Outbreak Of Pneumonia Research Articles

Evaluation of inhibition effect and interaction mechanism of antiviral drugs on main protease of novel coronavirus: Molecular docking and molecular dynamics studies

The outbreak of pneumonia caused by the novel coronavirus (SARS-CoV-2) has presented a challenge to public health. The identification and development of effective antiviral drugs is essential. The main protease (3CLpro) plays an important role in the viral replication of SARS-CoV-2 and is considered to be an effective therapeutic target. In this study, according to the principle of drug repurposing, a variety of antiviral drugs commonly used were studied by molecular docking and molecular dynamics (MD) simulations to obtain potential inhibitors of main proteases. 24 antiviral drugs were docked with 5 potential action sites of 3CLpro, and the drugs with high binding strength were further simulated by MD and the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations. The results showed that the drugs with high flexibility could bind to 3CLpro better than those with low flexibility. The interaction mechanism between antiviral drugs and main protease was analyzed in detail by calculating the root mean square displacement (RMSD), root mean square fluctuation (RMSF) and interaction residues properties. The results showed that the six drugs with high flexibility (Remdesivir, Simnotrelvir, Sofosbuvir, Ledipasvir, Indinavir and Raltegravir) had strong binding strength with 3CLpro, and the last four antiviral drugs can be used as potential candidates for main protease inhibitors.

Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

Advances in the study of cytokine storm mechanisms in pneumonia: An example of IL-6 and IL-33

In the past decades, pneumonia has been plaguing countries around the world, with great implications for medical and public health. Systems pose a serious challenge. Many pneumonia outbreaks have occurred in local areas and even in the world. Since the end of 2019, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has once again attracted the world's attention to viral pneumonia. At present, a large number of basic research, drug development and clinical trials are being carried out for SARS-CoV-2 and COVID-19. However, patients are generally given symptomatic and supportive treatment, antiviral therapy, immunotherapy, glucocorticoid therapy and respiratory support. Therefore, finding new and effective treatment methods based on the pathogenic mechanism of SARS-CoV-2 is still the current practical demand and research focus. This review focuses on the occurrence and development of CS and the immunopathological damage caused by CS in viral pneumonia, especially in severe pneumonia, and summarizes the potential means of treating CS through IL-related pathways. Research found that different viruses cause CS, cytokines involved in the type and severity of differences, therefore, should also be targeted in the treatment strategy. In addition, in addition to focusing on the treatment of CS, in order to better study the pathophysiological mechanism of virus-induced CS and screen intervention drugs efficiently, how to safely and effectively establish cell and animal models of CS and improve the scientific evaluation system are also key issues worthy of attention.

Synthetic macrolides overcoming MLSBK-resistant pathogens

Conventional macrolide-lincosamide-streptogramin B-ketolide (MLSBK) antibiotics are unable to counter the growing challenge of antibiotic resistance that is conferred by the constitutive methylation of rRNA base A2058 or its G2058 mutation, while the presence of unmodified A2058 is crucial for high selectivity of traditional MLSBK in targeting pathogens over human cells. The absence of effective modes of action reinforces the prevailing belief that constitutively antibiotic-resistant Staphylococcus aureus remains impervious to existing macrolides including telithromycin. Here, we report the design and synthesis of a novel series of macrolides, featuring the strategic fusion of ketolide and quinolone moieties. Our effort led to the discovery of two potent compounds, MCX-219 and MCX-190, demonstrating enhanced antibacterial efficacy against a broad spectrum of formidable pathogens, including A2058-methylated Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, and notably, the clinical Mycoplasma pneumoniae isolates harboring A2058G mutations which are implicated in the recent pneumonia outbreak in China. Mechanistic studies reveal that the modified quinolone moiety of MCX-190 establishes a distinctive secondary binding site within the nascent peptide exit tunnel. Structure-activity relationship analysis underscores the importance of this secondary binding, maintained by a sandwich-like π–π stacking interaction and a water–magnesium bridge, for effective engagement with A2058-methylated ribosomes rather than topoisomerases targeted by quinolone antibiotics. Our findings not only highlight MCX-219 and MCX-190 as promising candidates for next-generation MLSBK antibiotics to combat antibiotic resistance, but also pave the way for the future rational design of the class of MLSBK antibiotics, offering a strategic framework to overcome the challenges posed by escalating antibiotic resistance.

