A comparative study of genotyping and antimicrobial resistance between carbapenem-resistant Klebsiella pneumoniae and Acinetobacter baumannii isolates at a tertiary pediatric hospital in China.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) clinical isolations have rapidly increased in pediatric patients. To investigate a possible health care-associated infections of CRKP in a tertiary pediatric hospital, the circulating clones and carbapenem-resistant pattern between CRKP and carbapenem-resistant Acinetobacter baumannii (CRAB) isolates were compared to classify their epidemiological characteristics. The results will help to identify the epidemic pattern of the CRKP transmission in the hospital. Methods: Ninety-six CRKP and forty-eight CRAB isolates were collected in Kunming Children's Hospital from 2019 through 2022. These isolates were genotyped using repetitive extragenic palindromic-PCR (REP-PCR). Carbapenemase phenotypic and genetic characterization were investigated using a disk diffusion test and singleplex PCR, respectively. In addition, these characteristics of the two pathogens were compared. Results: The rates of CRKP and CRAB ranged from 15.8% to 37.0% at the hospital. Forty-nine and sixteen REP genotypes were identified among the 96 and 48 CRKP and CRAB isolates tested, respectively. The CRKP isolates showed more genetic diversity than the CRAB isolates. Of the 96 CRKP isolates, 69 (72%) produced Class B carbapenemases. However, all 48 CRAB isolates produced Class D carbapenemase or extended-spectrum ß-lactamases (ESBL) combined with the downregulation of membrane pore proteins. Furthermore, the carbapenemase genes bla KPC, bla IMP, and bla NDM were detected in CRKP isolates. However, CRAB isolates were all positive for the bla VIM, bla OXA-23, and bla OXA-51 genes. Conclusions: These CRKP isolates exhibited different biological and genetic characteristics with dynamic changes, suggesting widespread communities. Continuous epidemiological surveillance and multicenter research should be carried out to strengthen the prevention and control of infections.

Enhanced recovery after surgery in congenital duodenal obstruction.

BACKGROUND: The present study aims to explore the clinical application of enhanced recovery after surgery (ERAS) in pediatric patients with congenital upper gastrointestinal obstruction (CUGIO). METHODS: A total of 82 pediatric patients with CUGIO admitted to the neonatal intensive care unit in Kunming Children's Hospital between June 2017 and June 2021 were enrolled in the present study and divided into two groups: the ERAS group (n = 46) and the control group (n = 36). The ERAS management mode was adopted in the ERAS group, and the conventional perioperative management mode was adopted in the control group. RESULTS: In the ERAS group and the control group, the time to the first postoperative bowel movement was 49.2 ± 16.6 h and 58.4 ± 18.8 h, respectively, and the time to the first postoperative feeding was 79 ± 7.1 h and 125.2 ± 8.3 h, respectively. The differences in the above two indicators between the two groups were statistically significant (P < 0.05). In the ERAS group, the days of parenteral nutrition and the length of hospital stay were 14.5 ± 2.3 d and 18.8 ± 6.4 d, respectively. In the control group, 17.6 ± 2.2 d and 23.1 ± 8.1 d, respectively. The differences in these two indicators between the two groups were statistically significant (P < 0.05). CONCLUSION: The ERAS management model had a positive effect on early postoperative recovery in pediatric patients with CUGIO.

Viral etiology of acute respiratory tract infection among children under 5 years of age in Kunming City, China: a matched case-case-control study.

AIMS: Nucleic acid-based molecular techniques in current laboratory practice allow the identification of a broad range of respiratory viruses. However, due to asymptomatic carriage, the detection of viruses in the respiratory tract does not necessarily indicate disease. The study aimed to investigate infections of different viruses that colonize the airways, the viral combinations in coinfection, and the viral association with the occurrence of either upper respiratory tract infection (AURTI) or lower respiratory tract infection (ALRTI) in children. METHODS AND RESULTS: A matched case-case-control study included ALRTI cases, AURTI cases, and healthy controls was conducted at Kunming Children's Hospital. Oropharyngeal swabs from the three groups were collected for eight viral pathogens detection by multiplex RT-PCR. The association of each pathogen with disease status was determined by comparing the results between cases and controls. From 1 March 2021 through 28 February 2022, 278 participants in each group were investigated. Viral infection was detected in 54.0%, 37.1%, and 12.2% of the ALRTI cases, AURTI cases, and healthy controls, respectively. Human respiratory syncytial virus (RSV), adenovirus (ADV), and parainfluenza virus-3 (PIV-3) were the most frequently documented viruses. RSV/ADV was the most frequent combination detected in coinfection. When compared to healthy controls, RSV and PIV-3 were independently associated with both ALRTI and AURTI. CONCLUSIONS: RSV and PIV-3 were causes of both ALRTI and AURTI cases. These results provide initial evidence of the potential of microbiota-based diagnostics for the differential diagnosis of severe acute respiratory infections using oropharyngeal swab samples.

