Cases Of Bacterial Pneumonia Research Articles

Impact of the COVID-19 Pandemic on Pediatric Bacterial Community-Acquired Pneumonia: A Multicenter Retrospective Study in Madrid (Spain).

This study conducted in Madrid (Spain) between 2018 and 2023 shows a significant decrease in the pediatric bacterial community-acquired pneumonia cases during the COVID-19 pandemic, followed by a notable postpandemic increase surpassing prepandemic incidence. Streptococcus pneumoniae remains predominant, with an increasing prevalence of serotype 3, while Streptococcus pyogenes was the second most common pathogen.

Sentinel surveillance in bacterial pneumonia in children under 5 years old in a fourth-level pediatric hospital in Colombia 2016-2022

ObjectivesSentinel surveillance for bacterial pneumonia (SSBP) allows the monitoring of immunopreventable diseases. The results of the SSBP carried out at HOMI, Fundación Hospital pediátrico de la Misericordia, are presented. MethodsThe daily active search for cases was carried out in accordance with the protocol of the Pan American Health Organization from January 2016 to December 2022. ResultsThere were 6970 suspected cases of bacterial pneumonia (BP). Among the 3048 (43.7%) patients with probable BP, cultures were obtained from 2777 (91.1%), and BP was confirmed in 190 (6.2%). The causes were Streptococcus pneumoniae in 98 (51.6%) cases, Spn19A in 47 (48%), Spn3 in 16 (16%), and Spn6C in 4 (4%). Haemophilus influenzae was found in 32 (16.8%) cases: non-typeable H. influenzae in 20 (62.5%), H. influenzae type b in five (15.6%), and H. influenzae type a in two (6.2%). Other bacteria were found in 60 (31.6%) cases. A total of 51.6% and 42.9% of patients with S. pneumoniae had decreased sensitivity to penicillin and ceftriaxone, respectively. ConclusionsThe serotypes included in the conjugate vaccines for pneumococcus and H. influenzae b decreased significantly, and serotype turnover was observed. Among the S. pneumoniae cases, Spn19A, Spn3, and Spn6C were the most frequent serotypes and associated with multiresistance. The SSBP allows us to determine the clinical behavior of the disease and to detect the serotypes emergence.

Efficacy and safety of carrimycin in ten patients with severe pneumonia following solid organ transplantation.

The number of patients undergoing solid organ transplantation has increased annually. However, infections in solid organ transplant recipients can have a severe effect on patient survival owing to the continued use of immunosuppressants. Carrimycin is a novel macrolide antibiotic produced by genetically engineered streptomyces spiramyceticus harboring a 4''-O-isovaleryltransferase gene (ist) from streptomyces thermotoleran. Carrimycin has good antibacterial and antiviral effects. However, no relevant studies have been conducted on the efficacy and safety of carrimycin in patients with severe pneumonia (SP) after solid organ transplantation. To explore the efficacy and safety of carrimycin in patients with SP after solid organ transplantation to provide a medication reference for clinical treatment. In March 2022, ten patients with SP following solid-organ transplantation were treated at our hospital between January 2021 and March 2022. When the condition was critical and difficult to control with other drugs, carrimycin was administered. These ten patients' clinical features and treatment protocols were retrospectively analyzed, and the efficacy and safety of carrimycin for treating SP following solid organ transplantation were evaluated. All ten patients were included in the analysis. Regarding etiological agent detection, there were three cases of fungal pneumonia, two cases of bacterial pneumonia, two cases of Pneumocystis pneumonia, and three cases of mixed infections. After treatment with carrimycin, the disease in seven patients significantly improved, the course of the disease was significantly shortened, fever was quickly controlled, chest computed tomography was significantly improved, and oxygenation was significantly improved. Finally, the patients were discharged after curing. One patient died of acute respiratory distress syndrome, and two patients discontinued treatment. Carrimycin is a safe and effective treatment modality for SP following solid organ transplantation. Carrimycin may have antibacterial and antiviral effects in patients with SP following solid organ transplantation.

Effectiveness of Drip Infusion of Lascufloxacin, a Novel Fluoroquinolone Antibiotic, for Patients with Pneumonia Including Chronic Lung Disease Exacerbations and Lung Abscesses.

