As forensic scientists, we are required to accurately test for certain substances. This may necessitate the use of presumptive tests such as the One Step Multi-Drug Screen Test Card with the Integrated iCup®/iCup®A.D . There are many circumstances where these tests are applicable, such as job-related drug testing, custody and parole cases. An immunoassay, or presumptive test, is designed to give the analyst, even a non-scientific analyst, a general idea of what substance(s) are present in the individuals system, so that he or she is able to more accurately confirm what substances, if any, the individual may have used or consumed. The goal of the validation study of the One Step Multi-Drug Screen Test Card with the Integrated iCup®/iCup®A.D was to determine the sensitivity of various THC containing compounds (delta-9-THC, 11-nor-9-carboxy-delta9-THC, and 11-hydroxy-delta-9-THC) as well as different solutions containing Synthetic Cannabinoids at various concentrations and stored at different temperatures. Each of the drugs were tested below, at and above the cut-off of the drug stated by the manufacturer. The cut-off of 11-nor-9-carboxy-delta9-THC, given by the manufacturer, was 50 ng/mL. For every trial that was conducted, the drug could be detected in the iCup® at this limit of detection of 50 ng/mL, except when the drug had been stored in the freezer for approximately two months prior to use. Delta-9-THC was given a cut-off of 15,000 ng/mL, which is a high concentration, especially when these assays are used in custodial cases and job-related drug tests, where living individuals are providing a fresh specimen. The concentrations of delta-9-THC and 11-hydroxy-delta-9-THC were higher than the cut-off for a positive result of 11-nor-9-carboxy-delta9-THC, but it was tested below the 15,000 ng/mL cut-off for delta-9-THC, established by the manufacturer. After these adjustments were made, both delta-9-THC and 11-hydroxy-delta-9-THC could be detected in a range between 1,500 ng/mL and 5,000 ng/mL. While 1,500 ng/mL is still high for a living specimen, it is substantially lower than 15,000 ng/mL. Analyzing the higher concentration of the synthetic cannabinoid working stock solution of 10,000 ng/mL, positive results were detected at 3,500 ng/mL and 5,000 ng/mL. There were eight cannabinoids, metabolites, and synthetic cannabinoids found in the working stock solution: (1) THC, (2) 11-Hyroxy-delta-9-THC, (3) 11-nor-9-Carboxy-delta-9-THC, (4) AB-FUBINACA, (5) AB-FUBINACA Met. 3, (6) AB-FUBINACA Met. 2a, (7) AB-PINACA-blood, and (8) AB-PINACA Pentatonic Acid metabolite. As the concentrations decreased, a positive result was not produced. Ultimately, the final conclusions of all the testing was that the One Step Multi-Drug Screen Test Card with the Integrated iCup®/iCup®A.D is not as sensitive when it comes to the synthetic cannabinoids, the primary compound present in marijuana (delta-9-THC), and the active metabolite of marijuana (11-hydroxy-delta-9-THC). In order to gain more accurate results using this presumptive test, the sensitivity of the iCup® for a detection of delta-9-THC and 11-hydroxy-delta-9-THC at a lower concentration should be done. By, doing this, an analyst can be more confident when deciding what confirmatory test to use based on what substances are present in a given sample.

