High intestinal carriage of Clostridium perfringens in healthy individuals and ICU patients in Hangzhou, China.

Clostridium perfringens has emerged as a growing public health concern due to its ability to cause various infections and its increasing resistance to antibiotics. To assess its current epidemiology in clinical settings, we conducted a survey involving 426 healthy individuals and 273 ICU inpatients at a provincial hospital in China. Our findings revealed a high prevalence of C. perfringens in healthy individuals (45.77%, 95% CI: 41.0%-50.6%) and ICU patients (12.82%, 95% CI: 9.1%-17.4%). The identified 220 C. perfringens isolates displayed substantial resistance to erythromycin (57.9%), clindamycin (50.7%), and tetracycline (32.0%), primarily attributed to the presence of erm(Q) (54.4%), lnu(P) (13.8%), tetB(P) (83.6%), and tetA(P) (66.7%). Notably, C. perfringens isolates from this particular hospital demonstrated a high degree of sequence type diversity and phylogenic variation, suggesting that the potential risk of infection primarily arises from the bacteria's gut colonization rather than clonal transmissions within the clinical environment. This study provides an updated analysis of the current epidemiology of C. perfringens in healthy individuals and ICU patients in China and emphasizes the need to optimize intervention strategies against its public health threat. IMPORTANCE: Clostridium perfringens is a bacterium of growing public health concern due to its ability to cause infections and its increasing resistance to antibiotics. Understanding its epidemiology in clinical settings is essential for intervention strategies. This study surveyed healthy individuals and ICU inpatients in a provincial hospital in China. It found a high prevalence of C. perfringens, indicating infection risk. The isolates also showed significant antibiotic resistance. Importantly, the study revealed diverse sequence types and phylogenetic variation, suggesting infection risk from intestinal colonization rather than clonal transmission in hospitals. This analysis emphasizes the need to optimize intervention strategies against this public health threat.

Prevalence and genomic characterization of clinical Escherichia coli strains that harbor the plasmid-borne tet(X4) gene in China.

The tigecycline resistance gene tet(X4) has been widely reported in animals and animal products in some Asian countries including China in recent years but only sporadically detected in human. In this study, we investigated the prevalence and genetic features of tet(X4)-positive clinical E. coli strains. A total of 462 fecal samples were collected from patients in four hospitals located in four provinces in China in 2023. Nine tet(X4)-positive E. coli strains were isolated and subjected to characterization of their genetic and phenotypic features by performing antimicrobial susceptibility test, whole-genome sequencing, bioinformatic and phylogenetic analysis. The majority of the test strains were found to exhibit resistance to multiple antimicrobial agents including tigecycline but remained susceptible to colistin and meropenem. A total of seven different sequence types (STs) and an unknown ST type were identified among the nine tet(X4)-positive strains. Notably, the tet(X4) gene in six out of these nine tet(X4)-positive E. coli strains was located in a IncFIA-HI1A-HI1B hybrid plasmid, which was an tet(X4)-bearing epidemic plasmid responsible for dissemination of the tet(X4) gene in China. Furthermore, the tet(X4) gene in four out of nine tet(X4)-positive E. coli isolates could be successfully transferred to E. coli EC600 through conjugation. In conclusion, this study characterized the epidemic tet(X4)-bearing plasmids and tet(X4)-associated genetic environment in clinical E. coli strains, suggested the importance of continuous surveillance of such tet(X4)-bearing plasmids to control the increasingly widespread dissemination of tigecycline-resistant pathogens in clinical settings in China.

National genomic epidemiology investigation revealed the spread of carbapenem-resistant Escherichia coli in healthy populations and the impact on public health.