Lascufloxacin (LSFX), a novel fluoroquinolone antibacterial agent, has recently been used as a drip infusion for treating pneumonia, apparently with good effectiveness against various bacteria, including anaerobes, and good intrapulmonary penetration. The clinical effectiveness of LSFX was retrospectively investigated for the 55 patients admitted to our hospital with pneumonia, including chronic lung disease exacerbations and lung abscesses, from May 2021 to July 2023. The median age of the 55 patients was 76.1 (34.1-93.1) years, 45 (81.8%) were male, and 48 (87.5%) patients had underlying disease. Community-acquired pneumonia was seen in 47 (85.5%) patients, including 9 (16.4%) with lung abscess, and the other 8 (14.5%) had nursing and healthcare-associated pneumonia/hospital-acquired pneumonia. Moderate pneumonia was present in 33 (61.8%) of 55 patients, and LSFX was used as a second-line treatment for 28 (50.9%) patients in whom first-line antibiotics were ineffective. The median duration of intravenous LSFX administration was 9 (2.0-49) days. Streptococcus pneumoniae and methicillin-susceptible Staphylococcus aureus were isolated from 3 (7.1%) and 2 (4.8%) patients, respectively. Of the 55 patients, 45 (81.5%) improved clinically with intravenous LSFX administration; 20 (95.2%) of 21 community-acquired pneumonia cases, including 9 (100.0%) of 9 bacterial pneumonia cases, were improved by LSFX as first-line treatment, and 8 (88.9%) of 9 lung abscess patients also showed clinical improvement with LSFX as a second-line treatment. There were no severe adverse effects in any of the 55 patients. Based on these data, intravenous administration of LSFX seems effective for bacterial pneumonia, including chronic lung disease exacerbations and lung abscesses, and it appears to have broad antimicrobial activity and good tissue penetration into the lung.

Compare Clinical Characteristics of Psittacosis Pneumonia in 35 Patients and of Non Psittacosis Bacterial Pneumonia in 46 Patients.

We aimed to describe the difference between Chlamydia psittaci pneumonia group and non C. psittaci bacterial pneumonia group in community acquired pneumonia in this single-center clinical study. We collected the data of 35 patients with C. psittaci pneumonia cases and 46 patients with non C. psittaci bacterial pneumonia cases diagnosed with metagenomic next-generation sequencing assays from February 2019 to December 2021 in Huaihua First People's Hospital in China. In the C. psittaci pneumonia group, 35 patients (100%) had a chance of exposure to poultry or birds, and their body temperature was greaterthanorequalto 39.0°C. The other common symptoms were a slow pulse (68.6%), cough (65.7%), expectoration (54.3%), chills (51.4%) and a shortness of breath (37.1%). Laboratory tests showed that >90% of the cases had markedly elevated infection indicators, and 97.1% of the cases had markedly declined calcium.The most common imaging finding was patchyshadows (94.3%), pleuraleffusion (68.6%), bilateral in 54.3% (n = 19) and unilateral in 45.7% (n = 16) participants, and 51.4% (n = 18) of cases met the criteria for severe pneumonia. In the non C. psittaci bacterial pneumonia group, 18 patients (39.1%) had a chance of exposure to poultry or birds, and 11 patients (23.9%) body temperature was greaterthanorequal to 39.0°C. Laboratory tests showed that >67% of cases had a mildlyelevated infection indicators, and mildly declined serumalbumin. The following characteristics are more likely to help distinguish C. psittaci pneumonia from non C. psittaci bacterial pneumonia. Including had a chance of exposure to poultry or birds, high fever, exhibit chills, expectoration, relatively slow pulse, and progress into severe pneumonia. Percentage of neutrophils, C-reactive protein, procalcitonin, lactate dehydrogenase, and myoglobin levels are higher. Blood calcium and corrected calcium are lower. Chest CT showed pleural effusion, pericardial effusion, and mediastinal lymphadenopathy.

A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information.