In Mediterranean countries, sheep and goat’s milk production has traditionally been destined for the manufacture of cheese, often as raw milk. Cheese quality is closely related to milk composition but also to hygienic aspects such as somatic cell count, bacteriology or presence of antibiotic residues, currently regulated by European legislation. The implications of the presence of antibiotic residues in milk as a result of veterinary treatments include negative effects on consumer’s health such as allergies or antibiotic resistance and problems on the manufacturing processes of fermented products. For the screening of milk samples for antimicrobial residues, there are various methods available, microbial inhibitor tests and assays based on specific receptors, both widely used, especially in farms, the dairy industry and control laboratories. Screening methods have been validated for the use in raw milk from cows, but information on the performance of these tests in sheep and goat’s milk is rather limited. The aim of this study was to evaluate the performance of some microbial and receptor-binding screening tests to detect antibiotics in sheep and goat’s milk according to Commission Decision 2002/657/EC to determine their suitability to monitor the presence of antibiotic residues in milk and establish the most convenient analytical strategy in Spain. The Detection capability (CCβ) of microbial screening tests, the BRT MRL, the Delvotest MCS SP-NT, the Delvotest MCS Accelerator and the Eclipse 100, was at or below the maximum residue limits (MRLs) for most beta-lactam antibiotics assessed and other non-beta-lactam drugs such as neomycin, tylosin, sulfadiazine and sulfadimethoxine. However, they were less sensitive in the detection of quinolones and tetracyclines at safety levels. When individual milk samples were analysed, microbiological tests showed a higher occurrence of non-compliant results in sheep milk than in goat’s milk, being related in all cases to an elevated somatic cell count (SCC). The microbiological system consisting of two complemetary microtiter plates containing Geobacillus stereathermophilus var. calidolactis and Bacillus subtilis, respectively, allows improving the detection level in sheep milk with respect to the use of a single commercial test using G. stearothermophilus, detecting some quinolone and macrolide substances more closely related to their respective MRLs. The rapid receptor-binding assays (the Betastar Combo, the Charm MRL BLTET, the SNAP Betalactam, the SNAP Tetracycline and the TwinsensorBT) were able to detect most beta-lactams and tetracyclines at or below MRLs (CCβ ≤ MRL). A higher specificity of the rapid receptor tests was obtained in all cases even when individual milk samples were analysed. Only the TwinsensorBT test presented non-compliant results when antibiotic-free milk samples from individual animals were analysed, especially in the last weeks of lactation. No cross-reactions were found when drugs belonging to antimicrobial groups other than beta-lactams or tetracyclines were present in milk. Azidiol, used as a preservative, had no effect on the performance of the rapid receptor tests. Moreover, differences between the visual and instrumental classification of the test results were not found. Taking into account the frequency of use of antibiotics commonly employed in Spain and the screening test sensitivity at MRLs equivalent to antibiotic concentrations, total detection rates have been calculated. In general, the use of a single test allows detecting 62.8-82.4 % of the antibiotics employed. For sheep milk, the total detection range achieved with microbial tests was significantly higher than that reached with rapid receptor tests. However, no significant differences between the two types of tests were found when goat's milk was analysed. In both types of milk, the simultaneous use of two screening tests with a different analytical basis increases the total detection range significantly, reaching values ≥ 90 % in some cases. However, antibiotics such as enrofloxacin, marbofloxacin, spiramycin, and streptomycin also used to treat mastitis and other infectious diseases could not be detected by the screening tests assessed. Therefore, the improvement of the analytical strategy through the periodical implementation of screening tests able to detect these substances at safety levels would be recommended. / Beltrán Martínez, MC. (2014). Analytical strategy for the detection of antibiotic residues in milk from small ruminants [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48164 / TESIS

Sleep is an important and necessary part of life. While the biological need for sleep among school-aged individuals has not changed over the years, the quality and quantity of their sleep has. School-aged children reporting sleep deficits in the United States is on the rise and a significant body of research establishes associations between a lack of sleep and lower cognition as well as psychological and behavioral problems. These associations can heavily influence the academic progress and success of students. School psychologists are in a unique position to identify and provide support to individuals with sleep issues. Unfortunately, there is little evidence in the literature to suggest that school psychologists are regularly accounting for sleep. Additionally, no studies have attempted to explore the barriers and facilitating factors for regular sleep screening/assessment among school psychologists. The present study surveyed 105 school psychologists to ascertain the proportion of practitioners who report assessing or screening for sleep issues. School psychologists were also asked to identify potential barriers and facilitating factors related to treating sleep challenges. The results indicated that many school psychologists are not assessing, screening, or treating students for sleep issues or disorders. Many school psychologists report minimal exposure to sleep training within their graduate programs and practitioners cite a lack of training and awareness to sleep-related resources as both major barriers and facilitating factors to implementing sleep related practices. The results highlight the continued lack of emphasis on sleep training within the profession of school psychology. Practitioner's responses indicated a need for better sleep training and education within professional training programs, including equipping such programs with faculty possessing expertise and experience with sleep training/treatment. Results also indicated a need for professional school psychology associations and organizations, such as the National Association of School Psychologists, to provide better awareness, training, professional development opportunities, along with increased visibility to resources on sleep for practitioners to utilize. Furthermore, this article provides practitioners a variety of sleep-related resources to better prepare and educate themselves to provide the best possible services to students and families.

