Background: Gastric cancer is one of the most common cancers in the world. There are many genomic and molecular factors that cause gastric cancer to occur. Also, many markers that associate with tumor invasiveness have been known. E-cadherin is a calcium- mediated cell adhesion molecule. In some studies, abnormal expression of E-cadherin has been seen in gastric carcinoma. However, in the studies done there has been some conflicting information about abnormal expression of this marker in a variety of gastric carcinoma and also about the expression of this marker and its correlation with various clinicopathologic factors of tumor.Subjects and Methods: A case control study was performed on total or partial gastrectomy tissue samples obtained from 70 patients with gastric cancer and adjacent non-neoplastic tissues. The immunohistochemistry was used to assess E-cadherin expression. The correlation between abnormal E-cadherin expression and tumor histopathology was evaluated in all patients.Results: Among 70 patients who were analyzed, 48.6% showed abnormal E-cadherin expression. A significant correlation was seen between abnormal E-cadherin expression and tumor stage, grade, lymph node metastasis, tumor phenotype, tumor type, depth of invasion and age.Conclusion: Abnormal E-cadherin expression is a common phenomenon in gastric cancer. Because there was a strong correlation between abnormal E-cadherin expression and tumor stage, tumor grade, depth of invasion and regional lymph node involvement, this marker may be used as a predictive factor for tumor invasiveness in gastric cancer. In some studies, abnormal expression of E-cadherin is found in gastric carcinoma.13 However, in current studies, there are some conflicting information about abnormal expression of this marker in a variety of gastric carcinoma and also about its correlation with various clinicopathologic factors of tumor.Cadherin is a group of cell adhesion molecules with glycoprotein nature that are located in the membrane of the cell. These molecules are located in combination with other cell adhesion molecules (such as Catenins) and regulate various biological important processes such as cell migration, differentiation, proliferation and cell death (apoptosis).Two types of Cadherins that have been most studied are E-cadherin  and N-Cadherin .14increasing the prevalence of gastric cancer in different communities, including our country, Iran, lack of appropriate treatment response , shortcomings and contradictions that exist in many previous studies as well as insufficient studies in this field, we decided to investigate the expression of E-cadherin in all different kinds of gastric cancer and its correlation with each of mentioned parameters. It is hoped that this study can be a step towards better recognition of gastric cancer behavior and it can also be effective in the way of intervention and follow-up of these patients.Due to direct correlation between the risk of metastatic disease that is directly related to patient prognosis, and clinicopathologic factors We reviewed the tissue obtained from gastrectomy samples in 70 patients. We obtained two different tissue samples from each patient. One, from the area with malignant cells and one from the area with normal cells. This study was an analytic case-control design and was done on two groups. Group one included tissue samples with cancerous cells and group two included tissue samples with normal cells. It aimed to investigate the correlation between abnormal expressions of E-cadherin and clinicopathologic features of tumor in gastric cancer, on paraffin blocks available at Archives of Pathology in Imam Khomeini Hospital in Sari, Iran. There are 70 samples in both groups.Sample size was calculated through the formula Pokak. The clinicopathologic parameters that were evaluated in this study included age, gender, tumor type, tumor location, tumor size, tumor phenotype, histological grade, lymph node metastasis (N), distant metastasis (M) and stage.In order to compare the expression of E-cadherin and intensity of staining in clinicopathologic parameters, the patients were classified according to age, tumor size and lymph node metastasis. The patients' age were divided into two groups (under and above 55 years). Tumor size and lymph node metastasis were divided into three groups  and four groups , respectively.Patients who had preoperative chemotherapy or radiotherapy were excluded. The patient data were entered in the questionnaire. In order to follow-up patients, their addresses and telephone numbers were used. Tumor blocks or slides that were not suitable for immuno-histochemical staining were omitted from the study.To continue the study, the needed paraffin blocks were removed from the archive and several slides with hematoxylin – eosin staining were prepared from the areas of tumor and adjacent normal looking tissue. During the investigation of slides, in addition to detecting tumor, other microscopic parameters such as depth of invasion, neurovascular invasion and differentiation  were also evaluated.15,16 The slides were studied and reported by two expert pathologists who had no clinicopathologic knowledge of the patients' data, E-cadherin expression and intensity of staining.Immunohistochemical staining was performed on the samples for assessing the expression of E-cadherin. The staining step followed the routine process. In order to examine the specificity of immune staining, both positive and negative controls were run at the same time in each experiment.To improve the accuracy of diagnosis and to determine the staining of cells, multiple microscopic fields in low and high-power (X100-X400) were examined and the findings were reported according to the estimated percentage of the stained tumor cells.Staining intensity of E-cadherin was reported on a scale with three grades:Positive staining in less than 10% of tumor cells.:0Positive staining between 10% to 90% of tumor cells. :+1Positive staining Over 90% of the tumor cells.:+2170 to 1+ grade as low staining and +2 grade as high staining were considered for E-cadherin.E-cadherin protein was a membranous marker. The obtained results were analyzed by statistical software SPSS (IBM SPSS Statistics 20.0.1). Chi –square, Gama and Fisher's exact tests were used to analyze the correlation between the expression of E-cadherin in gastric cancer and clinicopathologic features. A p-value less than 0.05 was considered as a statistically significant level. A total of 70 patients (49 men and 21 women) were enrolled and evaluated in this study. The clinicopathologic findings in patients are summarized in By definition  in the case group, 51.4% n=36) samples showed high staining (2+) and 48.6% (n=34) revealed low staining , but in the control group all of them showed high staining  -3.6 samplesBy comparing E-cadherin expression with clinicopathologic features in patients with gastric cancer and statistical analysis of data, a significant correlation between the expression of tumor marker and patients’ age, tumor type, tumor phenotype, depth of invasion, histologic grade, number of involved lymph nodes, and the stage of the disease was observed. . E-cadherin is a tumor suppressor gene that is located on chromosome 16, and produces a membrane protein.E-cadherin is a calcium-mediated membrane molecule that plays an important role in adhesion and differentiation of gastric epithelial cells which is a very important protective mechanism against tumor formation.18, 19Mutation in E-cadherin gene has had different prevalence in various epithelial cancers.20 49% in Dr. Lazar et al.’s study in Romania,21 and 57% in Dr. Chu et al.’s study in China.17In this study, 48.6% of cases suffering from gastric cancer showed abnormal expression of E-cadherin in IHC staining. This frequency among cases suffering from gastric cancer was reported 38% in Dr. Anbiaee et al.’s study in Iran,These differences may be related to the methods of mutation assessment (expression of E-cadherin by gene methylation or IHC).20 In Dr. Saad et al.’s study in Egypt, there was a significant correlation between this marker and lymph node involvement and tumor stage, but the depth of invasion and tumor grade were not linked.22In Dr. Chu et al.’s study in China, a correlation between depth of invasion and lymph node involvement was seen, but it was not associated with grade.17In this study, a significant correlation was found between abnormal expression of E-cadherin and tumor grade, stage, depth of invasion and lymph node involvement. These parameters show the invasiveness of tumor. Also, in Dr. Anbiaee et al.’s study in Iran, a correlation between E-cadherin mutation and the grade and lymph node involvement was seen, but this marker was not correlated with depth of invasion.Although there are different results about the correlation between mutation of E-cadherin, histopathology and tumor invasiveness, most studies indicate that this mutation is associated with more aggressive tumors.So, E-cadherin mutation can be used as a significant prognostic factor, and the rate of invasive cancers can be decreased by detecting the causes of mutation and preventing them.21 Also, in Dr. Zhu’s study in China a correlation with tumor type was observed.13In Dr. Micu et al.’s study in Romania23and Dr. Anbiaee et al.’s study in Iran20no significant correlation was found between abnormal expression of marker and tumor type.In this study, such a significant correlation was seen between the expression of abnormal marker and tumor phenotype and type that was similar to Dr. Lazar et al.’s in Romania.20, 21Also, in this study a significant correlation was observed between E-cadherin mutation and patients’ age, but this correlation was not seen in other studies.22 and in Dr. Chu et al.’s study in China a correlation with neuro- vascular invasion was observed.17In this study, no significant correlation was seen between abnormal expression of marker and sex, tumor location, size, shape and neurovascular invasion. In Dr. Saad et al.’s study in Egypt, a correlation with vascular involvement 22 and also Dr. Chu et al.’s studies,17 there was a significant correlation between abnormal E-cadherin expression and metastasis. This difference is probably due to lack of enough time for patient follow-up. In this study, no significant correlation was found between abnormal expression of the marker and distant metastasis. In Dr. Saad et al.’s This study showed abnormal E-cadherin expression in 48.6% of cases suffering from gastric cancer. Since abnormal expression of E-cadherin is associated with more aggressive gastric tumors, this marker can be used as a negative prognostic factor.To achieve more conclusive results, we recommend evaluating the correlation between E-cadherin mutation and survival of patients in future studies. Also, we recommend studying the possible environmental factors that may cause mutation in this gene because it is possible to decrease the rate of invasive gastric cancers by detecting these factors and preventing them.


