Kisspeptin (Kp-10) inhibits in vitro osteogenic differentiation of multipotent mesenchymal stromal cells extracted from the bone marrow of adult rats.

Kisspeptin (Kp-10) is a neuropeptide that binds to GPR54 receptors, exerting several functions mainly in the nervous and reproductive systems of the body. However, its effects and mechanisms of action on the skeletal system remain poorly understood. This study evaluated the effects of different concentrations of Kp-10 on in vitro osteogenic differentiation of multipotent mesenchymal stromal cells (MSCs) extracted from the bone marrow (BM) of adult Wistar rats. Two-month-old female rats were euthanized to extract BM from long bones to obtain MSCs. Four experimental groups were established in vitro: a control and Kp-10 at concentrations of 0.01, 0.05 and, 0.1 µg/mL. After induction of osteogenic differentiation, cell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)- 2,5-diphenyl tetrazolium bromide (MTT) assay, alkaline phosphatase activity, collagen synthesis, percentage of area covered by MSCs/field and mineralized nodules/field, and immunocytochemistry of the GPR54 receptor tests. Furthermore, evaluation of gene transcripts for type I collagen, Runx-2, Bmp-2, bone sialoprotein, osteocalcin and osteopontin was performed using real-time RT-qPCR. It was observed that MSCs expressed GPR54 receptor to which Kp-10 binds during osteogenic differentiation, promoting a negative effect on osteogenic differentiation. This effect was observed at all the Kp-10 concentrations in a concentration-dependent manner, characterized by a decrease in the activity of alkaline phosphatase, collagen synthesis, mineralized nodules, and decreased expression of gene transcripts for type I collagen, osteocalcin, osteopontin, and Runx-2. Thus, Kp-10 inhibits in vitro osteogenic differentiation of MSCs extracted from the BM of adult Wistar rats.

Statistical control of the production of blood components by control charts of attribute to improve quality characteristics and to comply with current specifications.

Statistical process control (SPC) is closely related to good quality control practices in the manufacturing process. One of the primary goals is to detect unnatural patterns, allowing the production service to control the conformity of the blood components produced. Despite being recommended by national and international standards, its exercise is not uniform, and sometimes the methodology used is misinterpreted as SPC. When the input data has a Gaussian distribution, control charts for variables are proposed. However, when the data distribution is not normal, control charts for attributes are suggested. This article presents and discusses four statistical procedures for the control of attributes using p-, np-, u-, and c-charts. An empirical demonstration shows these models are reliable for in routine use in the Blood Establishment quality control, as also suggests the use when the control charts for variables are inapplicable.

Statistical methods to the control of the production of blood components: principles and control charts for variables.

General quality control good practices require the control of the production of blood components using statistical techniques, such as mandatory by the European Commission Directives and the American Association of Blood Banks standards. Sometimes, the control procedure is exclusively in favor of the compliance verification with specifications per individual component or to compute the number of defective parts usually on a monthly basis. However, this is a critical restriction to detect unnatural patterns such as to guarantee that the production has a non-significance chance to manufacturing nonconforming components. Therefore, a crucial issue in Blood Establishments is the application of a reliable statistical process control methodology to assure products reliable and consistent to specifications. Statistical principles and control charts for variables are reviewed, discussed and recommended, based on current good practices. The empirical data demonstrate the consistency of these models on blood establishment routine. A flowchart to select the type of control chart is suggested.

Sampling methods to the statistical control of the production of blood components.

The control of blood components specifications is a requirement generalized in Europe by the European Commission Directives and in the US by the AABB standards. The use of a statistical process control methodology is recommended in the related literature, including the EDQM guideline. The control reliability is dependent of the sampling. However, a correct sampling methodology seems not to be systematically applied. Commonly, the sampling is intended to comply uniquely with the 1% specification to the produced blood components. Nevertheless, on a purely statistical viewpoint, this model could be argued not to be related to a consistent sampling technique. This could be a severe limitation to detect abnormal patterns and to assure that the production has a non-significant probability of producing nonconforming components. This article discusses what is happening in blood establishments. Three statistical methodologies are proposed: simple random sampling, sampling based on the proportion of a finite population, and sampling based on the inspection level. The empirical results demonstrate that these models are practicable in blood establishments contributing to the robustness of sampling and related statistical process control decisions for the purpose they are suggested for.