation

It is the process of transformation of a drug within the body to make it more hydrophilic so that it can be excreted out from the body by the kidneys. This needs to be done since drugs and chemicals are foreign substances in our body. If the drug continues to be in the lipohilic state and is going to be filtered by the glomerulus then it will be reabsorbed and remain in the body for prolonged periods. Hence metabolism deals with making the drug more hydrophilic such that it can be excreted out from the body. In some cases the metabolites can be more active than the drug itself e.g. anxiolytic benzodiazepines.

Some enzymes are highly specific and will breakdown only compounds that they recognize for e.g. glucose dehydrogenase. But there are some enzymes such as pepsin which are not specific and will breakdown most soluble proteins into smaller polypeptides or amino acids. This enzyme and many other proteolytic enzymes attack the peptide bond that joins the amino acids to make proteins, and in this way break the protein down.

Two types of enzymes are involved in metabolism:

Phase I Metabolism

These enzymes modify the drug chemically by processes such as oxidation, reduction and hydrolysis or by the removal and addition of an active group.

Phase II Metabolism

These include the conjugation of a drug or a phase I metabolite with a polar group to render it possible for excretion. e.g. sulphates and glucuronide

The deconjugation of the drug by bacterial enzymes is called the enterohepatic cycle. Sometimes this deconjugation can lead to increased levels of drugs in the body. But some times due to treatment with antibiotics there may be less or no deconjugation as a result of which there will be less drug in the body.

Principal sites of metabolism are Liver and Kidney and once the drug is rendered hydrophilic they can be easily excreted out by the bile and urine without significant reabsorption.

Enzyme Induction

There are some drugs that can lead to an increase in the production of the enzyme and as a result speed up the metabolism of the drug and hence a higher dose of the drug is required to achieve the therapeutic effect.

Enzyme Inhibition

Some drugs result in the inhibition of certain enzymes and as a result there is an accumulation of the drug in the body and can lead to drug toxicity. This is also a form of drug – drug interaction.


Are nonsteroidal anti-inflammatory agents safe?

NSAIDs are one of the most widely prescribed group of medicines however, they are associated with some serious side effects.

NSAIDs can increase your risk of a fatal heart attack or stroke. The risk increases the higher the dosage and the longer the length of time you remain on an NSAID for. People with pre-existing heart disease are more at risk and certain NSAIDs, such as diclofenac and celecoxib, have been linked to more heart-related side effects than others. NSAIDs should never be used just before or after heart bypass surgery (coronary artery bypass graft, or CABG).

Gastrointestinal (GI) side effects are also common, and usually related to dosage and duration of treatment although some NSAIDs, such as ketorolac, aspirin and indomethacin, are associated with a higher risk. Elderly people or those taking other medicines that irritate the stomach are more likely to experience life-threatening GI side effects, such as stomach or intestinal bleeding.

Most NSAIDs are not suitable for children or adolescents under the age of 18 years. Ibuprofen is the only NSAID approved for children aged three months and older.

Most NSAIDs should not be taken during the last three months of pregnancy or while breastfeeding except on a doctor&rsquos advice.


Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi-221 005, India

* Corresponding author: R. P. Gupta e-mail: [email protected], [email protected]

Received October 2020 Revised March 2021 Published June 2021

Fund Project: The work of the third author (Shivam Saxena) is supported by Council of Scientific and Industrial Research (No.09/013(0721)/2017)

The manuscript aims to investigate the qualitative analysis of a plankton-fish interaction with food limited growth rate of plankton population and non-constant harvesting of fish population. The ecological feasibility of population densities of both plankton and fish in terms of positivity and boundedness of solutions is shown. The conditions for the existence of various equilibrium points and their stability are derived thoroughly. This study mainly focuses on how the harvesting affects equilibrium points, their stability, periodic solutions and bifurcations in the proposed system. It is shown that the system exhibits saddle-node bifurcation in the form of a collision of two interior equilibrium points. Existence conditions for the occurrence of Hopf-bifurcation around interior equilibrium points are discussed. Lyapunov coefficients are examined to check the stability properties of these periodic solutions. We have also plotted the bifurcation diagrams for saddle-node, transcritical and Hopf bifurcations. A detailed algorithm for the occurrence of Bogdanov-Takens bifurcation is derived and finally some numerical simulations are also carried out to validate the theoretical results. This work suggests that the harvesting of fish population can change the dynamics of the system, which may be useful for the ecological management.

