The pathophysiology of coronavirus disease-2019 (COVID-19)-related acute respiratory distress syndrome (ARDS) varies from other pneumonia-related ARDS. We evaluated whether the mortality rates differed for COVID-19 and non-COVID-19-related ARDS in the Asian population in 2021. This single center retrospective observational cohort study included patients with COVID-19 and non-COVID-19-related ARDS that required invasive mechanical ventilation. The primary outcome was all-cause in-hospital mortality. The secondary outcomes included hospital length of stay, ICU length of stay, duration of mechanical ventilation, and ventilator-free days (VFDs) during the first 28 days. A 1:1 propensity score matching was performed to correct potential confounders by age, obesity or not, and ARDS severity. One-hundred-and-sixty-four patients fulfilled the inclusion criteria. After 1:1 propensity score matching, there were 50 patients in each group. The all-cause in-hospital mortality of all patients was 38 (38%), and no significant differences were found between COVID-19 and non-COVID-19-related ARDS (17 [34%) vs. 21 [42%], p = 0.410). Both groups had length of stay (30.0 [20.0-46.0] vs. 27.0 [13.0-45.0] days, p = 0.312), ICU length of stay (19.0 [13.0-35.0] vs. 16.0 [10.0-32.0] days, p = 0.249), length of mechanical ventilation (19.0 [10.0-36.0] vs. 14.0 [9.0-29.0] days, p = 0.488), and ventilator-free days during the first 28 days (5.5 [0.0-17.0] vs. 0.0 [0.0-14.0] days, p = 0.320). Immunocompromised status (Hazard ratio: 3.63; 95% CI: 1.51-8.74, p = 0.004) and progress to severe ARDS (Hazard ratio: 2.92; 95% CI: 1.18-7.22, p = 0.020) were significant in-hospital mortality-related confounders. There were no significant difference in mortality among both groups. Immunocompromised status and progression to severe ARDS are two possible risk factors for patients with ARDS; COVID-19 is not a mortality-related risk exposure.

During the COVID-19 pandemic, TB mortality increased while diagnoses decreased, likely due to care disruption. In March, 2020, Uganda-a country with high TB burden, implemented a COVID-19 lockdown with associated decrease in TB diagnoses. This study aims to examine patient level risk factors for disruption in TB care during the COVID-19 pandemic in Uganda. This retrospective cross-sectional cohort study included six TB clinics in Uganda. Clustered sampling included phases of TB care and three time-periods: pre-lockdown, lockdown and post-lockdown. Characteristics of patients with TB care disruption (TBCD), defined as those with > 2 months of symptoms prior to diagnosis or who missed a TB clinic, and those without TB care disruption (non-TBCD) were analyzed between time-periods. 1,624 charts were reviewed; 1322 were contacted, 672 consented and completed phone interview; pre-lockdown (n = 213), lockdown (n = 189) and post-lockdown (n = 270). TBCD occurred in 57% (385/672) of patients. There was an increase in the proportion of urban patients in the TBCD and non-TBCD groups during post-lockdown (p <0.001). There was no difference in demographics, HIV co-infection, socioeconomic status, or distance to TB clinic between TBCD and non-TBCD groups or within TBCD by time-period. There were few differences amongst TBCD and all TB patients by time-period. The increase in urban patients' post-lockdown may represent a portion of urban patients who delayed care until post-lockdown. Insignificant trends suggesting more TBCD amongst those who lived further from clinics and those without HIV-coinfection require more investigation.

Cardiovascular involvement has been described in acute and recovered COVID-19 patients. Here, we present a case of symptomatic pericarditis with persistent symptoms for at least six months after the acute infection and report 66 published cases of pericarditis in discharged COVID patients. Patient mean age ± SD was 49.7 ± 13.3 years, ranging from 15 to 75 years and 57.6% were female. A proportion of 89.4% patients reported at least one comorbidity, with autoimmune and allergic disorders, hypertension and dyslipidaemia, as the most frequent. Only 8.3% of patients experienced severe symptoms of acute COVID-19. The time between acute COVID and pericarditis symptoms varied from 14 to 255 days. Chest pain (90.9%), tachycardia (60.0%) and dyspnoea (38.2%) were the most frequent symptoms in post-acute pericarditis. A proportion of 45.5% and 87% of patients had an abnormal electrocardiogram and abnormal transthoracic ultrasound, respectively. Colchicine combined with non-steroidal anti-inflammatory drug (NSAID) or acetylsalicylic acid (aspirin) were prescribed to 39/54 (72%) patients. Of them, 12 were switched to corticosteroid therapy due to non-response to the first-line treatment. Only 6 patients had persisting symptoms and were considered as non-respondent to therapy.Our report highlights that pericarditis should be suspected in COVID-19 patients with persistent chest pain and dyspnoea when pulmonary function is normal. Treatment with non-steroidal anti-inflammatory and colchicine is usually effective but corticosteroids are sometimes required.

