Traditional Diagnosis Methods Research Articles

Case report: Metagenomic next-generation sequencing for the diagnosis of rare Nocardia aobensis infection in a patient with immune thrombocytopenia

BackgroundNocardiosis poses a diagnostic challenge due to its rarity in clinical practice, non-specific clinical symptoms and imaging features, and the limitations of traditional detection methods. Nocardia aobensis (N. aobensis) is rarely detected in clinical samples. Metagenomic next-generation sequencing (mNGS) offers significant advantages over traditional methods for rapid and accurate diagnosis of infectious diseases, especially for rare pathogens.Case presentationA 52-year-old woman with a history of immune thrombocytopenia for over 2 years was hospitalized for recurrent fever and cough lasting for 10 days. Her initial diagnosis on admission was community-acquired pneumonia, based on chest computed tomography findings of lung inflammation lesion. Empirical treatment with moxifloxacin and trimethoprim-sulfamethoxazole (TMP-SMZ) was initiated. However, her condition failed to improve significantly even after 1 week of treatment. Bronchoalveolar lavage fluid (BALF) subjected to mNGS revealed the presence of N. aobensis, resulting in a diagnosis of pulmonary nocardiosis caused by N. aobensis. This diagnosis was also supported by Sanger sequencing of the BALF. After adjusting the antibiotic regimen to include TMP-SMZ in combination with imipenem, the patient’s condition significantly improved. She was finally discharged with instructions to continue oral treatment with TMP-SMZ and linezolid for 6 months. The patient’s first follow-up 1 month after discharge showed good treatment outcomes but with obvious side effects of the drugs. Consequently, the antibiotic regimen was changed to doxycycline, and the patient continued to improve.ConclusionWe report the first detailed case of pulmonary nocardiosis caused by N. aobensis diagnosed by mNGS. mNGS could be an effective method that facilitates early diagnosis and timely decision-making for the treatment of nocardiosis, especially in cases that involve rare pathogens.

Alginate‑carbon dot nanocomposite: A green approach towards designing turn-on aptasenor for Candida albicans fungus

Candida albicans fungus, an opportunistic microorganism that becomes a pathogen in its host, can often cause superficial and severe infections in human body. In this work, a turn-on fluorescent aptasenor based on Alginate‑carbon dot nanocomposite has been designed through a green approach. Carbon dots (CDs) with average size of 4 ± 1 nm were synthesized hydrothermally using Philodendron bipinnatifidum plant extract as precursor. Then, mixture of carbon dot and alginate was cross-linked by Ca2+ ions to make Alginate-CD nanocomposite with negligible effect on fluorescence intensity of CDs (λex = 360 nm, λem = 400 nm). The fluorescence quantum yield and lifetime were found as 14.1 % and 3.15 ns, respectively. The nanocomposite was characterize using different spectroscopic (Photoluminescence, UV–visible, XPS, FT-IR) and microscopic (TEM and SEM) analytical techniques. An aptamer was used to selectively target (1 → 3)-β-D-glucan on the surface of the Candida albicans cell wall. It was found that the fluorescence energy resonance transfer between nanocomposite and aptamer quenches the fluorescence intensity of nanocomposite, making it suitable for designing turn-on fluorescent probe. It was found that the fluorescence could be recovered in the presence of Candida albicans fungus (in <30 min), owing to higher affinity between aptamer and (1 → 3)-β-D-glucan on the surface of Candida albicans fungus, where there were two linear ranges: low concentration of 50–200 cells/mL and high concentration of 1000–6000 cells/mL (R2 ≥ 0.989 for both linear ranges), with limit of detection of 40 cells/mL. The selectivity of the designed probe was examined against a mixture of microbial agents (Staphylococcus aureus and Escherichia coli) and Curdlan polysaccharide (1→3)-b-D-Glucan (L). The designed biosensor showed an excellent ability to directly detect Candida albicans fungus via a simple and fast (<30 min) approach. The results of this experimental study show that Alginate-CD-aptamer nanocomposite is a bright prospect in clinical applications and a suitable alternative to traditional methods of Candida albicans diagnosis.