Consumption of Ultra-Processed Foods in the Brazilian Amazon during COVID-19.

A COVID-19 pandemic erupted, causing a global viral pneumonia outbreak, marking the most significant public health crisis of the 21st century. These changes profoundly impacted population health and well-being, leading to shifts in dietary habits. This study aimed to evaluate the consumption of ultra-processed foods in the Brazilian Amazon before, during, and after the COVID-19 pandemic. This is a secondary data analysis study derived from the Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey (Vigitel, 2019-2021) of the Brazilian Ministry of Health. All statistical analyses were performed using the Stata 17 statistical program in the survey module (svy). We found an increased frequency in the subgroups of consumption of ultra-processed foods in the capital of the Brazilian Amazon region between the years 2019 and 2021. In the cities of Boa Vista and Macapá, there was a significant increase in the consumption of snacks, salty snacks, cookies, and meat products. Boa Vista and Macapá showed an increase in the percentage difference in the consumption ≥5 of ultra-processed subgroups, being 30.4% (p = 0.014) and 53.7% (p = 0.014), respectively. The study indicated an increase in the consumption of ultra-processed foods in the Brazilian Amazon region during and after social distancing.

Population and spatial dynamics of desert bighorn sheep in Grand Canyon during an outbreak of respiratory pneumonia

IntroductionTerrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation.MethodsWe compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000–2018 and captured bighorn in 2010–2016 to deploy GPS collars and test for disease. From 2011–2015, we non-invasively collected fecal samples and genotyped them at 9–16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capture-recapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model.Results and discussionFrom 2010–2018, 3,176 sightings of bighorn were reported, with sightings of 56–145 bighorn annually on formal surveys. From 2012–2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011–2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022–0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536–552 (95% CrI range 451–647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems.

Multi‐locus sequence typing indicates multiple strains of Mycoplasma in desert bighorn sheep and aoudad in Texas

AbstractEpizootic events of pneumonia, presumably caused by Mycoplasma ovipneumoniae, in bighorn sheep (Ovis canadensis) have been observed in the western United States and Canada. Until recently, it was thought that populations of Mexican (O. c. mexicana) and Nelson's (O. c. nelsoni) desert bighorn sheep in Texas, USA, had not been exposed to Mycoplasma. Evidence of disease and potential population decline from outbreaks of M. ovipneumoniae are now known from several populations across the Trans‐Pecos Ecoregion with documented instances of pneumonia and bluetongue in desert bighorn sheep from the Van Horn Mountains and Black Gap Wildlife Management Area. These disease events, especially those in 2019–2021, may be a result of increasing populations of aoudad (Ammotragus lervia), an introduced and invasive ungulate, in the region. With large population sizes and similar movement patterns as desert bighorn sheep, aoudad potentially are the reservoirs for bacterial and viral diseases, such as pneumonia and bluetongue, and are possibly contributing to the decline of desert bighorn sheep. Herein, we optimized the multi‐locus sequence typing (MLST) with modifications in the Taq polymerase and annealing temperatures to determine the genetic identity of Mycoplasma strains or species within the nasal passages of desert bighorn sheep and aoudad in the Trans‐Pecos Ecoregion of Texas. Four loci (small ribosomal unit, 16S; 16S‐23S intergenic spacer region, IGS; RNA polymerase B, rpoB; gyrase B, gyrB) were characterized using MLST. Based on results from the modified MLST technique, we identified 9 desert bighorn sheep and 5 aoudad with M. ovipneumoniae, 9 aoudad with bacterial sequences genetically similar to M. conjunctivae, and 10 aoudad with bacterial sequences genetically similar M. hyopneumoniae. Of these, 9 aoudad possessed bacterial sequences genetically similar to both M. conjunctivae and M. hyopneumoniae. Among the 4 diagnostic loci, genetic divergence of M. ovipneumoniae ranged from 0.00–0.90% among desert bighorn sheep and aoudad. Future sampling efforts of seemingly asymptomatic aoudad, and asymptomatic, visibly sick, or deceased desert bighorn sheep, are important to monitor the spread of disease in desert bighorn sheep populations across mountain ranges in western Texas. It is imperative that aoudad removal plans are implemented to reduce and eliminate current infections and putative transmission of M. ovipneumoniae, prevent future disease outbreaks of pneumonia, and ultimately conserve desert bighorn sheep for future generations.

