Xuebijing injection, a Chinese patent medicine, against severe pneumonia: Current research progress and future perspectives.

Severe pneumonia is one of the most common infectious diseases and the leading cause of sepsis and septic shock. Preventing infection, balancing the patient's immune status, and anti-coagulation therapy are all important elements in the treatment of severe pneumonia. As multi-target agents, Xuebijing injection (XBJ) has shown unique advantages in targeting complex conditions and saving the lives of patients with severe pneumonia. This review outlines progress in the understanding of XBJ's anti-inflammatory, endotoxin antagonism, and anticoagulation effects. From the hundreds of publications released over the past few years, the key results from representative clinical studies of XBJ in the treatment of severe pneumonia were selected and summarized. XBJ was observed to effectively suppress the release of pro-inflammatory cytokines, counter the effects of endotoxin, and assert an anticoagulation effect in most clinical trials, which are consistent with experimental studies. Collectively, this evidence suggests that XBJ could play an important and expanding role in clinical medicine, especially for sepsis, septic shock and severe pneumonia. Please cite this article as: Zhang M, Zheng R, Liu WJ, Hou JL, Yang YL, Shang HC. Xuebijing injection, a Chinese patent medicine, against severe pneumonia: Current research progress and future perspectives. J Integr Med. 2023; 21(5): 413-422.

Lung abscess due to Streptococcus intermedius associated with SARS CoV-2 infection in pregnancy: Unusual presentation and complication of a commensal bacteria during pregnancy.

Streptococcus intermedius is a commensal bacterium reported in a few cases as the causative agent of brain and lung abscesses, pneumonia, and endocarditis. Lung abscesses due to Streptococcus intermedius are rare, especially in pregnancy. We describe the first case of lung abscess due to Streptococcus intermedius in a pregnant woman.

TRACHEOBRONCHIAL MORPHOMETRY CORRELATES WITH DEMOGRAPHIC CHARACTERISTICS AND INFECTIONS IN CRITICALLY ILL PATIENTS.

Tracheal measurements in the intensive care unit (ICU) are important for the choice of endotracheal tube and may correlate with patient demographic characteristics and infections. The study included 42 surgical patients, age 60 [48-71] years, who underwent diagnostic chest computed tomography (CT) scans during treatment in the ICU, Osijek University Hospital, in 2019 and 2020. CT scans were analyzed using AW Server 3.2. Measurement analysis showed that the diameters of the tracheobronchial tree, the length of the trachea and left main bronchus were significantly larger in men compared to women (p<0.05 all). The smallest tracheal upper diameter was 15.25 [IQR 11.8-18.8] mm vs. 17.95 [13.55-20.05] mm in septic and nonseptic patients, respectively (p=0.028). A total of 26 patients who underwent CT scans developed nosocomial pneumonia. It was right-sided in 15, left-sided in 6 and bilateral in 5 patients, and correlated significantly with the left main bronchus length (ρ=0.515, p=0.007). No correlation was observed between tracheobronchial measurements and length of ICU treatment, number of hours spent on mechanical ventilation, or survival. A larger study could provide better data on the importance of tracheobronchial tree measurements in ICU patients.

Xuebijing injection, a Chinese patent medicine, against severe pneumonia: Current research progress and future perspectives / 中西医结合学报

Severe pneumonia is one of the most common infectious diseases and the leading cause of sepsis and septic shock. Preventing infection, balancing the patient's immune status, and anti-coagulation therapy are all important elements in the treatment of severe pneumonia. As multi-target agents, Xuebijing injection (XBJ) has shown unique advantages in targeting complex conditions and saving the lives of patients with severe pneumonia. This review outlines progress in the understanding of XBJ's anti-inflammatory, endotoxin antagonism, and anticoagulation effects. From the hundreds of publications released over the past few years, the key results from representative clinical studies of XBJ in the treatment of severe pneumonia were selected and summarized. XBJ was observed to effectively suppress the release of pro-inflammatory cytokines, counter the effects of endotoxin, and assert an anticoagulation effect in most clinical trials, which are consistent with experimental studies. Collectively, this evidence suggests that XBJ could play an important and expanding role in clinical medicine, especially for sepsis, septic shock and severe pneumonia. Please cite this article as: Zhang M, Zheng R, Liu WJ, Hou JL, Yang YL, Shang HC. Xuebijing injection, a Chinese patent medicine, against severe pneumonia: Current research progress and future perspectives. J Integr Med. 2023; 21(5): 413-422.

