Effectiveness of virtual reality on pain and anxiety in patients undergoing cardiac procedures: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Cardiac procedures often induce pain and anxiety in patients, adversely impacting recovery. Pharmachological approaches have limitations, prompting exploration of innovative digital solutions like virtual reality (VR). Although early evidence suggests a potential favourable benefit with VR, it remains unclear whether the implementation of this technology can improve pain and anxiety. We aimed to assess by a systematic review and meta-analysis the effectiveness of VR in alleviating anxiety and pain on patients undergoing cardiac procedures. METHODS: Our study adhered to the PRISMA method and was registered in PROSPERO under the code CRD42024504563. The search was carried out in the PubMed, Web of Science, Scopus, and the Cochrane Library databases in January 2024. Four randomized controlled trials were included (a total of 382 patients). Risk of bias was employed to assess the quality of individual studies, and a random-effects model was utilized to examine the overall effect. RESULTS: The results showed that VR, when compared to the standard of care, had a statistically significant impact on anxiety (SMD = -0.51, 95 % CI: -0.86 to -0.16, p = 0.004), with a heterogeneity I2 = 57 %. VR did not show a significant difference in terms of pain when compared to standard care (SMD= -0.34, 95 % CI: -0.75 to -0.07, p = 0.10). The included trials exhibited small sample sizes, substantial heterogeneity, and variations in VR technology types, lengths, and frequencies. CONCLUSIONS: VR effectively lowers anxiety levels in patients undergoing cardiac procedures, however, did not show a statistically significant difference on pain.

Validation of a complex needs indicator for veterans in the UK using a composite indicators' method.

Objective: To construct an indicator for assessing the complexity of UK veterans' needs. Study design: Cross-sectional, secondary analysis. Methods: The study applied principal component (PCA) analysis as the method to determine the weights of different needs based on their interactions with each other, the effectiveness of the model was evaluated using bootstrapping. The dataset on UK veterans' support provided by the "Soldiers, Sailors, Airmen and Families Associations" (SSAFA) (N = 35,208) was considered. The grant applications for different categories of support were used as indicators of different needs. The dimensions of breadth (number of different needs) and depth (number of grant applications to address the need) were incorporated in the assessment of complexity. Results: The complex needs indicator for the current sample was validated. The majority of cases had a complexity score of 1 or less. Conclusions: The research suggested and tested an assessment method for the complexity of veterans' needs, that may be positively associated with higher risk of adverse health outcomes. This indicator can be used by decision-makers for risk stratification of the veteran population, thus supporting the allocation of resources in a more effective way.

Clinical Outcomes of Thermal Ablation Re-Treatment of Benign Thyroid Nodules: A Multicenter Study from the Italian Minimally Invasive Treatments of the Thyroid Group.

Background: Thermal ablation (TA) is an established therapeutic option alternative to surgery in patients with solid benign thyroid nodules causing local symptoms. However, a variable part of thyroid nodules remain viable after these nonsurgical treatments, and as many as 15% of nodules treated with TA may require a second treatment over time. This study aimed to evaluate the outcomes of TA re-treatment on symptomatic benign thyroid nodules where the volume decreased by <50% after the first procedure ( = technique inefficacy). Methods: We performed a multicenter retrospective cohort study including patients who underwent re-treatment with TA for benign thyroid nodules, whose volume decreased by <50% after initial treatment. The primary aim was to evaluate volume and volume reduction ratio (VRR) over time and compare the 6- and 12-month VRR after first versus second treatment. The secondary aim was to identify protective or risk factors for technique inefficacy, regrowth, and further treatments, expressed as adjusted hazard ratios (HRs) and confidence interval [CI], after adjustment for sex, age, nodule volume, structure and function, nodule regrowth or symptom relapse, technique used and if the same technique was used for the first and second TA and time between them. Results: We included 135 patients. Re-treatment led to VRR of 50% and 52.2% after 6 and 12 months. VRR after re-treatment was greater than after first treatment in small and medium size nodules (<30 mL), while there were no differences for large nodules (>30 mL). After re-treatment technique inefficacy rate was 51.9%, regrowth rate was 12.6%, and further treatment rate was 15.6%. Radiofrequency ablation (RFA) was protective toward technique inefficacy (HR = 0.40 [CI 0.24-0.65]) and need of further treatments (HR = 0.30 [CI 0.12-0.76]). Large nodule volume (>30 mL) was associated with increased risk of re-treatment (HR = 4.52 [CI 1.38-14.82]). Conclusions: This is the first study evaluating the outcomes of re-treatment on symptomatic benign thyroid nodules with a VRR <50% after the initial TA treatment. Best results were seen in small and medium nodules (<30 mL) and after RFA. Prospective confirmatory studies are needed.

Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19.

Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers' behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers.

Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation.