The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly. Research investigating the treatment of dementia has been performed worldwide for more than 20 years; however, no curative therapy has been established . Dementipara-aminobenzoic acid, coumarin, flavonoids, and pyrrolo-isoxazole, among others, they have no effect on the fundamental treatment of dementia, but only delay the progression of the disease [Although there are approved medicines for dementia treatment, including donepezil, rivastigmine, galantamine,  disease . In 2011 disease . Therefo disease . Thus, p disease .Twitter [Many investigators have been conducting research on predicting diseases using “big data” analysis techniques. To predict disease, The National Health Service of the United Kingdom collected and analyzed prescription data from pharmacies and hospitals nationwide . The UniTwitter . The SetTwitter . ResearcTwitter .The proposed research aimed to early predict dementia and improve an individual’s diagnostic performance, and used a biomedical big data approach that included personal disease history, sociodemographic data, and personal health examination, referred to herein as “personal medical history”. The KNHIS-SC DB contains information regarding participant insurance eligibility, medical treatment, and general health examination data for all Koreans . SeveralThe work of many researchers in traditional studies investigating dementia have revealed the impact of medical history that includes sociodemographic and lifestyle data, medical records, and biophysical properties ,18,19,20Shim et al.  investigAllen et al.  conducteStefano et al.  investigDementia prediction in individuals of the United States and Europe has been a relatively popular research topic compared with that of Asian-based studies. The proposed approach in the present article aims to predict dementia in a representative Korean population using KNHIS data. Because all medical records of Koreans have been automatically collected by KNHIS under a national policy, the KNHIS DB is a genuinely representative source. The proposed approach analyzed sociodemographic, health examination, and personal disease history data in the KNHIS-SC DB to determine the most salient features and the optimal observation period for dementia prediction.To predict dementia, the proposed approach uses personal medical history including sociodemographic, health examination history, and personal disease history. Since 1989, the KNHIS has been a legal institution of the Korean medical insurance program, and has managed all health care costs for health care recipients, providers and government agencies in the Republic of Korea. The KNHIS can collect medical records of every citizen in Korea through every registered hospital, drug store, and medical care institution. Moreover, all personal medical history data is linked with other databases of government agencies. Thus, personal, family, and socioeconomic information can be used through the network, as shown in By establishing the Korea National Health Information DB, the KNHIS has been able to centrally manage all medical records, including personal, sociodemographic, and medical treatment data . CertifiThe KNHIS-SC DB consists of 558,147 seniors, representing 10% of all Korean population, consisting of 5.5 million seniors. Population statistics for 5.5 million seniors of 2013 was compared with that of all Korean population to confirm the KNHIS-SC DB can be considered as a representative sample of Korean population. Statistics Korea (KOSTAT) calculate Korean population statistics based on resident registration every month  and we uPersonal medical history, including sociodemographic data, lifestyle, personal disease history, biophysical properties, and other factors have been widely used to predict dementia. The proposed approach selected those items as features for applying ML techniques. Among the items in the KNHIS-SC DB, the proposed approach selected: (i) sociodemographic data  in PIE-DB. In addition, (i) body measurement data ; (ii) blood test results ; (iii) urinary test results; (iv) history of personal illness ; (v) history of family illness ; and (vi) smoking status in GHE-DB; and finally (vii) personal disease history in MT-DB.International Classification of Diseases-10 (ICD-10) codes [Personal disease history of MT-DB is composed of three-characters of approximately 2600 0) codes . The ICD0) codes . AccordiIn the preprocessing step, the selected medical history features are processed into feature types suitable for ML. In PIE-DB, sex was divided into male and female. After that, age was used as seven levels and income quintile was classified as three levels. In MT-DB, the proposed approach verified personal disease history with their ICD-10 codes. Finally, in GHE-DB, the height of 101–230 cm was classified into 13 levels in 10 cm increments and the weight of 26–300 kg was classified into 11 levels in 5 kg increments. Waist, body mass index, blood test and urine test were classified as normal and abnormal according to the criteria of the health examination standards . Each feature was categorized in every year from 2003 to 2013 to identify time-series patterns. In particular, feature items of GHE-DB measured change between the corresponding year and 2013. Trends of increase/decrease and normal/abnormal change compared with 2013 were featured.Instances are classified by DM and NC based on whether the corresponding instance was diagnosed with dementia based on MT-DB diagnosis data during 2013. The government of Korea supports medical expenses on dementia patients diagnosed by the following ICD-10 codes: “Dementia in Alzheimer disease” , “Vascular dementia” (F01), “Dementia in other diseases classified elsewhere ” (F02), and “Unspecified dementia” (F03). The proposed approach used the same criteria to classify dementia ,37.The proposed approach aimed to prove the following two hypotheses: (1) personal medical history has an effect on improving the performance of dementia prediction; and (2) personal disease history is the most important information in the medical history. To prove the first hypothesis, the proposed approach used personal medical history from 2003 to 2013. PIE-DB, MT-DB and GHE-DB include various personal medical information. To compare the performance of the medical history, baseline experiments used only information from 2013. Furthermore, the proposed approach tried to determine the best period for predicting dementia. To find out the best period, the proposed approach set the period as the last three, five, seven, nine and 11 years from 2013, and analyzed changes between values of the corresponding year and those of 2013. Not only increase/decrease of values but also changes among classes were considered. The classes were normal/abnormal or predefined levels. To show the effectiveness of personal disease history in the second hypothesis, features from personal disease history were compared with other features and tried to make the best combination of features. In addition, the proposed approach examined to confirm the predictive power for the known dementia-related diseases and to reveal the effect of the other diseases to prediction. Because the number of instances was insufficient to apply ML techniques, the proposed approach followed some heuristic rules for sampling: (i) KNHIS provide free health examination for individuals older than 65 years of age, for every two years and the proposed approach used the health examination results from 2003 to 2013; (ii) after step (i), 11,443 instances remained and they consisted of 850 DMs and 10,593 NC; (iii) 850 NC and 850 DM were randomly selected and used in the experiments. We explored appropriate ML techniques for the prediction of DM and performed optimization to derive a DM prediction model. From the KNHIS-SC DB, we selected 850 seniors with dementia and 850 seniors without dementia. As for the features, four types of features were selected from the PIE-DB, 70 types from the GHE-DB, and, 2600 types from the MT-DB.The proposed approach used changes between values of the corresponding year and those of 2013 as well as features in the corresponding year. To find out the best period for predicting dementia, we set the periods as the last three, five, seven, nine and 11 years. The proposed approach builds a dementia prediction model focused on personal medical history, and two experiments were conducted to determine the best method to use personal disease history and the optimal personal medical history period. First, longitudinal model 1 used the primary disease groups for personal disease history and longitudinal model 2 used the extended disease groups for personal disease history. Through the above experiments, we determined that the best method was to use personal disease history. To prove the effectiveness of personal medical history, we set a baseline experiment that used features of only one year, 2013. In addition, the above experiments tried to compare periods to determine the optimal personal medical history period to predict dementia.Since the proposed approach did not focus on algorithmic analysis of ML techniques, we focused on an analysis of feature combinations to show the best performance. Therefore, WEKA, which can easily compare and analyze the influence of features through the existing algorithm, was used. WEKA is the most useful for academic purposes because it contains most of the existing algorithms and most of the functions required for data mining from feature selection to model evaluation . First, The longitudinal features reflected the time-series changes in the baseline features over the years 2003–2012. The results of the longitudinal model 1 and baseline feature models are shown in In the next experiments, longitudinal model 2 used the extended disease group instead of the primary disease group . (1)KNHIS-SC DB is a database of records created by doctors’ medical service activities. The diagnosis rate of dementia in Korea is 73.6% (as of 2015) , which i(2)The proposed approach considered the NC based on only a diagnostic history of dementia. However, the elderly may have symptoms or diseases related with dementia. Such cases have ambiguous attributes to classify DM and NC, and may have a negative effect on the performance of dementia prediction. Therefore, the proposed approach involved an extra experiment excluding NC who had dementia-related symptoms or diseases in their medical history. The experimental result showed 81.4% F-measure . Compared with the previous experiments (including NC who have dementia-related symptoms or diseases), dementia prediction slightly increased performance (+0.5% F-measure). This was because the attributes of NC became clearly different from DM.(3)Since the current approach used the personal history of dementia, the results may be biased. However, the approach tried to follow the same method with the same data with human physicians. As for the reference, the experiment excluding dementia features demonstrated 73.3% accuracy, 77.2% precision, 66.1% recall, and 71.2% F-measure.(4)Since the data from the KNHIS-SC DB in the proposed approach are newly released, there has been no previous work to handle it. To compare with the proposed method and the previous methods, the baseline experiment used only data from a particular year, and other experiments used personal medical history to prove the effectiveness of personal medical history.(5)The proposed approach dealt with personal medical history according to year, i.e., if a person has a medical record for such a disease at least one time during the corresponding year, the proposed approach considered him or her as having that disease in one total year. Thus, the current method to deal with personal medical history is not sophisticated and it needs to handle personal medical history by the month, week, or day for a more specific dementia prediction.(6)In addition, the proposed approach did not consider disease phase, but considered whether a person had certain diseases or not. To analyze disease patterns more exquisitely, we will develop and apply an algorithm that can allocate the phase for any diseases based on drug dosage and dose schedule screening.The current approach had several issues and limitations.A dementia prediction model for all of Korea was derived using the KNHIS-SC DB and ML techniques. Various features were analyzed and optimized to improve dementia prediction performance. Several experiments supported the effectiveness of personal medical history with promising performance. This study was the first attempt to construct a dementia prediction model based on a representative sample of the Korean population; this is especially significant because the proposed approach demonstrated state-of-the-art performance . The results confirm the possibility of dementia prediction using personal medical history data. Longitudinal models 1 and 2 showed that a seven-year period and a three-year period were optimal observation periods. Relatively recent information was more effective in predicting dementia. In other words, a longer observation period did not result in better performance.The results demonstrate that the personal disease history of the personal medical history has an important role in dementia prediction. Five attributes related to blood tests of personal health examination and 58 diseases included in personal disease history have been extensively studied, individually or in part, in association with dementia in previous studies. The proposed approach confirmed the same results with the previous research based on a representative cohort DB housing information about the entire Korean population. Furthermore, 18 newly detected diseases were considered to be associated with dementia in personal disease history. Because dementia is strongly related to aging, mental illness, and head injury, the diseases listed in Although the proposed approach focuses on improving performance of individual dementia diagnoses, the experimental results may contribute to a reduction in the incidence of dementia, not only in Korea, but around the world.


