Background Indeterminate fine-needle aspiration cytology (FNAC) imposes challenges in the management of thyroid nodules. This study aimed to examine whether preoperative anti-thyroid antibodies (Abs) and TSH are indicators of thyroid malignancy and aggressive behavior in patients with indeterminate FNAC. Methods This was a retrospective study of thyroidectomy patients from 2008 to 2016. We analyzed Abs and TSH levels, FNAC, and histopathology. Serum antibody levels were categorized as 'Undetectable', 'In-range'i f detectable but within normal range, and 'Elevated' if above upper limit of normal. 'Detectable' levels referred to 'Inrange' and 'Elevated' combined. Results There were 531 patients included. Of 402 patients with preoperative FNAC, 104 (25.9%) had indeterminate cytology (Bethesda III-V). Of these, 39 (37.5%) were malignant and 65 (62.5%) benign on histopathology. In the setting of indeterminate FNAC, an increased risk of malignancy was associated with 'Elevated' thyroglobulin antibodies (TgAb) (OR 7.25, 95% CI 1.13-77.15, P = 0.01) and 'Elevated' thyroid peroxidase antibodies (TPOAb) (OR 6.79, 95% CI 1.23-45.88, P = 0.008). Similarly, while still 'In-range', TSH C 1 mIU/L was associated with an increased risk of malignancy (OR 3.23, 95% CI 1.14-9.33, P = 0.01). In all patients with malignancy, the mean tumor size was 8 mm larger in those with TSH C 1 mIU/L (P = 0.03); furthermore, in PTC patients, 'Detectable' TgAb conferred a 4 9 risk of lymph node metastasis (95% CI 1.03-13.77, P = 0.02). Conclusion In this cohort, in indeterminate FNAC patients, Abs and TSH were associated with an increased risk of malignancy. Additionally, TgAb and TSH were potential markers of aggressive biology. As such, they may be diagnostic and prognostic adjuncts.

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the ‘Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies’ (‘CHARMS’) framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (10.3%), both climate change and demographic factors (10.3%), vector factors (5.1%), and demographic factors (5.1%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

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