The primary objective of this study was to evaluate the associations between psychosocial stressors, urine sugar levels, and subsequent birth outcomes, specifically high birth weight babies and Caesarean section births. In a prospective cohort study, 506 Black and White women of childbearing age were followed for the duration of one pregnancy in Tuscaloosa and Mobile counties in Alabama from 1990 to 2001. Participants were interviewed twice throughout pregnancy, during the first and third trimesters, respectively, and birth outcome data were collected via medical chart reviews. Six percent (6.1%) of the women in the sample had a high birth weight baby, and 18.4% received a C-section during childbirth. Adjusted logistic regression results indicate that urine sugar levels are predictive of high-weight births, with women who have higher urine sugar levels were more than three times likely to birth a high weight baby compared with women who have no detectable urine sugar spill (OR 3.25; 95% CI 1.30, 8.10). In addition, the interaction of familial social support throughout pregnancy, physical or verbal abuse during the second and third trimesters, and ethnicity is significantly associated with increased risk of having a high birth weight baby. For C-section, single participants are over two times less likely to receive a C-section during childbirth compared with currently married participants (OR 0.46; 95% CI 0.21-1.00). Examining structural equation modeling results; pathways leading from urine sugar levels, physical or verbal abuse during the latter half of the pregnancy, and a mother's social support among White participants are indicative of high weight births (R² = 0.65). White abused women who receive their mother's social support are more likely to have a high birth weight baby compared with both White and Black women who are not abused and receive the same amount of social support. Recommendations to public health practitioners include primary prevention through promotion of familial support during pregnancy, secondary prevention through urine sugar screening at every prenatal visit, and direct intervention by identifying and inquiring about instances of suspected abuse during pregnancy.

Although a great deal is known about the etiology of falls in elderly individuals, fall accidents continue to represent a significant burden to elders residing in long-term care facilities. It has been stated that 75% of deaths due to falls in the United States occur in the 13% of the population age 65 and over. The first objective of the study was to identify which fall-predictor variables acknowledged in the research literature are associated with increased fall frequency with the older population. Identifying specific predictor variables related to a high occurrence of falls in long-term care setting can assist in the redesign of tools and programs aimed to recognize fall risk, and prevent fall-related accidents and fatalities in the geriatric population. The second objective of the study was to identify which combination of predictor variables could better predict the frequency of falls. A history of falls variable was the only predictive variable that differed significantly between groups of residents who had sustained subsequent falls and those who had not. Other variables including age, mental status, day number of stay, elimination, visual impairment, confinement, blood pressure drop, gait and balance, and medication were found to not be statistically significant between groups of fallers and non-fallers. In this setting, the current design of the tool had limited accuracy and exhibited an inability to effectively discriminate between resident populations at risk of falling and those not at risk of falling. Consequently, the current fall risk assessment tool is not adequate for assessing fall risk in this clinical setting. / Master of Science