A Retrospective Analysis of Broad-Range Microbial PCR-Sequencing Findings and Culture Results in 101 Bronchoalveolar Lavage Specimens

Abstract Introduction/Objective Pneumonia is a significant cause of illness and death, identifying its cause is challenging and critical for effective antibiotic treatment and for reducing the length of stay. Bronchoalveolar lavage is commonly performed to obtain high-quality lower respiratory tract samples. While traditional culture methods have been the standard for pathogen detection, microbial rRNA broad-range polymerase chain reaction (BR-PCR) has emerged as an alternative method. Methods/Case Report A retrospective analysis was conducted on 101 patients suspected of severe pulmonary infections at Oregon Health and Science University (OHSU). Results (if a Case Study enter NA) When important microbial etiology is considered, culture detected 26%, while BR- PCR detected 20%. Both methods proved effective in detecting common pathogens; however, culture demonstrated superiority in detecting fungal pathogens due to potential sampling biases associated with PCR. Furthermore, culture holds the advantages of being faster and more cost-effective, although it may fail to detect difficult to non-cultivable organisms, like Legionella spp. Culture is indispensable for susceptibility testing and strain typing purposes. More often than not, BR-PCR captures airway microbiota indiscriminately with minimal clinical significance, potentially confusing clinical interpretation. Conclusion Novel technologies like Biofire Pneumonia Panel (BPP), a multiplex PCR, have shown promise in the rapid detection of pneumonia pathogens including bacteria, viruses, and resistance genes (&amp;lt; 2 hours), complementing the traditional culture methods for microbiological diagnosis. Based on this analysis we recommend introducing BPP at OHSU.

Diagnosis of Schizophrenia Using EEG Sensor Data: A Novel Approach with Automated Log Energy-Based Empirical Wavelet Reconstruction and Cepstral Features.

Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual's life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This study presents an innovative automated approach for detecting SZ acquired through electroencephalogram (EEG) sensor signals, aiming to improve diagnostic efficiency and accuracy. We utilised Fast Independent Component Analysis to remove artefacts from raw EEG sensor data. A novel Automated Log Energy-based Empirical Wavelet Reconstruction (ALEEWR) technique was introduced to reconstruct decomposed modes based on their variability, ensuring effective extraction of meaningful EEG signatures. Cepstral-based features-cepstral activity, cepstral mobility, and cepstral complexity-were used to capture the power, rate of change, and irregularity of the cepstrum of preprocessed EEG signals. ANOVA-based feature selection was applied to refine these features before classification using the K-Nearest Neighbour (KNN) algorithm. Our approach achieved an exceptional accuracy of 99.4%, significantly surpassing previous methods. The proposed ALEEWR and cepstral analysis demonstrated high precision, sensitivity, and specificity in the automated diagnosis of schizophrenia. This study introduces a highly accurate and efficient method for SZ detection using EEG technology. The proposed techniques offer significant improvements in diagnostic accuracy, with potential implications for enhancing SZ diagnosis and patient care through automated systems.

Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients.

Anemia diagnosis is crucial for pediatric patients due to its impact on growth and development. Traditional methods, like blood tests, are effective but pose challenges, such as discomfort, infection risk, and frequent monitoring difficulties, underscoring the need for non-intrusive diagnostic methods. In light of this, this study proposes a novel method that combines image processing with learning-driven data representation and model behavior for non-intrusive anemia diagnosis in pediatric patients. The contributions of this study are threefold. First, it uses an image-processing pipeline to extract 181 features from 13 categories, with a feature-selection process identifying the most crucial data for learning. Second, a deep multilayered network based on long short-term memory (LSTM) is utilized to train a model for classifying images into anemic and non-anemic cases, where hyperparameters are optimized using Bayesian approaches. Third, the trained LSTM model is integrated as a layer into a learning model developed based on recurrent expansion rules, forming a part of a new deep network called a recurrent expansion network (RexNet). RexNet is designed to learn data representations akin to traditional deep-learning methods while also understanding the interaction between dependent and independent variables. The proposed approach is applied to three public datasets, namely conjunctival eye images, palmar images, and fingernail images of children aged up to 6 years. RexNet achieves an overall evaluation of 99.83 ± 0.02% across all classification metrics, demonstrating significant improvements in diagnostic results and generalization compared to LSTM networks and existing methods. This highlights RexNet's potential as a promising alternative to traditional blood-based methods for non-intrusive anemia diagnosis.