Correction to: Hum Ecol (2017) 45:795–80710.1007/s10745-017-9956-8et al., “Children and wild foods in the context of deforestation in rural Malawi” is incorrect. The correct funding credits should read:Information on research funding presented in the paper by Maseko Funding We acknowledge financial contributions for the fieldwork and bursary (to HM) from the ASSETS project (NE-J002267–1) funded under the ESPA programme of DFID, ESRC and NERC, as well as the National Research Foundation (South Africa).http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.The article Children and Wild Foods in the Context of Deforestation in Rural Malawi, written by H. Maseko, Charlie M. Shackleton, J. Nagoli and D. Pullanikkatil, was originally published Online First without open access. After publication in volume 45, issue 6, page 795–807 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2017 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (


Purpose. The purpose of this study was to assess the feasibility of the Spanish Diabetes Self-Management Program (SDSMP) in the primary care setting of the Basque Health Service and offer initial estimations of the randomized controlled trial (RCT) effects. Methods. Ten health centers (HCs) participated in a single-arm pilot study with a 6-month follow-up period between February 2011 and June 2012. Recruitment was performed via invitation letters, health professionals, and the local media. Each intervention group consisted of 8–15 people. The ability of each HC in forming up to 2 groups, participants' compliance with the course, and coordination and data collection issues were evaluated. Glycated haemoglobin (HbA1c) was the main outcome variable. Secondary outcomes were cardiovascular risk factors, drugs consumption, medical visits, quality of life, self-efficacy, physical exercise, and diet. Results. Two HCs did not organize a course. A total of 173 patients initiated the program, 2 dropped out without baseline data, and 90% completed it. No pre-post HbA1c differences existed. Certain improvements were observed in blood pressure control, self-efficacy, physical activity, and some dietary habits. Conclusion. The SDSMP is feasible in our setting. Our experience can be of interest when planning and conducting this program in similar health settings. The trial is registered with ClinicalTrials.gov identifier NCT01642394. Diabetes is one of the most prevalent chronic diseases, with 422 million people worldwide having diagnosed diabetes in 2014 . What isAbove all, T2DM is associated with cardiovascular system conditions, such problems being the cause of death in three-quarters of the patients. In the Basque Country, 44% of people with diabetes are obese . The diaPatients' education can play an important role in improving glycemic control and reducing cardiovascular risk . The curResearch indicates that activated patients are more likely to adhere to treatment regimens, get preventive care, and participate to a greater degree in decisions about their care . They arLike other chronic illnesses, diabetes requires patients to take responsibility for their own health (self-care) to minimize long-term complications. Among programs on patient self- management, the most widely used structured approach is the Chronic Disease Self-Management Program (CDSMP)  developeSuccessive systematic reviews have been published on the efficacy of various educational models in self-care and patient activation –16. ThesPaciente Activo”) follows the SDSMP methodology and has been proposed as an instrument to promote self-care in people with T2DM. In our health system, these educational interventions take place in the primary care health centers. These centers mainly host general practitioners, pediatricians, and nurses, with the latter bearing the responsibility of most educational activities.In Spain, the few data available on self-management programs suggest favorable results, but the latter have not been assessed through prospective studies or compared with usual care . The DepA single-arm pilot study was conducted for assessing the feasibility of this educational intervention in our context . AcceptaThe feasibility aspects assessed by the pilot study were the interest of the target population in the proposed educational program and the enrollment rate; compliance with the program's schedule; adequacy of the battery of the administered questions, and finally participation and coordination of several health centers (HCs).In addition, the main and secondary outcome pre-post effects were estimated. The standard deviation (SD) estimation of the main outcome of interest (HbA1c) was implemented in the sample size estimation of the clinical trial. All primary and secondary derived effects offer an initial idea of the results that may be expected in the clinical trial.This preliminary research was a prospective pre-post pilot study without a control group. Recruitment took place in 10 participating HCs across 4 healthcare organizations  in the Basque Country (Spain) from February 2011 to June 2012. Between 2 and 9 health professionals (HPs) participated per center.Patients with T2DM between the ages of 18 and 79 years were included. Individuals with mental health problems  or other health problems, that might have affected their ability to participate in the study, were excluded.Recruitment was carried out in several ways. Invitations letters were sent to 120 T2DM subjects of each participating HC, fulfilling the age criteria. These subjects were selected via a computer generated random numbers sequence. In addition, the participating HPs were instructed to inform and invite patients to the study. Finally, awareness about the program was also spread in the local media. All the patients who agreed to participate gave written informed consent, after receiving information about the purpose of the research project.Sociodemographic and baseline clinical data were collected on age, sex, years since diagnosis, and comorbidities. Assessed comorbidities were hypertension, heart disease, macroangiopathy , microangiopathy , depression, asthma, chronic obstructive pulmonary disease, and cancer.Self-efficacy enhancement was the key element of the applied educational intervention. The teaching process was structured to include the following four self-efficacy components: performance mastery, which shows participants how to make specific action plans; modeling, which can be accomplished by involving peers as instructors of self-management programs; symptom interpretation, helping patients to form alternative interpretations of their physiological symptoms, as such interpretations can subsequently lead to new self-management behaviors; and, finally, social persuasion, which refers to the positive effect experienced by the majority of the group members and the way in which this can influence other group members . On the Patients were trained to set their own targets, solve problems related to their condition, and communicate more effectively, with their relatives and healthcare professionals, by sharing their feelings, in order to enable them to play a more active role in the management of their disease. The final goal of all this was to promote changes towards healthier lifestyles.All sessions were supported by educational material specifically developed for the program: books, leaflets, and CDs. Each group was supervised by two leaders previously trained and certified in the SDSMP. At least one of the leaders was required to be a T2DM patient or a caregiver for a person with this condition, while the other was allowed to be a HP. These leaders introduced themselves to participants as SDSMP leaders, not referring to their professional position, promoting the concept of peer-learning, as recommended in the implementation manual of the SDSMP. Patients not attending at least four sessions were considered not to have completed the program.The recruitment capacity of the centers and their ability in forming up to 2 intervention groups each was assessed. Each center was asked to recruit between 8 and 30 subjects. For the needs of this study, 8 and 15 were the minimum and maximum acceptable number of participants in any group. At least 65% of the participants initiating the intervention were expected to complete it [ Glycated haemoglobin (HbA1c) level was a primary outcome variable.Cardiovascular-Related Factors. The factors are body mass index (BMI), systolic and diastolic blood pressure (SBP and DBP), and total and HDL cholesterol levels. Cardiovascular risk was assessed with the Registre Gironí del COR (REGICOR) score, an adaptation of the original Framingham risk score for Mediterranean populations, calculated for persons between 35 and 74 years of age [s of age .Use of Medications. Antidiabetes, antihypertensives, and antiplatelets were studied. Quality of Life. The Spanish version of the self-administered instrument, Audit of Diabetes-Dependent Quality of Life (ADDQoL-19), was used [was used . This scSelf-Efficacy. The Spanish Diabetes Self-Efficacy Scale developed at Stanford University [iversity  was admiPhysical Exercise. Physical exercise was assessed with the 7-Day Physical Activity Recall (PAR) interview [nterview , 26. ThiDiet. Diet quality was examined using the food frequency questionnaire of the PREDIMED study [ED study . This quPatient Satisfaction with the Program. It was measured with an anonymous specific satisfaction survey consisting of 10 questions related to sociodemographic and process variables, 20 satisfaction questions rated on 5-point Likert-type scale ranging from 1 = minimum to 5 = maximum and 3 open questions. Questions were divided into three sections referring to the material presented, organization of the program, and behavior change .Use of Healthcare Services. Number of visits to the general practitioner and nurse, number of visits to the emergency department, and number of times of hospitalization are compared during a 6-month period before and after the intervention. Only cardiovascular morbidity and diabetes-related complications  were considered for the emergency department visits and hospital admissions.Patients were assessed twice, 1 month before starting the intervention and 6 months after the end of the intervention. Sociodemographic and self-report questionnaires were given to patients to fill in, in their own homes. All participating HPs, previously trained by the research team, were in charge of collecting the following data. Medication consumption and clinical visits were assessed from the electronic clinical history files and corroborated by the participants. Body mass index (BMI) and systolic (SBP) and diastolic blood pressure (DBP) were recorded, while HbA1c and cholesterol levels were assessed by blood samples. These samples were extracted in participants' HCs and were analyzed in the reference laboratories of the four participating health districts of the Basque Health System (Osakidetza). When necessary, the referring HP helped the participants complete the questionnaires.Finally, the 