Novel Variant and Known Mutation in 23S rRNA Gene of Mycoplasma pneumoniae, Northern Vietnam, 2023.

During a 2023 outbreak of Mycoplasma pneumoniae-associated community-acquired pneumonia among children in northern Vietnam, we analyzed M. pneumoniae isolated from nasopharyngeal samples. In almost half (6 of 13) of samples tested, we found known A2063G mutations (macrolide resistance) and a novel C2353T variant on the 23S rRNA gene.

The role of pre-onset hair hormone in predicting the prognosis of patients with severe pneumonia and acute COVID-19 outbreak

Numerous research works have investigated the potential impact of endocrine hormones on the severity of COVID-19-related pneumonia in individuals. However, there are few studies on the effect of pre-onset neuroendocrine hormones on the prognosis of COVID-19 patients. This study looked into the prognostic value of pre-onset hair hormone levels in COVID-19 infected individuals. This study included 27 patients with COVID-19 and collected patient information and laboratory indicators. The hormone levels in hair were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Within 28 days, 63 % of the patients in this study passed away. With 28-day mortality as the outcome index, urea nitrogen, CURB-65 score and pneumonia severity score (PSI) of 2 groups were statistically significant (P < 0.05). Among all hormone levels detected in hair, only progesterone level was substantially correlated negatively with COVID-19 patients' 28-day mortality(P < 0.05). The level of progesterone in hair was substantially adversely connected with the death rate at 28 days of COVID-19 patients, according to correlation and logistic regression analysis(P < 0.05). Among patients with COVID-19 pneumonia, hair progesterone levels were strongly associated with 28-day mortality, which emphasizes hair progesterone's importance as a prognostic factor.

Relationship between health behavior compliance and prospect theory-based risk preferences during a pandemic of COVID-19

The outbreak of COVID-19 forced people to change their lifestyles. In this study, a questionnaire was administered to 1000 Chinese residents to investigate the effects of risk preferences based on prospect theory from behavioral economics, trust in a trust game, other people's expectations, and personal attributes on the adherence to quarantine measures during a pandemic.Factor analysis divided the preventive behaviors into (1) compliance for self-health care, (2) compliance for going outside, and (3) compliance for staying home alone. In Category 1, those who were more sensitive to gains than losses were more likely to have a health code, wash their hands longer, report instances of COVID-19, and disinfect often. In Category 2, they avoided volunteering at large hospitals, avoided trips to see acquaintances, avoided public walks, avoided meeting strangers, avoided going to garden centers, and wore masks when out and about. On the other hand, those more sensitive to 'losses' than 'gains' were found to work from home and socialize with non-family members in Category 3.Based on prospect theory, it was found that whether one values gains or losses has a strong influence on preventative behavior. In particular, the fact that those who place greater value on gains than on losses tend to be more proactive in preventative behavior is expected to shed new light on future government guidance for citizens and subtle nudges or prompts to influence behavior.

X-ray Image Recognition of Pneumonia Based on Three Different Neural Networks

With the outbreak of new coronary pneumonia, a variety of pneumonia diseases are emerging, doctors often have misdiagnoses and omissions. To assist doctors in better clinical diagnosis, a variety of high-performance neural network structures have been applied to the X-ray recognition of pneumonia, and the X-ray image recognition accuracy will be improved, will effectively improve the efficiency of the hospital detection, at the same time, can also avoid the patient to miss the best rescue time. To this end, this paper compares and analyses the recognition performance of the three currently used neural network structures in the recognition of pneumonia X-ray images, uses some publicly available datasets and a mixed set composed of different datasets to conduct experiments, and summarises the advantages of the models and the direction of possible improvement. The three neural network models are presented, compared, and analysed to suggest useful references for the recognition of pneumonia X-ray images. Finally, an analysis and outlook for future neural network models for pneumonia x-ray image recognition is presented.