Automated early ovarian cancer detection system based on bioinformatics

Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient’s serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.

Optimal preservation condition for the extraction of cell-free DNA from bile of patients with biliary tract cancer.

Patients with biliary tract cancer (BTC) often have dismal outcomes due to the poor performance of traditional methods for early diagnosis. Recently, bile cell-free DNA (cfDNA) has been reported as a potential liquid biopsy material for BTC diagnosis. However, bile is a complex alkaline aqueous medium, and the proper storage conditions for bile remain to be explored. The aim of this study is to explore the effects of storing bile under various conditions on the stability of bile cfDNA and to determine the optimal conditions, thereby establishing a foundation for the subsequent application of bile cfDNA in liquid biopsy for early diagnostic and prognosis monitoring of patients with malignant BTC. We evaluated the storage temperature and storage time for the preservation of bile samples. Bile samples were collected in cfDNA tubes with protectant covered inside or regular tubes without, and the stability of bile cfDNA was analyzed during 10 days at room temperature (RT) or after 2 months of storage at low temperatures. Bile cfDNA remained stable for bile samples being collected with cfDNA tubes and stored for 10 days at RT, while degraded with time for the case with regular tubes. When bile samples were collected with cfDNA tubes and stored for 2 months at 4 ℃, bile cfDNA remained stable, however, if collected with regular tubes, bile cfDNA exhibited a slight loss of integrity. No significant difference was observed for 2 months storage at -20 or -80 ℃. Our findings suggested that for bile cfDNA research, bile samples should be collected with cfDNA tubes and it can be transported for short-term shipment at RT, and could be stored at 4 ℃ with cfDNA tubes, or frozen at -20 ℃ with regular tubes.

Nanomaterials for Plant Disease Diagnosis and Treatment: A Review.

Currently, the excessive use of pesticides has generated environmental pollution and harmful effects on human health. The controlled release of active ingredients through the use of nanomaterials (NMs) appears to reduce human exposure and ecosystem alteration. Although the use of NMs can offer an alternative to traditional methods of disease diagnosis and control, it is necessary to review the current approach to the application of these NMs. This review describes the most recent and significant advances in using NMs for diagnosing and treating plant diseases (bacteria, phytopathogenic fungi, viruses, and phytopathogenic nematodes) in cultivated plants. Most studies have focused on reducing, delaying, or eliminating bacteria, fungi, viruses, and nematodes in plants. Both metallic (including metal oxides) and organic nanoparticles (NPs) and composites are widely used in diagnosing and controlling plant diseases due to their biocompatibility and ease of synthesis. Few studies have been carried out with regard to carbon-based NPs due to their toxicity, so future studies should address the development of detection tools, ecological and economic impacts, and human health. The synergistic effect of NMs as fertilizers and pesticides opens new areas of knowledge on the mechanisms of action (plant-pathogen-NMs interaction), the interaction of NMs with nutrients, the effects on plant metabolism, and the traceability of NMs to implement sustainable approaches. More studies are needed involving in vivo models under international regulations to ensure their safety. There is still controversy in the release of NMs into the environment because they could threaten the stability and functioning of biological systems, so research in this area needs to be improved.

Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model's decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system's performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Liquid biopsy: Comprehensive overview of circulating tumor DNA (Review).

