SARS-CoV-2 Omicron-B.1.1.529 leads to widespread escape from neutralizing antibody responses.

On 24th November 2021, the sequence of a new SARS-CoV-2 viral isolate Omicron-B.1.1.529 was announced, containing far more mutations in Spike (S) than previously reported variants. Neutralization titers of Omicron by sera from vaccinees and convalescent subjects infected with early pandemic Alpha, Beta, Gamma, or Delta are substantially reduced, or the sera failed to neutralize. Titers against Omicron are boosted by third vaccine doses and are high in both vaccinated individuals and those infected by Delta. Mutations in Omicron knock out or substantially reduce neutralization by most of the large panel of potent monoclonal antibodies and antibodies under commercial development. Omicron S has structural changes from earlier viruses and uses mutations that confer tight binding to ACE2 to unleash evolution driven by immune escape. This leads to a large number of mutations in the ACE2 binding site and rebalances receptor affinity to that of earlier pandemic viruses.

Antibody evasion by the P.1 strain of SARS-CoV-2.

Terminating the SARS-CoV-2 pandemic relies upon pan-global vaccination. Current vaccines elicit neutralizing antibody responses to the virus spike derived from early isolates. However, new strains have emerged with multiple mutations, including P.1 from Brazil, B.1.351 from South Africa, and B.1.1.7 from the UK (12, 10, and 9 changes in the spike, respectively). All have mutations in the ACE2 binding site, with P.1 and B.1.351 having a virtually identical triplet (E484K, K417N/T, and N501Y), which we show confer similar increased affinity for ACE2. We show that, surprisingly, P.1 is significantly less resistant to naturally acquired or vaccine-induced antibody responses than B.1.351, suggesting that changes outside the receptor-binding domain (RBD) impact neutralization. Monoclonal antibody (mAb) 222 neutralizes all three variants despite interacting with two of the ACE2-binding site mutations. We explain this through structural analysis and use the 222 light chain to largely restore neutralization potency to a major class of public antibodies.

Reduced neutralization of SARS-CoV-2 B.1.617 by vaccine and convalescent serum.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone progressive change, with variants conferring advantage rapidly becoming dominant lineages, e.g., B.1.617. With apparent increased transmissibility, variant B.1.617.2 has contributed to the current wave of infection ravaging the Indian subcontinent and has been designated a variant of concern in the United Kingdom. Here we study the ability of monoclonal antibodies and convalescent and vaccine sera to neutralize B.1.617.1 and B.1.617.2, complement this with structural analyses of Fab/receptor binding domain (RBD) complexes, and map the antigenic space of current variants. Neutralization of both viruses is reduced compared with ancestral Wuhan-related strains, but there is no evidence of widespread antibody escape as seen with B.1.351. However, B.1.351 and P.1 sera showed markedly more reduction in neutralization of B.1.617.2, suggesting that individuals infected previously by these variants may be more susceptible to reinfection by B.1.617.2. This observation provides important new insights for immunization policy with future variant vaccines in non-immune populations.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach.

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces.

Despite research has massively focused on how emotions conveyed by faces are perceived, the perception of emotions' authenticity is a topic that has been surprisingly overlooked. Here, we present the Emotion Authenticity Recognition (EAR) test, a test specifically developed using dynamic stimuli depicting authentic and posed emotions to evaluate the ability of individuals to correctly identify an emotion (emotion recognition index, ER Index) and classify its authenticity (authenticity recognition index (EA Index). The EAR test has been validated on 522 healthy participants and normative values are provided. Correlations with demographic characteristics, empathy and general cognitive status have been obtained revealing that both indices are negatively correlated with age, and positively with education, cognitive status and different facets of empathy. The EAR test offers a new ecological test to assess the ability to detect emotion authenticity that allow to explore the eventual social cognitive deficit even in patients otherwise cognitively intact.

In silico analysis of non-structural protein 12 sequences from SARS-COV-2 found in Manaus, Amazonas, Brazil, reveals mutations linked to higher transmissibility.

