Abnormal Sounds Research Articles

Leveraging Real-World Evidence to Define Severe RSV Lower Respiratory Tract Disease in Adults.

As no standard case definitions for respiratory syncytial virus-associated lower respiratory tract disease (RSV-LRTD) in adults are available, this study analyzed definitions for severe RSV-LRTD from previously published data in hospital and community cohorts of adults with RSV-associated symptoms. The frequency, sensitivity, and specificity of acute respiratory disease symptoms among hospitalized and community cohorts of adults with RSV were analyzed. RSV-LRTD signs/symptoms assessed included shortness of breath (dyspnea), cough and/or fever, wheezing/rales/rhonchi (abnormal lung sounds by auscultation), sputum production, tachypnea, hypoxemia, and pleuritic chest pain. Dyspnea and tachypnea provided the best differentiation between hospitalized and community RSV-positive cases. The severe RSV-LRTD case definition yielding one of the highest and best-balanced sensitivity and specificity was dyspnea paired with either abnormal lung sounds by auscultation, hypoxemia, tachypnea, cough and/or fever, sputum, or chest pain. Dyspnea alone, and in combination with certain other lower respiratory tract disease signs/symptoms, was a leading symptomatic indicator for severe RSV outcomes. These results contribute to the harmonization of case definitions for RSV disease.

Clinical application of CAD/CAM technology in 3D printed dental model used for removable splints fabrication in the treatment of traumatized anterior permanent teeth in children

ObjectivesTo evaluate the effectiveness of computer-aided design (CAD)/computer-aided manufacturing (CAM) in designing and manufacturing 3D-printed dental models used to make thermoplastic removable splints for children with traumatized anterior permanent teeth. MethodsA total of 28 patients with dental trauma (subluxation, extrusive luxation, and lateral luxation) were divided into experimental (14 patients) and control groups (14 patients). The experimental group was treated with the removable splint made using a new 3D printed dental model designed with CAD/CAM technology, while the control group received a wire-composite splint. Follow-up was performed at week 2 and 1, 2, 3, 6, 12, 18, and 24 months post-treatment, and every year after that. The traumatized teeth were evaluated according to clinical symptoms, tooth color, mobility, position, percussion pain, abnormal percussion sounds indicating ankylosis or other pathosis, swelling, fistula of gums, vitality test, and radiographic examination. ResultsNo significant difference was found in gender and age between the two groups. The experimental group had 23 traumatic teeth, and the control group had 24 traumatic teeth. Traumatic teeth in the two groups most commonly involved open apex teeth (>90 %). The average follow-up time for the experimental and control group was 35.47±14.23 and 32.92±14.79 months (P > 0.05), respectively. The pulp survival rate and periodontal healing rate in the experimental group (86.96 %, 91.30 %) were higher compared to the control group (62.50 %, 75.00 %) (all P > 0.05). ConclusionA removable splint made by a 3D printed dental model designed with CAD/CAM technology achieved an equivalent treatment effect to a wire-composite splint. Clinical significanceA 3D-printed dental model designed with CAD/CAM technology can be used to fabricate removable splints, which in turn can decrease iatrogenic injuries frequently encountered during the traditional model production process. For example, taking the impression directly from the patient's mouth can cause displacement of a traumatized tooth or even exfoliation. The removable splints produced using a new 3D-printed dental model can meet clinical treatment requirements of traumatized teeth, especially for children and adolescents with occlusal trauma in mixed dentition.

Evaluation of the chronic oral toxicity of the classical ancient prescription Kai-Xin-San