Alcohol consumption during pregnancy remains a common occurrence and is associated with a multitude of adverse birth and long-term outcomes. Binge drinking in particular is shown to be particularly harmful to the developing fetus. Effects include full fetal alcohol syndrome, with characteristic facial dysmorphology, growth restriction, and developmental to delays. Exposed children may also have partial fetal alcohol syndrome, alcohol-related birth defects, and alcohol-related neurodevelopmental disorders. These effects are preventable, and efforts must begin with accurate identification of women who consume alcohol during pregnancy. Several screening tools have been developed and validated for use in prenatal care settings, and the most recently proposed brief and easy to use T-ACER3 has demonstrated high sensitivity and specificity in both identifying risk drinking during pregnancy and predicting long-term neurobehavioral outcomes in exposed children. Once identified, effective interventions are available for use with pregnant women consuming alcohol. Brief interventions, which can be delivered by a health professional and involve motivational interviewing, have been demonstrated to significantly reduce alcohol consumption during pregnancy. These approaches, recommended by American College of Obstetricians and Gynecologist (ACOG), help move patients toward increased readiness to positively change their drinking behavior. Ultimately, all prenatal care providers should routinely screen all patients for alcohol use using validated tools, and where appropriate, should offer intervention.

Alcohol consumption during pregnancy remains a common occurrence and is associated with a multitude of adverse birth and long-term outcomes. Binge drinking in particular is shown to be particularly harmful to the developing fetus. Effects include full fetal alcohol syndrome, with characteristic facial dysmorphology, growth restriction, and developmental to delays. Exposed children may also have partial fetal alcohol syndrome, alcohol-related birth defects, and alcohol-related neurodevelopmental disorders. These effects are preventable, and efforts must begin with accurate identification of women who consume alcohol during pregnancy. Several screening tools have been developed and validated for use in prenatal care settings, and the most recently proposed brief and easy to use T-ACER3 has demonstrated high sensitivity and specificity in both identifying risk drinking during pregnancy and predicting long-term neurobehavioral outcomes in exposed children. Once identified, effective interventions are available for use with pregnant women consuming alcohol. Brief interventions, which can be delivered by a health professional and involve motivational interviewing, have been demonstrated to significantly reduce alcohol consumption during pregnancy. These approaches, recommended by American College of Obstetricians and Gynecologist (ACOG), help move patients toward increased readiness to positively change their drinking behavior. Ultimately, all prenatal care providers should routinely screen all patients for alcohol use using validated tools, and where appropriate, should offer intervention.

No description available.

<p>In diagnostic accuracy studies, sensitivity and specificity are the most common measures to assess the performance of diagnostic or screening tests. The estimation of these measures can be done using empirical or model-based methods. The primary objective of this thesis is to use both the empirical and the model-based (logistic regression) approach to assess the effects of covariates on the performance of the visual inspection with acetic acid (VIA) and lugol iodine (VILI) tests using the data from women screened for cervical cancer in Kinshasa, the Democratic Republic of Congo. The secondary objectives are: first, to adjust for the false negative and false positive error rates by the two tests through latent class models (LCM), and second, to evaluate the effects of covariates on the agreement between the measurements of the two tests taken by nurse and physician through Kappa statistic.</p> <p>No particular pattern could be observed in the trend of empirically estimated sensitivity and specificity of the VIA and VILI tests measured by the nurse and by the physician across age and parity categories. From the logistic regression models, both age, parity, and their respective quadratic terms have significant effects on the probability of VIA and VILI tests to detect cervical cancer. However, there is no significant effect of marital status, smoking, and hybrid capture2 (HPV DNA) on the probability of VIA and VILI tests measured by nurse to detect cervical cancer while HPV DNA does in the probability of VIA and VILI tests measured by physician to detect cervical cancer. The trend of the estimated sensitivity of VIA and VILI tests measured by the nurse is not different across age groups but the specificity does vary. The trend of both the sensitivity and specificity of VIA and VILI tests are significantly different across parity groups. The reverse is the case for the sensitivity and specificity of VIA and VILI tests measured by physician across age and parity groups. The false negative and false positive error rates in the sensitivity and specificity of VIA and VILI tests measured by nurse are higher compared to that of physician. With Kappa statistic results, there is almost perfect agreement between the ratings by the nurse and physician for the dichotomized VIA and VILI test outcomes.</p> <p>In conclusion, there is a significant effects of age, parity and the quadratic term of age on the performance of VIA and VILI tests outcomes measured by nurse. On the VIA and VILI test outcomes measured by physician, age, parity, HPV DNA and quadratic term of age have shown significant effects on the performance of VIA and VILI tests outcomes measured by physician alone.</p> / Master of Science (MSc)