7-Day PAR and PREDIMED questionnaires were administered over the telephone by trained interviewers from a centralized call center.n) and percentages (%) and continuous variables as means and standard deviations (SD) when normally distributed or as medians and interquartile ranges  when they did not follow a normal distribution.Categorical variables were expressed as frequencies , while, for nonnormally distributed variables, such as the number of visits, differences are expressed as medians and their corresponding 95% CI. Comparisons were considered as statistically significant when p < 0.05. For the needs of this study, all results are based on available data. Statistical analyses were carried out using the SAS v.9.3.The comparisons between categorical variables before and after the intervention were carried out with McNemar's test. Comparisons between continuous variables were performed with Student'sThe research protocol was approved by the Clinical Research Ethics Committee of the Basque Country (Ref. number: 11/2010).n = 3 and 5) had to follow the program in a different HC, for being less than the required minimum for a course. The 5 additional subjects were absorbed without affecting the respective number of program groups. Of the 1200 invitation letters sent, 46 were undelivered. Many patients visited their corresponding HP with an invitation letter and requested more details on the program. However, frequency of patients who responded to the letter's invitation, of patients who showed initial interest, and of patients informed exclusively by the HP was not registered.The participating HCs recruited between 3 and 27 patients each, while 5 patients corresponded to nonparticipating HCs. Five of the centers obtained two program groups; three centers obtained one group, while patients recruited in two centers  completed the training program .Between 1 and 9% of the self-reported questions were not answered at baseline. At the postintervention assessment, the main outcome of interest, along with other cardiovascular data, was missing for 5 of the 171 participants, while the amount of missing data of the self-reported variables had increased.Overall, 52% of the sample were male, with mean (SD) age of 63.4 (8.1) years. Among the most common comorbidities were hypertension (57%) and heart disease (26%), while 25% and 14% of the participants had a history of macro- and microangiopathy, respectively. Baseline data are summarized in p = 0.348)  nor in patients with HbA1c ≥ 8% (diff: −0.06 (95% CI: −0.54 to 0.42); p = 0.791; n = 38) between the two time points. Further, no differences in BMI, total cholesterol levels, or cardiovascular risk were found (HbA1c levels at the two pilot study moments were 7.3% (1.1) and 7.4% (1.3), respectively, with the mean difference between the measurements being 0.1%  . It was re found .p = 0.002) and −1.3 mmHg , respectively. These reductions were also reflected in an increased percentage of patients who simultaneously achieved good control of both SBP and DBP (SBP < 140 and DBP < 90 mmHg) after the intervention, with 10%  of the participants improving the control of their blood pressure during the study. However, this improvement was not reflected in a coronary risk reduction  and primary nurse visits (p < 0.0001). Frequency of emergency department visits and hospitalization remained 0 at both time points. Finally, no differences were seen neither in the total number of medications per patient nor in the percentage of patients taking antidiabetics, antihypertensives, or antiplatelets drugs . On the other hand, no differences were observed in the general item, “If I did not have diabetes, my quality of life would be…” (p = 0.263) or in the total ADDQoL-19 score (p = 0.877) between the two moments  to 0.8 (95% CI: 0.5 to 1.2) .p = 0.007), while this improvement was not captured when physical activity was measured in minutes and METs (p = 0.020) and cold cured meats consumption was reduced (p = 0.035). However, none of the other main dietary habits was altered , with only one item “This course is going to help me to manage my emotions better,” having a lower median score of 4 (95% CI: 4-5).Based on the current pilot study, we concluded that performing a randomized clinical trial (RCT) for evaluating the effectiveness of an educational program for diabetic patients was feasible in our context. Results were acceptable as far as overall recruitment, course participation, and patients' satisfaction and collaboration across various centers was concerned.However, several important observations were also made. Two of the participating centers, with 3 HPs collaborators each, did not manage to fulfill the minimum number of required participants. The number of involved staff, their motivation, and understanding of the study goals were important aspects to consider in the future RCT. This was seen as a key aspect for the successful RCT fulfillment, especially considering that the latter would involve a great number of centers dispersed over the whole Basque Country. Therefore, it was decided that at least 5 or 6 HPs per center should be achieved for the future study. In addition, during the RCT informative sessions for capturing participating HPs, more effort should be made on highlighting the positive aspects of the educational intervention; this was expected to improve patients' health control and relieve, in the long term, the workload of the professionals themselves. It was also thought that the HP motivation would increase, if pilot study patients participated actively in those informative sessions.During the pilot study many participating HPs complained that their workload did not allow them to devote any time to the current project. For this reason, HPs participating in the RCT were going to be allowed (by Osakidetza) a certain amount of working hours devoted exclusively to the needs of that study.It was also observed that missing information, especially on subjective and patient self-reported data, increased at six months postintervention, compared to baseline. This fact was taken into consideration when estimating the RCT sample size, but also it indicated the need for a closer patient follow-up during the RCT data collection.The pre-post differences obtained in the current single-arm study may offer an initial estimation of the expected RCT results. The baseline characteristics of the current sample were comparable to those of the average diabetic patient in the Basque Country , except Changes in several dimensions including improvements in the self-efficacy scale, levels of exercise, and diet were observed. However, these changes were not accompanied by a greater glycaemic control in terms of HbA1c levels or changes in other variables, related to vascular morbidity, like coronary risk for example. The good initial control of the local T2DM population and the short follow-up of the pilot study could be possible explanations of this lack of difference. It is recognized that diabetic people with poorer HbA1c levels have a greater room for improvement and tend to respond better to any type of intervention . HoweverIt is important to note that although the target HbA1c level is the most widely used variable to date for assessing diabetes interventions, its use as the only method is currently being questioned. Recent evidence has shown that lower HbA1c levels are not always accompanied by a decrease in cardiovascular morbidity and mortality, especially in older patients or those with comorbidities . Hence, The pilot study results suggested a reduction in blood pressure, similar to that obtained using nondrug approaches, such as a salt-free diet and physical exercise, and this was reflected to a significant increase in the percentage of patients with good blood pressure control, without changing the prescription of antihypertensives. The effect on blood pressure has not been included in any of the evaluations of the DSMP we identified in our review of the literature , 34, 35.An improvement in scores on the specific self-efficacy questionnaire is a common finding in all evaluations on diabetes self-management , 36, 37.The effects seen in the current study cannot be generalized and no causal relationship can be claimed between those effects and the applied educational intervention. The fact that the DSMP has been assessed in other populations and contexts, resulting in modest short-term positive results in outcomes such as depression, dietary habits, exercise, medication adherence, symptoms of hypoglycemia, communication with physicians, and health status –36, makeThis pilot study has certain limitations. Firstly, its single-arm nature was not a replicate of the future RCT study. However, its design permitted testing the feasibility of the educational program, in terms of participation and multiple-sites coordination. Even though the minimum number of participants was met overall, great variability was seen among participating HCs. Motivation and time availability of participating HPs are issues that will be treated more carefully in the follow-up RCT study. What is more, exact patient participation rates cannot be calculated. On one hand, the invitation letters were sent to T2DM patients, irrespective of whether or not they were fulfilling any other inclusion criteria than the age. This was due to the data confidentially, which did not permit access to further medical information of the population of interest. Furthermore, no records of interested patients and patients informed exclusively by the HPs were kept. However, given the number of the participating centers, health professional and recruitment approaches, such a record would have been very difficult to manage. Finally, missing data information, especially at 6 months, is another important limitation. Patients that were not satisfied may have been more reluctant to reply after the intervention and thus the initially obtained and presented results may have been biased. Given the importance of missing and the effect they can have on the study's conclusions, a closer patient follow-up, at all stages, should be assured in the RCT.The Spanish Diabetes Self-Management Program is feasible in our health system and well accepted by patients. The single-arm pilot study results suggest that the program may induce improvements in self-efficacy and blood pressure, but its effectiveness will have to be confirmed by a RCT.The current pilot study could contribute to the debate on the most adequate outcome variables, for evaluating diabetic patients' interventions. In addition, the experiences and lessons learned during this phase will serve for better coordinating the future RCT study. Avoiding organizational and communicational flaws, motivating HPs, improving patients' follow-up, and refining the study manual will result in enhancing the RCT quality at all levels. Finally, the experience and lessons learned may be beneficial to similar health settings for planning and conducting diabetes self-management programs.Satisfaction survey questions.