[Etiology of bacterial pneumonias in patients with COVID-19]. / Etiología de neumonías bacterianas en pacientes con COVID-19.

Background: Aggregate bacterial pneumonia plays a fundamental role in mortality of patients hospitalized with COVID-19. Objective: To estimate the association of aggregated bacterial pneumonia with mortality in patients at Hospital Especialidades "La Raza". Material and methods: Analytical cross-sectional study, 252 COVID-19 patients, chest x-ray and culture of bronchial secretion or expectoration. Data taken from the SIOC electronic file and the IZASAlab platform. Results: 252 participants, positive culture, 89 patients, 35.3%, isolation of K. pneumoniae (22.5%), A. baumannii (20.2%), P. aeruginosa (13.5%) and S. aureus (11.2%), antimicrobial resistance 37.1%. 43.7% died, lung damage greater than 50% RMa 2.25 (95% CI 1.01-5.11) p=0.04 against minor lung damage; microorganism in culture RMa 9.04 (95% CI 3.06-26.74) p=0.000; antimicrobial resistance RMa 7.57 (95% CI 1.34-42.79) p=0.02; S. aureus RMa 1.24 (95% CI 0.36-4.23) p=0.73; A. baumannii RMa 3.74 (95% CI 1.41-9.91) p=0.008; K. pneumoniae RMa 4.12 (95% CI 1.55-10.97) p=0.005; and P. aeruginosa RMa 6.89 (95% CI 1.62-17.61) p=0.01. Uncontrolled Diabetes RMa 1.61 (IC95% 1.1-2.9) p=0.018. Conclusions: The development of added bacterial pneumonia increases the probability of death 2 times more, it amounts to 6 times more if there is antimicrobial resistance, it is observed to a greater extent for A. baumannii, K. pneumoniae and P. aeruginosa.

Introducción: la neumonía bacteriana agregada en pacientes COVID-19 tiene un papel determinante en la mortalidad hospitalaria. Objetivo: estimar la asociación de neumonía bacteriana agregada con la mortalidad de pacientes COVID-19 en el Hospital Especialidades de "La Raza". Material y métodos: estudio transversal analítico con 252 pacientes con COVID-19; se obtuvieron los datos del expediente electrónico y plataforma IZASAlab, se tomó Rx de tórax y cultivo de secreción bronquial o expectoración. Resultados: de 252 participantes resultó cultivo positivo en 89 pacientes (35.3%), aislamiento de K. pneumoniae (22.5%), A. baumannii (20.2%), P. aeruginosa (13.5%) y S. aureus (11.2%); hubo resistencia antimicrobiana en 37.1% y fallecieron 43.7%. El daño pulmonar mayor al 50% en la Rx de tórax tuvo RMa 2.25 (IC95%: 1.01-5.11) p = 0.04 para mortalidad; cultivo positivo RMa 9.04 (IC95%: 3.06-26.74) p = 0.000; resistencia antimicrobiana RMa 7.57 (IC95%: 1.34-42.79) p = 0.02; S. aureus RMa 1.24 (IC95%: 0.36-4.23) p = 0.73; A. baumannii RMa 3.74 (IC95%: 1.41-9.91) p = 0.008; K. pneumoniae RMa 4.12 (IC95%: 1.55-10.97) p = 0.005, y P. aeruginosa RMa 6.89 (IC95%: 1.62-17.61) p = 0.01. Diabetes Mellitus descontrolada RMa 1.61 (IC95%: 1.1-2.9) p = 0.018. Conclusiones: el desarrollo neumonía bacteriana agregada en pacientes COVID-19 incrementa dos veces más la probabilidad de muerte y seis veces más si existe resistencia antimicrobiana de A. baumannii, K. pneumoniae o P. aeruginosa.