Traditional tumor diagnosis methods rely on tissue biopsy, which can be invasive and unsuitable for long-term monitoring of tumor dynamics. The advent of liquid biopsy has notably improved the overall management of patients with cancer. Liquid biopsy techniques primarily involve detection of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). The present review focuses on ctDNA because of its significance in tumor diagnosis, monitoring and treatment. The use of ctDNA-based liquid biopsy offers several advantages, including non-invasive or minimally invasive collection methods, the ability to conduct repeated assessment and comprehensive insights into tumor biology. It serves crucial roles in disease management by facilitating screening of high-risk patients, dynamically monitoring therapeutic responses and diagnosis. Furthermore, ctDNA can be used to demonstrate pseudo-progression, monitor postoperative tumor status and guide adaptive treatment plans. The present study provides a comprehensive review of ctDNA, exploring its origins, metabolism, detection methods, clinical role and the current challenges associated with its application.

Precision Medicine in Periodontics: A Literature Review.

Periodontitis, a widespread health issue, requires effective prevention and management strategies due to its increasing prevalence and detrimental social consequences. The chronic inflammation associated with periodontitis also exacerbates systemic conditions, emphasizing the need for advanced approaches in addressing this public health concern. The traditional methods of periodontal diagnosis, which primarily rely on clinical indicators such as pocket depth, clinical attachment loss, mobility, and radiographic measurements of alveolar bone loss, have limitations in guiding therapy due to the intricate and multifaceted nature of periodontal diseases. Precision periodontics is the amalgamation of genomics, bioinformatics, and advanced technology, mainly biomarkers reflecting a precise patient-centered treatment. However, implementing this approach in periodontology is new due to the lack of validated periodontal biomarkers for diagnostic use. This article explores the foundations of personalized therapy in periodontal diagnosis. It discusses the current state and prospects of periodontal biomarkers as a crucial step toward realizing a precision approach in periodontal practice.

Intraluminal administration of indocyanine green as a method of intraoperative diagnostic of staple line leak during sleeve gastrectomy in an experiment.

Background: Bariatric surgery is an actively developing branch of surgery. At the same time, thanks to the use of modern hardware methods for disconnecting and stitching tissue, the number of postoperative complications have significantly decreased. However, staple line failure is still quiet high and the cause of severe postoperative complications. Traditional methods for intraoperative diagnosis of leakage are provocative tests: tests with methylene blue and air. One of the promising methods of intraoperative control in surgery is the use of indocyanine green (ICG) test . Aims: The purpose of the study is to assess the diagnostic value of intraoperative using fluorescent imaging with indocyanine green (ICG) in case of staple line leak during sleeve gastrectomy (SG) by modeling in pig models various causes of staple line failure using morphological studies. Methods: The study was carried out on 20 pigs, each of which underwent SG. The animals were divided into few groups: a control group standard SG (n=4), a group of SG with mechanical failure of staple line(n=12), and a group of SG with local ischemia of staple line (n=4). To assess a staple line leak methylene blue and indocynin green was injected into the stomach. The appearance of the methylene blue or visualization of indocyanine green fluorescence was assessed on the staple line. In the ischemia group ICG was administered intravenously. On the 7th day after surgery, the histological material for morphology examination was collected. Results: In the group of SG with mechanical failure of staple line (n=11) positive ISG test was in 10 cases and positive methylene blue test was in 2 cases. In 90% of cases, ICG leakage corresponded to the localization of modeling ischemia zone, while methylene blue leakage was noted in only 20% of cases. Conclusions: The intraluminal administration of indocyanine green in cases of "mechanical" failure of staple line during sleeve gastrectomy is more informative compared to the standart methylene blue test. ICG angiography data fully corresponds to the localization of modeling ischemia zone.