Binary measurement systems (BMS) are widely used in both manufacturing industry and medicine. In industry, a BMS is often used to measure various characteristics of parts and then classify them as pass or fail, according to some quality standards. Good measurement systems are essential both for problem solving (i.e., reducing the rate of defectives) and to protect customers from receiving defective products. As a result, it is desirable to assess the performance of the BMS as well as to separate the effects of the measurement system and the production process on the observed classifications. In medicine, BMSs are known as diagnostic or screening tests, and are used to detect a target condition in subjects, thus classifying them as positive or negative. Assessing the performance of a medical test is essential in quantifying the costs due to misclassification of patients, and in the future prevention of these errors. In both industry and medicine, the most commonly used characteristics to quantify the performance a BMS are the two misclassification rates, defined as the chance of passing a nonconforming (non-diseased) unit, called the consumer's risk (false positive), and the chance of failing a conforming (diseased) unit, called the producer's risk (false negative). In most assessment studies, it is also of interest to estimate the conforming (prevalence) rate, i.e. probability that a randomly selected unit is conforming (diseased). There are two main approaches for assessing the performance of a BMS. Both approaches involve measuring a number of units one or more times with the BMS. The first one, called the "gold standard" approach, requires the use of a gold-standard measurement system that can determine the state of units with no classification errors. When a gold standard does not exist, is too expensive or time-consuming, another option is to repeatedly measure units with the BMS, and then use a latent class approach to estimate the parameters of interest. In industry, for both approaches, the standard sampling plan involves randomly selecting parts from the population of manufactured parts. In this thesis, we focus on a specific context commonly found in the manufacturing industry. First, the BMS under study is nondestructive. Second, the BMS is used for 100% inspection or any kind of systematic inspection of the production yield. In this context, we are likely to have available a large number of previously passed and failed parts. Furthermore, the inspection system typically tracks the number of parts passed and failed; that is, we often have baseline data about the current pass rate, separate from the assessment study. Finally, we assume that during the time of the evaluation, the process is under statistical control and the BMS is stable. Our main goal is to investigate the effect of using sampling plans that involve random selection of parts from the available populations of previously passed and failed parts, i.e. conditional selection, on the estimation procedure and the main characteristics of the estimators. Also, we demonstrate the value of combining the additional information provided by the baseline data with those collected in the assessment study, in improving the overall estimation procedure. We also examine how the availability of baseline data and using a conditional selection sampling plan affect recommendations on the design of the assessment study. In Chapter 2, we give a summary of the existing estimation methods and sampling plans for a BMS assessment study in both industrial and medical settings, that are relevant in our context. In Chapters 3 and 4, we investigate the assessment of a BMS in the case where we assume that the misclassification rates are common for all conforming/nonconforming parts and that repeated measurements on the same part are independent, conditional on the true state of the part, i.e. conditional independence. We call models using these assumptions fixed-effects models. In Chapter 3, we look at the case where a gold standard is available, whereas in Chapter 4, we investigate the "no gold standard" case. In both cases, we show that using a conditional selection plan, along with the baseline information, substantially improves the accuracy and precision of the estimators, compared to the standard sampling plan. In Chapters 5 and 6, we investigate the case where we allow for possible variation in the misclassification rates within conforming and nonconforming parts, by proposing some new random-effects models. These models relax the fixed-effects model assumptions regarding constant misclassification rates and conditional independence. As in the previous chapters, we focus on investigating the effect of using conditional selection and baseline information on the properties of the estimators, and give study design recommendations based on our findings. In Chapter 7, we discuss other potential applications of the conditional selection plan, where the study data are augmented with the baseline information on the pass rate, especially in the context where there are multiple BMSs under investigation.