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1.
Braz. j. biol ; 84: e254487, 2024. tab, ilus
Artículo en Inglés | LILACS, VETINDEX | ID: biblio-1364508

RESUMEN

Biological samples obtained from a small temporary pond of northern Colombia yielded the first record Coronatella undata Sousa, Elmoor-Loureiro and Santos, 2015 and of the male of C. monacantha (Sars, 1901) for Colombia. In this study, the morphology of female of Coronatella undata and female and male of C. monacantha was described and compared to other species within the genus. C. undata was originally described from Brazil and, among the species of the Coronatella monacantha complex, seems to be closely related to C. acuticostata (Sars, 1903). C. undata shows some similarities with C. monacantha, but it can be identified by important diagnostic characters such as: 1) posterior-ventral corner of valve with two denticles, 2) seta on exopodite of trunk limb II rudimentary, 3) filter comb of trunk limb II with six setae, 4) ODL seta of trunk limb I shorter than longest seta of IDL. C. monacantha is the most reported species in the Neotropical region and the male most resemble C. paulinae Sousa, Elmoor-Loureiro & Santos, 2015 in relation to (i), length/wide of postabdomen ratio (ii) basal spine almost straight and (iii)) long basal spine reaching the mid-length of basal spine. However, they can be separated by (i) number of lateral seta on the antennule, (ii) postanal angle, (iii) position of gonopore (iv) presence of a denticle on posterior-ventral corner of valve.


Amostras biológicas obtidas de uma pequena lagoa temporária do norte da Colômbia proporcionaram o primeiro registro de Coronatella undata Sousa, Elmoor-Loureiro e Santos, 2015 e do macho de Coronatella monacantha (Sars, 1901) na Colômbia. Neste estudo, foi descrita a morfologia de fêmeas de C. undata e de fêmeas e machos de C. monacantha, comparando-a com outras espécies do gênero. Coronatella undata foi descrita originalmente no Brasil e, entre as espécies do complexo C. monacantha, parece estar intimamente relacionada com Coronatella acuticostata (Sars, 1903). Coronatella undata apresenta algumas semelhanças com C. monacantha, mas pode ser identificada por seus principais caracteres, tais como: 1) ângulo posterior ventral da valva com dois dentículos; 2) cerda rudimentar no exopodito do ramo do tronco II; 3) filtro da gnatobase do apêndice torácico II com seis cerdas; 4) cerda ODL do membro do tronco I mais curta que a cerda mais longa do IDL. Coronatella monacantha é a espécie mais relatada na região neotropical, e o macho se assemelha mais a Coronatella paulinae Sousa, Elmoor-Loureiro & Santos em relação à/ao: (i) razão comprimento / largura do pós-abdômen, (ii) espinho basal quase reto e (iii) espinho basal longo com a metade do comprimento do espinho basal. No entanto, eles podem ser separados pelo/pela: (i) número de cerdas laterais na antênula, (ii) ângulo postanal, (iii) posição do gonóporo e (iv) presença de dentículo no canto ventral posterior da valva.


Asunto(s)
Animales , Estanques , Registros , Crustáceos , Colombia
2.
Travel Behav Soc ; 30: 1-10, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35965603

RESUMEN

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper's findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

3.
Neural Regen Res ; 18(2): 284-288, 2023 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-35900404

RESUMEN

In the last two years, a new severe acute respiratory syndrome coronavirus (SARS-CoV) infection has spread worldwide leading to the death of millions. Vaccination represents the key factor in the global strategy against this pandemic, but it also poses several problems, especially for vulnerable people such as patients with multiple sclerosis. In this review, we have briefly summarized the main findings of the safety, efficacy, and acceptability of Coronavirus Disease 2019 (COVID-19) vaccination for multiple sclerosis patients. Although the acceptability of COVID-19 vaccines has progressively increased in the last year, a small but significant part of patients with multiple sclerosis still has relevant concerns about vaccination that make them hesitant about receiving the COVID-19 vaccine. Overall, available data suggest that the COVID-19 vaccination is safe and effective in multiple sclerosis patients, even though some pharmacological treatments such as anti-CD20 therapies or sphingosine l-phosphate receptor modulators can reduce the immune response to vaccination. Accordingly, COVID-19 vaccination should be strongly recommended for people with multiple sclerosis and, in patients treated with anti-CD20 therapies and sphingosine l-phosphate receptor modulators, and clinicians should evaluate the appropriate timing for vaccine administration. Further studies are necessary to understand the role of cellular immunity in COVID-19 vaccination and the possible usefulness of booster jabs. On the other hand, it is mandatory to learn more about the reasons why people refuse vaccination. This would help to design a more effective communication campaign aimed at increasing vaccination coverage among vulnerable people.