BACKGROUND: Carbapenem-resistant Escherichia coli (CREC) has been considered as WHO priority pathogens, causing a great public health concern globally. While CREC from patients has been thoroughly investigated, the prevalence and underlying risks of CREC in healthy populations have been overlooked. Systematic research on the prevalence of CREC in healthy individuals was conducted here. We aimed to characterize CREC collected from healthy populations in China between 2020 and 2022 and to compare the genomes of CREC isolates isolated from healthy individuals and clinical patients. METHODS: We present a nationwide investigation of CREC isolates among healthy populations in China, employing robust molecular and genomic analyses. Antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatics were utilized to analyze a cohort of CREC isolates (n = 113) obtained from fecal samples of 5 064 healthy individuals. Representative plasmids were extracted for third-generation nanopore sequencing. We previously collected 113 non-duplicate CREC isolates (59 in 2018, 54 in 2020) collected from ICU patients in 15 provinces and municipalities in China, and these clinical isolates were used to compare with the isolates in this study. Furthermore, we employ comparative genomics approaches to elucidate molecular variations and potential correlations between clinical and non-clinical CREC isolates. RESULTS: A total of 147 CREC isolates were identified from 5 064 samples collected across 11 provinces in China. These isolates were classified into 64 known sequence types (STs), but no dominant STs were observed. In total, seven carbapenemase genes were detected with blaNDM-5 (n = 116) being the most prevalent one. Genetic environments and plasmid backbones of blaNDM were conserved in CREC isolated from healthy individuals. Furthermore, we compared clinical and healthy human-originated CRECs, revealing noteworthy distinctions in 23 resistance genes, including blaNDM-1, blaNDM-5, and blaKPC (χ2 test, p < 0.05). Clinical isolates contained more virulence factors associated with iron uptake, adhesion, and invasion than those obtained from healthy individuals. Notably, CREC isolates generally found healthy people are detected in hospitalized patients. CONCLUSIONS: Our findings underscore the significance of healthy populations-derived CRECs as a crucial reservoir of antibiotic resistance genes (ARGs). This highlights the need for ongoing monitoring of CREC isolates in healthy populations to accurately assess the potential risks posed by clinical CREC isolates.

MALDI-TOF MS combined with AUC method for tigecycline susceptibility testing in Escherichia coli.

Objectives: The wide spread of tet(X4) gene orthologues in the environment, food, poultry and humans is causing serious tigecycline resistance. Consequently, developing a fast and universal method to detect tigecycline resistance has become increasingly important. Methods: During 2019-2022, 116 Escherichia coli isolates were obtained from nine provinces in China. All isolates were tested for their susceptibility to antimicrobial agents by the microdilution broth method and for the tet(X4) gene by PCR. Ten tet(X4)-positive E. coli isolates were used to confirm certain conditions, including the optimal incubation time, the optimal concentration of tigecycline, and the cut-off of the relative growth (RG) value. Results: The optimal time and concentration of tigecycline for separation of susceptible and resistant isolates was 2 h and 4 mg/L, and the RG cut-off value was 0.4. We validated whether the experiment was feasible using 116 isolates of E. coli. The method yielded a susceptibility of 94.9% (95% CI: 81.4%-99.1%) and a specificity of 96.1% (95% CI: 88.3%-99.0%). Conclusions: This research has shown that this optical antimicrobial susceptibility testing method can rapidly differentiate between susceptible and resistant phenotypes in isolates of E. coli. In the same range as the current gold-standard methods, the clinical turnaround time is reduced from 48 h to 2.5 h. The above results suggest that the method has splendid specificity and operationality.

Effects of meteorological factors on influenza transmissibility by virus type/subtype.

BACKGROUND: Quantitative evidence on the impact of meteorological factors on influenza transmissibility across different virus types/subtypes is scarce, and no previous studies have reported the effect of hourly temperature variability (HTV) on influenza transmissibility. Herein, we explored the associations between meteorological factors and influenza transmissibility according to the influenza type and subtype in Guangzhou, a subtropical city in China. METHODS: We collected influenza surveillance and meteorological data of Guangzhou between October 2010 and December 2019. Influenza transmissibility was measured using the instantaneous effective reproductive number (Rt). A gamma regression with a log link combined with a distributed lag non-linear model was used to assess the associations of daily meteorological factors with Rt by influenza types/subtypes. RESULTS: The exposure-response relationship between ambient temperature and Rt was non-linear, with elevated transmissibility at low and high temperatures. Influenza transmissibility increased as HTV increased when HTV < around 4.5 °C. A non-linear association was observed between absolute humidity and Rt, with increased transmissibility at low absolute humidity and at around 19 g/m3. Relative humidity had a U-shaped association with influenza transmissibility. The associations between meteorological factors and influenza transmissibility varied according to the influenza type and subtype: elevated transmissibility was observed at high ambient temperatures for influenza A(H3N2), but not for influenza A(H1N1)pdm09; transmissibility of influenza A(H1N1)pdm09 increased as HTV increased when HTV < around 4.5 °C, but the transmissibility decreased with HTV when HTV < 2.5 °C and 3.0 °C for influenza A(H3N2) and B, respectively; positive association of Rt with absolute humidity was witnessed for influenza A(H3N2) even when absolute humidity was larger than 19 g/m3, which was different from that for influenza A(H1N1)pdm09 and influenza B. CONCLUSIONS: Temperature variability has an impact on influenza transmissibility. Ambient temperature, temperature variability, and humidity influence the transmissibility of different influenza types/subtypes discrepantly. Our findings have important implications for improving preparedness for influenza epidemics, especially under climate change conditions.