Prevalence of COVID-19 among high school teachers in Erbil city

Background and objective: An outbreak of pneumonia of unknown origin was reported in Wuhan, Hubei Province, China, in December 2019. Cases of epidemic pneumonia have been linked to the Huanan Seafood Market; thus, it has been dubbed the severe acute respiratory syndrome coronavirus (SARS-CoV-2). The global spread of thousands of deaths from the coronavirus disease (COVID-19) has led the WHO to declare a pandemic on March 12, 2020. This study aimed to find out the prevalence of COVID-19 disease and vaccination coverage among public preparatory school teachers in Erbil. Methods: A cross-sectional study was conducted among the target group of 772 teachers in 76 public preparatory schools. The study population was 3157 teachers, 1263 male and 1894 female, in Erbil city, from September 1, 2021, to March 30, 2022. A questionnaire was used to collect data through face-to-face interviews using a simple random sampling method. Results: The study sample ages ranged from 26 to 62 years; the mean age, ±standard deviation, was 40.53 ± 9.75. The prevalence rate of COVID-19 was 63.1%. Of the study samples, 58.5% received information about COVID-19 disease from social media and 23.8% from satellite channels. 68.9% of the study samples received the COVID-19 vaccine, and the majority preferred the Pfizer vaccine. Conclusion: The majority of the study samples were infected with COVID-19 disease; many of the study samples received the COVID-19 vaccine; misconceptions need further correction; an increase in updating information regarding mode of transmission; an explanation of COVID-19 disease preventive measures; raising awareness of COVID-19 vaccination; more interesting health campaigns; and using mass media and community programs to change health behavior and reduce future health consequences of COVID-19 disease.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Evidence of transmission of New Delhi metallo-β-lactamase-producing Klebsiella pneumoniae through a gastrointestinal endoscope without an elevator channel.

To investigate the source and transmission dynamics of an endoscope-associated New Delhi metallo-β-lactamase-producing Klebsiella pneumonia (NDM-KP) outbreak. Epidemiological and genomic investigation. Academic acute care hospital in New Jersey. Five patients with active NDM-KP infection identified on clinical isolates, and four NDM-KP colonized patients identified via rectal swab screening. Over a twelve-month period, nine patients were identified with NDM-KP infection or colonization. Whole-genome sequencing (WGS) revealed that all of the identified cases were related by 25 mutational events or less. Seven of the cases were linked to gastrointestinal endoscopic procedures (four clinical cases and three positive screens among patients exposed to endoscopes suspected of transmission). Two cases demonstrated delayed transmission that occurred five months after the initial outbreak, likely through shared usage of a non-therapeutic gastroscope without an elevator channel. Although all endoscope cultures in our investigation were negative, the epidemiological link to gastrointestinal endoscopes, the high degree of relatedness via WGS, and the identification of asymptomatic NDM-KP colonization among patients exposed to shared endoscopes make the endoscopic mode of transmission most likely. This investigation highlights the probable transmission of NDM-KP via a gastroscope without an elevator channel, observed several months after an initial outbreak. We hypothesize that persistent mechanical defects may have contributed to the delayed device-related transmission of NDM-KP.

Rapid detection of SARS-CoV-2 spike protein using a magnetic-assisted electrochemical biosensor based on functionalized CoFe2O4 magnetic nanomaterials

The outbreak of novel coronavirus pneumonia (COVID-19) in 2019 has garnered widespread attention. The virus exhibits high contagiousness, and in certain cases, it can lead to recurrent infections. Therefore, it is imperative to develop portable, sensitive, and accurate sensors to promptly detect infected individuals, control the virus's transmission, and determine suitable treatment strategies. In this study, we proposed a magnetically-assisted method employing CFO@CS-Au MNP as the substrate material, which was functionalized with human angiotensin-converting enzyme (ACE2) for efficient capture of SARS-CoV-2 spike protein in solution. Subsequently, the captured protein was sensitively detected through differential pulse voltammetry (DPV) electrical analysis. The linear detection range of the labeled GCE/MNP/GA/ACE2/BSA electrochemical sensor is from 1 pg/mL to 10 μg/mL, with a minimum detection limit of 0.15 pg/mL. Furthermore, the fabricated GCE/MNP/GA/ACE2/BSA sensor achieved satisfactory recoveries of SARS-CoV-2 spike protein in saliva and nasal swab samples within 10 min. These results indicate that this magnetically-assisted biosensor has established a solid foundation for the swift on-site detection of COVID-19.