Comparison of deep vein thrombosis risks in acute respiratory distress syndrome caused by COVID-19 and bacterial pneumonia: a retrospective cohort study.

BACKGROUND: High incidence of deep vein thrombosis (DVT) has been observed in patients with acute respiratory distress syndrome (ARDS) caused by COVID-19 and those by bacterial pneumonia. However, the differences of incidence and risk factors of DVT in these two groups of ARDS had not been reported before. STUDY DESIGN AND METHODS: We performed a retrospective cohort study to investigate the difference of DVT in incidence and risk factors between the two independent cohorts of ARDS and eventually enrolled 240 patients, 105 of whom with ARDS caused by COVID-19 and 135 caused by bacterial pneumonia. Lower extremity venous compression ultrasound scanning was performed whenever possible regardless of clinical symptoms in the lower limbs. Clinical characteristics, including demographic information, clinical history, vital signs, laboratory findings, treatments, complications, and outcomes, were analyzed for patients with and without DVT in these two cohorts. RESULTS: The 28-days incidence of DVT was higher in patients with COVID-19 than in those with bacterial pneumonia (57.1% vs 41.5%, P = 0.016). Taking death as a competitive risk, the Fine-Gray test showed no significant difference in the 28-day cumulative incidence of DVT between these two groups (P = 0.220). Fine-Gray competing risk analysis also showed an association between increased CK (creatine kinase isoenzyme)-MB levels (P = 0.003), decreased PaO2 (partial pressure of arterial oxygen)/FiO2 (fraction of inspired oxygen) ratios (P = 0.081), increased D-dimer levels (P = 0.064) and increased incidence of DVT in COVID-19 cohort, and an association between invasive mechanical ventilation (IMV; P = 0.001) and higher incidence of DVT and an association between VTE prophylaxis (P = 0.007) and lower incidence of DVT in bacterial pneumonia cohort. The sensitivity and specificity of the corresponding receiver operating characteristic curve originating from the combination of CK-MB levels, PaO2/FiO2 ratios, and D-dimer levels ≥0.5 µg/mL were higher than that of the DVT Wells score (P = 0.020) and were not inferior to that of the Padua prediction score (P = 0.363) for assessing the risk of DVT in COVID-19 cohort. CONCLUSIONS: The incidence of DVT in patients with ARDS caused by COVID-19 is higher than those caused by bacterial pneumonia. Furthermore, the risk factors for DVT are completely different between these two ARDS cohorts. It is suggested that COVID-19 is probably an additional risk factor for DVT in ARDS patients.

Exploration of animal model of sepsis caused by lung bacterial infection / 中国医师杂志

