The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia

Ross River virus (RRV) is Australia’s most epidemiologically important mosquito-borne disease.During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications canaccount for up to 30% of national RRV notifications. However, little is known about factors which canforecast RRV transmission in Victoria. We aimed to understand factors associated with RRVtransmission in epidemiologically important regions of Victoria and establish an early warningforecast system. We developed negative binomial regression models to forecast human RRVnotifications across 11 Local Government Areas (LGAs) using climatic, environmental, andoceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 toJune 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing dataset. Evapotranspiration and precipitation were found to be common factors for forecasting RRVnotifications across sites. Several site-specific factors were also important in forecasting RRVnotifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreakin 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreakin 2016/17, however only six LGAs could predict the outbreak using the same model. We documentsimilarities and differences in factors useful for forecasting RRV notifications across Victoria anddemonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexityof RRV transmission where additional epidemiological information is needed to accurately predictRRV activity. Our findings have been applied to produce a Ross River virus Outbreak SurveillanceSystem (ROSS) to aid in public health decision making in Victoria

Optimising predictive modelling of Ross River virus using meteorological variables

Background:Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/Principal findings:We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model’s ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/Significance:We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance

Initial experience of a large, self-expanding, and fully recapturable transcatheter aortic valve: The UK & Ireland Implanters' registry.

OBJECTIVES: The UK & Ireland Implanters' registry is a multicenter registry which reports on real-world experience with novel transcatheter heart valves. BACKGROUND: The 34 mm Evolut R transcatheter aortic valve is a self-expanding and fully recapturable transcatheter aortic valve, designed to treat patients with a large aortic annulus. METHODS: Between January 2017 and April 2018, clinical, procedural and 30-day outcome data were prospectively collected from all patients receiving the 34 mm Evolut R valve across 17 participating centers in the United Kingdom and Ireland. The primary efficacy outcome was the Valve Academic Research Consortium-2(VARC-2)-defined endpoint of device success. The primary safety outcome was the VARC-2-defined composite endpoint of early safety at 30 days. RESULTS: A total of 217 patients underwent attempted implant. Mean age was 79.5 ± 8.8 years and Society of Thoracic Surgeons Predicted Risk of Mortality Score 5.2% ± 3.4%. Iliofemoral access was used in 91.2% of patients. Device success was 79.7%. Mean gradient was 7.0 ± 4.6 mmHg and effective orifice area 2.0 ± 0.6 cm2 . Paravalvular regurgitation was more than mild in 7.2%. A new permanent pacemaker was implanted in 15.7%. Early safety was demonstrated in 91.2%. At 30 days, all-cause mortality was 3.2%, stroke 3.7%, and major vascular complication 2.3%. CONCLUSIONS: Real-world experience of the 34 mm Evolut R transcatheter aortic valve demonstrated acceptable procedural success, safety, valve function, and incidence of new permanent pacemaker implantation

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Using grayscale images for object recognition with convolutional-recursive neural network

© 2016 IEEE. There is a common tendency in object recognition research to accumulate large volumes of image features to improve performance. However, whether using more information contributes to higher accuracy is still controversial given the increased computational cost. This work investigates the performance of grayscale images compared to RGB counterparts for visual object classification. A comparison between object recognition based on RGB images and RGB images converted to grayscale was conducted using a cascaded CNN-RNN neural network structure, and compared with other types of commonly used classifiers such as Random Forest, SVM and SP-HMP. Experimental results showed that classification with grayscale images resulted in higher accuracy classification than with RGB images across the different types of classifiers. Results also demonstrated that utilizing a small receptive field CNN and edgy feature selection on grayscale images can result in higher classification accuracy with the advantage of reduced computational cost

Object Recognition Using Deep Convolutional Features Transformed by a Recursive Network Structure

© 2017 IEEE. Deep neural networks (DNNs) trained on large data sets have been shown to be able to capture high-quality features describing image data. Numerous studies have proposed various ways to transfer DNN structures trained on large data sets to perform classification tasks represented by relatively small data sets. Due to the limitations of these proposals, it is not well known how to effectively adapt the pre-trained model into the new task. Typically, the transfer process uses a combination of fine-tuning and training of adaptation layers; however, both tasks are susceptible to problems with data shortage and high computational complexity. This paper proposes an improvement to the well-known AlexNet feature extraction technique. The proposed approach applies a recursive neural network structure on features extracted by a deep convolutional neural network pre-trained on a large data set. Object recognition experiments conducted on the Washington RGBD image data set have shown that the proposed method has the advantages of structural simplicity combined with the ability to provide higher recognition accuracy at a low computational cost compared with other relevant methods. The new approach requires no training at the feature extraction phase, and can be performed very efficiently as the output features are compact and highly discriminative, and can be used with a simple classifier in object recognition settings

Clinical utility of brain stimulation modalities following traumatic brain injury: current evidence

Shasha Li,1,2 Ana Luiza Zaninotto,2,3 Iuri Santana Neville,4 Wellingson Silva Paiva,4 Danuza Nunn,2 Felipe Fregni21Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People&rsquo;s Republic of China; 2Spaulding Neuromodulation Center, Harvard Medical School, Boston, MA, USA; 3Division of Psychology, Hospital das Cl&iacute;nicas, University of S&atilde;o Paulo, S&atilde;o Paulo, Brazil; 4Division of Neurosurgery, University of S&atilde;o Paulo Medical School, S&atilde;o Paulo, S&atilde;o Paulo, BrazilAbstract: Traumatic brain injury (TBI) remains the main cause of disability and a major public health problem worldwide. This review focuses on the neurophysiology of TBI, and the rationale and current state of evidence of clinical application of brain stimulation to promote TBI recovery, particularly on consciousness, cognitive function, motor impairments, and psychiatric conditions. We discuss the mechanisms of different brain stimulation techniques including major noninvasive and invasive stimulations. Thus far, most noninvasive brain stimulation interventions have been nontargeted and focused on the chronic phase of recovery after TBI. In the acute stages, there is limited available evidence of the efficacy and safety of brain stimulation to improve functional outcomes. Comparing the studies across different techniques, transcranial direct current stimulation is the intervention that currently has the higher number of properly designed clinical trials, though total number is still small. We recognize the need for larger studies with target neuroplasticity modulation to fully explore the benefits of brain stimulation to effect TBI recovery during different stages of recovery.Keywords: traumatic brain injury, brain stimulation, neuroplasticit