5.
Comput Human Behav ; 138: 107424, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35945974

RESUMEN

Background: There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time. Methods: A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis. Results: The internet addiction symptoms "escape" and "irritable," and depression symptoms "energy" and "guilty" were the central symptoms for both waves. At the same time, "guilty" and "escape" were identified as bridge symptoms. Notably, the correlation between "anhedonia" and "withdrawal" significantly increased, and that between "guilty" and "escape" significantly decreased over time. Conclusions: This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, "guilty" and "escape," two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students.

6.
Comput Human Behav ; 138: 107439, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35974879

RESUMEN

Given the amount of misinformation being circulated on social media during the COVID-19 pandemic and its potential threat to public health, it is imperative to investigate ways to hinder its transmission. To this end, this study aimed to identify message features that may contribute to misinformation sharing on social media. Based on the theory of social sharing of emotion and the extant research on message credibility, this study examined if emotions and message credibility serve as mechanisms through which novelty and efficacy of misinformation influence sharing intention. An online experiment concerning COVID-19 misinformation was conducted by employing a 2 (novelty conditions: high vs. low) × 2 (efficacy conditions: high vs. low) between-subjects design using a national quota sample in South Korea (N = 1,012). The findings suggested that, contrary to the expectation, the overall effects of novelty on sharing intention were negative. The specific mechanisms played significant and unique roles in different directions: novelty increased sharing intention by evoking surprise, while also exerting a negative influence on sharing intention through an increase in negative emotions and a decrease in positive emotions and message credibility. Consistent with the expectation, efficacy exhibited positive total effects on sharing intention, which was explained by higher levels of (self- and response-) efficacy of protective action increasing positive emotions and message credibility but decreasing negative emotions. The implications and limitations of the study are discussed.

7.
Biomed Signal Process Control ; 79: 104099, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35996574

RESUMEN

At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.

8.
Expert Syst Appl ; 211: 118604, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35999828

RESUMEN

The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting "if a shipment can be exported from one source to another", despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs.

10.
Eur J Oper Res ; 304(1): 207-218, 2023 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-35013638

RESUMEN

We describe the models we built for predicting hospital admissions and bed occupancy of COVID-19 patients in the Netherlands. These models were used to make short-term decisions about transfers of patients between regions and for long-term policy making. For forecasting admissions we developed a new technique using linear programming. To predict occupancy we fitted residual lengths of stay and used results from queueing theory. Our models increased the accuracy of and trust in the predictions and helped manage the pandemic, minimizing the impact in terms of beds and maximizing remaining capacity for other types of care.

11.
Expert Syst Appl ; 211: 118545, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-35996556

RESUMEN

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users' rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts.

15.
Expert Syst Appl ; 211: 118576, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36062267

RESUMEN

In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models.

16.
Omega ; 114: 102750, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36090537

RESUMEN

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

17.
Comput Human Behav ; 138: 107479, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36091923

RESUMEN

Taking advantage of 3 million English-language posts by Facebook public pages, this study answers the following questions: How did the amount of COVID-19 vaccine-related messages evolve? How did the moral expressions in the messages differ among sources? How did both the sources and the five moral foundations in posts influence the number of likes to posts, after controlling for the public page's features (e.g., age, followers)? Our research findings suggest that moral expression is prevalent in the COVID-19 vaccination posts, surpassing nonmoral content. Media sources, despite the high volume of posts, on average elicited fewer likes than all other sources. Although care and fairness were the two most used moral foundations, they were negatively related to likes. In contrast, the least used two moral values of authority and sanctity were positively related to likes. We conclude with a discussion of theoretical contributions and a recommendation of possible interventions.