[Spatial Characterization of Stable Isotope Composition of Organic Carbon from Farmland Soils in Chongqing].

Soil was sampled from 182 profiles in typical farmlands of Chongqing and analyzed for the stable carbon isotope composition of organic matter (δ13CSOC). The results showed that the values of δ13CSOC for each soil profile were gradually increasing with increasing soil depth, and the mean values were (-23.63±1.53)‰, (-22.43±1.59)‰, and (-21.42±1.90)‰ for surface, middle, and bottom layers, respectively. The δ13CSOC values in the northeastern region of Chongqing tended to be more negative, whereas those in central Chongqing were less negative. Paddy fields showed the most negative values of δ13CSOC, followed by rice-upland rotating fields and upland fields, with the average being (-25.32±0.93)‰, (-23.17±1.37)‰, and (-24.75±1.28)‰ for the surface layers, respectively. For different soil types, the δ13C values in the surface layers were in the order of paddy soil

[A pedigree study of laryngo-onycho-cutaneous syndrome with a novel mutation on LAMA3 gene].

Objective:To detect genetic mutations in a case of laryngo-onycho-cutaneous syndrome, and to explore the possible molecular biological pathogenic causes. Methods:With informed consent, the family clinical data of the child with laryngo-onycho-cutaneous syndrome were collected, peripheral blood of the protester and his parents was collected and DNA was extracted, and gene detection was performed by high-throughput sequencing method. Sanger sequencing was used to verify and analyze the mutation sites of the probs and their families. Results:Genetic testing of the proband revealed homozygous mutation of LAMA3 gene c.171+1G>A site, which is splicing mutation. There was no report in the literature, which was a new mutation site. The parents of the proband had normal phenotype and heterozygous mutation at this locus was detected. Conclusion:Homozygous mutation of LAMA3 c.171+1G>A is the likely pathogenic of the proband, and this study expands the mutant spectrum of LAMA3. The clinical phenotype of laryngo-onycho-cutaneous syndrome is highly variable, and the multidisciplinary diagnosis and treatment can effectively avoid missed diagnosis and misdiagnosis.

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation.

BACKGROUND: Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. OBJECTIVE: We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. METHODS: Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. RESULTS: Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs <10 and 187 (87.0%) had Pearson correlation coefficients >0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). CONCLUSIONS: By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

Improved Automated Foveal Avascular Zone Measurement in Cirrus Optical Coherence Tomography Angiography Using the Level Sets Macro.

Purpose: To evaluate automated measurements of the foveal avascular zone (FAZ) using the Level Sets macro (LSM) in ImageJ as compared with the Cirrus optical coherence tomography angiography (OCTA) inbuilt algorithm and the Kanno-Saitama macro (KSM). Methods: The eyes of healthy volunteers were scanned four times consecutively on the Zeiss Cirrus HD-OCT 5000 system. The FAZ metrics (area, perimeter, and circularity) were measured manually and automatically by the Cirrus inbuilt algorithm, the KSM, and the LSM. The accuracy and repeatability of all methods and agreement between automated and manual methods were evaluated. Results: The LSM segmented the FAZ with an average Dice coefficient of 0.9243. Compared with the KSM and the Cirrus inbuilt algorithm, the LSM outperformed them by 0.02 and 0.19, respectively, for Dice coefficients. Both the LSM (intraclass correlation coefficient [ICC] = 0.908; coefficient of variation [CoV] = 9.664%) and manual methods (ICC ≥ 0.921, CoV ≤ 8.727%) showed excellent repeatability for the FAZ area, whereas the other methods presented moderate to good repeatability (ICC ≤ 0.789, CoV ≥ 15.788%). Agreement with manual FAZ area measurement was excellent for both the LSM and KSM but not for the Cirrus inbuilt algorithm (LSM, ICC = 0.930; KSM, ICC = 0.928; Cirrus, ICC = 0.254). Conclusions: The LSM exhibited greater accuracy and reliability compared to the KSM and inbuilt automated methods and may be an improved and accessible option for automated FAZ segmentation. Translational Relevance: The LSM may be a suitable automated and customizable tool for FAZ quantification of Cirrus HD-OCT 5000 images, providing results comparable to those for manual measurement.