In the last decade, an increasing number of users have started reporting adverse drug events (ADEs) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use natural language processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question. Negations and speculations are pervasive phenomena in natural language and can severely hamper the ability of an automated system to discriminate between factual and non-factual statements in text. In this article, we take into consideration four state-of-the-art systems for ADE detection on social media texts. We introduce SNAX, a benchmark to test their performance against samples containing negated and speculated ADEs, showing their fragility against these phenomena. We then introduce two possible strategies to increase the robustness of these models, showing that both of them bring significant increases in performance, lowering the number of spurious entities predicted by the models by 60% for negation and 80% for speculations.

Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets.

BACKGROUND: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. OBJECTIVE: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. METHODS: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. RESULTS: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot-related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. CONCLUSIONS: We present a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model.

Excreta quality and digestive function of singly versus couple caged Sardinian partridges (Alectoris barbara barbara Bonnaterre, 1790) as non-invasive indicators of birds' coping ability to forced pairing.

The Sardinian partridge is a monogamous wild bird species of least concern according to IUCN list at present, though formerly accounted among threatened species for decades. The creation of couples is crucial in captivity, because forced pairing in cages can lead to poor welfare of birds, specially of females. This study investigated the impact of single versus couple caging of Sardinian partridges during non-laying period based on the collection and interpretation of indirect and non-invasive parameters (feed intake; space availability; excreta quality; and nutrient utilization). A total of 24 couples of breeding partridges were enrolled for two consequent phases of a same investigation (lasting 15 days each). During phase 1, all couples were fed ad libitum with a pelleted complete diet (DM, 905 g kg-1 of diet; CP, 160 g kg-1 ; and EE, 25 g kg-1 ). In phase 2, each male from 12 out of 24 couples was moved to an identical cage close to that where the female remained alone, to keep visual contact. The rest of couples continued to be kept like in phase 1. During phases 1 and 2, feed intake and excreta outputs were recorded daily. Pooled excreta of the last 3 days from couples and single birds were assessed for pH and DM, CP and ash content. Nutrient digestibility was calculated. No significant differences were noted between single versus couple caging regarding body weight (438 vs. 422 g respectively) or nutrient digestibility. However, singly housed males reduced daily feed intakes and females increased daily feed intake per g-1 BW (0.041 vs. 0.052 g, respectively, p < 0.001). Our results suggest that singly caged partridges from permanent couples can improve the access to feed and reduce competition during the non-mating season.

Generalized Born radii computation using linear models and neural networks.

MOTIVATION: Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. RESULTS: GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error. AVAILABILITY AND IMPLEMENTATION: We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic response of yellow mealworm larvae to two alternative rearing substrates.

INTRODUCTION: Mass insect rearing is becoming increasingly pursued by food and feed industry due to its high sustainability and low environmental impact. Yellow mealworm larvae (Tenebrio molitor) are conventionally reared on wheat bran (WB), but alternative substrates, such as several by-products of the agri-food industry, have shown good prospects for insect rearing. OBJECTIVES: The objective of this study was to investigate on the metabolic and nutritional response of yellow mealworm larvae to dried brewer's spent grains (BSG) and WB used as rearing substrates. METHODS: Proximate, fibre and fatty acid compositions of durum WB and dried BSG were first characterized. Mealworm larvae were fed either WB (WB-L) or BSG (BSG-L) in a trial reproducing a scale rearing condition. Feed efficiency parameters together with proximate and FA composition were determined. Metabolic pathways affected by the dietary treatments were evaluated by means of a targeted metabolomics approach. Proton nuclear magnetic resonance (1H NMR) spectra were acquired on lipid and polar extracts of WB-L and BSG-L and then coupled to multivariate data analysis. Absolute quantitative 1H NMR data were carried out on selected metabolites. RESULTS: BSG-L exhibited better feed conversion ratio and efficiency in conversion of ingested food (P < 0.05) and almost half fat content (P < 0.001) than WB-L. BSG-L also showed higher ω-3 and ω-6 poliunsaturated fatty acids (P < 0.001) and lower content of monounsaturated fatty acids (P < 0.001) than WB-L. BSG-L mobilized body fat towards methylamine accumulation and led to enhanced trehalose catabolism. CONCLUSIONS: Our findings are useful to gain knowledge on the metabolic features that finally affect growth and body composition in reared yellow mealworm larvae.

Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model.

Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this paper we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state of the art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios.

Reactivation of hepatitis B virus during targeted therapies for cancer and immune-mediated disorders.

INTRODUCTION: Targeted therapies have gained popularity in the treatment of several oncologic and immune-mediated diseases. Immunosuppression caused by these drugs has been associated to reactivation of hepatitis B virus (HBV) in both hepatitis B surface antigen (HBsAg) positive patients (overt infection) and HBsAg negative/anti-hepatitis B core antigen (anti-HBc) positive carriers (resolved infection), leading to premature discontinuation of therapy and potentially fatal hepatitis. AREAS COVERED: This review summarizes the evidence of HBV reactivation in patients with overt or resolved HBV infection undergoing targeted therapies for cancer or immune-mediated disorders, providing recommendations for the management of these patients. EXPERT OPINION: The risk of HBV reactivation relies on the immunosuppressive potency and duration of these therapies, the underlying disease and the virological patient's profile. However, HBV reactivation is preventable by screening for HBV markers in all patients scheduled to receive targeted therapies, assessing the virological profile and patient's clinical state, followed by appropriate antiviral treatment or prophylaxis in those patients at high risk of HBV reactivation. Close monitoring of HBV carriers at low risk of reactivation is warranted with the aim to start antiviral therapy as soon as HBV reactivates.