19.
Arch Argent Pediatr ; 121(1): e202202595, 2023 02 01.
Artículo en Inglés, Español | MEDLINE | ID: mdl-35984671

RESUMEN

Introduction. In Argentina, health care workers have been the first ones to receive the COVID-19 vaccine, but there are still few data on the production of anti-S IgG antibodies. Objectives. To assess specific IgG against the SARS-CoV-2 spike protein (anti-S IgG) after the vaccination of health care workers from a children's hospital. To explore the association between the presence of these antibodies, age, and history of prior infection. Population and methods. Cross-sectional study in 193 workers who received both doses of the two- component Sputnik V vaccine. The anti-S IgG antibody titer was measured and age, history of prior SARS-CoV-2 infection, and date of vaccination were recorded. Results. Anti-S IgG antibodies were produced in 98.6% of the subjects. The titer was higher in those with prior infection (p < 0.001), but no relationship was established with subjects' age. Conclusion. We provide data on post-vaccination production of IgG anti-S antibodies among health care workers from a children's hospital and explore some predictors.


Introducción. En Argentina, el personal de salud ha sido el primero en vacunarse contra COVID-19, pero todavía existen pocos datos sobre la producción de anticuerpos IgG anti-S. Objetivos. Evaluar IgG específica contra glicoproteína spike del SARS-CoV-2 (IgG anti-S) posvacunación en personal de un hospital pediátrico. Explorar la asociación entre presencia de dichos anticuerpos, edad y antecedente de infección previa. Población y métodos. Estudio transversal que incluyó 193 trabajadores vacunados con los dos componentes de la vacuna Sputnik V. Se pesquisó el título de IgG anti-S y se registraron edad, antecedente de infección previa por SARS-CoV-2 y fecha de la vacunación. Resultados. El 98,6 % de los sujetos generó IgG anti-S. El título fue mayor en quienes habían cursado infección previamente (p <0,001), pero no hubo relación con la edad de los sujetos. Conclusión. Aportamos datos de generación de anticuerpos IgG anti-S posvacunación en personal de salud de un hospital pediátrico y exploramos algunos predictores.


Asunto(s)
COVID-19 , Personal de Salud , SARS-CoV-2 , Anticuerpos Antivirales , COVID-19/inmunología , Vacunas contra la COVID-19 , Estudios Transversales , Hospitales Pediátricos , Humanos , Inmunoglobulina G , SARS-CoV-2/inmunología , Glicoproteína de la Espiga del Coronavirus
20.
Eur J Oper Res ; 304(1): 42-56, 2023 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-35035055

RESUMEN

A critical operations management problem in the ongoing COVID-19 pandemic is cognizance of (a) the number of all carriers at large (CaL) conveying the SARS-CoV-2, including asymptomatic ones and (b) the infection rate (IR). Both are random and unobservable, affecting the spread of the disease, patient arrivals to health care units (HCUs) and the number of deaths. A novel, inventory perspective of COVID-19 is proposed, with random inflow, random losses and retrials (recurrent cases) and delayed/distributed exit, with randomly varying fractions of the exit distribution. A minimal construal, it enables representation of COVID-19 evolution with close fit of national incidence profiles, including single and multiple pattern outbreaks, oscillatory, periodic or non-periodic evolution, followed by retraction, leveling off, or strong resurgence. Furthermore, based on asymptotic laws, the minimum number of variables that must be monitored for identifying CaL and IR is determined and a real-time identification method is presented. The method is data-driven, utilizing the entry rate to HCUs and scaled, or dimensionless variables, including the mean residence time of symptomatic carriers in CaL and the mean residence time in CaL of patients entering HCUs. As manifested by several robust case studies of national COVID-19 incidence profiles, it provides efficient identification in real-time under unbiased monitoring error, without relying on any model. The propagation factor, a stochastic process, is reconstructed from the identified trajectories of CaL and IR, enabling evaluation of control measures. The results are useful towards the design of policies restricting COVID-19 and encumbrance to HCUs and mitigating economic contraction.

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