This study aimed to develop a deep learning model based on chest radiography (CXR) images and clinical data to accurately classify gram-positive and gram-negative bacterial pneumonia in children to guide the use of antibiotics. We retrospectively collected CXR images along with clinical information for gram-positive (n=447) and gram-negative (n=395) bacterial pneumonia in children from January 1, 2016, to June 30, 2021. Four types of machine learning models based on clinical data and six types of deep learning algorithm models based on image data were constructed, and multi-modal decision fusion was performed. In the machine learning models, CatBoost, which only used clinical data, had the best performance; its area under the receiver operating characteristic curve (AUC) was significantly higher than that of the other models (P<0.05). The incorporation of clinical information improved the performance of deep learning models that relied solely on image-based classification. Consequently, AUC and F1 increased by 5.6% and 10.2% on average, respectively. The best quality was achieved with ResNet101 (model accuracy: 0.75, recall rate: 0.84, AUC: 0.803, F1: 0.782). Our study established a pediatric bacterial pneumonia model that utilizes CXR and clinical data to accurately classify cases of gram-negative and gram-positive bacterial pneumonia. The results confirmed that the addition of image data to the convolutional neural network model significantly improved its performance. While the CatBoost-based classifier had greater advantages owing to a smaller dataset, the quality of the Resnet101 model trained using multi-modal data was comparable to that of the CatBoost model, even with a limited number of samples.

Large‐scaled detection of COVID‐19 from X‐ray using transfer learning

AbstractThe pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures.

DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images.

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images.

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

First publication of endemic channels as part of a pediatric Antimicrobial Stewardship Program: when to turn on the alarms? Recommendations of a pediatric ASP program

BackgroundPediatric Antimicrobial Stewardship Programs (ASP) consider DOT a fundamental measure to quantify the impact of ASP. Novel strategies have been described, but no endemic channels (EC) have been reported to compare antibiotic use within historical patterns. This report describes the process of constructing an EC and analyzing its interpretation.MethodsThis was a descriptive study of the construction, implementation, and analysis of EC. The median and quartile method, as well as the geometric mean (GM) and confidence interval (CI) methods using DOT for the last 4 years were used. ECs have also been elaborated on in critical services (PICU).ResultsGM and CI method seem to be more sensitive in identifying changes in antimicrobial use. Ceftriaxone increased its use starting in December 2021, reaching the warning zone in March 2022 in relation to increased cases of bacterial and complicated pneumonia. Piperacillin–tazobactam showed an important increase in PICU during the first 8 months of 2021, reaching the alert zone until August 2021; thereafter, its use decreased, and this variation was related to a modification in the presentation of complicated appendicitis during the COVID 19 pandemic restrictions. The use of ampicillin-sulbactam has increased since January 2022 because of a change in local guidelines regarding its use in appendicitis and peritonitis. The changes identified in each EC allowed ASP to take different conducts.ConclusionEC allowed us to construct a new tool to measure ASP impact, internal comparison of antibiotic use facilitated taking timely interventions. EC could be useful for all pediatric and adult ASP.

The effect of Gaussian noise on pneumonia detection on chest radiographs, using convolutional neural networks

IntroductionChest X-rays (CXR) with under-exposure increase image noise and this may affect convolutional neural network (CNN) performance. This study aimed to train and validate CNNs for classifying pneumonia on CXR as normal or pneumonia acquired at different image noise levels. MethodsThe study used the curated and publicly available “Chest X-Ray Pneumonia” dataset of 5856 AP CXR classified into 1583 normal, 4273 viral and bacterial pneumonia cases. Gaussian noise with zero mean was added to the images, at 5 image noise variance levels, corresponding to decreasing exposure. Each noise-level dataset was split into 80% for training, 10% for validation, and 10% for test data and then classified using custom trained sequential CNN architecture. Six classification tasks were developed for five Gaussian noise levels and the original dataset. Sensitivity, specificity, predictive values and accuracy were used as evaluation performance metrics. ResultsCNN evaluation on the different datasets revealed no performance drop from the original dataset to the five datasets with different noise levels. Sensitivity, specificity and accuracy for the normal datasets were 98.7%, 76.1% and 90.2%. For the five Gaussian noise levels the sensitivity, specificity and accuracy ranged from 96.9% to 98.2%, 94.4%–98.7% and 96.8%–97.6%, respectively. A heat map was used for visual explanation of the CNNs. ConclusionThe CNNs sensitivity maintained, and the specificity increased in distinguishing between normal and pneumonia CXR with the introduction of image noise. Implications for practiceNo performance drops of CNNs in distinguishing cases with and without pneumonia CXR with different Gaussian noise levels was observed. This has potential for decreasing radiation dose to patients or maintaining exposure parameters for patients that require additional radiographs.