The aim of this study was to assess clinical findings, radiological data, pulmonary functions and physical capacity change over time and to investigate factors associated with radiological abnormalities after coronavirus disease 2019 (COVID-19) in non-comorbid patients. This prospective cohort study was conducted between April 2020 and June 2020. A total of 62 symptomatic in non-comorbid patients with COVID-19 pneumonia were included in the study. At baseline and the 2nd, 5th and 12th months, patients were scheduled for follow-up. Males represented 51.6% of the participants and overall mean age was 51.60 ± 12.45 years. The percentage of patients with radiological abnormalities at 2 months was significantly higher than at 5 months (P < .001). At 12 months, dyspnea frequency (P = .008), 6-minute walk test (6MWT) distance (P = .045), BORG-dyspnea (P < .001) and BORG-fatigue (P < .001) scores was significantly lower, while median SpO2 after 6MWT (P < .001) was significantly higher compared to results at 2 months. The presence of radiological abnormalities at 2 months was associated with the following values measured at 5 months: advanced age (P = .006), lung involvement at baseline (P = .046), low forced expiratory volume in 1 second (P = .018) and low forced vital capacity (P = .006). Even in COVID-19 patients without comorbidities, control computed tomography at 2 months and pulmonary rehabilitation may be beneficial, especially in COVID-19 patients with advanced age and greater baseline lung involvement.

Pulmonary infections have been a leading etiology of morbidity and mortality worldwide. Upper and lower respiratory tract infections have multifactorial causes, which include bacterial, viral, and rarely, fungal infections. Moreover, the recent emergence of SARS-CoV-2 has created havoc and imposes a huge healthcare burden. Drug and vaccine development against these pulmonary pathogens like respiratory syncytial virus, SARS-CoV-2, Mycobacteria, etc., requires a systematic set of tools for research and investigation. Currently, in vitro 2D cell culture models are widely used to emulate the in vivo physiologic environment. Although this approach holds a reasonable promise over pre-clinical animal models, it lacks the much-needed correlation to the in vivo tissue architecture, cellular organization, cell-to-cell interactions, downstream processes, and the biomechanical milieu. In view of these inadequacies, 3D cell culture models have recently acquired interest. Mammalian embryonic and induced pluripotent stem cells may display their remarkable self-organizing abilities in 3D culture, and the resulting organoids replicate important structural and functional characteristics of organs such the kidney, lung, gut, brain, and retina. 3D models range from scaffold-free systems to scaffold-based and hybrid models as well. Upsurge in organs-on-chip models for pulmonary conditions has anticipated encouraging results. Complexity and dexterity of developing 3D culture models and the lack of standardized working procedures are a few of the setbacks, which are expected to be overcome in the coming times. Herein, we have elaborated the significance and types of 3D cell culture models for scrutinizing pulmonary infections, along with the in vitro techniques, their applications, and additional systems under investigation.

[This retracts the article DOI: 10.1155/2022/6566982.].

Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) patients have a more severe COVID-19 course than the general population. Many patients report different persistent symptoms after SARS-CoV-2 infection. The aim of our study is to analyze the prevalence of long COVID-19 symptoms and assess if COVID-19 affects pulmonary hypertension (PH) prognosis. PAH/CTEPH patients who survived COVID-19 for at least 3 months before visiting the PH centers were included in the study. The patients were assessed for symptoms in acute phase of SARS-CoV-2 infection and persisting in follow-up visit, WHO functional class, 6-min walk distance, NT-proBNP concentration. The COMPERA 2.0 model was used to calculate 1-year risk of death due to PH at baseline and at follow-up. Sixty-nine patients-54 (77.3%) with PAH and 15 (21.7%) with CTEPH, 68% women, with a median age of 47.5 years (IQR 37-68)-were enrolled in the study. About 17.1% of patients were hospitalized due to COVID-19 but none in an ICU. At follow-up (median: 155 days after onset of SARS-CoV-2 symptoms), 62% of patients reported at least 1 COVID-19-related symptom and 20% at least 5 symptoms. The most frequently reported symptoms were: fatigue (30%), joint pain (23%), muscle pain (17%), nasal congestion (17%), anosmia (13%), insomnia (13%), and dyspnea (12%). Seventy-two percent of PH patients had a low or intermediate-low risk of 1-year death due to PH at baseline, and 68% after COVID-19 at follow-up. Over 60% of PAH/CTEPH patients who survived COVID-19 suffered from long COVID-19 syndrome, but the calculated 1-year risk of death due to PH did not change significantly after surviving mild or moderate COVID-19.