References:

N. D. Barlow, Harvesting models for resource-limited populations, N. Z. J. Ecol., 10 (1987), 129-133. Google Scholar

G. Birkhoff and G.-C. Rota, Ordinary Differential Equations, Ginn, Boston, 1982. Google Scholar

R. Bogdanov, Bifurcations of a limit cycle for a family of vector fields on the plan, Selecta. Math. Soviet., 1 (1981), 373-388. Google Scholar

R. Bogdanov, Versal deformations of a singular point on the plan in the case of zero eigenvalues, Selecta. Math. Soviet., 1 (1981), 389-421. Google Scholar

S. N. Chow and J. K. Hale, Methods of Bifurcation Theory, Springer-Verlag, New York, Berlin, 251, 1982. Google Scholar

S. N. Chow and H. D. Zhang, The qualitative analysis of two species predator-prey model with Holling's type III functional response, Appl. Math. Mech., 7 (1986), 73-80. doi: 10.1007/BF01896254. Google Scholar

X. Dou and Y. Li, Almost periodic solution for a food-limited population model with delay and feedback control, Int. J. Comput. Math. Sci., 5 (2011), 174-179. Google Scholar

K. Gopalsamy, M. R. S. Kulenovic and G. Ladas, Time lags in a food-limited population model, Appl. Anal., 31 (1988), 225-237. doi: 10.1080/00036818808839826. Google Scholar

K. Gopalsamy, M. R. S. Kulenovic and G. Ladas, Environmental periodicity and time delays in a food-limited population model, J. Math. Anal. Appl., 147 (1990), 545-555. doi: 10.1016/0022-247X(90)90369-Q. Google Scholar

R. P. Gupta and P. Chandra, Bifurcation analysis of modified Leslie-Gower predator-prey model with Michaelis-Menten type prey harvesting, J. Math. Anal. Appl., 398 (2013), 278-295. doi: 10.1016/j.jmaa.2012.08.057. Google Scholar

T. G. Hallam and J. T. De Luna, Effects of toxicants on populations: A qualitative approach III. Environmental and food chain pathways, J. Theor. Biol., 109 (1984), 411-429. doi: 10.1016/S0022-5193(84)80090-9. Google Scholar

J. Huang, Y. Gong and S. Ruan, Bifurcation analysis in a predator-prey model with constant-yield predator harvesting, Disc. Cont. Dyn. Syst. B, 18 (2013), 2101-2121. doi: 10.3934/dcdsb.2013.18.2101. Google Scholar

D. Jiang, N. Shi and Y. Zhao, Existence, uniqueness, and global stability of positive solutions to the food-limited population model with random perturbation, Math. and Comp. Modelling, 42 (2005), 651-658. doi: 10.1016/j.mcm.2004.03.011. Google Scholar

Y. A. Kuznetsov, Elements of Applied Bifurcation Theory, Appl. Math. Sciences, Springer-Verlag, New York, 112, 2004. doi: 10.1007/978-1-4757-3978-7. Google Scholar

B. Leard, C. Lewis and J. Rebaza, Dynamics of ratio-dependent predator prey models with non constant harvesting, Disc. Cont. Dyn. Syst. S, 1 (2008), 303-315. doi: 10.3934/dcdss.2008.1.303. Google Scholar

P. Lenzini and J. Rebaza, Nonconstant predator harvesting on ratio-dependent predator-prey models, Appl. Math. Sciences, 4 (2010), 791-803. Google Scholar

D. Li and M. Liu, Invariant measure of a stochastic food–limited population model with regime switching, Math. Comput. Simul., 178 (2020), 16-26. doi: 10.1016/j.matcom.2020.06.003. Google Scholar

Z. Li and M. He, Hopf bifurcation in a delayed food-limited model with feedback control, Nonlinear Dyn., 76 (2014), 1215-1224. doi: 10.1007/s11071-013-1205-0. Google Scholar

W. Liu, C. Fu and B. Chen, Hopf bifurcation and center stability for a predator–prey biological economic model with prey harvesting, Commun. Nonlinear. Sci. Numer. Simulat., 17 (2012), 3989-3998. doi: 10.1016/j.cnsns.2012.02.025. Google Scholar

P. Liu, J. Shi and Y. Wang, Periodic solutions of a logistic type population model with harvesting, J. Math. Anal. Appl., 369 (2010), 730-735. doi: 10.1016/j.jmaa.2010.04.027. Google Scholar

