Tag: nevin manimala

Acta Otorrinolaringol Esp. 2021 Aug 16:S0001-6519(21)00093-5. doi: 10.1016/j.otorri.2021.05.006. Online ahead of print.

ABSTRACT

BACKGROUND: Myringotomy with ventilation tube (VT) insertion is one the most performed procedures in children and adolescents worldwide. VTs usually remain in the eardrum between 6 and 12 months and during this period otorrhoea is the most frequent complication. For years, parents have been advised to protect the ears of children with VTs from contact with water, as water exposure in the middle ear is likely to cause acute otitis media. However, there is a growing evidence that water should not traverse VTs unless under significant pressure, so routine water precautions should not be prescribed. Despite these recommendations, many otolaryngologists and paediatricians continue to prescribe earplugs during bathing or swimming or advise against aquatic activities. There are already two reviews in the current literature on this topic: the first used strict selection criteria and included only 2 high-quality studies, while the second presented evidence up to 2005. The aim of this review is to identify, summarize and critically appraise the current evidence concerning water precautions for children with VTs.

METHODS: Two independent reviewers separately searched for related scientific papers. A qualitative synthesis analysis was performed considering the selected studies regarding the effects of water exposure on paediatric subjects with VTs.

RESULTS: Four randomized clinical trials (RCT) and five prospective cohort studies were included, for a total of 1299 patients aged from 3 months to 14 years. No statistically significant difference in otorrhoea incidence between water exposure with and without ear protection in children with VTs, and between water exposure and no water exposure in children with VTs, was found. Therefore avoiding water is at best inconvenient and at worst may delay learning to swim. The decision to protect the ear when exposed to water should be individualized and protection should be recommended during the first month after surgery and in cases of recurrent otorrhoea.

CONCLUSION: Based on the literature available, allowing water surface activities with no ear protection seems to present a minimum risk, so it is not necessary to prohibit patients from swimming. However, some recommendations should be followed.

PMID:34412895 | DOI:10.1016/j.otorri.2021.05.006

Int Dent J. 2021 Aug 16:S0020-6539(21)00132-5. doi: 10.1016/j.identj.2021.06.006. Online ahead of print.

ABSTRACT

AIM: The objective of this study was to evaluate the effect of additional light curing on the colour stability of composite resins.

MATERIALS AND METHODS: Four different composite resins-a nanofill, a nanohybrid, a microhybrid, and a bulk-fill composite resin-were tested. Eighty disc-shaped specimens were prepared from each material using either a quartz tungsten halogen or a light-emitting diode light source and were randomly divided into 2 groups according to the surface treatment: no polishing (nonpolished) or polishing with aluminum oxide discs (polished). Then additional light curing was applied to half of the specimens in each group. All specimens were immersed in coffee solution for 1 week. Colour was measured with a spectrophotometer at baseline and after 1 week of storage in coffee solution.

RESULTS: Statistically significant differences in colour stability were observed in the restorative materials according to the composition of composite resin, the polishing protocol, and additional light curing, whilst there were no significant differences according to the light source. Additional light curing reduced discolouration in all groups tested.

CONCLUSIONS: Additional light curing may be beneficial after finishing and polishing in order to maintain aesthetics and increase the resistance of composite resins to discolouration.

PMID:34412897 | DOI:10.1016/j.identj.2021.06.006

Artif Intell Med. 2021 Aug;118:102120. doi: 10.1016/j.artmed.2021.102120. Epub 2021 May 28.

ABSTRACT

BACKGROUND AND AIM: Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction.

METHODS: This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed.

RESULTS: Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results.

CONCLUSIONS: The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic.

PMID:34412843 | DOI:10.1016/j.artmed.2021.102120

Artif Intell Med. 2021 Aug;118:102131. doi: 10.1016/j.artmed.2021.102131. Epub 2021 Jun 25.

ABSTRACT

Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.

PMID:34412847 | DOI:10.1016/j.artmed.2021.102131

Artif Intell Med. 2021 Aug;118:102118. doi: 10.1016/j.artmed.2021.102118. Epub 2021 May 29.