Objective:Animal models of sepsis are mainly established by cecal ligation and puncture which causes mixed bacterial infections in the abdominal cavity. However in internal clinic, sepsis is more common to be caused by respiratory bacterial infections. Therefore, it is necessary to establish animal models of sepsis caused by lung Infection.Methods:According to the concentration of Staphylococcus aureus (S. aureus) suspension and Pseudomonas aeruginosa (P. aeruginosa) suspension, Sprague Dawley (SD) rats were equally divided into 10 groups, including S-Cont group, S-0.75 group, S-1.5 group, S-3 group, S-6 group and P-Cont group, P-1 group, P-2 group, P-4 group, P-8 group. Rats in the control group were treated with normal saline nasal drip. Rats in each experimental group were infected by nasal dripping bacterial suspension with 0.75×10 8 CFU/ml, 1.5×10 8 CFU/ml, 3×10 8 CFU/ml, 6×10 8 CFU/ml of S. aureus suspension or 1×10 8 CFU/ml, 2×10 8 CFU/ml, 4×10 8 CFU/ml, 8×10 8CFU/ml P. aeruginosa suspension. Our study detected the body temperature (T), blood pressure (BP), heart rate (HR) of rats in each group before and after infection, as well as blood lactic acid (Lac) and procalcitonin (PCT) level after infection. The lung infections of rats in each group were observed by hematoxylin-eosin (HE) staining. Results:The blood pressure(BP) of S-1.5 group, S-3 group, S-6 group and P-8 group was lower than before infection (all P<0.05). The Lac and PCT of each S. aureus experimental group were higher than that of the S-Cont group (all P<0.01); and they showed an increasing trend with the increase of the bacterial suspension concentration ( P<0.05), except for the S-3 and S-6 group ( P>0.05). The Lac and PCT of each P. aeruginosa experimental group were higher than that of the P-Cont group (all P<0.01); and they showed an increasing trend with the increase of the bacterial suspension concentration (all P<0.05), except for the Lac in the P-4 group and P-8 group ( P>0.05). HE staining showed that different degrees of inflammatory infiltration can be seen in the lungs of the experimental rats in each group. Conclusions:Infection of rats by nasal dripping with 3×10 8 CFU/ml of S. aureus suspension or 4×10 8 CFU/ml of P. aeruginosa suspension could establish relatively stable rat sepsis model induced by lung bacterial infection, of which the former could also establish a relatively stable septic shock model.

Application of blood routine and four inflammatory markers in children with lower respiratory tract infection / 中华检验医学杂志

Objective:To evaluate the differential expression of blood routine in different types of infection and the diagnostic value of C-reactive protein (CRP), procalcitonin (PT), ferritin (SF) and lactate dehydrogenase (LDH) in bacterial and mycoplasma pneumonia and their early warning value in severe cases.Method:A total of 627 patients, including 176 cases of bacterial pneumonia, 275 cases of mycoplasma pneumonia, 176 cases of viral infection and 180 cases of normal control were collected from May 2018 to December 2019 in children′s Hospital Affiliated to Capital Institute of Pediatrics. The mycoplasma pneumonia group was divided into mild group (151 cases) and severe group (124 cases) according to the results of lavage fluid RNA-examination. All patients received completed blood routine test at the first day of admission, patients in bacteria group and Mycoplasma group received the examination of four inflammatory indicators. The Kruskal-Wallis test was used to analyze the differences in blood routine results between different infection groups, and the differences of inflammatory indexes between bacterial group and Mycoplasma mild and severe group. The receiver operating characteristic (ROC)-curve method was used to analyze the predictive value of inflammatory indexes between different infection groups.Results:There were significant differences in leukocyte count, neutrophil, lymphocyte and monocyte percentage between bacterial pneumonia, mycoplasma pneumonia, viral infection and normal control group ( P<0.05). The differences of four inflammatory indexes in bacterial group, mild Mycoplasma group and severe group were statistically significant ( P<0.05). The rest of the index (CRP, PCT, LDH, SF and white blood cell count) were P<0.05 (CRP: area under curve [AUC] 0.799; PCT: AUC 0.579; LDH: AUC 0.651; SF: AUC 0.854), in mild and severe mycoplasma group, except WBC, by ROC-curves analysis. The AUC value of the area under the curve of CRP and SF is high, and the sensitivity and specificity at the diagnostic critical point are high, which has great diagnostic value (CRP: diagnostic critical point 12.55 mg/L, sensitivity 0.719, specificity 0.755; SF: diagnostic critical point 176.02 μg/L, sensitivity 0.765, specificity 0.960). ROC curve results also showed that of PCT, White blood cell and neutrophil percentage had the diagnostic value in bacterial infection and mycoplasma infection, P<0.05 (PCT: AUC 0.658; leukocyte: AUC 0.804; neutrophil: AUC 0.630). Leukocyte count is the best differential index (diagnostic critical point 9.585×10 9/L, sensitivity 0.778, specificity 0.698), PCT has higher sensitivity at the diagnostic critical point of 0.55 μg/L, but the specificity is slightly lower (diagnostic critical point of 0.55 μg/L, sensitivity 0.862, specificity 0.366). Conclusions:PCT and leukocyte count can be used as the preferred inflammatory indexes to distinguish bacterial and mycoplasma infection. CRP, LDH, PCT and SF can be used as early warning indexes to evaluate severe mycoplasma infection.

