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Till nowadays microbiological assay is still widely used with several antibiotics that are composed of a mixture of related active compounds. However, obtaining a reasonably valid determination of the potency is dependent on the validity and the suitability of the assay design. The present work aimed to validate an assay design of an aminoglycoside antibiotic (Gentamicin Sulfate) using a two-dose Parallel Line Model agar diffusion assay in a large 8×8 rectangular plate. All preparatory procedures were done following United States Pharmacopeia and the Inhibition Zones were measured using a digital caliper to the nearest 0.01 mm. Analysis of variance of compendial requirements of regression and parallelism were found to be satisfactorily meeting the acceptance criteria. Specificity was achieved for the product under investigation with no detectable IZ that could be found for all components except the antibiotic. The validation method showed acceptable linearity of r2≥0.98. Accuracy and precision parameters showed RSD (%)

Background: Cancer is one of the most devastating diseases that influence humanity in the modern era. The effect is not confined to morbidity and mortality, but it is also extended to social and economic consequences despite the advancements made in the medical and healthcare fields. The present case overview will focus on the overall long-term assessment of death rates from malignancy from 1960 to 2017 as part of a global study of the mortality trend of this disease. Methods: Statistical analysis and process control methodologies were used to study the death rate trend over this recorded period using statistical programs platform. A combination of techniques that could be used sequentially after database processing and stratification were used including distribution fitting, descriptive statistics, data fitting mathematical pattern, Gaussian Mixture Model (GMM) box plot and Individual-Moving Range (I-MR) trending chart. Results: Two-parameter Weibull distribution (or Weibull 2) was the most appropriate that has fitted the distribution pattern of data and used for the construction of the process-behavior chart. The last two decades of the dataset showed a progressive decline in the mortality rates, which were almost linear with a higher magnitude of a negative slope than that of the initial rising pattern from 1960 until the 1990s. The hump-shaped trend showed underlying two distribution clusters: An initial distribution (I) of 81% of data of higher average mortality rates and minor one (II) that covers the last decade of monitoring record. Discussion: A significant improvement in cancer healthcare was witnessed with a noticeable breakthrough in the last decade in the USA., {"references":["Weatherall D, Greenwood B, Chee HL, Wasi P. Science and technology for disease control: past, present, and future. 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PA, USA: Minitab Inc., State College; 2014:73.","Falzone L, Salomone S, Libra M. Evolution of cancer pharmacological treatments at the turn of the third millennium. Front Pharmacol 2018;9:1300. doi: 10.3389/fphar.2018.01300.","Zarros A, Tansey T. Pharmaceutical innovation after World War II: from rational drug discovery to biopharmaceuticals. Front Pharmacol. 2019;10:834. doi: 10.3389/fphar.2019.00834.","Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep. 2014;129(suppl 2):19-31. doi: 10.1177/00333549141291S206.","Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CMet al. Cancer treatment and survivorship statistics, 2019. CA: Cancer J Clin. 2019;69(5):363-85. doi: 10.3322/caac.21565.","Allart-Vorelli P, Porro B, Baguet F, Michel A, Cousson-Gélie F. Haematological cancer and quality of life: a systematic literature review. Blood Cancer J. 2015;5(4):e305. doi: 10.1038/bcj.2015.29.","Eissa ME, Seif M, Fares M. Assessment of purified water quality in pharmaceutical facility using six sigma tools. Int. J Pharm Qual Assur. 2015;6(2):54-72.","Eissa ME. Variable and attribute control charts in trend analysis of active pharmaceutical components: Process efficiency monitoring and comparative study. Experimental Medicine (EM) 2018;1(1):32-44. doi: 10.31058/j.em.2018.11003.","Eissa M. The attribute control charts for outbreak trends o f selected states in the USA: a brief report of the insight into the pattern. Int Med. 2019;1(1):11-4. doi: 10.5455/im.31744.","Sheikh K. Cancer Death Rate in U.S. Sees Sharpest One-Year Drop [Internet]. Nytimes.com. 2020. Available from: https://www.nytimes.com/2020/01/08/health/cancer-deaths-decline.html (accessed March 2020).","Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7-30. doi: 10.3322/caac.21590.","American Cancer Society. Cancer mortality continues steady decline, driven by progress against lung cancer: Drop of 2.2 percent from 2016 to 2017 is largest ever-reported [Internet]. ScienceDaily. 2020. Available from: https://www.sciencedaily.com/releases/2020/01/200108074809.htm (accessed March 2020).","Nadler DL, Zurbenko IG. Developing a weibull model extension to estimate cancer latency. ISRN Epidemiology 2012;2013. https://doi.org/10.5402/2013/750857.","Arteaga CL, Adamson PC, Engelman JA, Foti M, Gaynor RB, Hilsenbeck SG, et al. AACR Cancer Progress Report 2014. Clin Cancer Res. 2014; 20(19 Suppl):S1-S112. doi: 10.1158/1078-0432.CCR-14-2123."]}