Polylevodopa nanoplatform for lateral flow immunochromatography assay of SARS-CoV-2 and influenza A virus

At the end of 2019, an unprecedented outbreak of novel coronavirus pneumonia ravaged the global landscape, inflicting profound harm upon society. Following numerous cycles of transmission, we find ourselves in an epoch where the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coexists alongside influenza viruses (Flu A). Swift and accurate diagnosis of SARS-CoV-2 and Flu A is imperative to stem the spread of these maladies and administer appropriate treatment. Presently, colloidal gold-based lateral flow immunoassays (Au-LFIAs) constructed through electrostatic adsorption are beset by challenges such as diminished sensitivity and feeble binding stability. In this context, we propose the adoption of black polylevodopa nanoparticles (PLDA NPs) featuring abundant carboxyl groups as labeling nanomaterials in LFIA to bolster the stability and sensitivity of SARS-CoV-2 antigens and influenza A virus identifications. The engineered PLDA-LFIAs exhibit the capacity to detect SARS-CoV-2 and Flu A within 30 min, boasting a detection threshold of 5 pg/ml for the SARS-CoV-2 antigen and 0.1 ng/ml for the Flu A H1N1 antigen, thereby underscoring their heightened sensitivity relative to Au-LFIAs. These PLDA-LFIAs hold promise for the early detection of SARS-CoV-2 and Flu A, underscoring the potential of PLDA NPs as a discerning labeling probe to heighten the sensitivity of LFIA across diverse applications.

Clinical and Temporal Radiological Findings from Critical Patients with COVID-19 Pneumonia: A Descriptive Study.

In late December 2019, Wuhan, the capital of Hubei Province, China, became the center of an outbreak of pneumonia caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The radiological changes in the lungs of critical people with coronavirus disease 2019 (COVID-19) pneumonia at different times have not been fully characterized. We aim to describe the computed tomography findings of patients with critical COVID-19 pneumonia at different disease stages. Clinical and laboratory features of critical patients were assessed. CT scans were assigned to groups 1, 2, 3, or 4 based on the interval from symptom onset (within 2 weeks; ≥ 2-4 weeks; ≥ 4-6 weeks; or ≥ 6 weeks, respectively). Imaging features were analyzed and compared across the four groups. Total CT scores, corresponding periods of laboratory findings, and glucocorticoid dosages during the imaging intervals were longitudinally observed in five patients with complete data. All 11 critical patients (median age: 60 years [42-69]) underwent a total of 40 CT examinations, and the acquisition times ranged from 1-59 days after symptom onset. Median total CT scores were 18 (9-25.25); 44.5 (42.88-47.62); 43.75 (38.62-49.38); and 42 (32.25-53.25) in groups 1, 2, 3, and 4, respectively. The observed lesions were mainly bilateral (37 [92.5%]). The median values of involved lung segments were 10.5 (4.5-13.5); 17 (16-18.5); 18 (18-19.5); and 18 (18-19) in groups 1-4, respectively. The predominant patterns of observed abnormalities were ground-glass opacities (GGO) (9 [90%]); GGO with reticulation and mixed patterns (3 [37.5%] for both); GGO with consolidation (3 [30%]); and GGO with reticulation (8 [66.7%]) in groups 1-4, respectively. Patients developed fibrotic manifestations at later stages. Critical patients with COVID-19 infection generally presented with temporally changing abnormal CT features from focal unilateral to diffuse bilateral GGO and consolidation that progressed to or coexisted with reticulation in the long term after symptom onset. Low-dose glucocorticoids may be effective in patients with interstitial changes on CT findings.

