Harnessing three-dimensional (3D) cell culture models for pulmonary infections: State of the art and future directions.

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.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

Agents of mucormycosis outbreak associated with COVID-19

Mucormycosis is a life-threatening infection. Mucormycetes causes a wide range of diseases, including pneumonia, rhinosinusitis, internal organ spread, gastrointestinal tract involvement, and skin and soft tissue infection. It infects predominantly with hematological malignancies, transplantation, immunocompromised, and diabetes mellitus patients. The most severe type of the disease is a disseminated disease, which is linked to significant immunosuppression. Currently, this disease is more prevalent in the COVID-19 pandemic because of erroneous steroid use and untreated diabetes. However, there is a scarcity of study and information on the COVID-19 and mucormycosis connection. According to the latest research, mucormycosis cases are rising in developed and developing nations, and only a few therapies are available. The exact burden of mucormycosis is unclear;however, it is likely to be greater than recorded instances due to mucormycosis epidemiological changes. As a result of the delay in identifying this severe illness, appropriate antifungal medications are delayed, resulting in significant morbidity and death. A few drugs are underclinical trials for their efficacy. Other obstacles to treat patients are lack of reliable diagnostic non-invasive tests. This review article draws the attention of its readers and clinicians towards the agents of mucormycosis and discuss the various cases to manage this fungal infection.

Infections and spinal cord injury: Covid-19 and beyond

Spinal cord injuries cause not only a loss of mobility and sensibility, but also numerous chronic disorders such as: immunosuppression, higher rates of hypertension, neurogenic bladder, blood circulation impairments, and at T8 or above levels of injury, respiratory muscle weakness that can lead to breathing failure. All these conditions make chronic patients susceptible to infections due to a lowered immune system. The aim of this chapter is to analyze the clinical presentation of Covid-19 in patients with spinal cord injury. The authors pretend to make pause to understand if this emergent disease, which is deadly hitting our general population, behaves in the same way in these special patients, to understand if the spinal cord injury condition is acting as a risk factor for morbidity or not, and why. For this purpose, we want to explore the role that the immune system plays in causing infection in patients with spinal cord injury. Some spinal cord-injured patients develop a dysregulation of the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis, which negatively affects all immune processes. Therefore, the combination of this situation with other locally impaired conditions provide the suitable environment for developing an infection, as it occurs in urinary tract infections, the most frequent infection in these patients, because of the presence of a neurogenic bladder and the use of catheters to facilitate its voiding;or in pulmonary infections, the severest ones, because of the respiratory muscle weakness, dysphagia disorders, pulmonary edema, and the use of ventilators to assist with breathing. The physiopathology of these infections helps us to understand its appropriate diagnosis, treatment, and methods of prevention. Most of the published studies show a tendency of milder initial symptoms and a less severe evolution of the Covid-19 disease in spinal cord-injured patients, but currently further validation is needed to support or reject it. The altered immune response could play a critical role in the clinical presentation of these patients. Close observation of neurofunctional outcomes, especially with the help of the International Standards for Neurological Classification of the Spinal Cord Injury (ISNCSCI) Worksheet, is needed to conclude if this infection produces sensory and motor deficits in these patients. Telemedicine has demonstrated to be a useful and effective tool to provide access to medical healthcare to these chronically affected patients, especially under pandemic restriction. © 2022 Elsevier Inc. All rights reserved.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

COVID-19 and the alarming rise of "black fungus" (mucormycosis) infection.

COVID-19 which first raised its deadly head in December 2019, has now engulfed the entire planet with its fire and fury. Mankind has been literally held to ransom by this micro-beast which has caused so much pain, sorrow and suffering, leaving behind scores of people dead and millions sick and gasping for air (quite literally!) The whole world is in disarray since the past 16 months, and now a new deadly superadded fungal infection has appeared in COVID-19 patients, in parts of the Indian subcontinent; namely mucormycosis, the deadly "black fungus." This persistent, unrelenting fungal infection which is relatively resistant to conventional anti-fungal treatment, sometimes requires radical, extensive surgical intervention in order to stem the spread of infection to vital organs such as the heart, brain, orbital spaces and spleen. mucormycosis has been increasingly seen to occur in COVID-19 patients who are immunocompromised and have uncontrolled diabetes mellitus as a comorbidity. Commonly seen forms of mucormycosis in COVID-19 patients include, Rhinocerebral mucormycosis and Pulmonary mucormycosis, with some patients also developing the cutaneous form, while some manifesting the more serious disseminated form of mucormycosis.

Prospective selected biomarkers in COVID-19 diagnosis and treatment.

COVID-19 has become a global health concern, due to the high transmissible nature of its causal agent and lack of proper treatment. Early diagnosis and nonspecific medical supports of the patients appeared to be effective strategy so far to combat the pandemic caused by COVID-19 outbreak. Biomarkers can play pivotal roles in timely and proper diagnosis of COVID-19 patients, as well as for distinguishing them from other pulmonary infections. Besides, biomarkers can help in reducing the rate of mortality and evaluating viral pathogenesis with disease prognosis. This article intends to provide a broader overview of the roles and uses of different biomarkers in the early diagnosis of COVID-19, as well as in the classification of COVID-19 patients into multiple risk groups.

Incidence and risk factors for secondary pulmonary infections in patients hospitalized with coronavirus disease 2019 pneumonia.

BACKGROUND: Secondary pulmonary infections (SPI) have not been well described in COVID-19 patients. Our study aims to examine the incidence and risk factors of SPI in hospitalized COVID-19 patients with pneumonia. METHODS: This was a retrospective, single-center study of adult COVID-19 patients with radiographic evidence of pneumonia admitted to a regional tertiary care hospital. SPI was defined as microorganisms identified on the respiratory tract with or without concurrent positive blood culture results for the same microorganism obtained at least 48 h after admission. RESULTS: Thirteen out of 244 (5%) had developed SPI during hospitalization. The median of the nadir lymphocyte count during hospitalization was significantly lower in patients with SPI as compared to those without SPI [0.4 K/uL (IQR 0.3-0.5) versus 0.6 K/uL (IQR 0.3-0.9)]. Patients with lower nadir lymphocyte had an increased risk of developing SPI with odds ratio (OR) of 1.21 (95% CI: 1.00 to 1.47, p = 0.04) per 0.1 K/uL decrement in nadir lymphocyte. The baseline median inflammatory markers of CRP [166.4 mg/L vs. 100.0 mg/L, p = 0.01] and d-dimer (18.5 mg/L vs. 1.4 mg/L, p<0.01), and peak procalcitonin (1.4 ng/mL vs. 0.3 ng/mL, p<0.01) and CRP (273.5 mg/L vs. 153.7 mg/L, p<0.01) during hospitalization were significantly higher in SPI group. CONCLUSIONS: The incidence of SPI in hospitalized COVID-19 patients was 5%. Lower nadir median lymphocyte count during hospitalization was associated with an increased OR of developing SPI. The CRP and d-dimer levels on admission, and peak procalcitonin and CRP levels during hospitalization were higher in patients with SPI.