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Prevalence and Characterization of Asymptomatic Thyroid Nodules in Assin North District, Ghana
<p><strong>Study design and sampling </strong></p> <p>The study was cross-sectional involving six (6) communities in the Assin North District of the Central Region of Ghana. Ethical approval was obtained from the Institutional Review Board of the University of Cape Coast, Ghana, with this reference number: UCCIRB/EXT/2017/18. All community entry protocols with local authorities were observed before the study commenced. Further, protocol involving informed consent was also duly observed during and after this study. Both verbal and written consent were obtained from each participant prior to participation. Participation in the study was strictly on voluntary basis.</p> <p>Each of these six (6) communities was considered a stratum in which all households were listed to constitute a sampling frame from which the respective number of households in each community were sampled using systematic random sampling technique. The sampling interval (<em>K</em><sup>th</sup>) in each community was determined by dividing the total number of listed households (N) by the number of households respectively require (n) (based on proportion-to-population size) as shown in Table 1. The simple random sampling technique was then employed to select the first household (<em>i</em><<em>K</em>) in each community, from which every <em>K</em><sup>th</sup> household was selected until the expected number of households in each community was met. One eligible consenting participant in each selected household was then randomly selected for the study. In a few instances where there were no consenting or eligible participant, the next household on the roll was considered.</p> <p>Exclusion criteria included participants with anterior neck swelling or clinical evidence of thyroid disease, smokers, persons on lithium, phenytoin, oral contraceptive drugs, and women during menstruation, pregnant women or women who had delivered within the last 12 months and persons with any systemic disorder</p> <p><strong>Data collection</strong></p> <p>Data collection was conducted in July, 2019 in two phases. The first phase consisted of face-to-face interviews with participants using a structured interview guide to elicit socio-demographic information such as age, sex, marital status, and highest level of education; history dietary salt intake (often intake- at least 5g or one teaspoon of iodized salt per day and not often intake- less than 5g or one teaspoon of iodized salt per day or not at all); and history of alcohol intake (yes- consumed alcohol regardless of quantity and no- had not consume alcohol before). Anthropometric measurements of body weight (kg) and height (cm) were measured using standard anthropometric techniques and further computed to generate measures of body surface area (BSA) and body mass index (BMI). Data collection in the phase was conducted by six (6) trained research assistants from UCCSMS and duly supervised by key investigators (listed authors) of the study.</p> <p>The second phase of the data collection mainly focused on diagnostic imaging of the thyroid gland by a specialist radiologist with over five (5) working experience in thyroid examination using various imaging technologies. A screening center was staged in each of the study communities on different days while ensuring that such days did not conflict with market days or other important community events. Participants who were interviewed at the household level were given an identification chit to present with to the screening stage for easy synchronization of their interview data with thyroid data. Given that ultrasound has been recognized as the initial imaging modality of choice for the early detection of thyroid nodules [2,9,22,23], a real-time ultrasound scanner (MEDISON SA8000SE-MAI, 1003 Dachi-Dong, Gangnam-Gu, Seoul Korea) with a 7.5 MHz, 50 mm linear transducer was used in examining the thyroid gland of study participants.</p> <p>Participants were examined while in a supine position with hyperextended cervical spine. Ultrasound gel was applied over the thyroid area with the transducer directly placed on the skin over the thyroid gland. Longitudinal and transverse scans were performed, to obtain length and width in centimeters, of each thyroid nodule. If there were multiple nodules in a single thyroid lobe only the dimensions of the largest were recorded. Documented characteristics of thyroid nodules included the location of nodules in the thyroid lobe; number of nodules (solitary or multiple), nodule composition (cyst, solid or mixed), calcifications, and nodule size (length and width in centimeters). Out of the 343 participants interviewed in the initial phase of the data collection, 23 participants failed showed up for the thyroid screening in the second phase despite countless attempts to contact them. Hence the current study is based on 320 participants who were successfully interviewed and screened.</p> <p><strong>Statistical analysis</strong></p> <p> The data was captured using SPSS and later exported to STATA 11.0 for further management and analysis. A protocol was designed from the outset for imputing, ensuring data quality and preserving of data for reuse. Descriptive statistics including frequencies, percentages, means and standard deviation were used to summarize participants’ socio-demographic and thyroid characteristics. Bivariate and multivariate logistic regression analyses were conducted to determine factors associated with ATN. Odds ratios and corresponding confidence intervals were reported with statistical significance at p < 0.05</p>
Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
<p>This repository contains the dataset used in the paper "Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data" published in <strong>Scientific Reports</strong>. Please check our <a href="https://www.nature.com/articles/s41598-022-15342-z">formal publication</a> for the full details. The dataset contains 1232 nodules from 724 patients. Each row represents one nodule and each column represents one variable that describes the characteristics of the patient or nodule. The meaning of each variable is summarized below.</p> <ul> <li>id: the unique identity of the patient who carries the nodule</li> <li>age: the age of the patient</li> <li>FT3: triiodothyronine test result</li> <li>FT4: thyroxine test result</li> <li>TSH: thyroid-stimulating hormone test result</li> <li>TPO: thyroid peroxidase antibody test result</li> <li>TGAb: thyroglobulin antibodies test result</li> <li>site: the nodule location, 0: right, 1: left, 2: isthmus</li> <li>echo_pattern: thyroid echogenicity, 0: even, 1: uneven</li> <li>multifocality: if multiple nodules exist in one location, 0: no, 1: yes</li> <li>size: the nodule size in cm</li> <li>shape: the nodule shape, 0: regular, 1: irregular</li> <li>margin: the clarity of nodule margin, 0: clear; 1: unclear</li> <li>calcification: the nodule calcification, 0: absent, 1: present</li> <li>echo_strength: the nodule echogenicity, 0: none, 1: isoechoic, 2: medium-echogenic, 3: hyperechogenic, 4: hypoechogenic</li> <li>blood_flow: the nodule blood flow, 0: normal, 1: enriched</li> <li>composition: the nodule composition, 0: cystic, 1: mixed, 2: solid</li> <li>multilateral: if nodules occur in more than one location, 0: no, 1: yes</li> <li>mal: the nodule malignancy, 0: benign, 1: malignant</li> </ul>
Calculated Parameters of Thyroid Homeostasis: de-identified Data
<p>De-identified data of patients with different thyroid conditions. TI: Thyrotropic insufficiency; TT: Thyrotoxicosis; PH: Primary hypothyroidism; ST: Secondary hyperthyroidism. Parameters are reported with units of measurement and reference interval.</p>
Full summary statistics of mixQTL for GTEx v8 Thyroid
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Hypothyroidism Weight and serum thyroid hormones
<p>Dataset on 50 healthy controls (HC), 25 patients before starting treatment (Hyp1), 15 out of these 25 patients before starting treatment (Hyp12) and 15 patients (same 15 patients) after 6 months of treatment with L-thyroxine (Hyp2). Variables are: ID number, patient group (HC, Hyp1,Hyp12,Hyp2), sex (1=male), age (years), weight (kg), length (m), BSA (body surface area, m^2), BMI (kg/m^2), serum-TPO (IU/ml), serum-TSH (mIU/l), serum-free T3 (pmol/l), serum-free T4 (pmol/l), ratio serum-fT3/fT4, Zulewski score (points) and difference in weight before and after treatment (Hyp2-Hyp12), and difference in TSH (Hyp2-Hyp12) and dosage of L-thyroxine (Levaxin, mg).</p>
Selpercatinib treatment of RET mutated thyroid cancers is associated with gastrointestinal adverse effects
<p><strong>Background:</strong></p> <p>Metastatic medullary thyroid carcinoma (MTC) and radioactive iodine refractory differentiated thyroid carcinoma (RAI-R DTC) have poor prognosis and limited treatment options. Selpercatinib (LOXO-292), a selective kinase inhibitor targeting the <em>RET</em> gene, has shown a 69-79% objective responses have been observed in this cohort (1) with benefits in other tumours harbouring the same oncogenic driver (2). Published reports describe only 17% of patients experiencing gastrointestinal Adverse effects (AE) which is in contrast to our local experience. Here we characterise the AE and correlate with radiological and histopathological findings. </p> <p><strong>Methods:</strong></p> <p>Sequential patients enrolled in LIBRETTO-001 at Royal North Shore Hospital, Sydney with available imaging (n=22) were recruited. Patients had regular visits with AE documented and CT scans 3 monthly. CT at screening, at time of gastrointestinal AE and at most recent follow up were reviewed and scored. Endoscopic examination was performed in five patients.</p> <p><strong>Results:</strong></p> <p>Of 22 patients in this cohort, the majority had somatic <em>RET </em>alterations (n=18), most commonly <em>p.Met918Thr</em> (n=14). Ten patients (50%) developed gastrointestinal adverse effects. Dose reduction was required in 8 of the 10 patients, but none discontinued therapy. The majority had stable disease (n=17). Gastric and small bowel oedema was evident in symptomatic patients after a median time of 67 weeks’ treatment. Histological correlation in five patients revealed mucosal oedema correlating with radiological evidence of congestion and oedema.</p> <p><strong>Discussion: </strong></p> <p>Gastrointestinal AEs with selpercatinib may be more common than previously described. Most are self-limiting but often require dose adjustments. Histological evidence of mucosal oedema observed in conjunction with the radiological findings of congestion and wall thickening suggest that bowel wall oedema is a predominant mechanism of abdominal pain in these patients.</p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 2. Attributes of the classification models used in the experiments