ABSTRACT

Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learning algorithm that predicts simultaneously the mean arterial pressure (MAP) and the heart rate (HR). In an external validation dataset, our model exhibited very good calibration: R² of 0.747 (95% confidence interval, 0.692 to 0.794) and 0.850 (0.815 to 0.879) for respectively, MAP and HR prediction 60-minutes ahead of time. For acute hypotensive episodes defined as a MAP below 65 mmHg for 5 min, our MTL-PDL reached a predictive value of 90% for patients at very high risk (predicted MAP ≤ 60 mmHg) and 2‰ for patients at low risk (predicted MAP >70 mmHg). Based on its excellent prediction performance, the Physiological Deep Learner has the potential to help the clinician proactively adjust the treatment in order to avoid hypotensive episodes and end-organ hypoperfusion.

PMID:34412841 | DOI:10.1016/j.artmed.2021.102118

Ecol Lett. 2021 Aug 19. doi: 10.1111/ele.13862. Online ahead of print.

ABSTRACT

Predicting complex species-environment interactions is crucial for guiding conservation and mitigation strategies in a dynamically changing world. Phenotypic plasticity is a mechanism of trait variation that determines how individuals and populations adapt to changing and novel environments. For individuals, the effects of phenotypic plasticity can be quantified by measuring environment-trait relationships, but it is often difficult to predict how phenotypic plasticity affects populations. The assumption that environment-trait relationships validated for individuals indicate how populations respond to environmental change is commonly made without sufficient justification. Here we derive a novel general mathematical framework linking trait variation due to phenotypic plasticity to population dynamics. Applying the framework to the classical example of Nicholson’s blowflies, we show how seemingly sensible predictions made from environment-trait relationships do not generalise to population responses. As a consequence, trait-based analyses that do not incorporate population feedbacks risk mischaracterising the effect of environmental change on populations.

PMID:34412157 | DOI:10.1111/ele.13862

J Obstet Gynaecol Res. 2021 Aug 19. doi: 10.1111/jog.14993. Online ahead of print.

ABSTRACT

AIM: To determine whether preoperative genital hiatus at rest is predictive of medium-term prolapse recurrence.

METHODS: We conducted a retrospective study of women who underwent native tissue prolapse surgery from 2002 to 2017 with pelvic organ prolapse quantification data including resting genital hiatus at one of three time points: preoperatively, 6 weeks, and ≥1 year postoperatively. Demographics and clinical data were abstracted from the chart. Prolapse recurrence was defined by anatomic outcomes (Ba > 0, Bp > 0, and/or C ≥ -4) or retreatment. Descriptive statistics, bivariate analyses, and logistic regression analyses were performed.

RESULTS: Of the 165 women included, 36 (21.8%) had prolapse recurrence at an average of 1.5 years after surgery. Preoperative resting genital hiatus did not differ between women with surgical success versus recurrence (3.5 cm [interquartile range, IQR 2.25, 4.0) vs 3.5 cm (IQR 3.0, 4.0), p = 0.71). Point Bp was greater in the recurrence group at every time point. Preoperative Bp (odds ratio [OR] 1.24, confidence interval [CI] [1.06-1.45], p = 0.01) and days from surgery (OR 1.001, CI [1.000-1.001], p < 0.01) were independently associated with recurrence. Preoperative genital hiatus at rest and strain were significantly larger among women who underwent a colpoperineorrhaphy (rest: 4.0 [3.0, 4.5] cm vs 3.5 [3.0, 4.0] cm, p < 0.01; strain: 6.0 [4.0, 6.5] cm vs 5.0 [4.0, 6.0] cm, p = 0.01).

CONCLUSIONS: Preoperative genital hiatus at rest was not associated with prolapse recurrence when the majority of women underwent colpoperineorrhaphy. Preoperative Bp was more predictive of short-term prolapse recurrence. For every 1 cm increase in point Bp, there is a 24% increased odds of recurrence.