Analysis of the transmission chain of carbapenem-resistant Enterobacter cloacae complex infections in clinical, intestinal and healthcare settings in Zhejiang province, China (2022-2023).

Considerable attention is given to intensive care unit-acquired infections; however, research on the transmission dynamics of multichain carbapenemase-resistant Enterobacter cloacae complex (CRECC) outbreaks remains elusive. A total of 118 non-duplicated CRECC strains were isolated from the clinical, intestinal, and hospital sewage samples collected from Zhejiang province of China during 2022-2023. A total of 64 CRECC strains were isolated from the hospital sewage samples, and their prevalence increased from 10.0 % (95 % confidence interval, CI = 0.52-45.8 %) in 2022 to 63.6 % (95 % CI = 31.6-87.6 %) in 2023. Species-specific identification revealed that Enterobacter hormaechei was the predominant CRECC species isolated in this study (53.4 %, 95 % CI = 44.0-62.6 %). The antimicrobial susceptibility profiles indicated that all 118 CRECC strains conferred high-level resistance to ß-lactam antibiotics, ceftacillin/avibactam, and polymyxin. Furthermore, all CRECC strains exhibited resistance to ß-lactams, quinolones, and fosfomycin, with a higher colistin resistance rate observed in the hospital sewage samples (67.2 %, 95 % CI = 54.2-78.1 %). Several antibiotic resistance genes were identified in CRECC strains, including Class A carbapenemases (blaKPC-2) and Class B carbapenemases (blaNDM-1/blaIMP), but not Class D carbapenemases. The WGS analysis showed that the majority of the CRECC strains carried carbapenemase-encoding genes, with blaNDM-1 being the most prevalent (86.9 %, 95 % CI = 77.4-92.9 %). Furthermore, sequence typing revealed that the isolated CRECC strains belonged to diverse sequence types (STs), among which ST418 was the most prevalent blaNDM-positive strain. The high risk of carbapenemase-producing ST418 E. hormaechei and the blaNDM-harboring IncFIB-type plasmid (81.4 %, 95 % CI = 72.9-87.7 %) were detected and emphasized in this study. This study provides valuable insights into the prevalence, antimicrobial resistance, genomic characteristics, and plasmid analysis of CRECC strains in diverse populations and environments. The clonal relatedness analysis showed sporadic clonal transmission of ST418 E. hormaechei strains, supporting inter-hospital transmission.

Recovery and genetic characterization of clinically-relevant ST2 carbapenem-resistant Acinetobacter baumannii isolates from untreated hospital sewage in Zhejiang Province, China.

