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).

Kinetics of SARS-CoV-2 Specific and Neutralizing Antibodies over Seven Months after Symptom Onset in COVID-19 Patients.

To assess the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies produced by natural infection and describe the serological characteristics over 7 months after symptom onset among coronavirus disease 2019 (COVID-19) patients by age and severity group, we followed up COVID-19 convalescent patients confirmed from 1 January to 20 March 2020 in Jiangsu, China and collected serum samples for testing IgM/IgG and neutralizing antibodies against SARS-CoV-2 between 26 August and 28 October 2020. In total, 284 recovered participants with COVID-19 were enrolled in our study. Patients had a mean age of 46.72 years (standard deviation [SD], 17.09), and 138 (48.59%) were male. The median follow-up time after symptom onset was 225.5 (interquartile range [IQR], 219 to 232) days. During the follow-up period (162 to 282 days after symptom onset), the seropositive rate of IgM fluctuated around 25.70% (95% confidence interval [CI], 20.72% to 31.20%) and that of IgG fluctuated around 79.93% (95% CI, 74.79% to 84.43%). Of the 284 patients, 64 participants were tested when discharged from hospital. Compared with that at the acute phase, the IgM/IgG antibody levels and IgM seropositivity have decreased; however, the seropositivity of IgG was not significantly lower at this follow-up (78.13% versus 82.81%). Fifty percent inhibitory dilution (ID50) titers of neutralizing antibody for samples when discharged from hospital (geometric mean titer [GMT], 82; 95% CI, 56 to 121) were significantly higher than those at 6 to 7 months after discharge (GMT, 47; 95% CI, 35 to 63) (P < 0.001). After 7 months from symptom onset, the convalescent COVID-19 patients continued to have high IgG seropositive; however, many plasma samples decreased neutralizing activity. IMPORTANCE The long-term characteristics of anti-SARS-CoV-2 antibodies among COVID-19 patients remain largely unclear. Tracking the longevity of these antibodies can provide a forward-looking reference for monitoring COVID-19. We conducted a comprehensive assessment combining the kinetics of specific and neutralizing antibodies over 7 months with age and disease severity and revealed influencing factors of the protection period of convalescent patients. By observing the long-term antibody levels against SARS-CoV-2 and comparing antibody levels at two time points after symptom onset, we found that the convalescent COVID-19 patients continued to have a high IgG seropositive rate; however, their plasma samples decreased neutralizing activity. These findings provide evidence supporting that the neutralizing activity of SARS-CoV-2-infected persons should be monitored and the administration of vaccine may be needed.

A missense variant rs2585405 in clock gene PER1 is associated with the increased risk of noise-induced hearing loss in a Chinese occupational population.

OBJECTIVE: To investigate the potential association of cochlear clock genes (CRY1, CRY2, PER1, and PER2), the DNF gene (brain-derived neurotrophic factor), and the NTF3 gene (neurotrophin3) with susceptivity to noise-induced hearing loss (NIHL) among Chinese noise-exposed workers. METHODS: A nested case-control study was performed with 2056 noise-exposed workers from a chemical fiber factory and an energy company who underwent occupational health examinations in 2019 as study subjects. Propensity score matching was conducted to screen cases and controls by matching sex, age, and the consumption of tobacco and alcohol. A total of 1269 participants were enrolled. Then, general information and noise exposure of the study subjects were obtained through a questionnaire survey and on-site noise detection. According to the results of audiological evaluations, the participants were divided into the case group (n = 432, high-frequency threshold shift > 25 dB) and the matched control group (n = 837, high-frequency threshold shift ≤ 25 dB) by propensity score matching. Genotyping for PER1 rs2253820 and rs2585405; PER2 rs56386336 and rs934945; CRY1 rs1056560 and rs3809236; CRY2 rs2292910 and rs6798; BDNF rs11030099, rs7124442 and rs6265; and NTF3 rs1805149 was conducted using the TaqMan-PCR technique. RESULTS: In the dominant model and the co-dominant model, the distribution of PER1 rs2585405 genotypes between the case group and the control group was significantly different (P = 0.03, P = 0.01). The NIHL risk of the subjects with the GC genotype was 1.41 times the risk of those carrying the GG genotype (95% confidence interval (CI) of odds ratio (OR): 1.01-1.96), and the NIHL risk of the subjects with the CC genotype was 0.93 times the risk of those carrying the GG genotype (95%CI of OR: 0.71-1.21). After the noise exposure period and noise exposure intensities were stratified, in the co-dominant model, the adjusted OR values for noise intensities of ≤ 85 was 1.23 (95%CI: 0.99-1.53). In the dominant model, the adjusted OR values for noise exposure periods of ≤ 16 years and noise intensities of ≤ 85 were 1.88 (95%CI: 1.03-3.42) and 1.64 (95%CI: 1.12-2.38), respectively. CONCLUSION: The CC/CG genotype of rs2585405 in the PER1 gene was identified as a potential risk factor for NIHL in Chinese noise-exposed workers, and interaction between rs2585405 and high temperature was found to be associated with NIHL risk.