Previous research has discussed the factors associated with cognitive impairment, but the patterns of its development have been little described. Our aim was to examine long-term development of cognitive function and the related factors using longitudinal follow-up data. A group-based trajectory model and multinomial logistic regression were applied to identify trajectories and the associated baseline factors, and a mixed model was used to identify the time-varying factors associated with the trajectories. Three trajectories were identified: starting low and declining (30.8%), starting high and declining (51.8%), and high-stable (17.4%). These three trajectories were apparent at the beginning of the study and did not crossover during the study period. Smoking, diabetes, depression, and instrumental activities of daily living were significant variables for differentiating the starting high and declining group from the high-stable group. Similar patterns and emotional support as a contributing variable were observed in the starting low and declining group. Physical activity, self-rated health, cardiovascular diseases, depression score, physical function, and social support were related to the trajectories over time. Impaired physical function, cardiovascular diseases, depression symptom, and poor social support in late life may be potential risk factors affecting the decline of cognitive function. Preventive strategies should be designed accordingly. According to the World Health Organization, the world population aged 60 years and older is expected to reach more than 2 billion in 2050The growing number of people with dementia will increase the disability burden and will bring enormous costs from long-term care. In 2015, the total global societal costs of dementia were estimated to be US$818 billion, and more than 80% costs were attributed to informal care and residential careTaiwan is one of the most rapidly aging societies, and the prevalence of dementia in people aged 65 years and over is expected to increase from 1.02% of the total population in 2013 to 5.08% in 20601112131415This study is a secondary data analysis utilizing data from the Taiwan Longitudinal Study on Aging (TLSA), a nationally representative investigation first conducted by the Health Promotion Administration, Ministry of Health and Welfare of Taiwan in 1989For the current study, we used data collected from 1993 to 2007, since the cognitive function measurement was implemented in 1993. All the selected participants or proxies speaking on their behalf were interviewed. A proxy could only be used if the participant (1) was severely ill or too frail to answer questions; (2) had cognitive problems; (3) had speech or hearing problems; or (4) had dementia and could not answer the factual questions in the TLSA study. The proxies did not undergo assessment of cognitive function and are therefore excluded from our analysis. We analyzed data for 3155 persons whose cognitive function was measured in 1993. We further excluded subjects under 65 years of age (n = 61); those who died before the 1993 survey began (n = 1), had incorrect information on death (n = 14), or had a stroke in 1993 (n = 212); and those with fewer than two assessments of the Short Portable Mental Status Questionnaires (SPMSQ) score (n = 567) during 1993 to 2007. A total of 2300 elderly subjects were included in the analysis . This stWhen we initiated the study, the TLSA was publicly available, and we could use the data directly. Since 2014, the use of secondary data has required ethics approval. We obtained the ethics approval from the Internal Review Board of Academic Sinica. All methods were carried out in accordance with relevant guidelines and regulations.SPMSQ is a screening test for cognitive dysfunction23We used the baseline age, sex, and education level for analysis. Other variables, including health status, health behaviors, physical function, and social supports, were obtained in 1993, 1996, 1999, 2003, and 2007, and were thus analyzed as time-varying variables.Age was regarded as a continuous variable and was dichotomized into 65–74 and ≥75 years old, as shown in the descriptive analysis. Sex was a categorical variable coded as 1  or 2 . Education level was defined as (1) illiterate, (2) uneducated but literate, (3) primary, and (4) secondary or above based on the number of years of formal education a person completed.2), and we furthermore divided the participants into underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 24 kg/m2), overweight (24 ≤ BMI < 27 kg/m2), and obese (BMI ≥ 27 kg/m2) subgroups based on the criteria of the Ministry of Health and Welfare in TaiwanHealth-related variables included body mass index (BMI), self-rated health status, chronic diseases, and depression symptom score. Self-reported height and weight data were used to calculate BMI  joining organized group activities, such as folk dance, ballroom dance, and tai chi; (2) walking, jogging, hiking, or doing other outdoor fitness activities; or (3) planting or engaging in garden work. Respondents that participated in any one of these activities were classified as engaging in physical activity.Physical function was assessed by measurements of activities of daily living (ADL), instrumental activities of daily living (IADL), and mobility tasks. ADL included bathing, dressing, eating, movement in bed/transferring, ambulating indoors, and toilet use. IADL consisted of shopping, managing money, riding a bus or train independently, doing heavy work around the house or yard, doing light housework, and using the telephone. Mobility tasks comprised standing continuously for 15 minutes, squatting, lifting arms over head, holding or grasping with fingers, lifting or carrying 12 kg, running or jogging 20–30 m, climbing stairs, and walking 200–300 m. Each item was scored from 0 (no difficulty) to 3 (unable to do the activity). The total scores ranged from 0 to 18 for ADL, 0 to 18 for IADL, and 0 to 24 for mobility tasks. The higher scores represented worse functioning.Social support contained social interaction and emotional support. Social interaction was realized as two variables, playing games  and socializing with friends, neighbors, or relatives. Emotional support comprised respondents being cared for when ill and being listened to by friends or relatives. Each item was scored 0 (no) or 1 (yes), and the overall social support score was calculated based on the scores for social interaction and emotional support.http://www.andrew.cmu.edu/user/bjones/index.htm). PROC TRAJ has an option for censored normal. We applied it and set the minimum to 0 and maximum to 5 for SPMSQ. PROC TRAJ uses the maximum likelihood method for the estimation. It uses all the individuals in the data set, including those with missing values. However, only available data for each individual are incoporated in the analysis. In other words, individuals with partially missing data were not removed from the analysis. The optimal number of groups was determined by the Bayesian Information CriterionWe utilized the group-based trajectory model to identify the developmental trajectories of cognitive function based on the SPMSQ score. The group-based trajectory model combines the methods for finite mixture models and cluster analysis with longitudinal data. The model classifies individuals into clusters with similar trajectories according to their longitudinal data, assuming that individual differences in trajectories can be summarized by a finite set of different polynomial functions for age or time2830Baseline characteristics were expressed as mean and standard deviation (SD) for continuous variables and proportions for categorical variables. Chi-squared test  and analysis of variance (for continuous variables) were used to compare the differences among the trajectory groups. Multinomial logistic regression (MLR) was applied to identify baseline variables, including demographic, physical, mental, and social predictors of group membership. Least squares means (LSM) of SPMSQ adjusting for demographic characteristics were calculated, and Bonferroni correction was applied in the comparisons of SPMSQ score over time among the trajectory groupsWe used LSM to calculate the adjusted SPMSQ score, and used Bonferroni correction to compare the differences among trajectories. After adjustment for the socio-demographic variables, the SPMSQ score varied across time in comparison to the initial one in the starting high and declining group and the starting low and declining group . CompareAfter adjustment for age, sex, and education, MLR was used to calculate the relationships between baseline variables and trajectory groups . In a coTo account for the repeated measures, we used a mixed model to examine the association between the time-varying variables and the trajectories, adjusting for socio-demographic variables  and 4. FThis study utilized a longitudinal follow-up of elderly subjects to identify the developmental trajectories of cognitive impairment and found three trajectories with no crossover during the follow-up time. The trajectories begin prior to 65 years of age. Elderly subjects who smoked, did not drink alcohol, or had depression, diabetes, or poor physical function at baseline tended to be clustered into the starting high and declining and starting low and declining groups. Physical functioning, depression, cardiovascular diseases, social support, and physical activity were related to the progression of the decline in cognitive function.We found that elderly subjects who were smokers at baseline were more likely to have a decline in cognitive score over timeDepression is a major risk factor for the incidence of cognitive impairment35Self-rated health condition is an important factor that influences the quality of life and satisfaction15et al.We found that declining cognitive trajectories were significantly associated with physical function. This finding is similar to the study by Auyeung Vascular risk factors are associated with having worse cognitive performanceet al.et al.A positive relationship between social capital and cognitive function was found in our study, and this outcome corresponds to findings reported by Green The present study has several limitations. First, all variables were self-reported, thus recall bias is consequently unavoidable. Nevertheless, parts of the questionnaire have been validated, and the interviewers were well trained. We also made spot checks to ensure that the responses were correctly collected. Secondly, cognitive function was assessed by SPMSQ with five questions, which might restrict the results of our study. Many other instruments are available to evaluate cognitive function in the elderly, such as Mini-Mental State Examination (MMSE). MMSE, the most commonly used assessment for cognitive impairment, is a 30-point scale including tests of orientation, registration, attention and calculation, recall, and languageIn conclusion, the development of cognitive impairment trajectories varied. Impairment in the starting low and declining group appeared before 65 years of age. Physical function impairment, diabetes, heart disease, stroke, depression, and poor social capital in late life may be potential risk factors that affect the decline of cognitive function. Prevention and screening should begin early to delay deterioration. Different risk factors were associated with the trajectories of cognitive function, and preventive strategies should be designed accordingly.How to cite this article: Chen, T.-Y. and Chang, H.-Y. Developmental Patterns of Cognitive Function and Associated Factors among the Elderly in Taiwan. Sci. Rep.6, 33486; doi: 10.1038/srep33486 (2016).