Ethnopharmacological relevanceThe Kai-Xin-San (KXS), as an ancient classic prescription, has been used for the treatment of amnesia for thousands of years. Modern clinical and non-clinical pharmacological studies have found that it has significant therapeutic effects on dementia and depression, but there are relatively few studies on its safety. Aim of the studySubacute and chronic toxicity studies were conducted to investigate the symptoms, severity, target organs, development and recovery of toxic reactions, as well as the toxic dose. These studies provide technical data for ensuring the safety of KXS. Materials and methodsIn the sub-acute toxicity study, rats were orally administered KXS at doses of 0.80, 1.61, 3.22, and 6.43 g/kg body weight for a duration of 4 weeks. In the chronic toxicity study, rats were orally administered KXS at doses of 0.27, 0.81, and 2.43 g/kg body weight for a duration of 26 weeks, and a withdrawal study was conducted for a period of 4 weeks after the treatment.The rats were observed daily for clinical signs and mortality. Changes in body weight, food consumption, and water consumption were periodically monitored. Additionally, urinalysis results, hematological and biochemical parameters, relative organ weights, and pathology were monitored at specific observation time points. ResultsIn the sub-acute toxicity study, necropsy of dead and moribund rats revealed evident distension and swelling of the gastrointestinal tract, as well as thinning of the intestinal wall. The main adverse reactions observed included flatulence, piloerection, abnormal breathing sounds, and emaciation. Doses of 1.61 g/kg and below did not cause animal death. The gastrointestinal system is the main target organ of toxicity. In the chronic toxicity study, the no-observed-adverse-effect-level (NOAEL) of KXS was 0.27 g/kg, and its toxic effects were primarily concentrated in the gastrointestinal system. This led to secondary pathological changes in the immune system, hematopoietic system, and heart, suggesting that relevant indicators should be monitored when large doses are used clinically for an extended period of time. ConclusionsDuring the rodent toxicity evaluation, severe gastrointestinal damage was observed when KXS, powdered with crude drugs, was administered. The NOAEL for rats was found to be 0.27 g/kg/day.

Whistle or squeal? Distinguish and evaluate two typical abnormal sounds of collaborative robots

The whistle and squeal are two typical abnormal sounds among the radiated noise of running collaborative robots, indicating potential faults and causing discomfort through auditory perception. This study investigated whether psychoacoustic parameters for sound quality could distinguish the whistle and squeal and evaluate their discomfort. A total of 20 abnormal sound stimuli, including 6 whistles and 14 squeals, were obtained from various collaborative robots. The cepstrum characteristics and sound quality metrics (i.e., roughness, sharpness, fluctuation strength and tonality) of all stimuli were analyzed by controlling the equal loudness. Twenty-four participants rated the 'discomfort of abnormal sound in the stimuli' on an 11-point scale. The results show that the modulation frequencies of the fundamental frequency of the whistles were 2.9 Hz to 5.5 Hz, whereas the squeals had no obvious modulation characteristics. The modulation frequencies of the fundamental frequency were highly correlated (with a correlation coefficient of 0.89) to the discomfort responses for whistles, whereas tonality was significantly correlated (with a correlation coefficient of 0.82) to the discomfort responses for squeals. The linear models using single dependent variables of modulation frequency in cepstrum and tonality had determination coefficients of 0.77 and 0.66, respectively, in evaluating the discomfort of whistles and squeals.

Integrating Mixup and ArcFace for enhanced anomalous sound detection

In machine condition monitoring, accurate detection of anomalous sounds is essential for timely maintenance interventions. Traditional deep neural network models employ classification-based self-supervised learning to discriminate normal and abnormal operational sounds using classification confidence. However, these models often suffer from overfitting or excessive confidence, limiting their effectiveness. To overcome this, we introduce a hybrid technique that combines the strengths of Mixup and ArcFace techniques. Mixup enhances model generalization by training on mixed sound data, while ArcFace promotes greater inter-class distances with an angular margin loss. However, there is a challenge in combining these methods because the angular margin for ArcFace is defined for a single target class, and hence, can be hardly defined for the mixed data utilized in Mixup. The proposed technique employs the class-dependent angular margins based on the mixing ratio of data. SoftMax losses for individual classes are constructed using the class-dependent margins and then integrated into the Mixup loss. When applied to the DCASE2020 Task2 dataset, the proposed method significantly outperforms the state-of-the-art NoisyArcMix model in anomaly detection. This approach demonstrates the potential of leveraging class-dependent angular margins in mixed data for improved performance in machine condition monitoring.