<p>The authors used for their experiments a data set (UCI, 2016) containing 756 records about persons with thyroid dysfunctions. The classification model has 22 attributes; the class attribute is the target and it has three possible values: hypothyroidism, hyperthyroidism and normal. The current data set was extracted and preprocessed from the original file. A description of the attributes used in the experiments is given in Figure 2 (an extract from thyroid.arff test file). </p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 3. KNIME Diagram
<p>The proposed KNIME diagram representing the data mining models is given in Figure 3. The nodes that constitute the model diagram are: ARFF Reader – the input node used to load the data set in arff format, Partitioning – the node with the role of data set partition (for training and for the validation of the classification model), Naive Bayes Learner and Decision Tree Learner – the nodes used to build the classification model, Naive Bayes Predictor and Decision Tree Predictor – the nodes used to validate the model, Scorer – the node reports a confusion matrix and the accompanying quality measures in its view, Normalizer – the data set are normalized to be able to apply the neural network models, Multilayer Perceptron and RBFNetwork – the nodes corresponding to the neural network classification models, Weka Predictor – a node implemented in Weka to validate the models. </p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 1. Factors that Affect Thyroid Function (The Institute for Functional Medicine, 2014)
<p> In Figure 1 are presented the main factors that affect the thyroid function. It is obvious that factors such as stress, infection, toxins, trauma and certain medication are directly responsible for the improper production of thyroid hormones. Symptoms identification and the early detection of abnormal values of thyroid hormones after clinical investigation will help in establishing the proper diagnostic and to prescribe the right medication. The patient must periodically evaluate his clinical state in order to receive the treatment as long as he needs it. </p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1707">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Figure 4 in Identification of thyroid hormone receptors α and β genes and their expression profiles during metamorphosis in Rana chensinensis*
Figure 4. The levels of rcTRα and rcTRβ mRNA in several (n = 6) tadpoles as assessed by qRT-PCR. Different letters indicate significant differences (P <0.05) between developmental stages.
Figure 2 in Identification of thyroid hormone receptors α and β genes and their expression profiles during metamorphosis in Rana chensinensis*
Figure 2. Phylogenetic relationship of TRs among R. chensinensis and other species. Numbers at the branches are the bootstrap values.
Figure 3 in Identification of thyroid hormone receptors α and β genes and their expression profiles during metamorphosis in Rana chensinensis*
Figure 3. Organ-specific expression of rcTRα and rcTRβ in tadpoles during stages 33–46 by RT-PCR. M: DL2000 DNA marker.
Figure 1 in Identification of thyroid hormone receptors α and β genes and their expression profiles during metamorphosis in Rana chensinensis*
Figure 1. Alignment of TR polypeptide sequences in a few species of Rana. The A/B, DBD, D, and LBD domains are separated by arrows. Two conserved zinc fingers are boxed. Asterisks indicate conserved amino acid and hyphens represent spaces inserted to maximized similarity.
Figure 12 in The anatomy of the thyroid gland among "fishes": phylogenetic implications for the Vertebrata
Figure 12. - Distribution of the thyroidian patterns in Acanthomorphs. The interrelationship tree have been reconstructed from Li (2008), and Betancur- R et al. (2013b). See text and Appendix I for details.
Figure 11 in The anatomy of the thyroid gland among "fishes": phylogenetic implications for the Vertebrata
Figure 11. - Distribution of the thyroidian patterns in Teleostei. The interrelationship tree has been reconstructed from Li (2008), Broughton (2010) and Betancur-R et al. (2013b). See text and Appendix I for details.
Figure 10 in The anatomy of the thyroid gland among "fishes": phylogenetic implications for the Vertebrata
Figure 10. - Distribution of the thyroidian patterns in Vertebrates. The interrelationship tree has been reconstruct- ed from Li (2008), Janvier (2009), Broughton (2010) and Betancur-R et al. (2013b). See text and Appendix I for details.
Figure 9 in The anatomy of the thyroid gland among "fishes": phylogenetic implications for the Vertebrata
Figure 9. - Detail of the anatomy of the thyroid gland of an angler, Lophius piscatorius (Lophiidae); ventral view. The black arrow indicates the anterior part. Modified from Burne, 1927. ly.v = lymphatic vessel; th.g = thyroid gland; th.t = thyroidian tissue; v.ao = ventral aorta.
Figure 8 in The anatomy of the thyroid gland among "fishes": phylogenetic implications for the Vertebrata
Figure 8. - Close-up of the thyroid gland of an angler, Lophius piscatorius (Lophiidae); right lateral view. The thyroid is embedded in a blood sinus, pattern 5. The black arrow indicates the anterior part of the specimen. ly.v = lymphatic vessels; th.a = thyroidian artery; th.s = thyroidian sinus. Modified from Chanet et al., 2013. Scale bar = 3 mm.
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