Commercial products in markets must meet the regulatory and quality control criteria to be acceptable for the intended use. While it is mandatory that each product batch must meet specification limits, the stability and efficiency over the long term are underestimated. The present study was conducted on the chronological trend of a healthcare product using a statistical software package, including correlation matrix and multivariate analysis. The investigated quality characteristics were the average filling weight, relative density, pH and the relative potency of three active components, in addition to a chemical preservative. The database was processed in an Excel sheet and was subjected to descriptive statistical overview, histogram plot, box plot diagram, time series plot, correlation matrix table and Principal Component Analysis. The investigation showed that despite all batches passed quality control tests successfully yet there were signs of instability, fluctuations or oscillations and drifts in all quality metrics, with outlier values that could be observed. Non-parametric correlation demonstrated some level of association between some inspection characteristic indicators. PCA illustrated the major variability influencer and clustering tendency among studied quality markers that guided the grouping of the dataset. The study pinpointed the improvement needed to ensure product stability, efficiency and quality., Piyasalardaki ticari ürünler, kullanım amacına uygun olarak kabul edilebilir olması için düzenleyici ve kalite kontrol kriterlerini karşılamalıdır. Her ürün serisinin spesifikasyon limitlerini karşılaması zorunlu olmakla birlikte, uzun vadede stabilite ve verimlilik hafife alınmaktadır. Bu çalışma, korelasyon matrisi ve çok değişkenli analiz içeren istatistiksel bir yazılım paketi kullanılarak bir sağlık ürününün kronolojik eğilimi üzerinde yürütülmüştür. Araştırılan kalite özellikleri, bir kimyasal koruyucuya ek olarak, ortalama dolum ağırlığı, bağıl yoğunluk, pH ve üç aktif bileşenin bağıl gücüydü. Veritabanı bir Excel sayfasında işlendi ve tanımlayıcı istatistiksel genel bakışa, histogram grafiğine, kutu grafiğine, zaman serisi grafiğine, korelasyon matrisi tablosuna ve Temel Bileşen Analizine tabi tutuldu. Araştırma, tüm serilerin kalite kontrol testlerini başarıyla geçmesine rağmen, tüm kalite ölçümlerinde gözlemlenebilen aykırı değerlerle birlikte istikrarsızlık, dalgalanmalar veya salınımlar ve sapmalar olduğunu gösterdi. Parametrik olmayan korelasyon, bazı denetim karakteristik göstergeleri arasında bir düzeyde ilişki olduğunu göstermiştir. PCA, veri kümesinin gruplandırılmasına rehberlik eden incelenen kalite belirteçleri arasındaki ana değişkenlik etkileyicisini ve kümelenme eğilimini gösterdi. Çalışma, ürün istikrarını, verimliliğini ve kalitesini sağlamak için gereken iyileştirmeyi belirledi.

Inventory management and control represent a crucial activity required in any successful organization for the modern industry generally and the healthcare field specifically. Rigorous monitoring of the goods inspection properties is important for the delivery of products with appropriate quality that meets customer needs. In the present study, random records have been selected that cover a year simulation period of monitoring for container deliveries to a warehouse that were used as primary packaging materials for topical healthcare products from three different manufacturers. Statistical Process Control (SPC) methodologies and analyses such as box plots, histograms, Pareto diagrams, process-behavior charts and Gaussian Mixture Model (GMM) were applied for processed and stratified data to evaluate a single product property. Integration between the material stock database and statistical processing platform was established through excel dataset and/or Comma-Separated Values (CSV) files where the results of the monitored inspection characteristic were reported for each freightage. The analysis showed that the most dominant supplier dispatched products with specifications that have become very close to the target value, despite initial unstable variations in the inspection characteristic. The less common manufacturer showed product quality values that are shifted slightly above the previous one with a lower rate of out-of-control alarms. The least contacted vendor demonstrated the highest precision (which might be partially accounted to a very few numbers of the received batches of the packaging product) with the lowest accurate values that were very close to the upper specification limit. The study was useful in manufacturers' quality assessment and follow-ups.

Control charts are a valuable assessment tool in the healthcare industry. The ease of use of these trending charts is crucial to obtain timely important results with minimum time and efforts. The current case showed analysis of non-normal data to obtain control charts with useful output without using exhaustive different means of transformation and/or omitting aberrant numbers. Raw results for the quality of purified water from water treatment plant that converts municipal water to purified water were collected from two points-of-use - (ζ and Ψ). Data gathered were conductivity and total organic carbon (TOC) measurements. The statistical processing and control charts were done using commercial statistical software package. Statistical analysis of data showed that conductivity and TOC results of both points did not follow Gaussian distribution except TOC of point Ψ where it passed normality test, but they were closest to other distributions. There were several observations of outlier values from the results. Moreover, data normalization did not improve after removal of the extreme values. Data were switched to be interpreted using Laney-modified attribute control charts and compared with the original results drawn using individual-moving range (I-MR). Interestingly, both types of control charts agreed regarding control limits and some alarm points. I-MR and Laney-attribute charts could be used for non-normal data with unusual other types of distributions that may not be suitable for conventional types of control charts with the variable charts possess greater sensitivity of alarm detection over the attribute charts.