Protocolo clínico de diagnóstico y tratamiento de pacientes con neumonía adquirida en la comunidad que ingresan a la Unidad de Cuidados Intensivos / Clinical protocol for diagnosis and treatment of patients with community-acquired pneumonia admitted to the Intensive Care Unit

La neumonía es una infección frecuente que se presenta en todas las edades, en cualquier tipo de pacientes y a nivel co-munitario u hospitalario. La neumonía que se origina en la comunidad afecta a los pacientes con comorbilidades y en los extremos de la vida. La mortalidad de la neumonía comunitaria (NC) per-manece elevada, los sistemas de salud deben implementar estrategias para diagnosticar y tratar de forma rápida a estos pacientes. Cuando un paciente con neumonía comunitaria es ingresado en la emergencia de cualquier hospital se debe categorizar su estado para que reciba el mejor tratamiento posible. La Unidad de Cuidados Intensivos (UCI) participa en la detección de los pacientes con neu-monía adquirida en la comunidad grave, con el objetivo de priorizar su atención para lograr las metas de manejo lo más rápido posible y disminuir la mortalidad de estos pacientes.

Pneumonia is a common infection that occurs in all ages, in any type of patient and at the community or hospital level. Community-originating pneumonia affects patients with comorbidities and at the ex-tremes of life. Mortality from commu-nity pneumonia remains high, health sys-tems must implement strategies to quickly diagnose and treat these patients. When a patient with community pneumonia is admitted to any hospital emergency, their condition must be categorized so that they receive the best possible treat-ment. The Intensive Care Unit (ICU) participates in the detection of patients with severe community-acquired pneu-monia, with the objective of prioritizing their care to achieve management goals as quickly as possible and reduce the mortality of these patients.

COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning.

In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.

BACKGROUND: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. METHODS: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. RESULTS: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. CONCLUSION: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

BACKGROUND AND OBJECTIVE: The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient's immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits. METHODS: Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia). RESULTS: The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes. CONCLUSION: The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

Diagnostic value of TNF-α in patients with pulmonary infection after liver transplantation / 中国医师杂志

Objective:To explore diagnostic value of tumor necrosis factor-α (TNF-α) in patients with pulmonary infection after liver transplantation.Methods:The clinical data of 80 patients with pulmonary infection after liver transplantation in the the First Affiliated Hospital of Xinjiang Medical University from January 2016 to May 2019 were retrospectively analyzed. According to different pathogens, they were divided into bacteria infection group ( n=35) and non-bacteria infection group ( n=45). The general data, levels of serum TNF-α, C-reactive protein (CRP) and procalcitonin (PCT) were compared between the two groups. Logistic regression was performed to explore risk factors for pulmonary infection after liver transplantation. Receiver operating characteristic (ROC) curves were performed to analyze diagnostic value of TNF-α, CRP and PCT. Results:The levels of serum TNF-α, CRP and PCT in bacteria infection group were significantly higher than those in non-bacteria infection group ( P<0.05). Multivariate analysis showed that high TNF-α, CRP, and PCT levels were independent risk factors for bacterial pneumonia after liver transplantation. ROC analysis showed that sensitivity, specificity and areas under ROC curves (AUC) of TNF-α, CRP and PCT for diagnosis of bacterial pulmonary infection after liver transplantation were (80.12%, 72.12%, 80.18%), (83.45%, 73.46%, 83.38%) and (0.802, 0.751, 0.803), respectively. The AUC, sensitivity, and specificity between TNF-α and PCT for diagnosis of bacterial pulmonary infection after liver transplantation were similar ( P>0.05). The AUC, sensitivity and specificity of TNF-α for diagnosis of bacterial pulmonary infection after liver transplantation were better than those of CRP ( P<0.05). Conclusions:The diagnostic value of TNF-α for pulmonary infection after liver transplantation is similar to that of PCT, and is superior to CRP. It can be applied as a reliable index for identifying bacterial pneumonia and non-bacterial pneumonia.