Pneumonia hospitalizations of children aged

ABSTRACT As infection with Streptococcus pneumoniae is an important cause of pneumonia in children, the World Health Organization recommends childhood pneumococcal conjugate vaccines (PCVs). In January 2017, PCV universal mass vaccination (UMV) was introduced in Poland for children aged <2 years. The objective of this study was to estimate and describe the trends in the incidences of various types of pneumonia hospitalizations in Poland before (2013–2016) and after (2017–2018) introduction of the UMV program. The study was conducted at the regional hospitals of Opole and Bialystok and included all hospitalized children aged <2 years with a primary or secondary diagnosis of pneumonia in their electronic medical records. Pneumonia diagnoses were identified based on International Classification of Diseases 10th revision (ICD-10) codes for bacterial, viral, and other/unknown-cause pneumonias. The effect of the implementation of PCV UMV was modeled via an inferential multivariate model. Among 4,168 children included in the study, 64.3% were admitted before PCV UMV. The number of radiograph-confirmed likely bacterial pneumonia cases varied between 55 and 176 cases per 100,000 person-years, and no trend was observed over time. However, inferential modeling showed statistically significant decreasing trends in the incidence rates of bacterial-coded pneumonia (28.48%), viral-coded pneumonia (35.36%), all-cause pneumonia (24.60%), and radiograph-confirmed likely non-bacterial pneumonia (24.98%) among children eligible for UMV. This might be the first indication of the impact of the PCV UMV program in Poland.

A machine learning framework for identifying influenza pneumonia from bacterial pneumonia for medical decision making

Accurately identifying viruses from bacteria determine the therapy and outcome of patients suffering from pneumonia. This study focuses on exploring the capability of machine learning techniques in detecting the well-known influenza virus pneumonia from bacterial pneumonia based on the key variable quantity of blood tests and computer tomography (CT). In this study, we propose an improved Harris hawk’s optimization (GSHHO) based on the Gaussian mutation mechanism and simulated annealing method, and then combine GSHHO with Fuzzy k-nearest neighbor (FKNN) to form a GSHHO-FKNN machine learning model, GSHHO-FKNN. The experimental results have demonstrated that the proposed GSHHO-FKNN has identified that ground grass opacity (GGO) and bronchovascular bundle were distinctive CT phenomenon of influenza pneumonia. Furthermore, the number of procalcitonin (PCT) and white blood cells (WBC) in the cases of influenza pneumonia were notable from those in the cases of bacterial pneumonia. In addition, an interesting result was that the absolute value of eosinophils, platelet and flake shadow in influenza pneumonia cases was more abnormal than those of bacterial pneumonia cases. It proved that the GSHHO-FKNN model was an effective method for discriminating influenza pneumonia from community acquired pneumonia, leading to accurate diagnosis against pathogenic microorganisms.

Pneumonia bacteriana em equinos associada à infecção por Escherichia coli: relato de cinco casos

ABSTRACT Respiratory diseases have a major impact on sport horses and are often cited as the second most common reason for loss of training days and significant veterinary costs. Adult horses most commonly develop pneumonia when bacteria aspirated from the environment, nose, or oropharynx reach the lower airways and overwhelm the pulmonary defense mechanisms. This article report five cases of bacterial pneumonia in horses associated with infection by Eschericia coli. Five Quarter horses, three males and two females, with ages varying from 5 to 12 years, were examined for diagnosis of respiratory disease characterized by apathy, cough and lack of appetite. Auscultation of the thorax revealed increased harsh breath sounds dorsally, crackles, wheezes, and dullness of respiratory sounds ventrally. Manipulation of the trachea and larynx induced cough. Culture results were positive for Escherichia coli. The therapeutic protocol consisted of anti-inflammatory, antibiotic therapy, and supportive care. Flunixin meglumine was administered intravenously at 24-hour intervals over eight days. Ceftiofur sodium was prescribed intramuscularly, q.d., for 15 days. Horses were clinically monitored daily until complete remission of clinical signs. None of the animals relapsed, and all returned to routine athletic activities.

Characteristics of patients with viral infections of the lower respiratory tract: A retrospective study.

