[Preliminary Recommendations on the Timing of Lung Surgery after 
Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer].

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contracting COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.
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CRISPR/Cas12a system responsive DNA hydrogel for label-free detection of non-glucose targets with a portable personal glucose meter.

In this work, personal glucose meter (PGM) as a portable electrochemical device was utilized for sensitive detection of non-glucose targets: N-gene and PCB77, respectively. DNA hydrogel, which can respond to CRISPR/Cas system, was prepared for label-free encapsulating invertase. In the presence of targets, the repeated sequence for the activation of Cas12a was obtained due to the performance of RCA. Unlike "one-to-one" recognition, activated Cas12a can efficiently cleave multiple single-stranded linker DNAs on DNA hydrogels, thus releasing many invertase that can be used for PGM detection. With the amplification of RCA and CRISPR/Cas system, high detection sensitivity can be obtained even using portable PGM. The detection limits for N-gene and PCB77 were 2.6 fM and 3.2 × 10-5 µg/L, respectively, with high specificity and good practical application performance. The developed biosensor can be used for online monitoring with the merit of low cost, easy operation and can be used for various targets analysis.

Reinforcement Learning Based Diagnosis and Prediction for COVID-19 by Optimizing a Mixed Cost Function From CT Images.

A novel coronavirus disease (COVID-19) is a pandemic disease has caused 4 million deaths and more than 200 million infections worldwide (as of August 4, 2021). Rapid and accurate diagnosis of COVID-19 infection is critical to controlling the spread of the epidemic. In order to quickly and efficiently detect COVID-19 and reduce the threat of COVID-19 to human survival, we have firstly proposed a detection framework based on reinforcement learning for COVID-19 diagnosis, which constructs a mixed loss function that can integrate the advantages of multiple loss functions. This paper uses the accuracy of the validation set as the reward value, and obtains the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have proposed a prediction framework that integrates multiple detection frameworks using parameter sharing to predict the progression of patients' disease without additional training. This paper also constructed a higher-quality version of the CT image dataset containing 247 cases screened by professional physicians, and obtained more excellent results on this dataset. Meanwhile, we used the other two COVID-19 datasets as external verifications, and still achieved a high accuracy rate without additional training. Finally, the experimental results show that our classification accuracy can reach 98.31%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 98.82%, 97.99%, 98.67%, and 0.989, respectively. The accuracy of external verification can reach 93.34% and 91.05%. What's more, the accuracy of our prediction framework is 91.54%. A large number of experiments demonstrate that our proposed method is effective and robust for COVID-19 detection and prediction.

Heart-targeted amelioration of sepsis-induced myocardial dysfunction by microenvironment responsive nitric oxide nanogenerators in situ.

BACKGROUND: A balanced endogenous level of bioavailable nitric oxide (NO) plays a key role in maintaining cardiovascular homeostasis. The bioactive NO level in the cardiomyocytes was much reduced during sepsis. However, it is clinically challenging for the NO gas therapy due to the lack of spatial and temporal release system with precise control. The purpose of this study is to design a NO-releasing biomaterial with heart-targeted capability responsive to the infectious microenvironment, thus ameliorating lipopolysaccharide (LPS)-induced cardiac dysfunction. RESULTS: The heart-targeted NO delivery and in situ releasing system, PCM-MSN@LA, was synthesized using hollow mesoporous silica nanoparticles (MSN) as the carrier, and L-arginine (LA) as the NO donor. The myocardial delivery was successfully directed to heart by specific peptide (PCM) combined with low-intensity focused ultrasound (LIFU) guidance. The myocardial system synthesized NO from the LA released from PCM-MSN@LA in the presence of increased endogenous nitric oxide synthase (NOS) activity induced by LPS. This targeted NO release in situ achieved extraordinary protective effects against LPS-challenged myocardial injury by reducing the recruitment of inflammatory cells, inhibiting oxidative stress and maintaining the mitochondria integrity. In particular, this protection was not compromised by simultaneous circulation collapse as an adverse event in the context. CONCLUSIONS: PCM-MSN@LA + LIFU exhibited extraordinary cardioprotective effects against severe sepsis in the hearts of LPS-treated animals without the side effect of NO diffusion. This technology has great potential to be served as a novel therapeutic strategy for sepsis-induced myocardial injury.

Antimicrobial polymeric composites for high-touch surfaces in healthcare applications.