Reverse Transcription-polymerase Chain Reaction (RT-PCR) Based Detection and Identification of Monopartite Bean Common Mosaic Virus in Mungbean

Background: Bean common mosaic virus (BCMV) which belongs to the genus Potyvirus and the family Potyviridae, causes significant yield losses in legumes across the globe. Traditional methods for BCMV detection and diagnosis, such as visual inspection and serological assays, are often time-consuming, labour-intensive and may lack the sensitivity required for early detection. Advances in molecular techniques have made RT-PCR a powerful tool for the accurate detection of BCMV-infected plants. Methods: Three pairs of specific primers that flank the BCMV coat protein and polyprotein gene were designed using the Primer 3web tool to amplify fragments approximately ranging from 191 to 205 bp. The RT-PCR protocol was then standardized, including the optimization of annealing temperatures for all three primers, followed by the sequencing of the amplified PCR products. The, specificity and sensitivity of primer pair BCMV2 were then established. Result: Three pairs of specific primers were designed and RT-PCR protocol was standardized. The sequenced PCR products showed 98.03% to 99.50 % nucleotide similarity with the corresponding sequences of BCMV isolates used in primer designing. Further, the BCMV2 primer pair specifically amplified the target amplicon (200 bp) from the suspected leaf sample and no amplicon was observed when tested against other pathotypes of potyvirus such as BCMNV, BYMV, CABMV, SMV and PeMoV. The designed primer was sensitive enough to detect 0.05% of target cDNA. Additionally, the RT-PCR protocol was validated using field samples collected from Hyderabad, India. ELISA in combination with RT-PCR using the developed specific BCMV primer pair will ensure a foolproof, sensitive and rapid, procedure for diagnosis of BCMV in the quarantine processing of imported germplasm.

VoiceAD: A multilingual speech based classifier for early detection of Alzheimers disease

Alzheimer's Disease (AD) causes memory decline beyond normal aging and is the most common cause of dementia. Early detection and intervention can delay the progression of AD and alleviate symptoms. However, high expense, insensitivity, and inconvenience are among various deterrents to traditional diagnosis methods. Dementia impacts more than 55 million people globally, with this number set to double every 20 years. Much of this increase will be in low- and middle-income countries which often lack the resources for early diagnosis. Recently speech has emerged as an effective digital biomarker for AD detection. However, most studies in this area are limited to English speech due to lack of suitable data in other languages. This study aims to construct a speech-based classifier (VoiceAD) that can be trained on English data and still maintain its performance on other languages. Acoustic features were extracted from English, Spanish, and Greek data from DementiaBank with the eGeMAPS v2.0 feature set. Subsequently, an Extra Trees model was employed for classification. VoiceADs performance for English and Greek surpassed that of the baseline model and model incorporating only demographic data while the Spanish performance was lower due to task effect. In addition, VoiceADs satisfactory generalizability and interpretability positions it as a promising candidate for clinical applications. It can be implemented as a free screening tool such as a mobile application to monitor AD risk. VoiceADs crosslingual applicability can its enable worldwide deployment, especially in settings where traditional methods of diagnosis are less attainable.

Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Dental cavity analysis, prediction, localization, and quantification using computer vision

Dental health assessment is a critical component of overall well-being, and advancements in computer vision and deep learning have opened new avenues for automating and enhancing this process. In this study, we present a comprehensive approach to dental cavity analysis, spanning localization, quantification, and visualization. Our methodology leveraged a diverse dataset of colored dental images that had been meticulously augmented and annotated. The You Only Look Once model was employed for precise dental cavity localization, providing bounding box predictions. Remarkably, these results were obtained based on images from standard device cameras. Subsequently, we introduced the use of the segment anything model segmentation model, known for its zero-shot generalization capabilities, to focus on the exact areas of dental cavities. This approach enhanced the granularity of our analysis, providing dental professionals with detailed visualizations for precise diagnosis. During the quantification phase, we extracted cavity areas from bounding box coordinates, enabling accurate measurement of cavity sizes. The model achieved a notable mean average precision of 0.732, an accuracy of 0.789, and a recall of 0.701. Moreover, the model converged quickly, with most metrics achieving near-optimal results after 100 iterations. This quantitative data augments traditional diagnosis methods, facilitating more informed treatment decisions.

Application of metagenomic next-generation sequencing in the etiological diagnosis of refractory pneumonia in children.