While the impact of respiratory virus infections has been well researched in some respiratory diseases, no clinical studies have discussed the subject of who would be more likely to develop respiratory virus infections among patients with various respiratory illnesses who come from different backgrounds. This study aimed to identify respiratory diseases that are frequently associated with respiratory virus infections along with the characteristics of patients who develop such infections in clinical settings. Tested specimens were obtained from the lower respiratory tract by bronchoscopy to provide more accurate data. Data of bronchoscopies at Ryukyu University Hospital between August 2012 and September 2016 were reviewed, and patients who underwent multiplex polymerase chain reaction (PCR) tests for detecting respiratory viruses in bronchoscopy specimens were retrospectively recruited for descriptive statistics. Differences among patients’ primary pulmonary diseases and backgrounds were compared between the PCR-positive and -negative patients, and multivariate statistical analysis was performed to analyze factors associated with a positive PCR test result. Overall, 756 bronchoscopies were performed during the study period and PCR tests were performed for 177 patients. Of them, 27 tested positive for respiratory viruses, mainly parainfluenza virus and rhinovirus, and out of those, 7 were hospitalized for >1 month. Overall, all patients did not experience typical upper respiratory infection symptoms. In positive patients, 13 and 7 had diagnoses of interstitial lung disease and bacterial pneumonia, respectively. The diagnoses of 3 bacterial pneumonia cases were changed to viral pneumonia after receiving their PCR-positive tests. Respiratory virus infections were confirmed in 14 patients on immunosuppressant therapy and 4 on maintenance dialysis. Multivariate analysis revealed that immunosuppressant therapy and maintenance dialysis were independently associated with respiratory virus infections. Viruses were commonly detected in patients with interstitial lung diseases and bacterial pneumonia, while few patients were diagnosed with pure viral pneumonia. These illnesses were considered to be induced by respiratory infections. Immunosuppressant therapy and maintenance dialysis were associated with respiratory virus infections. Multiplex PCR testing is an essential diagnostic tool for respiratory virus infections in immunocompromised patients.

P16.13: COVID-19 Infection in Kidney Transplant Recipients From the IPS: Evolution of Kidney Function and Complications

Introduction: Transplant recipients, due to their immunocompromised condition and often other associated risk factors, present a high risk of infection and organ rejection. The objectives of this work have been to assess the evolution of transplant recipients with covid-19 infection and the development of kidney injury or rejection, comparing the unvaccinated (UV) and vaccinated (V) against sars-cov-2. Method: Descriptive, cross-sectional study with an analytical component in kidney transplant patients from our unit, with covid-19 infection, from March 2020 to February 2022. Statistical analysis: Excel 2010 program, includes medians, standard deviation, percentages, and t-test. Results: 29.4% of the total number of transplant patients (60/204) presented covid-19 infection; 37 unvaccinated patients (UV) and 23 vaccinated patients (V), mean age of 50±11 years and transplant time of 137±99 months. Kidney function one-month post-covid: creatinine 2.3±1.5mg/dl in UV and 1.8±1.1mg/dl in V (p= 0.07). MDRD4 clearance: 44.9±20 ml/min/1.73m2 in UV and 38.7±22.8 ml/min/1.73m2 V (p=0.03). The resistance index: 0.73±0.11 in UV and 0.69±0.04 in V (p= 0.023). Proteinuria in both populations without significant changes. Evolution: In the UV, 17 mild cases, 6 moderate, and 14 severe; in V, 15 mild cases, 4 moderate and 4 severe cases. Severity index: Ferritin in UV, 1069.4±678.8 ng/ml and in V, 397±441 ng/ml (P 0.0000099). D-dimer, 629.8±700.5 ng/ml in UV and 333.5±501.5 ng/ml in V (p= 0.03) Hospitalization was 56.7% and 34.7% in unvaccinated and vaccinated, respectively, with an average of 5.2±7.6 days of hospitalization, requiring invasive mechanical ventilation, 35% (13 cases) in UV and 4.3% (1 case) in V. Interstitial Pneumonitis due to covid-19 present in 28 unvaccinated and 10 vaccinated. Other complications: 2 activations of Cytomegalovirus; a case of Optic Nerve Neuritis; 23 cases of Bacterial pneumonia; 1 case of Pansinusitis; 3 cases of suspected kidney graft rejection. Mortality: 25% in UV (15/60 cases), and 1.6% (1/60 cases) in V. Conclusion: Renal function had a significant deterioration in UV patients with respect to V one month after covid. The unvaccinated patients evolved worse than the vaccinated ones with higher rates of severity, hospitalization, assisted respiration and deaths, which shows the importance of complete vaccination in immunosuppressed patients, from their pre-transplant preparation. Close follow-up is vital for early detection of post-covid complications.

Effects of Aerosol Inhalation Combined with Intravenous Drip of Polymyxin B on Bacterial Clearance, Symptoms Improvement, and Serum Infection Indexes in Patients with Pneumonia Induced by Multidrug-Resistant Gram-Negative Bacteria.