Breath-, air- and surface-borne SARS-CoV-2 in hospitals.

The COVID-19 pandemic has brought an unprecedented crisis to the global health sector. When discharging COVID-19 patients in accordance with throat or nasal swab protocols using RT-PCR, the potential risk of reintroducing the infection source to humans and the environment must be resolved. Here, 14 patients including 10 COVID-19 subjects were recruited; exhaled breath condensate (EBC), air samples and surface swabs were collected and analyzed for SARS-CoV-2 using reverse transcription-polymerase chain reaction (RT-PCR) in four hospitals with applied natural ventilation and disinfection practices in Wuhan. Here we discovered that 22.2% of COVID-19 patients (n = 9), who were ready for hospital discharge based on current guidelines, had SARS-CoV-2 in their exhaled breath (~105 RNA copies/m3). Although fewer surface swabs (3.1%, n = 318) tested positive, medical equipment such as face shield frequently contacted/used by healthcare workers and the work shift floor were contaminated by SARS-CoV-2 (3-8 viruses/cm2). Three of the air samples (n = 44) including those collected using a robot-assisted sampler were detected positive by a digital PCR with a concentration level of 9-219 viruses/m3. RT-PCR diagnosis using throat swab specimens had a failure rate of more than 22% in safely discharging COVID-19 patients who were otherwise still exhaling the SARS-CoV-2 by a rate of estimated ~1400 RNA copies per minute into the air. Direct surface contact might not represent a major transmission route, and lower positive rate of air sample (6.8%) was likely due to natural ventilation (1.6-3.3 m/s) and regular disinfection practices. While there is a critical need for strengthening hospital discharge standards in preventing re-emergence of COVID-19 spread, use of breath sample as a supplement specimen could further guard the hospital discharge to ensure the safety of the public and minimize the pandemic re-emergence risk.

A proof of concept for neutralizing antibody-guided vaccine design against SARS-CoV-2.

Mutations and transient conformational movements of the receptor binding domain (RBD) that make neutralizing epitopes momentarily unavailable present immune escape routes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To mitigate viral escape, we developed a cocktail of neutralizing antibodies (NAbs) targeting epitopes located on different domains of spike (S) protein. Screening of a library of monoclonal antibodies generated from peripheral blood mononuclear cells of COVID-19 convalescent patients yielded potent NAbs, targeting the N-terminal domain (NTD) and RBD domain of S, effective at nM concentrations. Remarkably, a combination of RBD-targeting NAbs and NTD-binding NAbs, FC05, enhanced the neutralization potency in cell-based assays and an animal model. Results of competitive surface plasmon resonance assays and cryo-electron microscopy (cryo-EM) structures of antigen-binding fragments bound to S unveil determinants of immunogenicity. Combinations of immunogens, identified in the NTD and RBD of S, when immunized in rabbits and macaques, elicited potent protective immune responses against SARS-CoV-2. More importantly, two immunizations of this combination of NTD and RBD immunogens provided complete protection in macaques against a SARS-CoV-2 challenge, without observable antibody-dependent enhancement of infection. These results provide a proof of concept for neutralization-based immunogen design targeting SARS-CoV-2 NTD and RBD.

Oropharyngeal Probiotic ENT-K12 Prevents Respiratory Tract Infections Among Frontline Medical Staff Fighting Against COVID-19: A Pilot Study.