The global transmission of carbapenem-resistant Acinetobacter baumannii (CRAB) poses a significant and grave threat to human health. To investigate the potential relationship between hospital sewage and the transmission of CRAB within healthcare facilities, isolates of Acinetobacter spp. obtained from untreated hospital sewage samples were subjected to antimicrobial susceptibility tests, genome sequencing, and bioinformatic and phylogenetic tree analysis, and that data were matched with those of the clinical isolates. Among the 70 Acinetobacter spp. sewage isolates tested, A. baumannii was the most prevalent and detectable in 5 hospitals, followed by A. nosocomialis and A. gerneri. Worryingly, 57.14 % (40/70) of the isolates were MDR, with 25.71 % (18/70) being resistant to carbapenem. When utilizing the Pasteur scheme, ST2 was the predominant type among these CRAB isolates, with Tn2006 (ΔISAba1-blaOXA-23-ATPase-yeeB-yeeA-ΔISAba1) and Tn2009 (ΔISAba1-blaOXA-23-ATPase-hp-parA-yeeC-hp-yeeB-ΔISAba1) being the key mobile genetic elements that encode carbapenem resistance. Seven A. gerneri isolates which harbored Tn2008 (ISAba1-blaOXA-23 -ATPase) and the blaPER-1 gene were also identified. Besides, an A. soil isolate was found to exhibit high-level of meropenem resistance (MIC ≥128 mg/L) and harbor a blaNDM-1 gene located in a core genetic structure of ISAba125-blaNDM-1-ble-trpF-dsbC-cutA. To investigate the genetic relatedness between isolates recovered from hospital sewage and those collected from ICUs, a phylogenetic tree was constructed for 242 clinical isolates and 9 sewage isolates. The results revealed the presence of two evolutionary clades, each containing isolates from both ICU and sewage water, suggesting that CRAB isolates in untreated sewage water were also the transmission clones or closely related evolutionary isolates recoverable in hospital settings. Findings in this work confirm that hospital sewage is a potential reservoir of CRAB.

SOC Estimation of Vanadium Redox Flow Batteries Based on the ISCSO-ELM Algorithm.

This study focuses on the stage of charge (SOC) estimation for vanadium redox flow batteries (VFBs), establishing an electrochemical model that provides parameters, including ion concentration. Second, considering the capacity decay of VFBs, an extreme learning machine (ELM) combined with an improved sand cat swarm optimization algorithm, named ISCSO-ELM, is integrated with SOC estimation to predict the battery's SOC more effectively.

Coal and Gangue Classification Based on Laser-Induced Breakdown Spectroscopy and Deep Learning.

During the extraction and processing of coal, a large amount of solid waste, collectively known as gangue, is produced. This gangue has a low carbon content but a high ash content, accounting for approximately 15 to 20% of the total coal yield. Before coal is used, coal and gangue must be effectively separated to reduce the gangue content in the raw coal and improve the efficiency of coal utilization. This study introduces a classification method for coal and gangue based on a combination of laser-induced breakdown spectroscopy (LIBS) and deep learning. The method employs Gramian angular summation fields (GASF) to convert 1D spectral data into 2D time-series data, visualizing them as 2D images, before employing a novel deep learning model-GASF-CNN-for coal and gangue classification. GASF-CNN enhances model focus on critical features by introducing the SimAM attention mechanism, and additionally, the fusion of various levels of spectral features is achieved through the introduction of residual connectivity. GASF-CNN was trained and tested using a spectral data set containing coal and gangue. Comparative experimental results demonstrate that GASF-CNN outperforms other machine learning and deep learning models across four evaluation metrics. Specifically, it achieves 98.33, 97.06, 100, and 98.51% in the accuracy, recall, precision, and F1 score metrics, respectively, thereby achieving an accurate classification of coal and gangue.

Effect of single intravenous injection of esketamine on postpartum depression after labor analgesia and potential mechanisms: a randomized, double-blinded controlled trial.

BACKGROUND: The study was designed to investigate effects of single intravenous injection of esketamine on the incidence of postpartum depression (PPD) after labor analgesia and explore the potential mechanisms. METHODS: A total of 120 women who underwent labor analgesia by epidural analgesia pump were enrolled and divided into two groups randomly. Esketamine at a dose of 0.2 mg/kg was intravenously injected after fetal disengagement in the test group and placebo was administered in the control group. The occurrence of PPD and side effects after delivery were recorded. Some indicators related to stress and inflammation were measured before labor analgesia and at 24 h, 1 week, and 6 weeks after delivery in this study. Data were analyzed by independent t-test, repeated measures analysis of variance and Chi-square test in SPSS software (version 25.0). It was considered statistically significant since a p value less than 0.05. RESULTS: The incidence of PPD was significantly decreased both for one week and six weeks after delivery by using of esketamine (3.4% vs. 15.3%, p = 0.004 and 5.2% vs. 18.6%, p = 0.006, respectively). There were also significant differences between the stress and inflammation-related indicators in different time points in this study, while the side effects for 48 h after delivery were similar between the two groups. CONCLUSIONS: Single intravenous injection of esketamine after delivery in participants underwent labor analgesia can decrease the occurrence of postpartum depression for one week and six weeks after delivery, while the side effects were not increased. The antidepressant effects of esketamine may be related to the reduction of stress response and inflammation. TRIAL REGISTRATION: The trial was registered at the Chinese Clinical Trial Registry on 5/30/2022 (CTRI registration number-ChiCTR2200060387). URL of registry: https://www.chictr.org.cn/bin/home .

