Factors associated with medication interruption among outpatients with severe mental illness exposed to COVID-19.

Many patients with severe mental illness (SMI) relapsed and deteriorated during the COVID-19 pandemic, as they experienced medication interruption. This study aimed to investigate factors affecting medication interruption in patients with SMI during the COVID-19 pandemic. A total of 2,077 patients with SMI participated in an online survey on medication interruption during the COVID-19 outbreak. The questionnaire comprised six parts: basic demographic information, COVID-19 exposure, state of disease, medication compliance before COVID-19, medication interruption during COVID-19, and the specific impact and needs. A total of 2,017 valid questionnaires were collected. Nearly 50% of patients with SMI have been affected to varying degrees of life expectancy and treatment. Among them, 74 patients stopped taking medicines for more than 14 days without a prescription. Logistic regression analysis showed that cohabitant exposure [OR = 26.629; 95% CI (3.293-215.323), p = 0.002], medication partial compliance and non-compliance pre-COVID-19 [OR = 11.109; 95% CI (6.093-20.251), p < 0.001; OR = 20.115; 95% CI (10.490-38.571), p < 0.001], and disease status [OR = 0.326; 95% CI (0.188-0.564), p < 0.001] were related to medication interruption. More than 50% of the patients wanted help in taking medications, follow-up, and receiving more financial support and protective materials. We found that the daily lives of patients with SMI were much more susceptible to impact during the pandemic. Patients with a history of partial or non-medication compliance before COVID-19 and an unstable disease state are more easily affected by pandemics and epidemics and need extra attention should similar large-scale outbreaks occur in the future.

Chinese expert consensus on intestinal microecology and management of digestive tract complications related to tumor treatment (version 2022).

The human gut microbiota represents a complex ecosystem that is composed of bacteria, fungi, viruses, and archaea. It affects many physiological functions including metabolism, inflammation, and the immune response. The gut microbiota also plays a role in preventing infection. Chemotherapy disrupts an organism's microbiome, increasing the risk of microbial invasive infection; therefore, restoring the gut microbiota composition is one potential strategy to reduce this risk. The gut microbiome can develop colonization resistance, in which pathogenic bacteria and other competing microorganisms are destroyed through attacks on bacterial cell walls by bacteriocins, antimicrobial peptides, and other proteins produced by symbiotic bacteria. There is also a direct way. For example, Escherichia coli colonized in the human body competes with pathogenic Escherichia coli 0157 for proline, which shows that symbiotic bacteria compete with pathogens for resources and niches, thus improving the host's ability to resist pathogenic bacteria. Increased attention has been given to the impact of microecological changes in the digestive tract on tumor treatment. After 2019, the global pandemic of novel coronavirus disease 2019 (COVID-19), the development of novel tumor-targeting drugs, immune checkpoint inhibitors, and the increased prevalence of antimicrobial resistance have posed serious challenges and threats to public health. Currently, it is becoming increasingly important to manage the adverse effects and complications after chemotherapy. Gastrointestinal reactions are a common clinical presentation in patients with solid and hematologic tumors after chemotherapy, which increases the treatment risks of patients and affects treatment efficacy and prognosis. Gastrointestinal symptoms after chemotherapy range from nausea, vomiting, and anorexia to severe oral and intestinal mucositis, abdominal pain, diarrhea, and constipation, which are often closely associated with the dose and toxicity of chemotherapeutic drugs. It is particularly important to profile the gastrointestinal microecological flora and monitor the impact of antibiotics in older patients, low immune function, neutropenia, and bone marrow suppression, especially in complex clinical situations involving special pathogenic microbial infections (such as clostridioides difficile, multidrug-resistant Escherichia coli, carbapenem-resistant bacteria, and norovirus).

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

Roles of Interleukin-6-mediated immunometabolic reprogramming in COVID-19 and other viral infection-associated diseases.

Interleukin-6 (IL-6) is a highly pleiotropic glycoprotein factor that can modulate innate and adaptive immunity as well as various aspects of metabolism, including glycolysis, fatty acid oxidation and oxidative phosphorylation. Recently, the expression and release of IL-6 is shown to be significantly increased in numerous diseases related to virus infection, and this increase is positively correlated with the disease severity. Immunity and metabolism are two highly integrated and interdependent systems, the balance between them plays a pivotal role in maintaining body homeostasis. IL-6-elicited inflammatory response is found to be closely associated with metabolic disorder in patients with viral infection. This brief review summarizes the regulatory role of IL-6 in immunometabolic reprogramming among seven viral infection-associated diseases.