The disease coronavirus COVID-19 has been the cause of millions of deaths worldwide. Among the proteins of SARS-CoV-2, non-structural protein 12 (NSP12) plays a key role during COVID infection and is part of the RNA-dependent RNA polymerase complex. The monitoring of NSP12 polymorphisms is extremely important for the design of new antiviral drugs and monitoring of viral evolution. This study analyzed the NSP12 mutations detected in circulating SARS-CoV-2 during the years 2020 to 2022 in the population of the city of Manaus, Amazonas, Brazil. The most frequent mutations found were P323L and G671S. Reports in the literature indicate that these mutations are related to transmissibility efficiency, which may have contributed to the extremely high numbers of cases in this location. In addition, two mutations described here (E796D and R914K) are close and have RMSD that is similar to the mutations M794V and N911K, which have been described in the literature as influential on the performance of the NSP12 enzyme. These data demonstrate the need to monitor the emergence of new mutations in NSP12 in order to better understand their consequences for the treatments currently used and in the design of new drugs.

Development and Validation of Conversational Agent to Pregnancy Safe-education.

Pregnant women constantly need some information to support nutritional decisions during pregnancy, and many do not receive such assistance at all. This study aims to present a conversational agent to provide reliable information to pregnant women, focusing on nutritional education and evaluating the perception of pregnant women and health professionals about the agent. As a scientific contribution, this article developed and implemented a conversational agent in a real environment capable of generating reliable responses on the basis of a set of health documents. We proposed an intervention study with 25 women and 10 healthcare providers through a survey to measure the perceptions of these groups towards conversational agents. The results show that the intended design could ensure positive support for pregnant women, clarify certain issues for the public, and remove some knowledge barriers. The results showed no significant difference between the groups (p-value = 0.713). Depending on the perception of the pregnant group, the conversational agent model can teach new knowledge during the prenatal period (Mean = 4.56). The model presented for health professionals could already be indicated as a support tool for pregnant women (Mean = 4.7).

Cluster of SARS-CoV-2 Gamma Variant Infections, Parintins, Brazil, March 2021.

High case counts after the Gamma (P. 1) variant of severe acute respiratory syndrome coronavirus 2 emerged in Brazil raised concerns that previously infected persons might become reinfected. Investigation of a cluster of coronavirus disease cases in Parintins, in the Brazilian Amazon, suggested household transmission but did not identify high rates of reinfection.

Phylogenomic Analysis of Velvet Worms (Onychophora) Uncovers an Evolutionary Radiation in the Neotropics.

Onychophora ("velvet worms") are charismatic soil invertebrates known for their status as a "living fossil," their phylogenetic affiliation to arthropods, and their distinctive biogeographic patterns. However, several aspects of their internal phylogenetic relationships remain unresolved, limiting our understanding of the group's evolutionary history, particularly with regard to changes in reproductive mode and dispersal ability. To address these gaps, we used RNA sequencing and phylogenomic analysis of transcriptomes to reconstruct the evolutionary relationships and infer divergence times within the phylum. We recovered a fully resolved and well-supported phylogeny for the circum-Antarctic family Peripatopsidae, which retains signals of Gondwanan vicariance and showcases the evolutionary lability of reproductive mode in the family. Within the Neotropical clade of Peripatidae, though, we found that amino acid-translated sequence data masked nearly all phylogenetic signal, resulting in highly unstable and poorly supported relationships. Analyses using nucleotide sequence data were able to resolve many more relationships, though we still saw discordant phylogenetic signal between genes, probably indicative of a rapid, mid-Cretaceous radiation in the group. Finally, we hypothesize that the unique reproductive mode of placentotrophic viviparity found in all Neotropical peripatids may have facilitated the multiple inferred instances of over-water dispersal and establishment on oceanic islands.

Strengthening therapeutic adherence and pharmacovigilance to antimalarial treatment in Manaus, Brazil: a multicomponent strategy using mHealth.