In recent years, the incidence of pneumonia caused by multidrug-resistant (MDR) Gram-negative bacteria (G−) has increased year by year. Polymyxin B has a good clinical effect in the treatment of MDR, but there is controversy about the administration route of this drug. In this study, we retrospectively analyzed the clinical data of 84 cases of MDR Gram-negative bacterial pneumonia, and aimed to explore the effects of aerosol inhalation combined with intravenous polymyxin B infusion on the bacterial clearance, symptom improvement, and serum infection indexes of MDR patients on the patients with Gram-negative (G−) bacterial pneumonia. The results show that aerosol inhalation combined with intravenous drip of polymyxin B can improve bacterial clearance rate, reduce levels of serum inflammatory factors, and improve clinical symptoms in patients with pneumonia induced by MDR G-bacteria.

Detection of causative agents of bacterial pneumonia in hospitalized hajj and umrah cases by multiplex real-time polymerase chain reaction.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is commonly detected in pneumonia patients who travel from the Middle East regions. Besides MERS-CoV, many other pathogenic agents cause pneumonia. Detection of such organisms must be done swiftly, especially in case of the negative MERS-CoV samples. The aim of this study was to identify the pathogenic agents that might account for bacterial pneumonia, from Hajj and Umrah pneumonia cases. We conducted a cross-sectional study, 38 pneumonia clinical samples from suffering of Hajj and Umrah in 2017 with negative MERS-CoV were selected. The laboratory testing was done at National Reference Laboratory in Jakarta and performed by multiplex real-time PCR using a FTD respiratory pathogens. Haemophilus influenzae (26.4%) was the most frequent bacteria detected. Other causative agents of bacterial pneumonia identified were Moraxella catarrhalis (20.8%), Klebsiella pneumoniae (13.2%), Streptococcus pneumoniae (9.4%), and Staphylococcus aureus (5.7%). From 38 samples showed that 25 (65.79%) samples were positive with bacteria, including five samples with coinfection. The coinfection were combinations among S. aureus and S. pneumoniae (1/20), S. pneumoniae and K. pneumoniae (1/20), S. pneumoniae and M. catarrhalis (2/20), S. pneumoniae and H. influenzae (2/20), K. pneumoniae and H. influenzae (5/20), and M. catarrhalis and H. influenzae (5/20). Haemophilus influenzae is the most recurrent bacteria to be identified in samples of pneumonia of hajj and umrah cases.

Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images

The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.

Artículo traducido] Mortalidad a 30 días en pacientes con fractura de fémur proximal durante la pandemia COVID-19 en Vizcaya (País Vasco)

ObjectivesCompare 30-day mortality rate following a proximal femur fracture (PFF) and SARS-CoV-2 infection versus a PFF and no SARS-CoV-2 infection. DesignRetrospective comparative study. SettingThree university hospitals in Biscay province (Basque Country, Spain). Patients77 patients over 65 years-old with PFF (AO 31-A and 31-B). InterventionStudy conducted between 9 March and 15 April 2020. The COVID-19 group included only patients with SARS-CoV-2 infection, confirmed by a positive RT-PCR test. Main outcome measurements30-Day mortality rate and risk factors for mortality. ResultsOf a total 77 patients, 10 were diagnosed with SARS-CoV-2 infection. Mean age was 85 years. Patients with SARS-CoV-2 infection had higher BMI (29.53 kg/m2) compared to patients without infection (24.09 kg/m2) (p = 0.001). No significant differences were found between both groups in terms of the Charlson Comorbidity Index, ASA score, use of oral anticoagulants or presence of cognitive impairment. Seven of the 10 patients in the COVID-19 group developed (viral) pneumonia, as compared to one single case (1/67) of (bacterial) pneumonia in the control group (p < 0.001). Thirty-days mortality was higher (p = 0.03) in COVID-19 patients (40%) than in those in the control group (11.9%). In the multivariate analysis, age ≥ 91 years-old, ASA class IV and BMI > 25 kg/m2 were significant predictors of 30-day mortality. All deaths in the COVID-19 group occurred while the patients were hospitalized. ConclusionsSARS-CoV-2 infection in patients with a PFF was seen to result in higher rates of early mortality, with COVID-19-related pneumonia being the leading direct cause of mortality. Level of evidenceLevel III study.