A lung sound classification model with a spatial and channel reconstruction convolutional module

To construct a model with a spatial and channel reconstruction convolutional module for accurate identification and classification of lung sound data. We propose a convolutional network architecture combining the spatial-channel reconstruction convolution (SCConv) module. A lung sound feature extraction method combining the dual tunable Q-factor wavelet transform (DTQWT) with the triple Wigner-Ville transform (WVT) was used to improve the model's ability to capture the key features of the lung sounds by adaptively focusing on the important channel and spatial features. The performance of the model for classification of normal, crackles, wheezes, and crackles with wheezes was tested using the ICBHI2017 dataset. The accuracy, sensitivity, specificity and F1 score of the proposed method reached 85.68%, 93.55%, 86.79% and 90.51%, respectively, demonstrating its good performance in classification tasks in the ICBHI2017 lung sound database, especially for distinguishing normal from abnormal lung sounds.

Acute leptospirosis in horses: A retrospective study of 11 cases (2015-2023).

Reports of leptospirosis in horses are limited. To describe the clinical and diagnostic findings of acute systemic leptospirosis in horses. Eleven client-owned horses presented to an equine hospital because of acute onset of disease between 2015 and 2023. Retrospective case series. Horses diagnosed with leptospirosis by 1 or more of urine PCR, serologic microscopic agglutination test (MAT), and histopathology. Common clinical signs included lethargy (10), anorexia (10), fever (9), tachypnea (9), abnormal lung sounds (9), and epistaxis (6). Acute kidney injury was present in all cases. Evidence of pulmonary hemorrhage and liver disease was found in 8 (73%) and 6 (55%) horses, respectively. In 6 (55%) horses, kidneys, lungs, and liver were affected. Urine quantitative polymerase chain reaction for detection of pathogenic Leptospira spp. was positive in 6 (55%) cases. On serology Leptospira interrogans serovar Australis, Autumnalis, and Bratislava accounted for 86% of all titers ≥1 : 800. Overall case fatality rate was 4/11 (36%). Main findings on necropsy were tubular necrosis, interstitial nephritis, hemorrhage in the alveoli, pulmonary edema, periportal hepatitis and necrosis, cholestasis, and cholangitis. Leptospirosis should be considered as a differential diagnosis in horses with evidence of acute systemic inflammation and acute renal injury, epistaxis, or hepatic disease. For increased likelihood of identifying positive cases, both MAT serology and urine PCR should be performed.

The Epidemiologic and Clinical Features of Radiographic-Confirmed Community-Acquired Pneumonia Among Chinese Children: A Retrospective Hospital-Based Study.

To help understand the disease burden of vaccine-preventable bacterial disease, we delineated the epidemiologic and clinical characteristics of radiographic-confirmed community-acquired pneumonia (CXR-CAP) among Chinese children. We retrospectively screened the electronic database of the hospital information system to identify all pediatric CAP cases admitted to the Children's Hospital of Soochow University between 2010 and 2014. Radiographic findings and clinical data were extracted from the medical charts through individual chart reviews. CXR-CAP cases were defined as the presence of consolidation or pleural effusion noted on chest radiograph reports. We employed a multivariate logistic regression model to identify the potential risk factors associated with CXR-CAP. Among the 27,485 hospitalized CAP cases with radiologic data, 6322 (23.00%) were identified as CXR-CAP cases, while 21,163 (77.00%) were categorized as non-CXR-CAP cases. Children with CXR-CAP were notably older than those without CXR-CAP (non-CXR-CAP; χ2 = 1313.22; P < 0.01). CXR-CAP cases exhibited a higher rate of intensive care unit admission (3.55% vs. 1.94%; P < 0.01), extended hospital stays (73.87% vs. 63.79%; P < 0.01) and increased mortality rates (0.19% vs. 0.04%; P < 0.01). The factors associated with CXR-CAP included age (>12 months), season (summer and autumn), fever, abnormal breath sounds, C-reactive protein (>8 mg/L) and alanine transaminase (>40 U/L). CXR-CAP cases consisted of a substantial proportion of hospitalized patients with CAP and had more severe clinical manifestations than in-patients without CXR-CAP among Chinese children.