BACKGROUND: Public health initiatives for improving adherence to primaquine based regimens and enhancing effective pharmacovigilance are needed to support the efforts for malaria elimination in real world conditions. METHODS: A multicomponent patient-oriented strategy using a Smart Safety Surveillance (3S) approach including: (1) educational materials for treatment counselling and identification of warning symptoms of haemolytic anaemia; (2) an mHealth component using Short Message Service (SMS) treatment reminders and (3) development and implementation of follow-up phone surveys three days after treatment completion, using a web-based platform linked to the local information system of malaria. Adherence was measured using the Morisky Medication Adherence Scale. Self-reported events were registered using a structured questionnaire and communicated to the Brazilian Health Regulatory Agency. RESULTS: Educational materials were disseminated to 5594 patients, of whom 1512 voluntarily entered the mHealth component through the local information system; 7323 SMS were sent, and 1062 participants completed a follow-up survey after treatment. The mean age of patients was 37.36 years (SD 13.65), 61.24% were male, 98.54% were infected with. Plasmodium vivax and 95.90% received a short regimen of chloroquine plus primaquine (CQ + PQ 7 days), as per malaria case management guidelines in Brazil. From the 1062 surveyed participants 93.31% were considered adherent to the treatment. Most of the patients (95.20%) reported at least one adverse event. Headache, lack of appetite and nausea/vomiting were the most frequently reported adverse events by 77.31%, 70.90% and 56.78% of the patients respectively. A quarter of the patients reported anxiety or depression symptoms; 57 (5.37%) patients reported 5 to 6 warning symptoms of haemolytic anaemia including jaundice and dark urine in 44 (4.14%). Overall, three patients presenting symptoms of haemolytic anaemia attended a hospital and were diagnosed with G6PD deficiency, and one had haemolysis. All of them recovered. CONCLUSIONS: Under real world conditions, a multicomponent patient-oriented strategy using information and communication technologies allowed health care providers to reinforce treatment adherence and enhance safety surveillance of adverse events associated with regimens using primaquine. Active monitoring through phone surveys also reduced under-reporting of ADRs. This approach is low-cost, scalable and able to support prioritized activities of the national malaria programme.

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach.

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

Brain activity during facial processing in autism spectrum disorder: an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies.

BACKGROUND: Though aberrant face processing is a hallmark of autistic spectrum disorder (ASD), findings on accompanying brain activity are divergent. Therefore, we conducted an activation likelihood estimation (ALE) meta-analysis of studies examining brain activity during face processing. METHODS: We searched PubMed and PsycINFO using combinations of terms as 'fMRI', 'Autism Spectrum Disorder', 'Face Perception'. Eligible studies reported on DSM-diagnosed ASD individuals, compared to controls (HC), using face stimuli presented in fMRI and reporting whole-brain analysis coordinates. We compared two approaches: 'convergence of differences' (primary analysis) using study-level coordinates from ASD vs. HC contrasts, and 'differences in convergence' (secondary) pooling coordinates within each group separately, and contrasting the resultant ALE maps. RESULTS: Thirty-five studies (655 ASD and 668 HC) were included. Primary analysis identified a cluster in amygdala/parahippocampus where HC showed greater convergence of activation. Secondary analysis yielded no significant results. CONCLUSIONS: Results suggest that ASD dysfunction in face processing relies on structures involved in emotional processing rather than perception. We also demonstrate that the two ALE methodologies lead to divergent results.

Internet of Things in active cancer Treatment: A systematic review.

The Internet of Things (IoT) applied to the treatment of cancer patients has been explored and the results are promising. This review aims to identify the applications and benefits of using IoT techniques, especially wearable devices, on the management of the adverse effects and symptoms, quality of life, and survival in cancer patients undergoing active treatment. The work also presents the architecture and taxonomy of the use of IoT, the challenges and the relevant results, as well as the association of the collected information with the type of treatment and the type of cancer. This study was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and considered articles from the last 10 years. Specific and general research questions and the PICOS approach were used to define the search string and to guide the selection of articles. The search retrieved 1678 publications, of which 121 were included for a full review. 67% of selected studies addressed the monitoring and follow-up of physical activities and their associations with the adverse effects and symptoms related to cancer treatment. Besides, 53% evaluated sleep patterns, heart rate, and oxygen saturation levels. One-third of the studies assessed patients with the indication for surgery and about one-half evaluated patients undergoing chemotherapy. Furthermore, the IoT allowed verifying associations of human behaviors with adverse effects and quality of life. IoT was observed to contribute to monitoring cancer patients, improve their quality of life and manage adverse effects related to cancer treatment. 53% were pilot studies and 93% were published in the last 5 years, which demonstrates to be a recent issue and therefore still has a lot to be explored.

A Survey About Real-Time Location Systems in Healthcare Environments.