Healthcare workers at the frontline are facing a substantial risk of respiratory tract infection during the COVID-19 outbreak due to an extremely stressful work schedule and public health event. A well-established first-line defense on oropharyngeal microbiome could be a promising strategy to protect individuals from respiratory tract infections including COVID-19. The most thoroughly studied oropharyngeal probiotic product which creates a stable upper respiratory tract microbiota capable of preventing upper respiratory tract infections was chosen to evaluate the safety and efficacy on reducing episodes of upper respiratory tract infections for COVID-19 healthcare workers. To our knowledge to date, this is the very first study describing the beneficial effects of oropharyngeal probiotic been administered by healthcare workers during the COVID-19 pandemic. In this randomized controlled trial, we provided the probiotics to frontline medical staff who work in the hospitals in Wuhan and had been in close contact with hospitalized COVID-19 patients for prophylactic use on a daily basis. Our finding suggests that oropharyngeal probiotic administration significantly reduced the incidence of respiratory tract infections by 64.8%, reduced the time experiencing respiratory tract infections and oral ulcer symptoms by 78%, shortened the days absent from work by 95.5%, and reduced the time under medication where there is no record of antibiotic and anti-viral drug intake in the probiotic group. Furthermore, medical staff treated with Bactoblis experienced sustained protection from respiratory tract infections since the 10th day of oropharyngeal probiotic administration resulting in an extremely low incidence rate of respiratory tract infections.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Serum Cytokine and Chemokine Profile in Relation to the Severity of Coronavirus Disease 2019 in China.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated the serum cytokine and chemokine levels in asymptomatic, mild, moderate, severe, and convalescent SARS-CoV-2-infected cases. Proinflammatory cytokine and chemokine production induced by SARS-CoV-2 were observed not only in symptomatic patients but also in asymptomatic cases, and returned to normal after recovery. IL-6, IL-7, IL-10, IL-18, G-CSF, M-CSF, MCP-1, MCP-3, IP-10, MIG, and MIP-1α were found to be associated with the severity of COVID-19. Moreover, a set of cytokine and chemokine profiles were significantly higher in SARS-CoV-2-infected male than female patients. The serum levels of MCP-1, G-CSF, and VEGF were weakly and positively correlated with viral titers. We suggest that combinatorial analysis of serum cytokines and chemokines with clinical classification may contribute to evaluation of the severity of COVID-19 and optimize the therapeutic strategies.

Toilets dominate environmental detection of severe acute respiratory syndrome coronavirus 2 in a hospital.

Respiratory and fecal aerosols play confirmed and suspected roles, respectively, in transmitting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). An extensive environmental sampling campaign of both toilet and non-toilet environments was performed in a dedicated hospital building for patients with coronavirus disease 2019 (COVID-19), and the associated environmental factors were analyzed. In total, 107 surface samples, 46 air samples, two exhaled condensate samples, and two expired air samples were collected within and beyond four three-bed isolation rooms. The data of the COVID-19 patients were collected. The building environmental design and the cleaning routines were reviewed. Field measurements of airflow and CO2 concentrations were conducted. The 107 surface samples comprised 37 from toilets, 34 from other surfaces in isolation rooms, and 36 from other surfaces outside the isolation rooms in the hospital. Four of these samples were positive, namely two ward door handles, one bathroom toilet seat cover, and one bathroom door handle. Three were weakly positive, namely one bathroom toilet seat, one bathroom washbasin tap lever, and one bathroom ceiling exhaust louver. Of the 46 air samples, one collected from a corridor was weakly positive. The two exhaled condensate samples and the two expired air samples were negative. The fecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 in the hospital, highlighting the importance of surface and hand hygiene for intervention.

A reverse-transcription recombinase-aided amplification assay for the rapid detection of N gene of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).

The current outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in China firstly. A rapid, highly sensitive, specific, and simple operational method was needed for the detection of SARS-CoV-2. Here, we established a real-time reverse-transcription recombinase-aided amplification assay (RT-RAA) to detect SARS-CoV-2 rapidly. The primers and probe were designed based on the nucleocapsid protein gene (N gene) sequence of SARS-CoV-2. The detection limit was 10 copies per reaction in this assay, which could be conducted within 15 min at a constant temperature (39 °C), without any cross-reactions with other respiratory tract pathogens, such as other coronaviruses. Furthermore, compared with commercial real-time RT-PCR assay, it showed a kappa value of 0.959 (p < 0.001) from 150 clinical specimens. These results indicated that this real-time RT-RAA assay may be a valuable tool for detecting SARS-CoV-2.

Association of viral load with serum biomakers among COVID-19 cases.

Since SARS-CoV-2 spreads rapidly around the world, data have been needed on the natural fluctuation of viral load and clinical indicators associated with it. We measured and compared viral loads of SARS-CoV-2 from pharyngeal swab, IgM anti-SARS-CoV-2, CRP and SAA from serum of 114 COVID-19 patients on admission. Positive rates of IgM anti-SARS-CoV-2, CRP and SAA were 80.7%, 36% and 75.4% respectively. Among IgM-positive patients, viral loads showed different trends among cases with different severity, While viral loads of IgM-negative patients tended to increase along with the time after onset. As the worsening of severity, the positive rates of CRP and SAA also showed trends of increase. Different CRP/SAA type showed associations with viral loads in patients in different severity and different time after onset. Combination of the IgM and CRP/SAA with time after onset and severity may give suggestions on the viral load and condition judgment of COVID-19 patients.