Metagenomic next-generation sequencing (mNGS) was used to analyze the etiological distribution of refractory pneumonia in children. We compared its efficacy in pathogen diagnosis against traditional methods to provide a basis for clinical adjustment and treatment. A total of 60 children with refractory pneumonia treated at the Department of Respiratory Medicine, Children's Hospital Affiliated with the Capital Institute of Paediatrics, from September 2019 to December 2021 were enrolled in this study. Clinical data (including sex, age, laboratory tests, complications, and discharge diagnosis) and lower respiratory tract specimens were collected, including bronchoalveolar lavage fluid (BALF), deep sputum, pleural effusion, lung abscess puncture fluid, traditional respiratory pathogens (culture, acid-fast staining, polymerase chain reaction, serological testing, etc.), and mNGS detection methods were used to determine the distribution of pathogens in children with refractory pneumonia and to compare the positive rate and diagnostic efficiency of mNGS and traditional pathogen detection for different types of pathogens. Among the 60 children with refractory pneumonia, 43 specimens were positive by mNGS, and 67 strains of pathogens were detected, including 20.90% (14 strains) of which were Mycoplasma pneumoniae, 11.94% (8 strains) were Streptococcus pneumoniae, 7.46% (5 strains) were cytomegalovirus, and 5.97% (4 strains) were Candida albicans. Thirty-nine strains of Mycoplasma pneumoniae (41.03%, 16 strains), Streptococcus pneumoniae (10.26%, 4 strains), Candida albicans (7.69%, 3 strains), and Aspergillus (5.13%, 2 strains) were detected using traditional methods. The positive rate of mNGS detection was 90.48%, and the positive rate of the traditional method was 61.90% (p = 0.050), especially for G+ bacteria. The positive rate of mNGS was greater than that of traditional methods (p < 0.05), but they had no significant difference in detecting G- bacteria, viruses, fungi, or Mycoplasma/Chlamydia. Among the 60 patients, 21 had mixed infections, 25 had single infections, and the other 14 had unknown pathogens. Mycoplasma pneumoniae was most common in both mixed infections and single infections. The sensitivity, specificity, positive predictive value, and negative predictive value of mNGS were 95.45, 37.50, 80.77, and 75.00%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the traditional methods were 72.72, 62.50, 84.21, and 45.45%, respectively. The clinical compliance of mNGS was 80.00%, and that of the traditional method was 70.00%. The sensitivity and negative predictive value of mNGS were high, and the difference in the sensitivity for detecting G+ bacteria was statistically significant (p < 0.05). However, the differences in G- bacteria, fungi, and Mycoplasma/Chlamydia were not statistically significant (p > 0.05). Due to the small sample size, statistical analysis could not be conducted on viral infections. mNGS has higher overall efficacy than traditional methods for the etiological diagnosis of refractory pneumonia in children. The application of mNGS can significantly improve the detection rate of pathogens in children with refractory pneumonia. The sensitivity and negative predictive value of mNGS for detecting G+ bacteria are greater than those of other methods, and it can exclude the original suspected pathogenic bacteria. Unnecessary antibiotic use was reduced, but there was no statistically significant difference in G- bacteria, fungi, or Mycoplasma/Chlamydia.

Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification

Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.

A quantitative internal-short-circuit diagnosis method of lithium-ion batteries for float charging systems

The diagnosis of internal short circuit (ISC) faults in lithium-ion batteries (LIBs) plays an important role in improving battery safety and reducing the occurrence of fire and explosion accidents. Traditional ISC diagnosis methods mainly focus on dynamic operating conditions, and rarely consider stable float charging scenarios with high risks. This paper proposes a quantitative diagnosis strategy for ISC faults in LIBs under different float charging conditions. First, a simulation model of ISC faults for single cells and battery modules under float charging conditions is established. Then, for the LIB packs without a balancing system, three quantitative diagnosis methods (namely Map-based, constant-voltage-source based, and intermittent-charging based) for the ISC faults applicable to different simple and complex float charging systems are proposed. Finally, the method is verified through simulation and experiments, and the results show that an ISC of 0–500 Ω can be detected in various scenarios, and the proposed methods are low-cost, high-precision, and easy to implement for early ISC diagnosis of float charging systems for energy storage.