Marantic Endocarditis Associated with Metastatic Fibrolamellar Hepatocellular Carcinoma in a Young Adult: About a Case

We describe the case of a 21-year-old patient presenting with marantic endocarditis of the aortic valve occurring in the context of underlying metastatic fibrolamellar hepatocellular carcinoma. The patient was admitted due to a pulmonary embolism complicating deep vein thrombosis in the right lower limb. Upon admission, the patient was hemodynamically stable and afebrile, with a poor general condition. Physical examination revealed a painful swelling in the right lower limb extending up to the thigh, hepatomegaly suggestive of a tumor, and moderate ascites. Cardiac auscultation did not reveal any murmurs or abnormal sounds. Laboratory tests showed a non-specific inflammatory syndrome, and blood cultures were negative. The electrocardiogram indicated regular sinus tachycardia. Echocardiography revealed a mobile, hyperechoic mass located on the ventricular aspect of the aortic valve, without regurgitation or stenosis. The patient was started on heparin therapy. Unfortunately, the patient passed away at home two weeks later, and no autopsy was performed.

Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds

The normal and abnormal sounds from the respiratory system shed great light on medical science by revealing the quality, diseases and changes in people’s lungs. In medicine, this easy and old method, realized by the stethoscope, facilitates the diagnosis of diseases by specialists. This manual method can sometimes lead to wrong decisions in terms of sound detection due to different audibility. Detailed sound analysis is crucial for accurately detecting lung diseases with high mortality rates. As technology advances, the development of automated approaches based on machine learning is of great interest as they provide modern and highly accurate analysis. In today’s most popular topic, i.e., the COVID-19 disaster, the conflict between early detection of respiratory disease and machine learning for sound signal processing is extremely important. In this study, a machine learning model was developed to detect respiratory system sounds such as sneezing/coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the “Pfizer digital medicine challenge” on the “OSFHOME” open access platform. In the database provided in this challenge, which consists of 3 parts, features that effectively showed coughing/sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. The sequential forward selection (SFS) feature selection method was used to select the relevant and dominant variables among the obtained features to represent the model fully and accurately. Three different classification techniques were considered for successful respiratory sound classification in the dataset containing more than 3800 sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, decision tree and ensemble aggregation classification methods were used as classification techniques. In an attempt to classify coughing/sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.

Manual and ventilator hyperinflation parameters used by intensive care physiotherapists in Sri Lanka: An online survey.

Hyperinflation is a common procedure to clear secretion, increase lung compliance and enhance oxygenation in mechanically ventilated patients. Hyperinflation can be provided as manual hyperinflation (MHI) or ventilator hyperinflation (VHI), where outcomes depend upon the methods of application. Hence it is crucial to assess the application of techniques employed in Sri Lanka due to observed variations from recommended practices. This study is aimed to evaluate the application and parameters used for MHI and VHI by physiotherapists in intensive care units (ICUs) in Sri Lanka. An online survey was conducted among physiotherapists who are working in ICUs in Sri Lanka using WhatsApp groups and other social media platforms. A total of 96 physiotherapists responded. The survey comprised of three sections to obtain information about socio-demographic data, MHI practices and VHI practices. Most of the respondents (47%) worked in general hospitals and 74% of participants had a bachelor's degree in physiotherapy; 31.3% had 3-6 years of experience; 93.8% used hyperinflation, and 78.9% used MHI. MHI was performed routinely and as needed to treat low oxygen levels, abnormal breath sounds, and per physician orders while avoiding contraindications. Self-inflation bags are frequently used for MHI (40.6%). Only a few participants (26%) used a manometer or tracked PIP. In addition to the supine position, some participants (37.5%) used the side-lying position. Most physiotherapists followed the recommended MHI technique: slow squeeze (57.3%), inspiratory pause (45.8%), and quick release (70.8%). VHI was practised by 19.8%, with medical approval and it was frequently performed by medical staff compared to physiotherapists. Treatment time, number of breaths, and patient positioning varied, and parameters were not well-defined. The study found that MHI was not applied with the recommended PIP, and VHI parameters were not identified. The study indicates a need to educate physiotherapists about current VHI and MHI practice guidelines.

Application of CNN Model on Medical Images (Chest X-Rays)

Chest diseases encompass a range of conditions affecting the lungs, including infections caused by bacteria, viruses, or other microorganisms. These diseases can vary in severity, posing a potential threat to vulnerable individuals such as young children, older adults, and those with weakened immune systems. Detecting chest diseases involves employing diverse methods, such as physical examination, chest x-rays, blood tests, and sputum culture. During a physical examination, healthcare professionals utilize a stethoscope to listen for abnormal breathing sounds or crackling noises in the chest. They also assess factors like fever and respiratory rate. Chest x-rays provide a visual representation of the lungs, enabling the identification of areas displaying inflammation or fluid accumulation. Sputum culture involves collecting a sample of mucus expelled from the lungs through coughing and testing it for the presence of microorganisms. Key Words: Deep Learning, CNN, Chest X-Ray Images.