Antimicrobial polymer composites have long been utilized in the healthcare field as part of the first line of defense. These composites are desirable in that they pose a minimal risk of developing contagions with antibiotic resistance. For this reason, the field of antimicrobial composites has seen steady growth over recent years and is becoming increasingly important during the current COVID-19 pandemic. In this article, we first review the need of the antimicrobial polymers in high tough surfaces, the antimicrobial mechanism, and then the recent advances in the development of antimicrobial polymer composite including the utilization of intrinsic antimicrobial polymers, the addition of antimicrobial additives, and new exploration of surface patterning. While there are many established and developing methods of imbuing a material with antimicrobial activity, there currently is no standard quantification method for these properties leading to difficulty comparing the efficacy of these materials within the literature. A discussion of the common antimicrobial characterization methods is provided along with highlights on the need of a standardized quantification of antiviral and antibacterial properties in testing to allow ease of comparison between generated libraries and to facilitate proper screening. We also discuss and comment on the current trends of the development of antimicrobial polymer composites with long-lasting and specific antimicrobial activities, nontoxic properties, and environmental friendliness against a broad-spectrum of microbes.

Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning.

Background: The outbreak of COVID-19 attracted the attention of the whole world. Our study aimed to explore the predictors for the survival of patients with COVID-19 by machine learning. Methods: We conducted a retrospective analysis and used the idea of machine learning to train the data of COVID-19 patients in Leishenshan Hospital through the logical regression algorithm provided by scikit-learn. Results: Of 2010 patients, 42 deaths were recorded until March 29, 2020. The mortality rate was 2.09%. There were 6,812 records after data features combination and data arrangement, 3,025 records with high-quality after deleting incomplete data by manual checking, and 5,738 records after data balancing finally by the method of Borderline-1 Smote. The results of 10 times of data training by logistic regression model showed that albumin, saturation of pulse oxygen at admission, alanine aminotransferase, and percentage of neutrophils were possibly associated with the survival of patients. The results of 10 times of data training including age, sex, and height beyond the laboratory measurements showed that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients. The rates of precision, recall, and f1-score of the two training models were all higher than 0.9 and relatively stable. Conclusions: We demonstrated that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients with COVID-19.

[Preliminary Recommendations for Lung Surgery during 2019 Novel Coronavirus Disease (COVID-19) Epidemic Period].

In December 2019, China diagnosed the first patient with 2019 novel coronavirus disease (COVID-19), and the following development of the epidemic had a huge impact on China and the whole world. For patients with lung occupying lesions, the whole process of diagnosis and treatment can not be carried out as usual due to the epidemic. For thoracic surgeons, the timing of surgical intervention should be very carefully considered. All thoracic surgeons in China should work together to develop the proper procedures for the diagnosis and treatment in this special situation, and continuously update the recommendations based on epidemic changes and further understanding of COVID-19. Here, we only offer some preliminary suggestions based on our own knowledge for further reference and discussion.

Chinese Expert Consensus on Protection for Ultrasound Healthcare Workers against COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has caused a worldwide pandemic and poses a serious public health risk. It has been proven that lung ultrasound can be extremely valuable in the diagnosis and treatment of the disease, which could also minimize the number of exposed healthcare workers and equipment. Because healthcare workers in ultrasound departments are in close contact with patients who might be infected or virus carriers, it is extremely important that they be provided sufficient protection. Extremely aggressive protection should be avoided because it might lead to a lack of protection equipment for the hospital. Guidance on proper protection management should be provided in detail, for example, how to choose personal protective equipment, how to disinfect the environment. To address these problems, on behalf of the Chinese Ultrasound Doctors Association, Chinese PLA Professional Committee of Ultrasound in Medicine, Beijing Institute of Ultrasound in Medicine and Chinese Research Hospital Association Ultrasound Professional Committee, the authors have summarized the recommendations for effective protection according to existing hygienic standards, their experience and available literature. After the recommendations were completed, two online conferences were held on January 31, 2020 and February 7, 2020, at which the recommendations were discussed in detail. A modified version of the work was circulated and finally approved by all authors, and is the present Chinese Expert Consensus on Protection for Ultrasound Healthcare Workers against COVID-19.

Prominent Hypercoagulability Associated With Inflammatory State Among Cancer Patients With SARS-CoV-2 Infection.