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm.

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea.

Purpose: Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients. Patients and Methods: We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires. Results: Severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥9.5×109/L, hemoglobin ≥175g/L, triglycerides ≥1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002). Conclusion: Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.

Impact of the first wave of COVID-19 on Crohn's disease after the end of "zero-COVID" policy in China.

Background: The incidence and severity of coronavirus disease 2019 (COVID-19) among Crohn's disease (CD) patients are unknown in China. This study aimed to clarify the clinical courses and outcomes of CD patients in the first COVID-19 wave after the end of "zero-COVID" policy in China. Methods: Clinical characteristics, including vaccination doses and medications of 880 CD patients from a prospective cohort were collected for analysis. Results: Of the enrolled patients (n = 880) who underwent nucleic acid or antigen testing for COVID-19 from Dec 7, 2022, to Jan 7, 2023, 779 (88.5%) were infected with COVID-19. Among the infected patients, 755 (96.9%) were mild, 14 (1.8%) were moderate, one patient with leukemia died of cerebral hemorrhage (mortality, 0.1%) and only 9 (1.2%) were asymptomatic. Fever, cough, headache and appetite loss were the most frequently observed symptoms in general, respiratory, neurological and gastrointestinal manifestations, respectively. The age and disease duration were significantly higher (40/32, 5.6/3.6, all p < 0.05) in moderate patients than those in mild patients. All other clinical characteristics, including CD activity and medication exposure, showed no significant differences between the above two groups. Furthermore, no significant difference in vaccination or comorbidities was observed between the two groups. Conclusion: Most CD patients contracted the Omicron infection and experienced mild disease courses in the first COVID-19 wave attack after China ended the "zero-COVID" policy irrespective of vaccination dose or comorbidities.

Impact of urbanization on morbidity of hepatitis A: a national panel study in China during 2005-2018.

BACKGROUND: The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China. METHODS: Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005-2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates. RESULTS: A total of 537,466 hepatitis A cases were reported in China during 2005-2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005-2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92-0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95% CI: 0.75-0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95% CI: 1.02-1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children. CONCLUSIONS: People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005-2018.

Comparison of the NG-Test Carba 5, Colloidal Gold Immunoassay (CGI) Test, and Xpert Carba-R for the Rapid Detection of Carbapenemases in Carbapenemase-Producing Organisms.