Exploring Architectural Details Through a Wearable Egocentric Vision Device.

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board, the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details at which he is currently looking. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience.

Spatial distribution and sequential sampling plans for Tuta absoluta (Lepidoptera: Gelechiidae) in greenhouse tomato crops.

BACKGROUND: The within- and between-plant distribution of the tomato leafminer, Tuta absoluta (Meyrick), was investigated in order to define action thresholds based on leaf infestation and to propose enumerative and binomial sequential sampling plans for pest management applications in protected crops. RESULTS: The pest spatial distribution was aggregated between plants, and median leaves were the most suitable sample to evaluate the pest density. Action thresholds of 36 and 48%, 43 and 56% and 60 and 73% infested leaves, corresponding to economic thresholds of 1 and 3% damaged fruits, were defined for tomato cultivars with big, medium and small fruits respectively. Green's method was a more suitable enumerative sampling plan as it required a lower sampling effort. Binomial sampling plans needed lower average sample sizes than enumerative plans to make a treatment decision, with probabilities of error of <0.10. CONCLUSIONS: The enumerative sampling plan required 87 or 343 leaves to estimate the population density in extensive or intensive ecological studies respectively. Binomial plans would be more practical and efficient for control purposes, needing average sample sizes of 17, 20 and 14 leaves to take a pest management decision in order to avoid fruit damage higher than 1% in cultivars with big, medium and small fruits respectively.

Mating disruption of Planococcus ficus (Hemiptera: Pseudococcidae) in vineyards using reservoir pheromone dispensers.

Mating disruption field experiments to control the vine mealybug, Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae), were carried out in 2008 and 2009 in two commercial vineyards in Sardinia (Italy). The effectiveness of mating disruption was evaluated by testing reservoir dispensers loaded with 100 mg (62.5 g/ha) and 150 mg (93.8 g/ha) of the sex pheromone in 2008 and 2009, respectively. The number of males captured in pheromone traps, the P. ficus population density and age structure, the parasitism rate, the percentage of ovipositing females, and the crop damage were compared between disrupted and untreated plots. In both field trials, the number of males captured in mating disruption plots was significantly reduced by 86% and 95%, respectively. Mating disruption at the initial dose of 62.5 g/ha of active ingredient gave inconclusive results, whereas the dose of 93.8 g/ha significantly lowered the mealybug density and modified the age structure, which showed a lower percentage of ovipositing females and a higher proportion of preovipositing females. Mating disruption did not affect negatively the parasitism rate, which was higher in the disrupted than in the control plots (>1.5-fold). Crop damage at harvest was very low in both field trials and did not differ between treatments. Mating disruption was effective in wide plots protected with dispensers loaded with 150 mg of the sex pheromone, showing its potential to be included in the overall integrated control programs in Mediterranean wine-growing regions.

Local Pyramidal Descriptors for Image Recognition.

In this paper, we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one's bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement. We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.

Context-dependent logo matching and recognition.

We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency. Finally, we show the validity of our method through extensive experiments on the challenging MICC-Logos dataset. Our method overtakes, by 20%, baseline as well as state-of-the-art matching/recognition procedures.

In vivo dynamics of the K103N mutation following the withdrawal of non-nucleoside reverse transcriptase inhibitors in Human Immunodeficiency Virus-infected patients.

To investigate the dynamics of the K103N mutation following the withdrawal of non-nucleoside reverse transcriptase inhibitors (NNRTIs), we selected the Human Immunodeficiency Virus (HIV)-infected patients with the mutation at the time or after the failure of an NNRTI-containing regimen from an observational database. Of 62 patients fulfilling the inclusion criteria, 39 continued antiretroviral treatment without NNRTIs (group A), whereas 23 discontinued all antiretrovirals after NNRTI failure (group B). A total of 149 tests were analysed, with a median (IQR) of two (2-3) tests/patient. The overgrowth of wild-type virus at position 103 was observed in 14 subjects in group A (36%) and nine in group B (39%). No significant trend was found in relation to the disappearance of K103N variants in either group, but patients tested while receiving antiretrovirals had a significantly higher probability of retaining the K103N mutation over 24 months than those tested during treatment interruption (P = 0.007). In conclusion, following NNRTI discontinuation, HIV variants carrying the K103N mutation are not overgrown for long by wild-type quasispecies at this position in the majority of patients, although treatment interruption favours their disappearance. This suggests that the K103N mutation per se has little impact on viral fitness in vivo.