PMID:34412156 | DOI:10.1111/jog.14993

Neural Comput. 2021 Aug 19;33(9):2473-2510. doi: 10.1162/neco_a_01416.

ABSTRACT

We investigate the use of parameterized families of information-theoretic measures to generalize the loss functions of generative adversarial networks (GANs) with the objective of improving performance. A new generator loss function, least kth-order GAN (LkGAN), is introduced, generalizing the least squares GANs (LSGANs) by using a kth-order absolute error distortion measure with k≥1 (which recovers the LSGAN loss function when k=2). It is shown that minimizing this generalized loss function under an (unconstrained) optimal discriminator is equivalent to minimizing the kth-order Pearson-Vajda divergence. Another novel GAN generator loss function is next proposed in terms of Rényi cross-entropy functionals with order α>0, α≠1. It is demonstrated that this Rényi-centric generalized loss function, which provably reduces to the original GAN loss function as α→1, preserves the equilibrium point satisfied by the original GAN based on the Jensen-Rényi divergence, a natural extension of the Jensen-Shannon divergence. Experimental results indicate that the proposed loss functions, applied to the MNIST and CelebA data sets, under both DCGAN and StyleGAN architectures, confer performance benefits by virtue of the extra degrees of freedom provided by the parameters k and α, respectively. More specifically, experiments show improvements with regard to the quality of the generated images as measured by the Fréchet inception distance score and training stability. While it was applied to GANs in this study, the proposed approach is generic and can be used in other applications of information theory to deep learning, for example, the issues of fairness or privacy in artificial intelligence.

PMID:34412112 | DOI:10.1162/neco_a_01416

Lasers Surg Med. 2021 Aug 19. doi: 10.1002/lsm.23453. Online ahead of print.

ABSTRACT

OBJECTIVES: Skin rejuvenation with radiofrequency has been a widely used treatment modality for the safe and efficient remodeling of the dermis and revision of textural irregularities, achieved with minimal downtime. The efficacy of fractional radiofrequency (FRF) specifically for acne scarring has not been widely established. The objective of this clinical trial was to establish the efficacy and safety of FRF for moderate to severe acne scarring in a wide range of Fitzpatrick skin types using two different applicator tips to deliver energy to the skin (80-pin of up to 124 mJ/pin and 160-pin of up to 62 mJ/pin).

METHODS: Enrolled subjects received a series of three FRF treatments to the full face, each 4 weeks apart. A visual analog scale was utilized to assess pain of the treatment. Subject satisfaction questionnaires were completed at follow-up visits at 6 and 12 weeks post final treatment. Photographs were graded for change by three blinded evaluators using the Global Aesthetic Improvement Scale (GAIS).

RESULTS: Image sets of 23 enrolled subjects were assessed by blinded evaluation, showing a statistically significant improvement (p = 0.009) from the baseline visit to the 12-week follow-up on the GAIS for acne scarring. Subject satisfaction was high with subjects giving an average satisfaction score of 3.27 (“satisfied”) out of 4. Pain was “mild” as treatments were rated an average of 2.15 on a 10-point visual analog scale. The GAIS score of the 80-pin tip improved patients’ acne scars treated with that applicator by 1.06 points and 0.85 for the 160-pin tip. Ninety-five percent (95.5%) of subjects reported either a mild, moderate, or significant improvement to their treatment area. Ninety-one percent of subjects reported that they would recommend the treatment to a friend.

CONCLUSION: FRF produced a statistically significant improvement in acne scarring when assessed by independent blinded evaluators. No serious adverse events resulted from treatment by either applicator tip. Treatment pain was low and tolerable among subjects of all Fitzpatrick skin types. Subjects had high levels of satisfaction with the results.

PMID:34412150 | DOI:10.1002/lsm.23453

Stat Med. 2021 Aug 19. doi: 10.1002/sim.9167. Online ahead of print.

ABSTRACT

Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.

PMID:34412151 | DOI:10.1002/sim.9167