Abnormal coagulation parameters and potential benefits of anticoagulant therapy in general population with novel coronavirus pneumonia (COVID-19) have been reported. However, limited data are available on cancer patients. Coagulation indexes and inflammation parameters in 57 cancer patients with SARS-CoV-2 infection with different severity were retrospectively analyzed. We found that D-dimer levels were increased in 33 patients (57.9%, median: 790 ng/mL). Compared with ordinary type patients, severe and critical ill patients had decreased MPV values (P = 0.006), prolonged PT (median: 13.3 vs. 11.5 vs. 11.4 s, P < 0.001), significant higher D-dimer levels (median: 2,400 vs. 940 vs. 280 ng/mL, P < 0.001), higher PCT levels (median: 0.17 vs. 0.055 vs. 0.045 ng/mL, P = 0.002), higher IL-6 (median: 20.6 vs. 2.3 vs. 3.0 pg/mL, P = 0.040), and decreased PaO2 (median: 68 vs. 84 vs. 96 mm Hg, P < 0.001). Importantly, three patients, one severe and two critical ill type, with increased D-dimer survived after anticoagulant therapy with continuous heparin infusion. Increased D-dimer levels positively correlated with increased PCT levels (r = 0.456, P = 0.002) and IL-6 levels (r = 0.501, P = 0.045). A negative correlation between D-dimer levels and PaO2 levels (r = -0.654, P = 0.021) were also existed. Cancer patients with COVID-19 showed prominent hypercoagulability associated with severe inflammation, anticoagulation therapy might be useful to improve the prognosis and should be immediately used after the onset of hypercoagulability.

Scoring systems for predicting mortality for severe patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, especially in patients with severe COVID-19. This analysis aimed to explore risk factors for mortality of severe COVID-19, and establish a scoring system to predict in-hospital deaths. METHODS: Patients with COVID-19 were retrospectively analyzed and clinical characteristics were compared. LASSO regression as well as multivariable analysis were used to screen variables and establish prediction model. FINDINGS: A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66•0 years while it was 73•0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. No differences were observed of antibody levels between severe and non-severe patients. About 60.2% of severe patients had complications. Among acute myocardial injury (AMI), acute kidney injury (AKI) and acute liver injury (ALI), the heart was the earliest injured organ, whereas the time from AKI to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariable analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established to divide patients into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P=0·264). Complications in low-risk and high-risk groups were significantly different (P<0·05). Use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P=0·036) and durations of disease by 7·5 days (P=0·012) compared with no corticosteroids. INTERPRETATION: Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis. FUNDING: This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500).

Novel Insights Into Illness Progression and Risk Profiles for Mortality in Non-survivors of COVID-19.

Background. The outbreak of COVID-19 has attracted the attention of the whole world. Our study aimed to describe illness progression and risk profiles for mortality in non-survivors. Methods. We retrospectively analyzed 155 patients with COVID-19 in Wuhan and focused on 18 non-survivors among them. Briefly, we compared the dynamic profile of biochemical and immune parameters and drew an epidemiological and clinical picture of disease progression from disease onset to death in non-survivors. The survival status of the cohort was indicated by a Kaplan-Meier curve. Results. Of the non-survivors, the median age was 73.5 years, and the proportion of males was 72.2%. Five and 13 patients were hospital-acquired and community-acquired infection of SARS-CoV-2, respectively. The interval between disease onset and diagnosis was 8.5 days (IQR, [4-11]). With the deterioration of disease, most patients experienced consecutive changes in biochemical parameters, including lymphopenia, leukocytosis, thrombocytopenia, hypoproteinemia, as well as elevated D-dimer and procalcitonin. Regarding the immune dysregulation, patients exhibited significantly decreased T lymphocytes in the peripheral blood, including CD3+T, CD3+CD4+Th, and CD3+CD8+Tc cells. By the end of the disease, most patients suffered from severe complications, including ARDS (17/18; 94.4%), acute cardiac injury (10/18; 55.6%), acute kidney injury (7/18; 38.9%), shock (6/18; 33.3%), gastrointestinal bleeding (1/18; 5.6%), as well as perforation of intestine (1/18; 5.6%). All patients died within 45 days after the initial hospital admission with a median survivor time of 13.5 days (IQR, 8-17). Conclusions. Our data show that patients experienced consecutive changes in biochemical and immune parameters with the deterioration of the disease, indicating the necessity of early intervention.