Carbapenem-resistant Enterobacterales (CRE) are increasingly recognized as an urgent public health concern. The rapid and accurate identification of carbapenemases could provide insights into antimicrobial therapy and infection control. In this study, we evaluated the efficacy of three different methods, including the NG-test Carba 5, colloidal gold immunoassay (CGI) test, and Xpert Carba-R assay, for the rapid detection of five carbapenemases (KPC, NDM, IMP, OXA-48, and VIM). A total of 207 Gram-negative strains collected from patients and hospital sewages were tested. The presence or absence of carbapenemase genes in the whole-genome sequences was used as the gold standard for evaluating the accuracy of the above-mentioned three methods. Among the 192 strains carrying only one carbapenemase gene, the accuracies of the NG-Test Carba 5, CGI test, and Xpert Carba-R were 96.88% (95% CI, 93.01-98.72%), 96.88% (95% CI, 93.01-98.72%), and 97.92% (95% CI, 94.41-99.33%), respectively. Xpert Carba-R was able to detect all 13 types of KPC variants, including KPC-2, KPC-3, KPC-25, KPC-33, KPC-35, KPC-51, KPC-52, KPC-71, KPC-76, KPC-77, KPC-78, KPC-93, and KPC-123, with a detection sensitivity of 100.00% (95% CI, 96.50-100.00%), a specificity of 100.00% (95% CI, 92.38-100.00%), and a κ index of 1.00. For IMP, Carba 5 was superior to the other two methods, with a sensitivity of 100% (95% CI, 71.66-100.00%), a specificity of 100% (95% CI, 97.38-100.00%), and a κ index of 1.00. For the remaining 15 strains carrying two or three kinds of carbapenemase genes, Carba 5 performed the best, which accurately identified all the target genes, followed by Xpert Carba-R (12/15, 80.00%) and the CGI test (10/15, 66.67%). Therefore, all three assays demonstrated reliable performances in carbapenemase detection, and Xpert Carba-R should be recommended for the detection of KPC variants, especially for patients at a high risk of infections caused by ceftazidime/avibactam-resistant strains. IMPORTANCE: CRE was listed as one of the top three pathogens that are in critical need of new antibiotics by the WHO. The rapid and accurate identification of carbapenemases is important for antimicrobial therapy and infection control. In recent years, new beta-lactam/beta-lactamase inhibitor combinations such as ceftazidime/avibactam (CZA) have been approved by the Food and Drug Administration (FDA) to cope with CRE challenges. CZA was effective against class A, class C, and some class D enzymes such as OXA-48-like. However, CZA-resistant KPC variants emerged at an alarming speed, which posed a new challenge for the accurate identification of KPC variants. In this study, we evaluated the performance of two lateral flow immunochromatographic assays, namely, NG-test Carba 5 and the CGI test, and the automated real-time quantitative PCR Xpert Carba-R in the rapid detection of carbapenemases. Notably, 13 types of KPC variants were enrolled in this study, which covered most KPC variants discovered in China. Carba-R was superior to NG-teat Carba 5 and the CGI test; it was able to detect all of the included KPC variants, including KPC-2, KPC-3, KPC-25, KPC-33, KPC-35, KPC-51, KPC-52, KPC-71, KPC-76, KPC-77, KPC-78, KPC-93, and KPC-123.

Influenza-Associated Excess Mortality by Age, Sex, and Subtype/Lineage: Population-Based Time-Series Study With a Distributed-Lag Nonlinear Model.

BACKGROUND: Accurate estimation of the influenza death burden is of great significance for influenza prevention and control. However, few studies have considered the short-term harvesting effects of influenza on mortality when estimating influenza-associated excess deaths by cause of death, age, sex, and subtype/lineage. OBJECTIVE: This study aimed to estimate the cause-, age-, and sex-specific excess mortality associated with influenza and its subtypes and lineages in Guangzhou from 2015 to 2018. METHODS: Distributed-lag nonlinear models were fitted to estimate the excess mortality related to influenza subtypes or lineages for different causes of death, age groups, and sex based on daily time-series data for mortality, influenza, and meteorological factors. RESULTS: A total of 199,777 death certificates were included in the study. The average annual influenza-associated excess mortality rate (EMR) was 25.06 (95% empirical CI [eCI] 19.85-30.16) per 100,000 persons; 7142 of 8791 (81.2%) deaths were due to respiratory or cardiovascular mortality (EMR 20.36, 95% eCI 16.75-23.74). Excess respiratory and cardiovascular deaths in people aged 60 to 79 years and those aged ≥80 years accounted for 32.9% (2346/7142) and 63.7% (4549/7142) of deaths, respectively. The male to female ratio (MFR) of excess death from respiratory diseases was 1.34 (95% CI 1.17-1.54), while the MFR for excess death from cardiovascular disease was 0.72 (95% CI 0.63-0.82). The average annual excess respiratory and cardiovascular mortality rates attributed to influenza A (H3N2), B/Yamagata, B/Victoria, and A (H1N1) were 8.47 (95% eCI 6.60-10.30), 5.81 (95% eCI 3.35-8.25), 3.68 (95% eCI 0.81-6.49), and 2.83 (95% eCI -1.26 to 6.71), respectively. Among these influenza subtypes/lineages, A (H3N2) had the highest excess respiratory and cardiovascular mortality rates for people aged 60 to 79 years (20.22, 95% eCI 14.56-25.63) and ≥80 years (180.15, 95% eCI 130.75-227.38), while younger people were more affected by A (H1N1), with an EMR of 1.29 (95% eCI 0.07-2.32). The mortality displacement of influenza A (H1N1), A (H3N2), and B/Yamagata was 2 to 5 days, but 5 to 13 days for B/Victoria. CONCLUSIONS: Influenza was associated with substantial mortality in Guangzhou, occurring predominantly in the elderly, even after considering mortality displacement. The mortality burden of influenza B, particularly B/Yamagata, cannot be ignored. Contrasting sex differences were found in influenza-associated excess mortality from respiratory diseases and from cardiovascular diseases; the underlying mechanisms need to be investigated in future studies. Our findings can help us better understand the magnitude and time-course of the effect of influenza on mortality and inform targeted interventions for mitigating the influenza mortality burden, such as immunizations with quadrivalent vaccines (especially for older people), behavioral campaigns, and treatment strategies.