Every year healthcare organizations suffer from several issues, such as unapropriated workflow, thousands of deaths caused by medical errors, counterfeit drugs, and increasing costs. To offer better patient care and increase profit, hospitals could adopt solutions that help remedy these problems. Real-Time Location Systems have the potential to deal with many of these issues, as well as offering means for developing new and intelligent solutions. This kind of system enables tracking assets and people, allowing several improvements. Even though the benefits of such solutions are well known and desired by healthcare providers, their large scale adoption is still distant. In this article, we surveyed Real-Time Location Systems usage in hospitals. While developing this survey, we observed a need for organizing important aspects of healthcare-oriented Real-Time Location Systems. Therefore, we analyzed challenges regarding this topic and a taxonomy proposed. This survey offers researchers and developers ways to comprehend the challenges surrounding this area while proposing a classification of aspects that a Real-Time Location System for healthcare environments must assess for it to be successful.

Genomic and phylogenetic characterisation of an imported case of SARS-CoV-2 in Amazonas State, Brazil.

A new coronavirus [severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] is currently causing a life-threatening pandemic. In this study, we report the complete genome sequencing and genetic characterisation of a SARS-CoV-2 detected in Manaus, Amazonas, Brazil, and the protocol we designed to generate high-quality SARS-CoV-2 full genome data. The isolate was obtained from an asymptomatic carrier returning from Madrid, Spain. Nucleotide sequence analysis showed a total of nine mutations in comparison with the original human case in Wuhan, China, and support this case as belonging to the recently proposed lineage A.2. Phylogeographic analysis further confirmed the likely European origin of this case. To our knowledge, this is the first SARS-CoV-2 genome obtained from the North Brazilian Region. We believe that the information generated in this study may contribute to the ongoing efforts toward the SARS-CoV-2 emergence.

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.

ROI and phobias: The effect of ROI approach on an ALE meta-analysis of specific phobias.

About 90% of fMRI findings on specific phobias (SP) include analysis of region of interest (ROI). This approach characterized by higher sensitivity may produce inflated results, particularly when findings are aggregated in meta-analytic maps. Here, we conducted a systematic review and activation likelihood estimation (ALE) meta-analysis on SP, testing the impact of the inclusion of ROI-based studies. ALE meta-analyses were carried out either including ROI-based results or focusing on whole-brain voxelwise studies exclusively. To assess the risk of bias in the neuroimaging field, we modified the Newcastle-Ottawa Scale (NOS) and measured the reliability of fMRI findings. Of the 31 selected investigations (564 patients and 485 controls) one-third did not motivate ROI selection: five studies did not report an explicit rationale, whereas four did not cite any specific reference in this regard. Analyses including ROI-based studies revealed differences between phobics and healthy subjects in several regions of the limbic circuit. However, when focusing on whole-brain analysis, only the anterior midcingulate cortex differentiated SP from controls. Notably, 13 studies were labeled with low risk of bias according to the adapted NOS. The inclusion of ROI-based results artificially inflates group differences in fMRI meta-analyses. Moreover, a priori, well-motivated selection of ROIs is desirable to improve quality and reproducibility in SP neuroimaging studies. Lastly, the use of modified NOS may represent a valuable way to assess and evaluate biases in fMRI studies: "low risk" of bias was reported for less than half of the included studies, indicating the need for better practices in fMRI.

Analyzing the performance of a blockchain-based personal health record implementation.

BACKGROUND: The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated. OBJECTIVE: This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard. We thus follow OmniPHR architecture model, which describes an infrastructure that supports the implementation of a distributed and interoperable PHR. METHODS: Our method involves implementing a prototype and then evaluating the integration and performance of medical records from different production databases. In addition to evaluating the unified view of records, our evaluation criteria also focused on non-functional performance requirements, such as response time, CPU usage, memory occupation, disk, and network usage. RESULTS: We evaluated our model implementation using the data set of more than 40 thousand adult patients anonymized from two hospital databases. We tested the distribution and reintegration of the data to compose a single view of health records. Moreover, we profiled the model by evaluating a scenario with 10 superpeers and thousands of competing sessions transacting operations on health records simultaneously, resulting in an average response time below 500 ms. The blockchain implemented in our prototype achieved 98% availability. CONCLUSION: Our performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios we tested. Our study also demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.

On Providing Multi-Level Quality of Service for Operating Rooms of the Future.

The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals.

Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost-benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.