However, the overall quality of the included studies was moderate. A meta-analysis of the ten trials reporting adverse events demonstrated no statistically significant differences in the incidence of adverse events between the intervention and control group . The results suggests that probiotic use may be beneficial in the prevention of AAD among outpatients. Furthermore, the use of probiotics appears safe.A common adverse effect of antibiotic use is diarrhea. Probiotics are living microorganisms, which, upon oral ingestion, may prevent antibiotic-associated diarrhea (AAD) by the normalization of an unbalanced gastrointestinal flora. The objective of this systematic review was to assess the benefits and harms of probiotics used for the prevention of AAD in an outpatient setting. A search of the PubMed database was conducted and yielded a total of 17 RCTs with 3631 participants to be included in the review. A meta-analysis was conducted for the primary outcome: the incidence of AAD. The pooled results found that AAD was present in 8.0% of the probiotic group compared to 17.7% in the control group (RR 0.49, 95% CI 0.36 to 0.66; I Clostridium difficile infections.Diarrhea is a common adverse effect of systemic antibiotic treatment. Antibiotic-associated diarrhea (AAD) occurs in 5% to 39% of patients, from the beginning and up to two months after the end of treatment . Any typThe primary care sector is responsible for the bulk of antibiotic consumption in humans . ReportsProbiotics are defined as “live microorganisms which when administered in adequate amounts confer a health benefit on the host” . The ratLactobacillus genus, Bifidobacterium genus, and Saccharomyces genus. Previous reviews suggest that probiotics are useful in the prevention of AAD, especially in a pediatric population  with a NNT of 10 [Numerous probiotic species have been tested, most commonly the The objective of this systematic review and meta-analysis was thus to assess the benefits and harms of probiotics used for the prevention of antibiotic-associated diarrhea in outpatients of all ages.A total of 637 studies were identified through MEDLINE/PubMed. An independent review of these titles and abstracts identified 53 potentially relevant studies for full-text reviews. Of these studies, 17 met the inclusion criteria. The details of the study flow, including reasons for exclusion, are documented in the study flow diagram in All included studies were prospective, randomized, controlled trials with placebo, active, or no treatment control arms. Additional information about each study can be found in the Characteristics of Included Studies table in The 17 studies included a total of 3631 patients. The patient population was restricted to outpatients taking oral antibiotics, as trials randomizing hospitalized patients were excluded. The recruitment and evaluation of patients took place in private practices, pharmacies, or hospitals .All studies included both males and females.Probiotics:Lactobacilli spp., Lactococcus spp., Bacillus spp., Bifidobacterium spp., Saccharomyces spp., Leuconostoc cremoris, Clostridium spp., or Streptococcus spp. Eight studies  used a combination of two or more probiotic strains as intervention. Additional information about the type of probiotic(s) that were used, including the dosages and treatment durations, can be found in The trials tested the prevention of AAD with Antibiotics:H. pylori eradication with a combination of clarithromycin and amoxicillin . One study (Ojetti 2012) used a combination of levofloxacin and amoxicillin for H. pylori eradication therapy. Two studies  reported the use of single beta-lactam antibiotics, while others included several antibiotics or were otherwise unspecified.The patients were treated with oral antibiotics for various clinical indications, but the most common reason was H. pylori infection, the most common indications for treatment with antibiotics were upper and lower respiratory tract infections, otitis media, and throat infections.Aside from N = 1557; Tankanow 1990, Arvola 1999, Vanderhoof 1999, Cindoruk 2007, Merenstein 2009, De Vrese 2011, Chatterjee 2013, Fox 2014, Olek 2017) or probiotics vs. no treatment .Most RCTs randomized a moderate number of participants  to either probiotics vs. placebo  applied the WHO definition of diarrhea (“the passage of three or more loose or liquid stools per day”) [N = 561; Cindoruk 2007, Kim 2008, Ojetti 2012) categorized diarrhea into groups , but did not include the frequency or consistency of bowel movements. Four studies  did not provide any definition of diarrhea. The individual studies’ definitions of diarrhea can be seen in All of the included studies provided data on the main outcome: the incidence of AAD. The outcomes were patient-reported. The definitions of diarrhea varied in each study in regard to the number of bowel movements per day and the consistency of stools . Seven studies  reported the incidence of adverse events . The definitions of adverse events varied widely. Four studies  reported the mean duration of diarrhea, but the data was not sufficient to make a quantitative analysis of this outcome. Instead these are summarized qualitatively and/or by descriptive statistics.Ten studies  given to the intervention group.Loss to follow-up was substantial  in three trials . 11 studies  did not perform an intention-to-treat analysis.Visual inspection of the funnel plot  for the All of the 17 included studies reported the incidence of diarrhea and the number of patients randomized to each group. The incidence of AAD in the probiotic group was 8.0%, compared to 17.7% in the control group. The overall pooled results showed that the use of probiotics produced a statistically significant reduction in the incidence of AAD: RR 0.49; 95% CI 0.36 to 0.66. The forest plot can be seen in p = 0.001), and this was moderate (I2 = 58%). A GRADE analysis , four studies using S. boulardii , and two studies using a combination of L. acidophilus La-5 and B. lactis Bb-12 .In addition to the pooled analysis of any probiotic, a strain-specific meta-analysis  was condThe results were similar to the overall pooled analysis and showed a beneficial effect of probiotics in the prevention of AAD. This effect was statistically significant in two of the three subgroups.L. rhamnosus GG and S. boulardii showed a statistically significant lower risk of AAD, while this was not the case regarding the combination probiotic supplement of L. acidophilus La-5 and B. lactis Bb-12 . The high level of heterogeneity in the overall pooled results could no longer be detected in these three subgroups (I2 = 0%). A GRADE analysis  applied the WHO definition of diarrhea, and the pooled results showed that the use of probiotics produced a statistically significant reduction in the incidence of AAD: RR 0.54; 95% CI 0.36 to 0.82 . A GRADE analysis  reported the mean duration of diarrhea (MDD). The results can be seen in Four studies  reported the number of participants with adverse events in each group. Adverse events consisted of a variety of different symptoms such as metallic taste, nausea, loss of appetite, epigastric discomfort, headache, flu-like symptoms, rash, etc. There were no serious adverse effects leading to major disabilities, hospitalization, or death. Three trials  found a statistically significant difference in adverse events between groups; one favors placebo and two favor probiotics. Kim et al. found a difference in the presence of metallic taste between the intervention group (16.7%) and the control group (7.3%). Fox et al., as well as Olek et al., reported more adverse events in their placebo-controlled patient group, and these were abdominal pain, loss of appetite, nausea, pyrexia, headache, and rash.None of the 17 included studies specifically defined adverse events prior to enrolment of participants. Ten trials , and, for doses less than 5 × 109 CFU/day, it was RR 0.61 . The difference in response rates between high-dose and low-dose probiotics were statistically significant (p < 0.002) by Fisher's exact test). The forest plot is provided in The analysis regarding dose-response relationships included ten studies of different probiotic species. The pooled effect size for doses larger than 5 × 10H. pylori eradication; (3) low risk of bias; and (4) intention-to-treat analyses. Forest plots from each subgroup analysis can be seen in  age groups; (2) trials with  seen in .N = 1446; Tankanow 1990, Arvola 1999, Vanderhoof 1999, Erdeve 2004, Merenstein 2009, Fox 2014, Olek 2017) targeted children specifically, and their results showed a statistically significant lower risk of AAD but with considerable heterogeneity .Two subgroups were based on the age of the participants, including children (<15 years of age) and adults (>15 years of age). Seven trials  targeting adults also showed a statistically significant reduction in the risk of AAD and moderate heterogeneity .Nine trials  included both children and adults and therefore was not included in this subgroup analysis.H. pylori eradication therapy , adjunct probiotic use was also associated with a lower risk of AAD .In the seven trials using a combination of clarithromycin and amoxicillin for N = 633; Merenstein 2009, Fox 2014, Olek 2017) were evaluated as having a low risk of bias. Combining these three low risk studies in a meta-analysis showed a non-significant lower risk of AAD and considerable heterogeneity .The trial quality was generally low, and only three studies  showed similar results to the overall pooled analysis, with a significantly lower risk of AAD and moderate heterogeneity .A meta-analysis combining the six studies using intention-to-treat analyses (2 = 58%), with no apparent increase in the risk of side effects . The number needed to treat (NNT) to prevent one case of diarrhea was 11 (95% CI 6 to 13). The quality of evidence for the main outcome was categorized as moderate due to a moderate degree of heterogeneity and a high risk of bias in some trials.The results of this review point towards a protective effect of the use of probiotics as adjunct therapy to prevent antibiotic-associated diarrhea in outpatients of all ages. Data from 17 studies with a total of 3631 patients found that the use of a probiotic may reduce the risk of AAD by 51% , followed by S. boulardii . Furthermore, with this subgroup analysis, the heterogeneity from the pooled analysis (I2 = 58%) disappeared in each of the three subgroups (I2 = 0%).A strain-specific subgroup analysis combining data from eight of the included trials showed a similar protective effect of probiotics in the prevention of AAD when compared to the overall pooled analysis. The most effective probiotic strain was 2 = 27%; p = 0.22). This explains some of the statistical heterogeneity, and it also demonstrates the importance of having clear and consistent definitions of outcomes in clinical trials. The quality of evidence for this outcome was categorized as high.Data from the seven studies applying the definition of diarrhea defined by WHO showed a similar protective effect of probiotic use to prevent AAD  but with no statistically significant heterogeneity . However, this result should be interpreted with caution as the analysis was on any probiotic species and not on specific strains. A review investigating different treatment regimens of probiotics in human studies concluded that a dose-response relationship exists within the commonly studied range of 108 to 1011 CFU, meaning that, within this range, a higher dose will lead to a better response [9 CFU/day) [We also provide preliminary evidence of a possible dose-response relationship, as results indicate that higher doses were associated with fewer ADD events , as well as a review, including hospitalized patients [2 = 54%, 11,811 participants).Our result was fairly consistent across a number of subgroup analyses in which RRs ranged from 0.36 to 0.58. All but two subgroup analyses yielded a statistically significant result. Of note, the analysis of studies with a low risk of bias did not produce a statistically significant result. This is concerning, and