O. P. Misra and R. Babu, A model for the effect of toxicant on a three species food chain system with Food–Limited growth of prey population, Glob. J. Math. Anal., 2 (2014), 120-145. doi: 10.14419/gjma.v2i3.2990. Google Scholar

P. Panja and S. K. Mondal, Stability analysis of coexistence of three species prey-predator model, Nonlinear Dyn., 81 (2015), 373-382. doi: 10.1007/s11071-015-1997-1. Google Scholar

P. Panja, S. K. Mondal and D. K. Jana, Effect of toxicants of phytoplankton-zooplankton-fish dynamics and harvesting, Chaos Soliton Fract., 104 (2017), 389-399. doi: 10.1016/j.chaos.2017.08.036. Google Scholar

P. Panja, Plankton population and cholera disease transmission: A mathematical modeling study, Int. J. Bifurcat. Chaos, 30 (2020), 2050054(16). doi: 10.1142/S0218127420500546. Google Scholar

L. Perko, Differential Equations and Dynamical Systems, Springer, New York, 1996. doi: 10.1007/978-1-4684-0249-0. Google Scholar

E. C. Pielou, An Introduction to Mathematical Ecology, Wiley, New York, 1969. Google Scholar

F. E. Smith, Population dynamics in Daphnia magna and a new model for population growth, Ecology, 44 (1963), 651-663. doi: 10.2307/1933011. Google Scholar

J. W.-H. So and J. S. Yu, On the uniform stability for a food-limited population model with time delay, Proc. Roy. Soc. Edinburgh Sect. A, 125 (1995), 991-1002. doi: 10.1017/S0308210500022605. Google Scholar

S. Tang and L. Chen, Global attractivity in a food-limited population model with impulsive effects, J. Math. Anal. Appl., 292 (2004), 211-221. doi: 10.1016/j.jmaa.2003.11.061. Google Scholar

Y. Tao, X. Wang and X. Song, Effect of prey refuge on a harvested predator-prey model with generalized functional response, Commun. Nonlinear. Sci. Numer. Simulat., 16 (2011), 1052-1059. doi: 10.1016/j.cnsns.2010.05.026. Google Scholar

A. Wan and J. Wei, Hopf bifurcation analysis of a food-limited population model with delay, Nonlinear Anal. Real World Appl., 11 (2010), 1087-1095. doi: 10.1016/j.nonrwa.2009.01.052. Google Scholar

J. Wang, L. Zhou and Y. Tang, Asymptotic periodicity of a food-limited diffusive population model with time-delay, J. Math. Anal. Appl., 313 (2006), 381-399. doi: 10.1016/j.jmaa.2005.03.036. Google Scholar

G. S. K. Wolkowicz, Bifurcation analysis of a predator-prey system involving group defence, SIAM J. Appl. Math., 48 (1988), 592-606. doi: 10.1137/0148033. Google Scholar

D. Xiao and S. Ruan, Bogdanov-Takens bifurcations in predator prey systems with constant rate harvesting, Fields Inst. Commun., 21 (1999), 493-506. Google Scholar

J. Zhou and J. Shi, The existence, bifurcation and stability of positive stationary solutions of a diffusive Leslie-Gower predator-prey model with Holling-type II functional responses, J. Math. Anal. Appl., 405 (2013), 618-630. doi: 10.1016/j.jmaa.2013.03.064. Google Scholar


Materials and methods

Data and preprocessing

Table S1 shows the statistics of samples and probes/genes in TCGA multi-omics data. See Supplementary Information for detail preprocesses.

Evaluating the robustness of LncRIndiv

Using LncRIndiv, the quantitative lncRNA expression profile from the atlas of non-coding RNAs in cancer was transformed to an IDElncRNA profile, which defines lncRNA expression as upregulated, downregulated, or unaltered in each breast invasive carcinoma (BRCA) sample. From the lncRNA expression profile of 105 paired cancer-normal samples, we randomly selected 80% of overall paired samples (84 pairs) as the training set and the rest as the test set to perform a five-fold cross-validation test. The sample size of the normal samples was sufficient for stable lncRNA pair identification [6]. For each iteration, the LncRIndiv was applied to the training set to generate the IDElncRNAs’ reference criterion. To evaluate the performance of LncRIndiv, we validated IDElncRNAs in the test set. For example, if lncRNA-A was identified as upregulated/downregulated in the training set, we calculated its delta value (cancer-normal) in the test set. The average accuracy of lncRNA-A was defined as the number of positive/negative delta values divided by the total number of test sets. The average accuracy of both lncRNAs and samples was calculated.