Rapid proximate analysis of coal based on reflectance spectroscopy and deep learning.

Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R2) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal.

A Multi-Age-Group Interrupted Time-Series Study for Evaluating the Effectiveness of National Expanded Program on Immunization on Mumps.

The national Expanded Program on Immunization (EPI) in China has covered vaccines for measles, mumps, and rubella, among children aged 18-24 months since September 2008. However, no previous studies have quantified the effectiveness of the EPI on mumps incidence. There are methodological challenges in assessing the effect of an intervention that targets a subpopulation but finally influences the whole population. In this study, monthly data on mumps incidence were collected in Guangzhou, China, during 2005-2019. We proposed a multi-age-group interrupted time-series design, setting the starting time of exerting effect separately for 14 different age groups. A mixed-effects quasi-Poisson regression was applied to analyze the effectiveness of the EPI on mumps incidence, after controlling for long-term and seasonal trends, and meteorological factors. The model also accounted for the first-order autocorrelation within each age group. Between-age-group correlations were expressed using the contact matrix of age groups. We found that 70,682 mumps cases were reported during 2005-2019, with an annual incidence rate of 37.91 cases per 100,000 population. The effect of EPI strengthened over time, resulting in a decrease in the incidence of mumps by 16.6% (EPI-associated excess risk% = -16.6%, 95% CI: -27.0% to -4.7%) in September 2009 to 40.1% (EPI-associated excess risk% = -40.1%, 95% CI: -46.1% to -33.3%) in September 2019. A reverse U-shape pattern was found in age-specific effect estimates, with the largest reduction of 129 cases per 100,000 population (95% CI: 14 to 1173) in those aged 4-5 years. The EPI is effective in reducing the mumps incidence in Guangzhou. The proposed modeling strategy can be applied for simultaneous assessment of the effectiveness of public health interventions across different age groups, with adequate adjustment for within- and between-group correlations.

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis.

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.

Sex Difference Trend in 5-Year Mortality Among Patients With Coronary Artery Disease: A 24,432 Chinese Cohort Study From 2007 to 2014.

Background: The sex difference trend of short-term mortality in coronary artery disease (CAD) is narrowing, which has been reported in the previous studies. However, no studies assess the sex difference temporal trends of CAD mortality in China especially long-term mortality trend. Methods: Based on the registry at Guangdong Provincial People's Hospital which is the largest cardiovascular center in South China, this retrospective cohort study included 24,432 hospitalized patients with CAD confirmed by coronary angiography from January 2007 to December 2014. Women and men were followed for 1-year and 5-year all-cause mortality. Results: From 2007 to 2014, 5-year age-standardized mortality increased from 10.0 to 11.7% in men (p for trend < 0.001) and from 11.5 to 8.1% in women (p for trend = 0.99). The multivariable-adjusted hazard ratios (95% CI), which compare women with men, were from 1.02 (0.39-2.67) to 0.66 (0.39-1.12) for 1-year all-cause mortality and 1.23 (0.64-2.36) to 0.59 (0.44-0.79) for 5-year all-cause mortality (p for trend = 0.04). Conclusion: Our study found that the mortality risk among men and women was similar in the 1-year prognosis of CAD, and there was no significant downward trend. In the 5-year long-term prognosis of CAD, the mortality risk among men continued to rise, while women had reached the peak, which means that the mortality risk continues to be higher among men than women.