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General General

Citation screening using crowdsourcing and machine learning produced accurate results: evaluation of Cochrane's modified Screen4Me service.

In Journal of clinical epidemiology ; h5-index 60.0

OBJECTIVE : To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review.

STUDY DESIGN AND SETTING : This was a sub-study to a larger randomised study; the main study sought to assess the performance of single screening search results versus dual screening. This sub-study assessed the performance in identifying relevant RCTs for a published Cochrane review of a modified version of Cochrane's Screen4Me workflow which uses crowdsourcing and machine learning. We included participants who had signed up for the main study but who were not eligible to be randomised to the two main arms of that study. The records were put through the modified workflow where a machine learning classifier divided the dataset into "Not RCTs" and "Possible RCTs". The records deemed "Possible RCTs" were then loaded into a task created on the Cochrane Crowd platform and participants classified those records as either "Potentially relevant" or "Not relevant" to the review. Using a pre-specified agreement algorithm we calculated the performance of the crowd in correctly identifying the studies that were included in the review (sensitivity) and correctly rejecting those that were not included (specificity).

RESULTS : The RCT machine learning classifier did not reject any of the included studies. In terms of the crowd, 112 participants were included in this sub-study. Of these, 81 completed the training module and went on to screen records in the live task. Applying the Cochrane Crowd agreement algorithm, the crowd achieved 100% sensitivity and 80.71% specificity.

CONCLUSIONS : Using a crowd to screen search results for systematic reviews can be an accurate method as long as the agreement algorithm in place is robust.

TRIAL REGISTRATION : Open Science Framework: https://osf.io/3jyqt.

Noel-Storr Anna, Dooley Gordon, Affengruber Lisa, Gartlehner Gerald

2020-Sep-29

accuracy, agreement algorithm, crowdsourcing, human computation, literature screening, machine learning, systematic reviews

General General

Seizure burden fluctuates with the female reproductive cycle in a mouse model of chronic temporal lobe epilepsy.

In Experimental neurology

Women with catamenial epilepsy often experience increased seizure burden near the time of ovulation (periovulatory) or menstruation (perimenstrual). To date, a rodent model of chronic temporal lobe epilepsy (TLE) that exhibits similar endogenous fluctuations in seizures has not been identified. Here, we investigated whether seizure burden changes with the estrous cycle in the intrahippocampal kainic acid (IHKA) mouse model of TLE. Adult female IHKA mice and saline-injected controls were implanted with EEG electrodes in the ipsilateral hippocampus. At one and two months post-injection, 24/7 video-EEG recordings were collected and estrous cycle stage was assessed daily. Seizures were detected using a custom convolutional neural network machine learning process. Seizure burden was compared within each mouse between diestrus and combined proestrus and estrus days (pro/estrus) at two months post-injection. IHKA mice showed higher seizure burden on pro/estrus compared with diestrus, characterized by increased time in seizures and longer seizure duration. When all IHKA mice were included, no group differences were observed in seizure frequency or EEG power. However, increased baseline seizure burden on diestrus was correlated with larger cycle-associated differences, and when analyses were restricted to mice that showed the severe epilepsy typical of the IHKA model, increased seizure frequency on pro/estrus was also revealed. Controls showed no differences in EEG parameters with cycle stage. These results suggest that the stages of proestrus and estrus are associated with higher seizure burden in IHKA mice. The IHKA model may thus recapitulate at least some aspects of reproductive cycle-associated seizure clustering.

Li Jiang, Leverton Leanna K, Naganatanahalli Laxmi Manisha, Christian-Hinman Catherine A

2020-Sep-29

Convolutional neural network, EEG, Epilepsy, Estrous cycle, Hippocampus, Kainic acid

Pathology Pathology

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

In PloS one ; h5-index 176.0

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Artigas Laura, Coma Mireia, Matos-Filipe Pedro, Aguirre-Plans Joaquim, Farrés Judith, Valls Raquel, Fernandez-Fuentes Narcis, de la Haba-Rodriguez Juan, Olvera Alex, Barbera Jose, Morales Rafael, Oliva Baldo, Mas Jose Manuel

2020

General General

Risk factor analysis combined with deep learning in the risk assessment of overseas investment of enterprises.

In PloS one ; h5-index 176.0

To evaluate the overseas investment risks of enterprises and expand the application and development of deep learning methods in risk assessment, 15 national clusters are utilized as samples to analyze and discuss the overseas investment risk indicators of enterprises. First, based on the indicator system of overseas investment risks, five major types of investment risks are identified. Second, the Deep Neural Network (DNN) is introduced; a risk evaluation model is constructed for enterprise overseas investment. Finally, the investment attractiveness index in the Fraser risk assessment learning label is adopted as the evaluation results of the model. According to the classification of risks, the model is trained and its performance is tested. The results show that the major source of overseas investment risks includes basic resources, political systems, economic and financial development, and environmental protection. The corresponding risk score is high. North American country clusters and Oceanian country clusters have lower investment risks, while the investment risks in Africa, Latin America, and Asia are affected by multiple factors of the specific cities. This is closely related to the resources and legal systems possessed by the country clusters. This is of great significance for enterprises to conduct risk assessment in overseas investment.

Xu Xiuyan

2020

General General

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes.

In Biosensors

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6-9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: -6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: -8.3 to -2.9%, HIIT MARD: 12.1 to 16.8% and MRD: -14.3 to -9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

Guillot Florian H, Jacobs Peter G, Wilson Leah M, Youssef Joseph El, Gabo Virginia B, Branigan Deborah L, Tyler Nichole S, Ramsey Katrina, Riddell Michael C, Castle Jessica R

2020-Sep-29

aerobic exercise, continuous glucose monitoring, exercise, glucose sensor accuracy, high intensity interval training, resistance exercise, type 1 diabetes

Pathology Pathology

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

In PloS one ; h5-index 176.0

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Artigas Laura, Coma Mireia, Matos-Filipe Pedro, Aguirre-Plans Joaquim, Farrés Judith, Valls Raquel, Fernandez-Fuentes Narcis, de la Haba-Rodriguez Juan, Olvera Alex, Barbera Jose, Morales Rafael, Oliva Baldo, Mas Jose Manuel

2020