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-Cov) and SARS-COV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study. Given this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset. Experimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides. The results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.- com/JYY625/EACVP.git.

Ultrasound COVID-19 Classification Based on the Novel Module-Based Dual-Path Network

With a large-scale novel coronavirus pneumonia (COVID-19) outbreak, more and more researchers have acquired convenient and efficient COVID-19 infection status through medical imaging. Here, due to the excellent features of zero-radiation and rapid clinical examination, ultrasound images have been used to assist doctors in COVID-19 diagnosis. To effectively identify the pathological differences between common pneumonia and novel coronavirus pneumonia, an ultrasound COVID-19 classification based on the novel module-based dual-path network (MD-DPNet) is proposed. Specifically, this paper effectively improves the generalization ability by adding the progressive heatmaps intuitively representing the lesion density with the original ultrasound images. Meanwhile, the proposed algorithm creates regular modular sets to reduce the calculating loads and the coupling between each module, which takes advantage of the fact that most pathological features are concentrated in relatively small regions. Furthermore, the proposed MD-DPNet model divides the complete task into multiple sub-tasks to alleviate interclass disequilibrium and the lesion artifact localization is obtained by an unsupervised method. In this paper, experiments show the effectiveness of the proposed innovation, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Dice reach 0.99, 0.99, 0.99, 0.98, and 0.99, respectively. Experiments on the proposed mixed dataset demonstrate satisfactory results on all the considered tasks, which can provide a new idea for effectively assisting doctors in COVID-19 diagnosis and follow-up treatment.

Association of Inflammatory Markers With Disease Progression and the Severity of COVID-19.

Introduction InDecember 2019, there was a massive outbreak of viral pneumonia, which had a high case fatality rate. Genetic sequencing of the virus showed similarity with severe acute respiratory syndrome coronavirus (SARS-CoV). It was later named novel coronavirus 2019 while the disease it caused was given the nomenclature of COVID-19. This deadly pneumonia outbreak was declared a pandemic by the World Health Organization (WHO). Aim To derive the strength of the correlation between blood levels of various inflammatory markers with the severity of COVID-19 pneumonia in patients affected with novel coronavirus 2019. Materials and methodology A prospective study was conducted on 300 confirmed cases of COVID-19 infection from August 2020 to July 2021 in SSG Hospital, Vadodara. Diagnosis of patients as confirmed cases of COVID-19 infection was done according to the WHO interim guidance for COVID-19. Their inflammatory markers were done for this study. All COVID-19-positive patients who had given negative consent for enrollment were excluded from the study. Patients were classified based on the severity of acute respiratory distress syndrome (ARDS). Comprehensive medical record information, encompassing biodata, clinical symptoms, comorbidities, and laboratory investigations, was systematically collected. Patients were given the standard treatment protocol as per guidelines. Patients were subjected to detailed investigations comprising complete blood counts and inflammatory markers like C-reactive protein (CRP), lactate dehydrogenase (LDH), serum ferritin, and D-dimer. Patients were further investigated by chest X-ray (posteroanterior view) or high-resolution computed tomography of the thorax. Results A total of 300 confirmed cases of COVID-19 infection were included in this study. Most of them were males (52%) with a mean age of 51 years and 48% were females with a mean age of 55 years. The majority of patients(40%) did not have ARDS, 23.3% of patients had mild, 16.7% of patients had moderate, and 20% of patients had severe ARDS. Higher CRP levels, serum ferritin, and serum D-dimer were significantly associated with the severity of COVID-19 infection as compared to those having no symptoms (p < 0.05). Increased levels were associated with severe clinical manifestations of COVID-19. The sensitivity of CRP is 69% and specificity is 100% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of ferritin is 88% and specificity is 81% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of D-dimer is 94% and specificity is 89% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of LDH is 93% and specificity is 84% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. Conclusions Current evidence from our study showed that higher levels of inflammatory markers such as CRP, LDH, D-dimer, and ferritin are associated with the severity of COVID-19 in terms of ARDS and thus could be used as significant prognostic factors of the disease.These indicators might support clinical decisions to identify highfatality casesand poor diagnosis in the initial admission phase.