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BACKGROUND: S. pneumoniae (SPN) is the most common cause of pneumonia. The disease can be effectively prevented through immunisation. Since December 2020, the Malaysian Government has included the 10-valent pneumococcal conjugate vaccine (PCV10) for all infants born on or after 1 January 2020 as part of the National Immunisation Programme (NIP). However, the epidemiology of pneumonia remains poorly understood. To fill the knowledge gap, we established a multicentre surveillance study to understand the burden of pneumococcal pneumonia among young children in Peninsular Malaysia. METHODS: MY-Pneumo is a multicentre prospective case-control study conducted in three sentinel sites located in three different states of Peninsular Malaysia - Kuala Lumpur, Pahang, and Kelantan. A cohort of at least 500 incident cases and 500 controls is enrolled beginning in October 2021 and matched for age. Cases are hospitalised children < 5 years with radiologically confirmed pneumonia, and the controls are children without any features suggestive of pneumonia. Clinical samples, including nasopharyngeal swabs (NPS) and urine, are collected according to the study protocol. Biological fluids such as blood, cerebrospinal fluid (CSF) and pleural fluid are obtained from invasive pneumonia disease (IPD) patients, if available. All children are tested for SPN using polymerase chain reaction (PCR) and pneumococcal urine antigen test (PUAT) using BinaxNow. DISCUSSION: Surveillance data, including carriage rate, serotype variations and the phylogeny data structure of SPN among young children in Malaysia during PCV implementation, will be generated from this study. Trends and patterns of pneumococcal serotypes by different regions are important for targeted public health strategies. Our data will provide baseline information for estimating the impact of PCV10 implementation and will influence policymakers' decisions regarding the upgrade from PCV10 to a higher-valency conjugate vaccine in Malaysia. TRIAL REGISTRATION: This project was registered at ClinicalTrials.gov (NCT04923035) on 2021, June 11. The study protocol was approved by the International Medical University Joint-Committee on Research & Ethics (4.15/JCM-216/2021) and the Institutional Review Board at sentinel sites (USM/JEPeM/21020190, IREC 2021-114, MREC ID No: 2021128-9769) and University of Southampton's Ethics and Research Governance (ERGo II 64844).

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Neumonía Neumocócica , Humanos , Malasia/epidemiología , Estudios de Casos y Controles , Lactante , Preescolar , Estudios Prospectivos , Neumonía Neumocócica/epidemiología , Neumonía Neumocócica/prevención & control , Vacunas Neumococicas/administración & dosificación , Masculino , Streptococcus pneumoniae/aislamiento & purificación , Femenino

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Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance.

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Pneumonia is a highly transmitted disease in children. According to the World Health Organization (WHO), the most affected regions include South Asia and sub-Saharan Africa. 15% deaths of children are due to pneumonia. In 2017, 0.88 million children were killed under the age of five years. An analysis of pneumonia disease is performed with the help of a delayed mathematical modelling technique. The epidemiological system contemplates subpopulations of susceptible, carriers, infected and recovered individuals, along with nonlinear interactions between the members of those subpopulations. The positivity and the boundedness of the ongoing problem for nonnegative initial data are thoroughly proved. The system possesses pneumonia-free and pneumonia existing equilibrium points, whose stability is studied rigorously. Moreover, the numerical simulations confirm the validity of these theoretical results.

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With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

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Five patients in a geropsychiatric unit of a psychiatric hospital became abruptly ill with pneumonia caused by Streptococcus pneumoniae serotype 6A. Four other residents were colonized with the same serotype, which has previously not been reported in association with pneumonia outbreaks. Furthermore, serotype 6A is not included in all vaccine types, which may be important for the choice of vaccine in some settings. All isolates showed identical pulsed-field gel electrophoresis restriction patterns.

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Brotes de Enfermedades , Neumonía Neumocócica/diagnóstico , Neumonía Neumocócica/microbiología , Streptococcus pneumoniae/clasificación , Streptococcus pneumoniae/aislamiento & purificación , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Masculino , Neumonía Neumocócica/epidemiología

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ObjectiveTo explore the surgical methods and its efficacy on severe chronic obstructive pneumonia disease (COPD) with spontaneous pneumothorax.MethodsClinical features and surgical efficacies of 16 cases of severe COPD were analyzed.Results No death occurred and all patients recovered and discharged.The lung function index including subjective symptoms,motor ability and endurance in 14 cases showed obvious improvement compared with those before combination of pneumothorax.Two patients improved to the level before surgery.ConclusionThe lung function index of patients with severe COPD and pnumothorax can be relatively less restricted and open-chest surgery should be performed as soon as possible.Linear nailing and simple lung volume reduction surgery are recommended to improve lung function,shorten operation time and reduce surgical risks.

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Objective To assess the clinical efficacy of budesonide in combination with salbutamol sulfate and ambroxol through fog inhalation in the treatment of elderly patients older than 70 years with acute exacerbation chronic obstructive pneumonia disease (AECOPD). Methods A total of 58 elderly patients older than 70 years with AECOPD were randomly assigned to receive medication of budesonide in combination with salbutamol sulfate and ambroxol through fog inhalation in addition to conventional therapy ( n = 58 treatment group). In the control group,56 AECOPD patients received conventional therapy (including oxygen uptaking,anti-infection, medication, relieving asthma, eliminating sputum, nutritional support, et al). Results The overall effective rate in the treatment group was 76.79% (43/56) ,which was significantly lower than that in the control group 89. 66% (52/58) ( χ2 = 6. 46, P = 0. 007 ). Blood gas analysis ( pH, PaCO2, PaO2 ) and lung function ( FEV1/FVC, FEV1 occupy prospect% ) outcome were significantly improved after treatment (Ps ＜ 0.05 ). We found no significant difference in the comparison of the blood gas analysis improvement between the treatment and control ( Ps ＞ 0. 05 ), whereas significant difference was observed in the improvement on lung function between the two groups (P ＜ 0. 05 ). Conclusion Combined budesonide with salbutamol sulfate and ambroxol through fog inhalation in addition to conventional therapy has a significant better effect and less side effects on the treatment of AECOPD patients older than 70 years.