This multicenter retrospective investigation aimed to identify predictors of pneumothorax (PTX), pneumomediastinum (PM), and subcutaneous emphysema (SE) in patients with COVID-19 pneumonia admitted to the ICU. A total of 256 patients were included, with 128 in the case group and 128 in the control group. The study sample consisted of predominantly male patients with a mean age of around 53 years and a high prevalence of comorbidities. Significant predictors of PTX, PM, and SE included the presence of coronary artery disease, non-rebreather mask usage, high-flow oxygen therapy, mechanical ventilation, pressor usage, inpatient dialysis, steroid usage, sedative usage, narcotic usage, paralytic usage, elevated C-reactive protein levels, increased lung infiltration, the presence of PM and SE, mode of ventilation, duration of various respiratory support interventions, and severity of illness as indicated by APACHE and SOFA scores. These findings have important implications for the clinical management of patients with COVID-19 pneumonia, as they may help identify and closely monitor at-risk individuals, allowing for timely intervention and potentially improving clinical outcomes. Future research should focus on validating these predictors in larger cohorts and investigating the underlying mechanisms to develop targeted preventive and therapeutic strategies.

Clostridioides difficile (C. difficile)and coronavirus disease 2019 (COVID-19) infections can have overlapping symptoms. Recently, the association and outcomes of coinfection have been studied. We present the case of an 83-year-old lady with Parkinson's disease (PD) who was admitted with pneumonia secondary to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. She was treated with empiric antibiotics ampicillin-sulbactam and azithromycin, along with antiviral therapy remdesivir and baricitinib, and dexamethasone. The patient developed severe C. difficile infection with a leukemoid reaction. She was treated with intravenous metronidazole and oral vancomycin without any improvement. Before she could receive a fecal microbiota transplant, her infection progressed to fulminant colitis, and she required emergent surgery. The patient developed several complications post-surgery and succumbed to the severe illness. Our patient's multiple comorbidities and an underlying COVID-19 infection predisposed her to severe illness. This case emphasizes the long-standing discussion on antibiotic stewardship and encourages a debate on the role of immunosuppressant antiviral medications and underlying PD in predisposing patients to a severe C. difficile infection.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

A 29-year-old woman was admitted with a diagnosis of ischemic enteritis. She had a coronavirus disease 2019 (COVID-19) infection four weeks before this visit and continued to experience a cough. Four months before, she received the third COVID-19 vaccine. Chest computer tomography revealed scattered ground-glass opacities in both upper lobes. Based on abnormalities in chest imaging, eosinophilia, and a high level of fractional exhaled nitric oxide, she was diagnosed with eosinophilic lower airway inflammation due to COVID-19. Since the visit, the patient had an intermittent fever and no radiological improvement, so systemic corticosteroid treatment was initiated, and the symptoms and clinical findings improved. Clinicians should know the potential association between COVID-19 and eosinophilic lower airway inflammation, which may still occur despite multiple vaccinations.

[This corrects the article DOI: 10.3389/fmed.2022.869818.].

SARS-CoV-2 has caused an estimated 7 million deaths worldwide to date. A secreted SARS-CoV-2 accessory protein, known as open reading frame 8 (ORF8), elicits inflammatory pulmonary cytokine responses and is associated with disease severity in COVID-19 patients. Recent reports proposed that ORF8 mediates downstream signals in macrophages and monocytes through the IL-17 receptor complex (IL-17RA, IL-17RC). However, generally IL-17 signals are found to be restricted to the nonhematopoietic compartment, thought to be due to rate-limiting expression of IL-17RC. Accordingly, we revisited the capacity of IL-17 and ORF8 to induce cytokine gene expression in mouse and human macrophages and monocytes. In SARS-CoV-2-infected human and mouse lungs, IL17RC mRNA was undetectable in monocyte/macrophage populations. In cultured mouse and human monocytes and macrophages, ORF8 but not IL-17 led to elevated expression of target cytokines. ORF8-induced signaling was fully preserved in the presence of anti-IL-17RA/RC neutralizing Abs and in Il17ra-/- cells. ORF8 signaling was also operative in Il1r1-/- bone marrow-derived macrophages. However, the TLR/IL-1R family adaptor MyD88, which is dispensable for IL-17R signaling, was required for ORF8 activity yet MyD88 is not required for IL-17 signaling. Thus, we conclude that ORF8 transduces inflammatory signaling in monocytes and macrophages via MyD88 independently of the IL-17R.