Identifying BRCA over-represented and subtype-specific lncRNAs

BRCA subtype information was available in The Cancer Genome Atlas (TCGA) following the classification standards: PAM50 and CSCO [18]. See Supplementary Information for details.

Identifying prognosis-related lncRNAs and TNBC classification

Pathway analysis of TNBC subtypes

Characterization of the tumor immune microenvironment

The immunomodulator list and single nucleotide variants (SNV)-derived neoantigens were obtained from Vesteinn et al. [19]. The homologous recombination deficiency (HRD) score based on the loss of heterozygosity, telomeric allelic imbalance, and large-scale transitions were attained from the study of Knijnenburg et al. [20].

We extracted TCGA BRCA mRNA expression profile characterized by transcripts per million from gene expression omnibus (Accession number GSE62944) (https://www.ncbi.nlm.nih.gov/geo/) and performed CIBERSORT, TIMER, and xCELL methods to evaluate immune cell compositions [21,22,23,24]. LncRNA and its related immune pathways were attained from the ImmLnc database which calculated enrichment score (lncRES scores) for lncRNAs-pathways pairs (http://bio-bigdata.hrbmu.edu.cn/ImmLnc/jt-download.jsp) [25]. See Supplementary Information for details.

Cell culture and transfection

Wound healing assay

Transwell assay

Immunofluorescence

RNA extraction and quantitative real-time PCR

Protein extraction and western blot

Validation of TNBC subtype in CCLE

Reverse phase protein array (RPPA) datasets of cell lines and pharmacologic profiles of 24 anticancer drugs across CCLE lines are available at https://data.broadinstitute.org/ccle/. The drug response was evaluated as activity area (ActArea) values. Cell lines were screened to obtain TNBC cell lines according to the receptor status reported in a previous review [26]. See Supplementary Information for details.

Identifying BRCA drug response-related IDElncRNA

Statistical analysis


Samenstelling werkgroep

Werkgroep Somatoforme klachten en stoornissen

Vice-voorzitter (en werkgroeplid)

Nederlandse Vereniging voor Psychiatrie

Nederlands Huisartsen Genootschap

Nederlands Huisartsen Genootschap

Nederlandse Vereniging voor Psychotherapie

Nederlandse Internisten Vereeniging

Mw. drs. K. Bozelie (vanaf 270807)

Verpleegkundigen & Verzorgenden Nederland,

Dhr. drs. R. Broeders (vanaf 010108)

Nederlandse Vereniging voor

Nederlandse Vereniging voor Psychiatrie

Dhr. dr. D.J.S. Donker (tot 011207)

Nederlands Instituut van Psychologen

Nederlands Instituut van Psychologen

Dhr. J.L.M. van Gestel (tot 050109)

Koninklijk Nederlands Genootschap voor

Nederlandse Vereniging van Revalidatieartsen

Mw. prof.dr. H.E. van der Horst

Nederlands Huisartsen Genootschap

Nederlandse Vereniging voor Arbeids- en

Mw. drs. C.H. Kaufmann (tot 200307)

Nederlandse Vereniging voor Psychotherapie

Nederlandse Vereniging voor Neurologie

Dhr. prof.dr. H.E. Kremer (tot 250907)

Nederlandse Vereniging voor Neurologie

Nederlands Huisartsen Genootschap

Nederlands Instituut van Psychologen

Nederlandse Vereniging voor Psychotherapie

Mw. B. Vanderschuren (tot 260607)

Verpleegkundigen & Verzorgenden Nederland,

Nederlandse Vereniging voor Obstetrie en

Mw. drs. J.W. Hagemeijer (tot 010908)

Kwaliteitsinstituut voor de Gezondheidszorg


18. Publications

Amitai Y, Almog S, Singer R, Hammer R, Bentur Y, Danon YL. Atropine poisoning in children during the Persian Gulf crisis. A national survey in Israel. JAMA 1992 Aug268(5):630-2. [PubMed Citation]

ATSDR. Medical Management Guidelines for Nerve Agents: Tabun (GA) Sarin (GB) Soman (GD) and VX

