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Measurement of Electrolytic Conductance
Published in Grinberg Nelu, Rodriguez Sonia, Ewing’s Analytical Instrumentation Handbook, Fourth Edition, 2019
Stacy L. Gelhaus, William R. LaCourse
In addition to monitoring water for trace contaminants, suppressed conductivity detection is widely used for drug monitoring. Metformin (N,N-dimethylimidodicarbonimidic diamide) is an antidiabetic drug used to treat type 2 diabetes and is also used in treating polycystic ovary syndrome. Metformin must be tested for residual reactants, as the reaction conditions can cause the formation of dimethylnitrosamine, a suspected human carcinogen. Thus, drug manufacturers must monitor impurities before releasing a drug product. Thermo Scientific uses IC with cation-exchange separation and suppressed conductivity detection to separate and detect dimethylamine.40 Similarly, tolterodine, a compound used for treating urinary incontinence caused by abnormal bladder contraction, must be assayed for the counter ion tartrate to ensure proper levels and check the amount of active pharmaceutical in the sample. Using IC with suppressed conductivity detection, an improved method for analyzing tolterodine drug products is reported.41 Drug analysis is also performed by IC with suppressed conductivity detection to determine the sulfate counter ion and anionic impurities in aminoglycoside drug substances, a class of antibiotics used for severe infections of the abdomen and urinary tract, with common drugs including gentamicin, tobramycin, amikacin, and streptomycin. 35
Signaling and Architectural Cues Necessary for 3D Diabetic Tissue Models
Published in Karen J.L. Burg, Didier Dréau, Timothy Burg, Engineering 3D Tissue Test Systems, 2017
Rosalyn D. Abbott, David L. Kaplan
Type I diabetes, historically referred to as juvenile diabetes or insulin-dependent diabetes, most commonly develops at an early age; however, can also develop in adults. In type I diabetes, the body's immune system attacks the pancreatic β-cells that secrete insulin, creating an insulin deficiency throughout the body. Daily insulin injections are required to control blood glucose levels. Type I diabetes usually involves either a genetic predisposition or is triggered by environmental cues such as an infection or other stresses (Leslie and Delli Castelli 2004; Loghmani 2005). Type II diabetes, historically referred to as adult-onset diabetes or noninsulin-dependent diabetes, can affect people at any age, even children. Type II diabetes is usually diagnosed when patients become insulin resistant—a condition that occurs when liver, skeletal muscle, and adipose tissues do not respond effectively to insulin stimulation. As a result, the body requires more insulin to regulate glucose properly. Initially, the pancreas increases insulin secretion to counter the decreased responsiveness of the tissues; however, over time the pancreas function declines and does not produce enough insulin (for instance after meals) and treatment is required. People who are more likely to develop type II diabetes are: Inactive, overweight, have a genetic predisposition, or have another condition associated with insulin resistance (for example, polycystic ovary syndrome) (Loghmani 2005).
Reprotoxic and Endocrine Substances
Published in Małgorzata Pośniak, Emerging Chemical Risks in the Work Environment, 2020
Katarzyna Miranowicz-Dzierżawska
Bisphenol A has also been found to cause uterine and ovarian cancers, diabetes, obesity, metabolic syndrome, precocious puberty, impaired genital development and fertility disorders, and difficulties with conception and maintaining pregnancy. It is assumed that the compound can also induce polycystic ovary syndrome. Studies also confirm its effects on sperm quality. Prolonged exposures to BPA can also contribute to an increased risk of prostate cancer [Kulik-Kupka et al. 2017].
Producing Parenthood: Islamic Bioethical Perspectives & Normative Implications
Published in The New Bioethics, 2020
Aasim I. Padela, Katherine Klima, Rosie Duivenbode
Female factor infertility may be caused by ovulation disorders, genetic factors or structural abnormalities. Ovulation disorders, such as polycystic ovary syndrome (PCOS), can be treated by lifestyle adjustments, medication, and at times, surgery. Endocrine disorders such as hyperprolactinemia, thyroid and adrenal disease also impact ovulation and are treated with medications, and at times, surgery. Should these treatments fail, ovulation induction can be attempted and combined with IUI or IVF When ovulation cannot be achieved, the only option is ovum donation with IVF or gestational surrogacy (Guillén Antón and García Velasco 2011, The American College of Obstetricians and Gynecologists 2019). Ova donation is an option primarily when genetic factors are in play, or when couples are concerned about passing on genetic diseases. Infertility in women may also be caused by structural abnormalities of the fallopian tubes, uterus, or pelvis. For tubal factor infertility IVF can be a viable option. Women with absolute uterine factor infertility, who are incapable of gestating a child, traditionally relied on gestational surrogacy or, adoption, but can now undergo uterine transplantation (Johannesson and Järvholm 2016, p. 43, Jones 2016, Chmel 2019). Finally, where state law permits, embryo donation may be an option for couples who are unable to conceive despite treatment.
The obesogen tributyltin induces features of polycystic ovary syndrome (PCOS): a review
Published in Journal of Toxicology and Environmental Health, Part B, 2018
Eduardo Merlo, Ian V. Silva, Rodolfo C. Cardoso, Jones B. Graceli
Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology and is the most common cause of anovulatory infertility in women (Dumesic et al. 2015; Norman et al. 2007). In addition to reproductive perturbations, PCOS is associated with cardio-metabolic risk factors, including insulin resistance, obesity, and dyslipidemia (Dunaif 1997; Ehrmann 2005; Essah and Nestler 2006; Franks 1995). Indeed, the incidence of PCOS is higher in overweight and/or obese women (Álvarez-Blasco et al. 2006).
Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence
Published in The Imaging Science Journal, 2023
M. Sumathi, P. Chitra, S. Sheela, C. Ishwarya
Artificial intelligence (AI) combines reasoning, learning, vision, problem-solving, language comprehension, etc. A basic introduction to the field of artificial intelligence heralds a new era in human history and opens the door for future descriptions of computers that replicate human nature in connection with ‘cognitive’ processes of the mind like ‘learning’ and ‘problem solving’. Real-world problems can be solved very easily and with high accuracy if AI is believed to exist. Healthcare industries are now using AI to diagnose patients more quickly and accurately than humans do. This technology was widely employed by researchers to automatically classify ultrasound images. In order to diagnose diseases, it is able to ‘learn’ traits from enormous amounts of data through clinical practices and also able to eliminate irrelevant information accurately and precisely identify diseases. A common endocrine condition called PCOS can have severe effects on a woman's ability to reproduce throughout her reproductive years [1]. A working party of the European Society of Human Reproduction and Embryology (ESHRE) has stressed the importance of being vigilant for the long-term health of women with PCOS [2–4]. The majority of endocrinology conditions affecting women include polycystic ovary syndrome (PCOS), which affects 8% to 13% of women of reproductive age [5,6]. A sizable international consortium was established to rigorously review the evidence, develop evidence-based guidelines on diagnosis and management, and publish them in 2018. This was done in response to the abundance of potential diagnostic schemes, treatment options, and frequently conflicting recommendations [7,8]. PCOS is a highly adaptable syndrome in contemporary products. However, it was made apparent in this published guideline that there are still a lot of difficulties in identifying and treating PCOS. Classification of data related to PCOS is necessary to identify the PCOS affected cases and not affected cases accurately. Here classification is done using various parameters of feature extraction along with comparing trained dataset. There are a few algorithms like SVM and CNN being used to classify an image.