Dromedary camel nanobodies broadly neutralize SARS-CoV-2 variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike is a trimer of S1/S2 heterodimers with three receptor-binding domains (RBDs) at the S1 subunit for human angiotensin-converting enzyme 2 (hACE2). Due to their small size, nanobodies can recognize protein cavities that are not accessible to conventional antibodies. To isolate high-affinity nanobodies, large libraries with great diversity are highly desirable. Dromedary camels (Camelus dromedarius) are natural reservoirs of coronaviruses like Middle East respiratory syndrome CoV (MERS-CoV) that are transmitted to humans. Here, we built large dromedary camel VHH phage libraries to isolate nanobodies that broadly neutralize SARS-CoV-2 variants. We isolated two VHH nanobodies, NCI-CoV-7A3 (7A3) and NCI-CoV-8A2 (8A2), which have a high affinity for the RBD via targeting nonoverlapping epitopes and show broad neutralization activity against SARS-CoV-2 and its emerging variants of concern. Cryoelectron microscopy (cryo-EM) complex structures revealed that 8A2 binds the RBD in its up mode with a long CDR3 loop directly involved in the ACE2 binding residues and that 7A3 targets a deeply buried region that uniquely extends from the S1 subunit to the apex of the S2 subunit regardless of the conformational state of the RBD. At a dose of ≥5 mg/kg, 7A3 efficiently protected transgenic mice expressing hACE2 from the lethal challenge of variants B.1.351 or B.1.617.2, suggesting its therapeutic use against COVID-19 variants. The dromedary camel VHH phage libraries could be helpful as a unique platform ready for quickly isolating potent nanobodies against future emerging viruses.

Correlation of SARS­CoV­2 to cancer: Carcinogenic or anticancer? (Review).

Severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) is highly infectious and pathogenic. Among patients with severe SARS­CoV­2­caused by corona virus disease 2019 (COVID­19), those complicated with malignant tumor are vulnerable to COVID­19 due to compromised immune function caused by tumor depletion, malnutrition and anti­tumor treatment. Cancer is closely related to the risk of severe illness and mortality in patients with COVID­19. SARS­CoV­2 could promote tumor progression and stimulate metabolism switching in tumor cells to initiate tumor metabolic modes with higher productivity efficiency, such as glycolysis, for facilitating the massive replication of SARS­CoV­2. However, it has been shown that infection with SARS­CoV­2 leads to a delay in tumor progression of patients with natural killer cell (NK cell) lymphoma and Hodgkin's lymphoma, while SARS­CoV­2 elicited anti­tumor immune response may exert a potential oncolytic role in lymphoma patients. The present review briefly summarized potential carcinogenicity and oncolytic characteristics of SARS­CoV­2 as well as strategies to protect patients with cancer during the COVID­19 pandemic.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Platelet-to-lymphocyte ratio is associated with prognosis in patients with coronavirus disease-19.

Since December 2019, novel coronavirus infected pneumonia emerged in Wuhan city and rapidly spread throughout China. In severe novel coronavirus pneumonia cases, the number of platelets, their dynamic changes during the treatment, platelet-to-lymphocyte ratio (PLR) were a concern. We sought to describe the platelet feature of these cases. Single-center case series of the 30 hospitalized patients with confirmed coronavirus disease (COVID)-19 in Huizhou municipal central hospital from January 2020 to February 2020 were retrospectively analyzed. Demographic, clinical, blood routine results, other laboratory results, and treatment data were collected and analyzed. Outcomes of severe patients and nonsevere patients were compared. Univariate analysis showed that: age, platelet peaks, and PLR at peak platelet were the influencing factors in severe patients, multivariate analysis showed that the PLR value at peak platelet during treatment was an independent influencing factor in severe patients. The average hospitalization day of patients with platelet peaks during treatment was longer than those without platelet peaks (P < .05). The average age of patients with platelet peaks during treatment was older than those without platelet peaks (P < .05). The patients with significantly elevated platelets during treatment had longer average hospitalization days. And the higher PLR of patients during treatment had longer average hospitalization days. Single-center case series of the 30 hospitalized patients with confirmed COVID-19 in Huizhou Municipal Central Hospital, presumed that the number of platelets and their dynamic changes during the treatment may have a suggestion on the severity and prognosis of the disease. The patient with markedly elevated platelets and longer average hospitalization days may be related to the cytokine storm. The PLR of patients means the degree of cytokine storm, which might provide a new indicator in the monitoring in patients with COVID-19.

Expert recommendations on blood purification treatment protocol for patients with severe COVID-19.

Coronavirus disease (COVID-19) was first diagnosed in Wuhan in December 2019. The World Health Organization defined the subsequent outbreak of COVID-19 worldwide as a public health emergency of international concern. Epidemiological data indicate that at least 20% of COVID-19 patients have severe disease. In addition to impairment of the respiratory system, acute kidney injury (AKI) is a major complication. Immune damage mediated by cytokine storms and concomitant AKI is a key factor for poor prognosis. Based on previous experience of blood purification for patients with severe acute respiratory syndrome and Middle East respiratory syndrome combined with clinical front-line practice, we developed a blood purification protocol for patients with severe COVID-19. This protocol is divided into four major steps. The first step is to assess whether patients with severe COVID-19 require blood purification. The second step is to prescribe a blood purification treatment for patients with COVID-19. The third step is to monitor and adjust parameters of blood purification. The fourth step is to evaluate the timing of discontinuation of blood purification. It is expected that blood purification will play a key role in effectively reducing the mortality of patients with severe COVID-19 through the standardized implementation of the present protocol.