Randomized controlled trial of the clinical effectiveness and safety of Jiuwei-Zhuhuang San plus amoxilcillin sodium and sulbactam sodium for the children with bacterial pneumonia / 国际中医中药杂志

Objective:To evaluate the clinical effectiveness and safety of Jiuwei-Zhuhuang San plus amoxilcillin sodium and sulbactam sodium in the treatment of bacterial pneumonia in children. Methods:A total of 120 patients with bacterial pneumonia in children from 1 year to 14 years old were randomly divided into the study group ( n=90) and the control group ( n=30) with ratio 3 to 1, the random sequence created by SAS software. Both groups were treated with amoxilcillin sodium for basic treatment, the observation group was treated with Jiuwei-Zhuhuang San. Both groups were treated for 1 week and followed up for 1 week. The cough frequency, clinical effective rate, symptoms and signs score, Traditonal Chinese medicine (TCM) pattern scores and adverse event rate were observed. Results:Eighteen cases were dropped and eliminated in the observation group, and 4 cases were dropped and eliminated in the control group, so 72 of observation group and 26 of control group were analyzed. After treatment, the clinical effective rate of the observation group was 27.8% (20/72), and the control group was 0% (0/26), where the difference was significant ( χ2=7.445, P=0.006). The difference of TCM syndrome score before and after treatment in the observation group (-16.8 ± 8.2 vs. -11.0 ± 5.8, t=-3.858) was lower than that of the control group ( P<0.01). There was significant difference between the two groups ( Z=-2.347, P= 0.019) in the TCM syndrome. The cough frequency of the observation group was 41.7% (30/72), and the control group was 26.9% (7/26). There wasn’t any significant differences in the cough frequency between two groups ( P>0.05). There was no statistical difference in symptoms and signs score or adverse event rate between two groups ( P>0.05). Conclusion:On the basis of amoxicillin sodium and sulbactam sodium, combined use of Jiuwei-Zhuhuang San can improve the clinical effectiveness of children with bacterial pneumonia.

Pneumonia associada à ventilação mecânica: evidências científicas / Ventilator-associated pneumonia: scientific evidence / Neumonía asociada al ventilador: evidencia científica

Justificativa e Objetivos: identificar os fatores relacionados à prevenção de Pneumonia Associada à Ventilação Mecânica em pacientes de unidades de terapia intensiva. Método: revisão integrativa com buscas, nas bases de dados LILACS, MEDLINE, SCOPUS e BDENF, entre 2007 e 2016, por estudos que apresentassem fatores relacionados ao desenvolvimento da pneumonia em questão. A amostra final foi composta por nove estudos que abordaram como fatores de proteção a manutenção da cabeceira elevada entre 30° e 45°, a higiene oral com clorexidina, a necessidade de aspiração prévia à mudança de decúbito e a adoção de sistema de aspiração subglótica. Conclusão: o conhecimento sobre os fatores de risco e a aplicação de medidas preventivas podem contribuir para reduzir a incidência deste agravo no âmbito intensivo.(AU)