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.

BACKGROUND: Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide. METHODS: The "interest over time" and "interest by region" Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. The features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries. RESULTS: Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods and the models with different numbers of features. CONCLUSIONS: Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.

Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020.

The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10-14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8-12 days for laboratory-confirmed cases and 6-8 days for suspected cases.

Reliability of foveal avascular zone metrics automatically measured by Cirrus optical coherence tomography angiography in healthy subjects.

PURPOSE: To investigate the reliability of the foveal avascular zone (FAZ) metrics automatically measured using Cirrus optical coherence tomography angiography (OCTA) embedded algorithm compared to human manual measurement. METHODS: Thirty-five eyes of 35 healthy subjects were enrolled and scanned four times continuously on Zeiss Cirrus HD-OCT 5000. The FAZ metrics (area, circularity and perimeter) of the superficial capillary plexus were measured automatically using the embedded tool and manually measured by the two independent observers using ImageJ. The repeatability of the four scans within all methods of measurements was calculated. The agreement of the manual vs automated measurement was also analyzed. RESULTS: The repeatability of the automated algorithm was only poor to moderate (intraclass correlation coefficients [ICCs] for the area, perimeter and circularity were 0.600, 0.405 and 0.221, respectively) while the repeatability of the manually measured FAZ area and perimeter was good [([ICCs] ranged from 0.845 to 0.877) except the circularity (ICC = 0.538 to 0.608)]. The ranges of 95% limits of agreement between the manual measurements by the two observers were only 20% to 31% of those of automated-manual agreement. The Cirrus inbuilt algorithm obviously outlined the border of FAZ wrongly in 22.9% cases. CONCLUSION: Caution should be taken when using the automated measurement results of FAZ metrics in Cirrus OCTA, because of the low repeatability and poor agreement compared with the manual measurement.

[Detection of gene expression alteration of myeloma cells treated with arsenic trioxide].

OBJECTIVE: To investigate the effect of arsenic trioxide on multiple myeloma (MM) cell gene expression and explore the molecular mechanism of arsenic trioxide therapy for MM. METHODS: U266 cells were divided into two groups, group A as control group and group B as test group. Cells were cultured for 48 hours, and total RNA and mRNA were extracted. Suppression subtractive hybridization (SSHs) was performed to distinguish the differentially expressed genes. The products were cloned into pGEM-T Easy Vector, and transfected into the competent host JM109 to construct two subtractive libraries. Positive colonies were selected by blue-white screening, and the plasmids were extracted. Homologous comparison was conducted in GenBank. RESULTS: Five downregulated clones were isolated in the first SSH: (1) Aminopeptidase N, (2) Homosapiens tumor translationally-controlled protein 1, (3) Human ATP synthetase A chain, (4) Signal recognition particle A10, (5) Mitochondrial ATP synthetase/ATPase subunit 6. Four upregulated clones were isolated in the second SSH: (1) Calcium-binding protein A10, (2) Keratin 6A, (3) 45 kD MIP repetitive element containing splicing factor and (4) poly(A)-binding protein. CONCLUSIONS: Arsenic trioxide exerts proliferation inhibition and apoptosis induction on MM cells by regulating genes expression.

The density distributions of the counterions and the coions confined in two similarly charged plates.

By using the field-theoretic method, we established a unified systematic formulation of a model of counterions and coions confined in two similarly charged plates, and calculated the density distributions of counterions and coions with various coupling parameters by the two methods: Poisson-Boltzmann (PB) approach and the strong coupling (SC) theory, respectively. We also performed Monte Carlo simulations, and obtained the density distributions of counterions and coions with several different coupling parameters. Comparing our theoretical results with those from Monte Carlo simulation, we find that the PB approach is valid when the coupling parameter Xi is smaller than 1, but, as Xi > or = 1, the results by the PB approach deviate from the corresponding Monte Carlo simulation data, and the deviation gets larger with the coupling parameter increasing. This shows that the PB approach is completely invalid when the coupling parameter is equal to 1 or larger than 1. For the latter case, the development trend of the distribution curve calculated by SC theory agrees with that from Monte Carlo simulation as the coupling parameter increases. This demonstrates that the SC theory can give a qualitative available explanation on the density distribution of the counterions in the system in which the coupling parameters are strictly confined.