Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores.

OBJECT: To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model. METHODS: All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII. RESULTS: Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%. CONCLUSION: Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.

High-Quality Genome Assembly of Pseudopestalotiopsis theae, the Pathogenic Fungus Causing Tea Gray Blight.

Tea gray blight is one of the most serious foliar diseases of tea tree, caused by the plant-pathogenic fungus Pseudopestalotiopsis theae, which can affect production and quality of tea worldwide. We generated a highly contiguous, 50.41-Mbp genome assembly (N50 = 1.30 Mbp) of P. theae strain CYF27 by combining PacBio long-read and Illumina short-read sequencing technologies. We identified a total of 15,626 gene models, of which 1,038 genes encode putative secreted proteins. The high-quality genome assembly and annotation resource reported here will be useful for the study of fungal infection mechanisms and pathogen-host interaction.

Primary care workforce and continuous medical education in China: lessons to learn from a nationwide cross-sectional survey.

OBJECTIVES: This study aimed to examine the education and training background of Chinese community health centres (CHCs) staff, continuous medical education (CME) and factors affecting participation in CME. DESIGN: Cross-sectional survey. SETTING: Community health centres (CHCs). PARTICIPANTS: All doctors and nurses working in selected CHCs (excluding those solely practising traditional Chinese Medicine). MAIN OUTCOME MEASURES: CME recorded by CHCs and self-reported CME participation. METHODS: A stratified random sample of CHCs based on geographical distribution and 2:1 urban-suburban ratio was selected covering three major regions of China. Two questionnaires, one for lead clinicians and another for frontline health professionals, were administered between September-December 2015, covering the demographics of clinic staff, staff training and CME activities. RESULTS: 149 lead clinicians (response rate 79%) and 1734 doctors and 1846 nurses completed the survey (response rate 86%). Of the doctors, 54.5% had a bachelor degree and only 47% were registered as general practitioners (GPs). Among the doctors, 10.5% carried senior titles. Few nurses (4.6%) had training in primary care. Those who have reported participating in CME were 91.6% doctors and 89.2% nurses. CME participation in doctors was more commonly reported by older doctors, females, those who were registered as a GP and those with intermediate or senior job titles. CME participation in nurses was more common among those with a bachelor degree or intermediate/senior job titles or those with longer working experience in the CHC. CONCLUSION: Only half of doctors have bachelor degrees or are registered as GPs as their prime registration in the primary care workforce in China. The vast majority of CHC staff participated in CME but there is room for improvement in how CME is organised.