Background and objectives: to identify factors related to the prevention of ventilator-associated pneumonia in patients of intensive care units. Method: this is an integrative review with searches for studies that presented factors related to the disease in question, in the LILACS, MEDLINE, SCOPUS and BDENF databases, between 2007 and 2016. The final sample consisted of nine studies that addressed as protective factors: maintenance of headboard elevation between 30° and 45°, oral hygiene with chlorhexidine, aspiration prior to decubitus change and adoption of Subglottic Aspiration System. Conclusion: the knowledge about risk factors and the application of preventive measures can contribute to reduce the incidence of this disease in the intensive care environment.(AU)

Justificación y Objetivos: identificar los factores relacionados con la prevención de la neumonía asociada al ventilador en pacientes en unidades de cuidados intensivos. Método: revisión integradora con búsquedas en las bases de datos LILACS, MEDLINE, SCOPUS y BDENF, entre 2007 y 2016, de estudios que tratan de los factores asociados al desarrollo de la referida neumonía. La muestra se compuso de nueve artículos, que abarcan como factores protectores el mantenimiento elevado de la cabecera entre 30° y 45°, la higiene oral con clorhexidina, la necesidad de aspiración antes del cambio de decúbito y la adopción del sistema de aspiración subglótica. Conclusiones: el conocimiento sobre los factores de riesgo y la aplicabilidad de medidas preventivas pueden contribuir a la reducción de la incidencia de este problema en el área intensiva.(AU)

[Analysis of risk factors and prognosis of cirrhosis combined with bacterial pneumonia].

Objective: To study the bacterial pathogen, the optimal plan of antibacterial drugs and the prognostic factors in patients with liver cirrhosis combined with bacterial pneumonia. Methods: Data of 324 cases with liver cirrhosis from the Department of Traditional and Western Medical Hepatology, the Third Hospital of Hebei Medical University were collected, including 217 cases of bacterial pneumonia. Baseline characteristics of the patients, factors affecting the efficacy of antibacterial treatment and prognosis were compared and analyzed. Logistic regression analysis was used to screen and predict the antibacterial efficacy indicators and a prediction model was established. Receiver operating characteristic curve was used to evaluate the value of the established model and Child-Turcortte-Pugh, model for end-stage liver disease, and model for end-stage liver disease combined with serum sodium concentration predict the therapeutic efficacy. Results: Chronic HBV and HCV infections were the main causes of cirrhosis, followed by cryptogenic cirrhosis and alcoholic cirrhosis. Diabetes, cardio-cerebrovascular and chronic obstructive pulmonary disease were susceptible factors for bacterial pneumonia. As infection occurred, the ratio of neutrophils to lymphocytes, serum C-reactive protein, procalcitonin, alanine aminotransferase, and total bilirubin levels had increased significantly. The results of pathogenic analysis showed that the top three pathogenic bacteria were Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Staphylococcus aureus. The resistance rate of Klebsiella pneumoniae to ceftriaxone was 50.0%, and that of ceftazidime, cefepime, and cefoperazone sulbactam were 27.8%. Imipenem and piperacillin tazobactam containing ß-lactamase inhibitors were the most effective antibacterial therapies. Regression analysis showed that age, procalcitonin, and albumin was significantly correlated with antibacterial effects. The PAA model was established and had predicted the efficacy of Child-Turcortte-Pugh, model for end-stage liver disease, and model for end-stage liver disease combined with serum sodium. The specificity and sensitivity of the PAA was confirmed to be 94.12% and 93.62%, which was significantly higher than other models. Conclusion: The main common pathogenic bacterium of cirrhosis combined with bacterial pneumonia is Klebsiella pneumonia (G-bacilli). In addition, gram positive cocci (Staphylococcus aureus) and other are also visible. The elderly, diabetics and patients using hormones are prone to secondary fungal infections. Age, procalcitonin and serum albumin can accurately predict the antibacterial effect, guide clinical treatment and judge the prognosis of the established PAA model.