although in part may be ascribed to a low number of trials (three), this finding calls for caution in its interpretation. Nevertheless, our results are in line with a previous Cochrane review [patients , on the Subgroup analyses did not further explain the substantial amount of heterogeneity across studies as heterogeneity remained evident throughout all these analyses. Combining data into a meta-analysis by probiotic species and strain level from all included studies would have been preferred, but this was not possible due to varying species, strains, and combinations of strains used in the included studies.In most of the included studies, the types of infections/diagnoses of the subjects in the included studies were not specified. This was due to inadequate reporting of the trials. Likewise, the antibiotics used were rarely specified, but, by excluding inpatients from the analysis, some similarity regarding the diagnoses of subjects can be expected. The five most important causes of antibacterial prescribing in primary care are upper respiratory tract infection, lower respiratory tract infection, sore throat, urinary tract infection, and otitis media . OutpatiProbiotics can be found in the form of yoghurt, tablets, and capsules, e.g., in dietary supplements and as non-prescription drugs from pharmacies. This makes the use of probiotics an easily available and relatively simple method of AAD prophylaxis. Furthermore, the ingestion of probiotics seems safe, and our meta-analysis found no increased risk of adverse events, including serious adverse events. This result is in line with a previous review on the safety of probiotics . The majH. pylori eradication, if the incidence of AAD was reported. Trials testing probiotics for the treatment of diarrhea were not included.All randomized controlled trials in which probiotics were given to prevent antibiotic-associated diarrhea and in which the use of probiotics was compared to either a placebo or an active alternative prophylaxis or in which no treatment were considered for inclusion. Trials were also included if probiotics were given together with antibiotics in H. pylori eradication therapy for otherwise healthy adults were also included, and the subjects were assumed to be outpatients (if not directly stated as inpatients) due to the nature of the trials because this kind of treatment is normally done in an ambulatory setting.Studies with outpatients of all ages being administered antibiotic therapy for any indication were considered for inclusion. An outpatient can be defined as “a person who goes to a health-care facility for a consultation, and who leaves the facility within three hours of the start of consultation. An outpatient is not formally admitted to the facility” . Trials Trials with inpatients were not included in this review because this patient group a priori was different with regard to the severity of their illness, the presence of comorbidity, and equally more comprehensive treatment .Intervention:The administration of an identified probiotic agent of any specified strain or dose, regardless of the administration form .Control:Administration of placebo or an active comparator or no treatment.Incidence of antibiotic-associated diarrhea (AAD)Primary outcome:This analysis used the original study’s definition of diarrhea.An overall pooled meta-analysis, as well as a strain-specific subgroup analysis, was conducted.Incidence of AAD using the criteria defined by WHO:Secondary outcomes:This analysis used the definition of diarrhea authored by WHO. Diarrhea is defined as “the passage of three or more loose or liquid stools per day ” . AntibioMean duration of diarrhea (MDD) in daysNumber and types of adverse eventsOn the 20th of July 2017 a search was conducted of the MEDLINE/PubMed database to identify relevant RCTs. Combinations of the keywords “probiotics”, “prevention”, “antibiotics”, and “diarrhea” were used.The exact search terms for PubMed can be seen in Two independent investigators screened all the titles and abstracts from the search results and retrieved the relevant articles. The articles were then assessed for inclusion according to the selection criteria defined previously.Data extraction and management was conducted by two independent investigators. The following data were extracted from each study: author, year of publication, patient characteristics (age group and mean age), country, study setting, diagnosis, antibiotic(s) administered, probiotic(s) used, comparator, outcome measures , the study’s definition of diarrhea, the number of patients allocated to each group, the presence/absence of intention-to-treat analysis, and the number of participants lost to follow-up or withdrawn from the study (including the reasons for this).Methodological quality assessment using the Cochrane Collaboration’s tool for assessing risk of bias  was alsoThe overall quality of the evidence supporting the main outcome (incidence of AAD) and the secondary outcomes  was evaluated using “the Grading of Recommendations, Assessment, Development and Evaluations” (GRADE) criteria . RCTs arDichotomous data (antibiotic-associated diarrhea vs. no diarrhea and adverse events vs. no adverse events) were combined in a random-effects meta-analysis using a pooled risk ratio (RR) or risk difference (RD) along with the corresponding 95% confidence interval (95% CI). All models were weighted based on study size. The number needed to treat (NNT) was calculated for the main outcome. The data were entered into Review Manager 5.3  for stat2 statistic, wherein a value of >50% may represent substantial heterogeneity [H. pylori eradication; (4) probiotic genus; (5) low risk of bias; and (6) intention-to-treat analyses.A high degree of heterogeneity was expected because of the broad inclusion criteria , and this was investigated using the Iogeneity . To explTo evaluate the potential for publication bias, a funnel plot was applied for the efficacy outcome (incidence of AAD).Lactobacillus rhamnosus GG and Saccharomyces boulardii. Furthermore, we found preliminary evidence to suggest a dose-response relationship.Using probiotics for the prevention of antibiotic-associated diarrhea reduces the risk of AAD by 51%  with a moderate quality of evidence according to GRADE. This result was confirmed in analyses of specific strains, namely The use of probiotics appears safe. However, our study still suggests that caution be applied prior to widespread introduction of probiotic treatment for AAD as only 18% of the included studies had a low risk of bias, and these studies did not find a statistical significant reduction in the prevention of AAD.Limitations to the findings include the paucity of data on probiotic strain level, and future studies on probiotics to prevent AAD should focus on identifying the most effective agent(s) preferable in head-to-head comparisons and follow a stringent approach to definitions of outcomes, as well as clinical scenarios, prior to the widespread recommendation of probiotics as adjunct therapy to antibiotics. Also, more data are needed to determine the safety of probiotics, and trials should define potential adverse events in advance.


Background: The present study tested Method: Adolescent competitive tennis players  completed measures of values, goal orientations, and moral attitudes. A sub-sample (n = 90) was observed in 45 competitive tennis matches by trained observers who recorded their cheating and gamesmanship behaviors on a validated checklist.Results: Consistent with hypotheses, athletes’ values predicted their moral attitudes through the effects of goal orientations. Anti-social attitudes directly predicted cheating behavior in actual match play providing support for a direct link between moral attitude and actual behavior.Conclusion: The present study findings support key propositions of Lee and colleagues’ model, and extended its application to competitive athletes in actual match play. Whether sports participation builds moral character and fosters moral functioning is a matter of debate . ResearcQuestions about moral behavior in sports are essentially concerned with how athletes conduct themselves when engaging in their sport . Developing an equivocally accepted definition of moral behavior in sports is difficult; definitions, operationalization, and interpretation of moral behavior vary according to the origin and perspective of those involved. One line of research has defined moral behavior in relation to sportspersonship, a concept that describes a range of honorable behaviors, including fair play, respecting the rules, respecting the opponents and officials, and accepting defeat and victory . AthleteExtending the work of Over the years, These two types of goal orientations have been shown to be conceptually and empirically related to the value systems that underpin moral behavior in sport, and prosocial and antisocial attitudes. With respect to values, existing research has shown that task-oriented individuals assign relative importance to, and are predominantly regulated by competence values, whereas, ego-oriented individuals are driven by and regulate their behavior based on status values . AccordiTaken together, the aforementioned evidence is in line with The present study intended to apply N = 314; 76.75% male, M age = 14.36 years, SD = 1.50, range: 12–17 years) agreed to participate in the study. Data were collected from mid-December 2011 to the beginning of January 2012. All players were recruited from the LEMONBOWL in Rome, one of the most important international tennis tournaments for young competitive tennis players in the world, with an annual participation of approximately 2000 players. Participants were randomly selected from the roster of Italian adolescent players (nearly 920 athletes). All the athletes who were approached agreed to participate to the study. Consent from the athletes’ parents and the athletes’ themselves was obtained prior to data collection. The study was approved by the Ethics Review Board of the Department of Social and Developmental Psychology, “La Sapienza” University of Rome, and participants were informed of the aims and purpose of the study, as well as their participation rights , in advance of data collection. All participating athletes individually completed study measures in a single questionnaire booklet administered in a quiet room located near the tennis courts. Tennis players were instructed that they could use as much time as needed to complete the survey. Participants typically spent approximately 25 min completing the survey. During the administration sessions, the participants were also informed that their matches during the tournament might have been randomly selected for an additional observation phase of the study.Adolescent tennis players , competence , and status  values. Responses were recorded on the original seven-point scales1, ranging from extremely important to me (5), to the opposite of what I believe (-1). Higher scores reflected stronger values. Previous studies  and ego orientation . Responses were recorded on five-point Likert scales ranging from strongly disagree (1) to strongly agree (5). Higher scores on each scale reflected greater achievement goals. The Italian version of the TEOSQ was developed by We assessed achievement goals using the Italian version of Task and Ego Orientation in Sport Questionnaire . The queProsocial attitudes were assessed with a translated version of the two MSOS subscales: Commitment to Sport Participation  and “Respect for Social Convention”  . ResponsAntisocial attitudes were assessed using the Italian version of the Attitudes to Moral Decision-Making in Youth Sport Questionnaire AMDYSQ; , includin = 90) of young tennis players randomly selected from the total sample. The choice of selecting a subsample of young tennis players’ matches was determined by the number of trained observers available (n = 8) at any one time to observe and code tournament matches. In fact, the maximum number of matches that could be subjected to observation was 45. It should be noted that, as in other youth tennis tournaments, the