CAS# Tabun (GA) 77-81-6, Sarin (GB) 107-44-8, Soman (GD) 96-64-0, VX 5078269-9

Cannard K. The acute treatment of nerve agent exposure J Neurol Sci 2006 249:86-94. [PubMed Citation]

DHHS/FDA Emergency Preparedness and Response-Counterterrorism and Emerging Threats (12/01/2011)

Ellenhorn, M.J., S. Schonwald, G. Ordog, J. Wasserberger, eds. Ellenhorn's Medical Toxicology: Diagnosis and Treatment of Human Poisoning, 2nd ed. Baltimore, MD: Williams and Wilkins, 1997, p. 843

Lynch M. Atropine Use in Children After Nerve Gas Exposure. J Pediatric Nursing 2005 20(6):477-84. [PubMed Citation]

Martin J, et al., eds. British National Formulary, No. 58. London, UK: BMJ Group, RPS Publishing, 2009 p. 703

McDonough JH Jr, Zoeffel LD, McMonagle J, Copeland TL, Smith CD, Shih TM. Anticonvulsant treatment of nerve agent seizures: anticholinergics versus diazepam in soman intoxicated guinea pigs. Epilepsy Research 2000 Jan38(1):1-14. [PubMed Citation]

McEvoy GK, ed. Drug Information 2012. Bethesda, MD: American Society of Health-System Pharmacists, 2012 p. 1277-89

McEvoy GK, ed. Drug Information 2012. Bethesda, MD: American Society of Health-System Pharmacists, 2012 p. 3628-30

Nambiar MP, Gordon RK, Rezk PE, Katos AM, Wajda NA, Moran TS, Steele KE, Doctor BP, Sciuto AM. Medical countermeasure against respiratory toxicity and acute lung injury following inhalation exposure to chemical warfare nerve agent VX.Toxicology and Applied Pharmacology 2007 Mar219(2-3):142-150 [PubMed Citation]

Product Label: ATNAA (atropine and pralidoxime chloride)
[Meridian Medical Technologies Inc.] Last revised May 2007[DailyMed]

Product Label: ATROPEN auto-injector (atropine sulfate) injection
[Meridian Medical Technologies, Inc.] Last revised: July 2007 [DailyMed]

Product Label: ATROPINE CARE (atropine sulfate) solution/drops [Akorn, Inc.] Last revised: June 2012 [DailyMed]

Product Label: ATROPINE SULFATE ointment
[Bausch & Lomb Incorporated] Last revised: Aug 2012 [DailyMed]

Product Label: ATROPINE SULFATE injection
[West-ward Pharmaceutical Corp.] Last revised: November 2011[DailyMed]

Product Label: DUODOTE (atropine and pralidoxime chloride) kit
[Meridian Medical Technologies , Inc.] Last revised Sep 2009[DailyMed]

Rajpal S, Mittal G, Sachdeva R, Chhillar M, Ali R, Agrawal SS, Kashyap R, Bhatnagar A. Development of atropine sulfate nasal drops and its pharmacokinetic and safety evaluation in healthy human volunteers. Environ Toxicol Pharmacol. 200927:206-11. [PubMed Citation]

Rotenberg JS, Jonathan Newmark J. Nerve Agent Attacks on Children: Diagnosis and Management. Pediatrics 2003 112:648-58. [PubMed Citation]

Schier JG, Ravikumar PR, Nelson LS, MD, Heller MB, Mary Ann Howland MA, Hoffman RS. Preparing for Chemical Terrorism: Stability of Injectable Atropine Sulfate. Acad Emerg Med. 2004 April 11(4):329-34. [PubMed Citation]

Shih TM, Duniho SM, McDonough JH. Control of nerve agent-induced seizures is critical for neuroprotection and survival. Toxicology and Applied Pharmacology 2003 188: 69-80. [PubMed Citation]

Weinbroum AA, Rudick V, Paret G, Kluger Y, Ben Abraham R. Anaesthesia and critical care considerations in nerve agent warfare trauma. Resuscitation 2000 Oct47(2):113-23. [PubMed Citation]

Yanagisawa N Morita H, Nakajima T. Sarin experiences in Japan: Acute toxicity and long-term effects. J Neurol Sci. 2006 Nov249(1):76-85. [PubMed Citation]


Watch the video: Salbutamol Ventolin: Mechanism of Action (December 2022).