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

BACKGROUND AND OBJECTIVE: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays. METHODS: In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases. RESULTS: CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. CONCLUSION: CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

Epidemiologic Survey of Legionella Urine Antigen Testing Within a Large Wisconsin-Based Health Care System.

PURPOSE: Legionella pneumophila pneumonia is a life-threatening, environmentally acquired infection identifiable via Legionella urine antigen tests (LUAT). We aimed to identify cumulative incidence, demographic distribution, and undetected disease outbreaks of Legionella pneumonia via positive LUAT in a single eastern Wisconsin health system, with a focus on urban Milwaukee County. METHODS: A multilevel descriptive ecologic study was conducted utilizing electronic medical record data from a large integrated health care system of patients who underwent LUAT from 2013 to 2017. A random sample inclusive of all positive tests was reviewed to investigate geodemographic differences among patients testing positive versus negative. Statistical comparisons used chi-squared or 2-sample t-tests; stepwise regression followed by binary logistic regression was used for multivariable analysis. Positive cases identified by LUAT were mapped to locate hotspots; positive cases versus total tests performed also were mapped by zip code. RESULTS: Of all LUAT performed (n=21,599), 0.68% were positive. Among those in the random sample (n=11,652), positive cases by LUAT were more prevalent in the June-November time period (86.2%) and younger patients (59.4 vs 67.7 years) and were disproportionately male (70.3% vs 29.7%) (P<0.0001 for each). Cumulative incidence was higher among nonwhite race/ethnicity (1.91% vs 1.01%, P<0.0001) but did not remain significant on multivariable analysis. Overall, 5507 tests were performed in Milwaukee County zip codes, yielding 82 positive cases by LUAT (60.7% of all positive cases in the random sample). A potential small 2016 outbreak was identified. CONCLUSIONS: Cumulative incidence of a positive LUAT was less than 1%. LUAT testing, if done in real time by cooperative health systems, may complement public health detection of Legionella pneumonia outbreaks.

Diagnóstico etiológico de la neumonía: un problema en la práctica clínica pediátrica / Etiological diagnosis of pneumonia: a problem of pediatric clinical practice

Resumen La neumonía en niños es causa frecuente de morbilidad y mortalidad, especialmente en países de bajos ingresos; es indispensable proporcionar una adecuada conducta terapéutica, idealmente orientada por etiología, pues la principal consecuencia de no establecer un diagnóstico etiológico preciso es el abuso de antibióticos. La evaluación clínica y radiológica son los pilares básicos para el diagnóstico de neumonía, y el conocimiento del comportamiento epidemiológico de los gérmenes y los biomarcadores ayudan a su aproximación etiológica. Se revisaron aspectos prácticos sobre el diagnóstico de la neumonía en niños, abordando criterios clínicos y epidemiológicos (edad y género), reactantes de fase aguda, hallazgos radiológicos y modelos de predicción etiológica utilizados como herramientas para la diferenciación de neumonía bacteriana de viral en menores de 18 años, en escenarios donde no se dispone rutinariamente de técnicas más precisas para diagnóstico rápido, como aquellas de tipo inmunológico o moleculares. MÉD.UIS.2020;33(1):39-52.

Abstract Pneumonia in children is a frequent cause of morbidity and mortality, especially in low-income countries. Due to this, it is indispensable to get a right therapeutic behavior, ideally focused by etiology, because the main consequence of not establishing an accurate etiological diagnosis is the abuse of antibiotics. The radiologic and clinic evaluations are basic pillars for pneumonia diagnosis and the knowledge in epidemiological behavior and biomarkers is very useful for an etiological approximation. Practical aspects were reviewed about pneumonia diagnosis in children, addressing clinic and epidemiological criteria (age and gender), acute phase reactants, radiological findings and etiological prediction models used as tools for differentiation between viral and bacterial pneumonia in children under 18 years old, in scenarios where it is not possible to find techniques for a right diagnostic, as those of immunologic and molecular types. MÉD.UIS.2020;33(1):39-52.