LEMONBOWL matches do not have an official umpire and players are required to officiate their own matches. Table 1 shows the list of cheating and gamesmanship behaviors assessed in the study and the number of tennis players who displayed each behavior at least once during the matches. The behaviors listed in Table 1 were selected based on data from a focus group interview. The focus group included six experienced and Masters-level tennis players who were asked to generate and reach consensus about instances of cheating and gamesmanship behaviors that typically occur during tennis matches in youth tennis tournaments. Their data was used to generate cheating and gamesmanship items for the observation protocol. Subsequently, an additional and independent group of six tennis experts was involved to verify the face and content validity of the items in the observation protocol. Specifically, the experts were initially provided with the operational definition of cheating and gamesmanship, and then asked to evaluate the soundness and representativeness of the instances of cheating and gamesmanship included in the observation protocol. Each expert in this panel individually confirmed the soundness and representativeness of the behaviors listed in Table 1.We used an observation protocol to record cheating and gamesmanship behaviors during competitive tennis matches in a subsample (Table 1). Observers were initially provided with the operational definition of each behavior, and were presented with a demonstration of each behavior on a tennis court. They were then trained on how to use the checklist during trial tennis games that simulated the LEMONBOWL tournament. For the actual behavioral assessment, 45 tennis matches from the tournament were randomly selected, and the two players involved in each of these matches were observed by a pair of observers, randomly assigned to observe the match. Each observer individually recorded, in real time, (a) the occurrence of cheating or gamesmanship behaviors displayed by the two players and, for each recorded behavior, and (b) the exact moment and match score when the behavior occurred on the checklist (these latter data were not used in the present study). For each behavior listed on the observation tool, observers assigned the player a “yes” score if they enacted the target behavior at least once during the match, or “no” if they did not enact that behavior during the match. Therefore, players receiving a “yes” one or more times across the listed cheating behaviors were classified overall as 1 = “engaged in cheating behaviors”, while players that did not register a “yes” for any of the listed cheating behaviors were scored as 0 = “did not engage in cheating behaviors”. The same procedure was applied to gamesmanship behaviors. Inter-rater reliability was calculated across all matches and showed a relatively high level (Cohen’s Kappa for each pair of observers ranged between 0.85 and 1.00). The limited cases of discrepancy in the two observers’ ratings were resolved by discussing the cases after the matches and by reaching a consensus. The use of observational checklists to measure behavior is a common approach in social science research  with the Mplus statistical software . In our Figure 1. We followed Figure 2.Focusing on Model 1, consistent with the Figures 1, 2 shows, the measurement models of athletes’ prosocial and antisocial attitudes were different across the two analyses. In Model 1, both types of social attitudes were hypothesized to indicate a second-order factor structure in which indicators were developed using the item parceling procedure. In contrast, in Model 2, we used a first-order factor structure in which indicators of prosocial and antisocial attitudes were identified by aggregating the observed scores utilized in the first analysis. These choices were made in lieu of the reduction in sample sizes across analyses: the replication model was tested in a relatively large sample (N = 314) while the behavioral model was tested in a substantially smaller subsample (n = 90). Second, scores for cheating and gamesmanship behaviors from the observation checklist were treated as dichotomous. In line with recommendations by There are two important addenda to the above analyses. First, as N = 314) are presented in Table 2. Intercorrelations for the study measures in the sub-sample (n = 90) are reported in Table 3.Descriptive statistics, internal consistency reliability coefficients, and intercorrelations for the study measures in the whole sample (2(299) = 472.57, p = 0.001; CFI = 0.93; TLI = 0.90; RMSEA = 0.04, 90% CI RMSEA = 0.04 to 0.05; SRMR = 0.06). The factor loadings of the latent constructs were statistically significant and the average variance extracted (AVE) was significantly different from zero for the moral values (0.31), competence values (0.36), status values (0.43), task orientation (0.48), ego orientation (0.51), prosocial attitudes (0.85), and antisocial attitudes (0.67) factors, respectively.Overall, goodness-of-fit indices suggested indicated that the measurement characteristics of the replication model adequately accounted for the data  = 516.42, p = 0.001; CFI = 0.91; TLI = 0.90; RMSEA = 0.05, 90% CI RMSEA = 0.04 to 0.05; SRMR = 0.07). Standardized path coefficients for the hypothesized pathways in the model indicated that moral values were negatively and directly associated with tennis players’ cheating and gamesmanship attitudes . They were also directly and positively associated with athletes’ prosocial attitudes . In contrast, competence and status values had no direct associations with prosocial  and antisocial  attitudes, whereas they were positively associated with task  and ego  orientations, respectively. Task and ego orientations were associated, respectively, with prosocial  and antisocial attitudes . Finally, the indirect effect of competence values on prosocial attitudes through task orientation was statistically significant . Similarly, the indirect effect of status values on antisocial attitudes through ego orientation was statistically significant .The structural model also demonstrated a good fit with the data (χ2(131) = 170.736, p = 0.01; CFI = 0.93; RMSEA = 0.06; SRMR = 0.08). The AVE for the latent variables were statistically significant for the moral values (0.47), competence values (0.24), status values (0.34), task orientation (0.62), ego orientation (0.52), prosocial attitudes (0.58) and antisocial attitudes (0.59), respectively.Turning to the behavioral model, the measurement model showed adequate fit with the data (χ2(175) = 203.02, p = 0.07; CFI = 0.91; RMSEA = 0.042; Weighted Root Mean Square Residual = 0.74). As reported in Figure 2, standardized path coefficients indicated an identical pattern of effects among players’ values, goal orientations, and social attitudes to that found in Model 1 with the entire sample, consistent with p = 0.019). There were no statistically significant direct effects of prosocial  and antisocial  attitudes on observed gamesmanship behaviors, or of prosocial attitudes on cheating behavior . Finally, with respect to possible indirect effects of sport values on cheating behavior, the indirect effect of moral values on cheating behavior through the antisocial attitudes , and the indirect effect of status values on cheating behavior through ego orientation and prosocial attitudes , were not statistically significant.The structural model showed an adequate fit statistics  and sport contexts .With respect to our aim of extending the model findings to objective measures of cheating behavior, findings indicated that moral attitudes exerted a direct, albeit small, effect on young tennis athletes’ cheating behaviors during actual tennis matches. This finding is also consistent with Taken together, the findings of the present study are important for three sets of reasons. First, although it provides a well-known theoretical basis for linking values, attitudes and behavior, the value-expressive model of attitudes  had not Second, our findings imply that, at least to some degree, the display of morally questionable behaviors  is guided by athletes’ antisocial attitudes about moral behavior and conduct in sport settings. This notwithstanding, the lack of any effects from prosocial attitudes to behavior does not rule out the possibility that the attitude-behavior relation, especially concerning gamesmanship behaviors, may also depend on social psychological antecedents that were not measured in the present study. In this regard, it also seems relevant to point out that, while cheating behaviors typically draw upon, and are evaluated with respect to, clear, explicit and well-defined sets of rules, gamesmanship behaviors call upon an individual’s personal representations of sport rules and codes of conduct. This distinction may well have had some impact on the differences in findings across cheating and gamesmanship behaviors.Third, given that cheating behavior is generally viewed as socially and culturally undesirable , self-reThe current study may serve as a platform for future research on the theoretical links between prosocial and antisocial attitudes, motivational factors, and cheating and gamesmanship behaviors. For instance, although our data does not allow a direct test of this assumption, findings imply that cheating attitudes guide cheating behavior directly without the need for conscious intention-formation . This hypothesis may partly account for the single effect found between antisocial attitudes and cheating  and, at the same time, for the lack of an attitude-behavior relation in the case of prosocial attitudes. Finally, the lack of any effect of antisocial attitudes on gamesmanship could be related to different levels of perceived acceptability of the two antisocial-behaviors by athletes . In line with this argument, a mounting body of evidence has shown that features of the context can trigger mental representations, such as attitudes or values, outside of awareness and accordingly drive behavior in an offhand manner . This maIn addition, we propose three additional explanations for the non-significant effect of pro-social attitudes on actual gamesmanship and cheating behavior. Firstly, attitude specificity may determine the influence of attitudes on behavior . More spIn terms of theoretical implications, our replication of Another important implication is concerned with the generalizability of the present research’s findings. Our findings extend those The present study was cross-sectional in design, which means we cannot infer causal relations among the constructs from the data, only from theory. Longitudinal designs, particularly cross-lagged panel designs may assist researchers in addressing hypotheses of the value-expressive model of moral attitudes in youth sport. In addition, although we systematically developed our observational checklist for cheating and moral behaviors in sport, we did not conduct a large-scale validity trial. It would be important that future research to corroborate our small-scale development of the measure on a larger sample with more raters. This limitation notwithstanding, we attained good inter-rater reliability and expert corroboration of face validity of our measure. Finally, the measures used to tap the model value constructs in the current study from the MSOS and the AMDYS, did not exhibit robust solution estimates . It may be the meaning of the constructs in contexts and cultural settings other than those in which the scales were originally developed may vary. Current results should be interpreted in light of the suboptimal validity of these scales and revision and confirmation of the construct validity of these scales in multiple contexts and cultures is warranted. In addition, we were unable to replicate Despite its correlational nature, the present study can provide some initial considerations as to the ways social agents in youth sport  may promote moral attitudes and behavior in young athletes. The present research provided empirical support for All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.