48 research outputs found
Wink testing for ocular dominance
The dominant eye is a clinical consideration for the optometrist when prescribing lenses. An effort was made to determine a patients dominant eye based on the hypothesis that a person winks with their non-dominant eye
Alkaline Aerosols: An Initial Investigation of Their Role in Determining Precipitation Acidity
published or submitted for publicationis peer reviewedOpe
Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools
Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between
schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies
have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to
date and novel statistical tools, we aimed to determine the extent to which migraine’s polygenic architecture
overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared
genetic loci.
Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine
(n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression
(n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine
and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci.
Loci were functionally characterized to provide biological insights.
Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100–12 300 disorder-influencing variants). Bivariate analysis estimated that
800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and
schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine
and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic
effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation
mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several
novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative
gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia.
Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority
of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar
overlap with other brain-related phenotypes suggests this represents a pool of ‘pleiotropic’ variants that influence
vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate
migraine genes for experimental validation
Historical Threads in the Development of Oncology Social Work
As the Association of Oncology Social Work celebrates its 25th year, we pause to reflect on the many historical threads that contributed to its development and hear from each of the presidents who helped create the organization, as we know it today. Set within hospitals, medical social work was born in the early 20th century. In the 1940s medical social work became necessary for hospital accreditation. Two additional historical shifts, one in medical improvements in treating cancer, the other a shift to a consumer-oriented American Cancer Society, contributed to the push for a greater role for the federal government in funding cancer research. Oncology social work came to full blossom in the 1970s, a result of the physicians' need for a member of the health care team who understood cancer, its treatment, and the patient's need to address his or her psychosocial needs resulting from cancer. Today, oncology social work is a fully developed profession with a national organization providing education and support to oncology social workers' in their use of psychosocial interventions and research in behalf of cancer patients and their families
Acute weight gain, gender, and therapeutic response to antipsychotics in the treatment of patients with schizophrenia
BACKGROUND: Previous research indicated that women are more vulnerable than men to adverse psychological consequences of weight gain. Other research has suggested that weight gain experienced during antipsychotic therapy may also psychologically impact women more negatively. This study assessed the impact of acute treatment-emergent weight gain on clinical and functional outcomes of patients with schizophrenia by patient gender and antipsychotic treatment (olanzapine or haloperidol). METHODS: Data were drawn from the acute phase (first 6-weeks) of a double-blind randomized clinical trial of olanzapine versus haloperidol in the treatment of 1296 men and 700 women with schizophrenia-spectrum disorders. The associations between weight change and change in core schizophrenia symptoms, depressive symptoms, and functional status were examined post-hoc for men and women and for each medication group. Core schizophrenia symptoms (positive and negative) were measured with the Brief Psychiatric Rating Scale (BPRS), depressive symptoms with the BPRS Anxiety/Depression Scale and the Montgomery-Asberg Depression Rating Scale, and functional status with the mental and physical component scores on the Medical Outcome Survey-Short Form 36. Statistical analysis included methods that controlled for treatment duration. RESULTS: Weight gain during 6-week treatment with olanzapine and haloperidol was significantly associated with improvements in core schizophrenia symptoms, depressive symptoms, mental functioning, and physical functioning for men and women alike. The conditional probability of clinical response (20% reduction in core schizophrenia symptom), given a clinically significant weight gain (at least 7% of baseline weight), showed that about half of the patients who lost weight responded to treatment, whereas three-quarters of the patients who had a clinically significant weight gain responded to treatment. The positive associations between therapeutic response and weight gain were similar for the olanzapine and haloperidol treatment groups. Improved outcomes were, however, more pronounced for the olanzapine-treated patients, and more olanzapine-treated patients gained weight. CONCLUSIONS: The findings of significant relationships between treatment-emergent weight gain and improvements in clinical and functional status at 6-weeks suggest that patients who have greater treatment-emergent weight gain are more likely to benefit from treatment with olanzapine or haloperidol regardless of gender
Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use
Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders 1 . They are heritable 2,3 and etiologically related 4,5 behaviors that have been resistant to gene discovery efforts 6–11 . In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures
Performance of data enhancements and training optimization for neural network: A polyp detection case study
Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no, and even a negative, effect
Performance of data enhancements and training optimization for neural network: A polyp detection case study
Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no, and even a negative, effect