In this retrospective cohort from three Missouri hospitals from January 2017 to August 2020, hospital-onset Clostridioides difficile infections (CDI) were more common during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic at the tertiary care hospital. Risk factors associated with hospital-onset CDI included year of hospitalization, age, high-risk antibiotic use, acid-reducing medications, chronic comorbidities, and SARS-CoV-2 infection.

Live attenuated vaccines (LAVs) administered via the mucosal route may offer better control of the COVID-19 pandemic than non-replicating vaccines injected intramuscularly. Conceptionally, LAVs have several advantages, including presentation of the entire antigenic repertoire of the virus, and the induction of strong mucosal immunity. Thus, immunity induced by LAV could offer superior protection against future surges of COVID-19 cases caused by emerging SARS-CoV-2 variants. However, LAVs carry the risk of unintentional transmission. To address this issue, we investigated whether transmission of a SARS-CoV-2 LAV candidate can be blocked by removing the furin cleavage site (FCS) from the spike protein. The level of protection and immunity induced by the attenuated virus with the intact FCS was virtually identical to the one induced by the attenuated virus lacking the FCS. Most importantly, removal of the FCS completely abolished horizontal transmission of vaccine virus between cohoused hamsters. Furthermore, the vaccine was safe in immunosuppressed animals and showed no tendency to recombine in vitro or in vivo with a SARS-CoV-2 field strain. These results indicate that removal of the FCS from SARS-CoV-2 LAV is a promising strategy to increase vaccine safety and prevent vaccine transmission without compromising vaccine efficacy.

The Jafron series of sorbent cartridges provides a comprehensive array of coated, highly biocompatible sorbent beads made of styrene-divinylbenzene copolymers. Such beads carry a mean diameter of 0.8 mm with a range from 0.60 to 1.18 mm. The maximal pore size of these coated beads and the volume of the cartridge vary according to the type of cartridge ranging between 50 Da and 60 kDa. The sorbents, the size of the cartridge, the volume of sorbent, and the pore size (which reaches 60 kDa with the HA330 cartridge) aim to take advantage of the principles of molecular adsorption in a variety of diseases from uremic toxin retention to poisoning and drug overdose, from kidney disease to liver failure, from acute respiratory distress syndrome to sepsis, from toxic skin injury to COVID-19. The preliminary data from ex vivo studies, animal investigations, and human pilot work look promising and justify a program of systematic investigation of these products to advance our understanding of how they may be incorporated into our therapeutic arsenal.

Intraorbital lymphatic-venous malformations are rare lesions that represent a therapeutic challenge given their location and high rate of recurrence, with only a few cases in adult patients having been published in the literature. We present the case of a 30-year-old male with a right intraorbital lymphatic-venous malformation treated with sirolimus at a dose of 4 mg/day with complete clinical and radiologic remission. Mild cold-like symptoms ensued during the first week of treatment and elevation of liver function enzymes and D-dimer occurred in the context of acute SARS-CoV-2 pneumonia. No major adverse effects were documented. After 18 months of treatment, the patient remains asymptomatic and ophthalmologic examinations including optical coherence tomography and visual field test are within normal values.

Due to its high efficiency and selectivity, cell-free biosynthesis has found broad utility in the fields of bioproduction, environment monitoring, and disease diagnostics. However, the practical application is limited by its low productivity. Here, we introduce the entropy-driven assembly of transcription templates as dynamic amplifying modules to accelerate the cell-free transcription process. The catalytic DNA circuit with high sensitivity and enzyme-free format contributes to the production of large amounts of transcription templates, drastically accelerating the as-designed cell-free transcription system without interference from multiple enzymes. The proposed approach was successfully applied to the ultrasensitive detection of SARS-CoV-2, improving the sensitivity by 3 orders of magnitude. Thanks to the high programmability and diverse light-up RNA pairs, the method can be adapted to multiplexing detection, successfully demonstrated by the analysis of two different sites of the SARS-CoV-2 gene in parallel. Further, the flexibility of the entropy-driven circuit enables a dynamic responding range by tuning the circuit layers, which is beneficial for responding to targets with different concentration ranges. The strategy was also applied to the analysis of clinical samples, providing an alternative for sensitively detecting the current SARS-CoV-2 RNA that quickly mutates.

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.