Approaches for Respiratory Sound Analysis in Identification of Respiratory Diseases

Purpose: Medical professionals throughout the world prefer to use conventional stethoscopes to listen to respiratory sounds. Listening to respiratory sounds through stethoscopes is a subjective matter, and proper diagnosis of the disease depends on the skills and ability of the doctor. Computerized analysis of respiratory sounds can help doctors and researchers to characterize different abnormal respiratory patterns and make informed decisions.&#x0D; Materials and Methods: This study includes previously reported work in different normal and abnormal respiratory sounds. The IEEE, PubMed, Google Scholar and Elsevier databases were searched and studies with the keywords of lung sound analysis, respiratory sound analysis, and respiratory sound classification were included. Detailed characteristics of normal and abnormal respiratory sounds are mentioned. In addition, Time-amplitude characteristics of different respiratory sound plots are obtained using MATLAB and ICBHI database. This study systematically discusses different approaches for respiratory sound analysis like visual analysis of the time-amplitude signals, frequency analysis, and spectral analysis using fast Fourier transform, statistical analysis, and machine learning approach. A list of relevant datasets is mentioned that can help researchers to do further analysis in this domain.&#x0D; Results: The careful observations and analysis show the possibility of predicting respiratory diseases by extracting suitable parameters such as the frequency response and spectral characteristics of the signal. Power spectral density can help us to calculate the maximum, median frequency over an extended period. Using machine learning we can estimate the energy, entropy, spectral features, and wavelets of the signals.&#x0D; Conclusion: Computer-based respiratory sound analysis can help medical professionals in making informed decisions. This will help in early diagnosis and devise effective treatment plans for the patients.

Laryngeal and oropharyngeal adenocarcinoma with pulmonary metastases in a common raven (Corvus corax).

A captive 15-year-old male common raven (Corvus corax) was presented for post-mortem examination. It had been previously presented to a local veterinarian due to a 3-4 weeks long history of abnormal respiratory sounds. Upon admission, the bird demonstrated severe dyspnea and a massive amount of mucous in the oropharynx. After symptomatic treatment, dyspnea deteriorated dramatically, and euthanasia was elicited because of poor prognosis. The necropsy revealed a 2.65 x 2.15 x 2.18 cm expansile and poorly delineated cauliflower-shaped mass around the glottis and extending inside the tracheal lumen. Additionally, a dilated salivary gland in the adjacent tissue and multifocal reddish-fleshy areas in the lung parenchyma were detected. Histopathological examination identified the mass as moderately differentiated, tubular adenocarcinoma with invasive growth and moderate to marked cellular atypia and numerous mitoses. The presumptive origin of the neoplasia was one of the salivary glands. Multiple metastases were identified in the lung both macroscopically and histologically. Bacterial culture and molecular testing for West Nile and Usutu viruses were negative. To the authors' knowledge, this is the first report of metastatic laryngeal and oropharyngeal adenocarcinoma in a common raven.

DeepRespNet: A deep neural network for classification of respiratory sounds

Respiratory sounds convey significant information about the pulmonary status. This study proposes a deep learning-based framework to create an automatic, non-invasive, diagnostic method of categorizing pulmonary sounds. A labelled database of pulmonary sounds has been collected using an electronic stethoscope and audio recording instrument. Two deep learning architectures, 1D DeepRespNet and 2D DeepRespNet are proposed in this work that were trained and evaluated with normalised 1-D time series and 2-D spectrograms of acoustic signals of six types of lung sounds, respectively. The models were highly optimized to yield superior performance on the considered dataset. Experimental results demonstrate that the 2D DeepRespNet model trained with spectrogram-based representations yields higher accuracy of 95.2% on the test data as compared to the 1D DeepRespNet trained on the time-series data. The proposed model may be deployed on a single board computer or integrated into a smartphone to develop a standalone diagnostic tool to accurately and objectively classify abnormal lung sounds with low time complexity.

A survey study on antibiotic prescription practices for acute asthma exacerbations: An European academy of allergy and clinical immunology task force report.

Guidelines recommend treating asthma exacerbations (AAEs) with bronchodilators combined with inhaled and/or systemic corticosteroids. Indications for antibiotic prescriptions for AAEs are usually not incorporated although the literature shows antibiotics are frequently prescribed. To investigate the antibiotic prescription rates in AAEs and explore the possible determining factors of those practices. A digital survey was created to determine the antibiotic prescription rates in AAEs and the influencing factors for the prescription practices. The survey was distributed among European academy of allergy and clinical immunology (EAACI) members by mass emailing and through regional/national societies in the Netherlands, Italy, Greece, and Poland. Furthermore, we retrieved local antibiotic prescription rates. In total, 252 participants completed the survey. Respondents stated that there is a lack of guidelines to prescribe antibiotics in AAEs. The median antibiotic prescription rate in this study was 19% [IQR: 0%-40%] and was significantly different between 4 professions: paediatrics 0% [IQR: 0%-37%], pulmonologists 25% [IQR: 10%-50%], general practitioners 25% [IQR: 0%-50%], and allergologists 17% [IQR: 0%-33%]) (p=0.046). Additional diagnostic tests were performed in 71.4% of patients before prescription and the most common antibiotic classes prescribed were macrolides (46.0%) and penicillin (42.9%). Important clinical factors for health care providers to prescribe antibiotics were colorised/purulent sputum, abnormal lung sounds during auscultation, fever, and presence of comorbidities. In 19% of patients with AAEs, antibiotics were prescribed in various classes with a broad range among different subspecialities. This study stresses the urgency to compose evidence-based guidelines to aim for more rational antibiotic prescriptions for AAE.

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method’s potential in the identification of indoor abnormal sound events.

Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial.

The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown. This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation. First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as "normal," "abnormal," or "undetermined." Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system. The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation. The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.

Single intratracheal administration toxicity study on safety of vapor inhalation of electrolyzed reduced water in rats.

The effects of acute intratracheal administration of electrolyzed reduced water (ERW; alkaline electrolyzed water) were investigated in rats. In this study, no deaths or near-deaths were recorded in either group, namely those treated with ERW or purified water (maximum doses of 900 mg/kg). The main symptoms observed in the rats were decreased spontaneous movements and abnormal breath sounds, which were considered to be transient symptoms caused by intratracheal administration. In addition, low values of alkaline phosphatase, total protein and lactate dehydrogenase were found in BALF tests, but these values were considered to be of low toxicological significance, since they are usually high in the presence of lung inflammation or cellular damage. This suggests that the alkalinity of ERW partially contributes to broken peptide bonds in proteins. There were no significant increases in bronchoalveolar lavage fluid protein in either group. ERW did not cause an increase in the influx of neutrophils, eosinophils, basophils, or lymphocytes, suggesting that intratracheal administration of ERW did not cause lung inflammation. ERW did not cause abnormalities in the body or pathological changes in the lungs. Aggregates of alveolar macrophages, as a measure of inflammation, were observed in both groups. These may be transient symptoms due to intratracheal administration, not due to ERW toxicity. This study confirmed the safety of intratracheal ERW infusion and demonstrated the low risk of acute toxicity for inhalation exposure to ERW aerosol or vapor. Therefore, ERW may be an effective air purifier against viruses or bacteria.

The Art of Diagnosis from Breath Sound: A Literature Review

Breath sounds provide relevant information related to lung abnormalities. It is often difficult to differentiate between breath sounds, this is due to the different characteristics of each breath sound. Differentiating the types of breath sounds is crucial in making an accurate diagnosis. Breath sounds are divided into normal breath sounds and abnormal (additional) breath sounds. Normal breath sounds are sounds that originate from the chest wall, such as tracheal, bronchial, bronchovesicular and vesicular breath sounds. Meanwhile, abnormal (additional) breath sounds are breath sounds that indicate an abnormal condition in the respiratory system. Normal and abnormal breath sounds have different characteristics such as intensity, duration, frequency, quality of air flow, air flow pattern, air distribution, body position, location of sound production, changes in pressure and vibration of dense tissue in the lungs.