Background Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency. Methods This retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance. Results The predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001). Conclusions The BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Necrotizing soft tissue infections (NSTI) are rapidly progressing and life-threatening conditions that require prompt diagnosis. However, differentiating NSTI from other non-necrotizing skin and soft tissue infections (SSTIs) remains challenging. We aimed to evaluate the diagnostic value of the biochemical analysis of soft tissue infectious fluid in distinguishing NSTIs from non-necrotizing SSTIs. This cohort study prospectively enrolled adult patients between May 2023 and April 2024, and retrospectively included patients from April 2019 to April 2023. Patients with a clinical suspicion of NSTI in the limbs who underwent successful ultrasound-guided aspiration to obtain soft tissue infectious fluid for biochemical analysis were evaluated and classified into the NSTI and non-necrotizing SSTI groups based on their final discharge diagnosis. Common extravascular body fluid (EBF) criteria were applied. Of the 72 patients who met the inclusion criteria, 10 patients with abscesses identified via ultrasound-guided aspiration were excluded. Based on discharge diagnoses, 39 and 23 patients were classified into the NSTI and non-necrotizing SSTI groups, respectively. Biochemical analysis revealed significantly higher albumin, lactate, lactate dehydrogenase (LDH), and total protein levels in the NSTI group than in the non-necrotizing SSTI group, and the NSTI group had significantly lower glucose levels and pH in soft tissue fluids. In the biochemical analysis, LDH demonstrated outstanding discrimination (area under the curve (AUC) = 0.955; p < 0.001) among the biochemical markers. Albumin (AUC = 0.884; p < 0.001), lactate (AUC = 0.891; p < 0.001), and total protein (AUC = 0.883; p < 0.001) levels also showed excellent discrimination. Glucose level (AUC = 0.774; p < 0.001) and pH (AUC = 0.780; p < 0.001) showed acceptable discrimination. When the EBF criteria were evaluated, the total scores of Light’s criteria (AUC = 0.925; p < 0.001), fluid-to-serum LDH ratio (AUC = 0.929; p < 0.001), and fluid-to-serum total protein ratio (AUC = 0.927; p < 0.001) demonstrated outstanding discrimination. Biochemical analysis and EBF criteria demonstrated diagnostic performances ranging from acceptable to outstanding for NSTI when analyzing soft tissue infectious fluid. These findings provide valuable diagnostic insights into the recognition of NSTI. Further research is required to validate these findings.

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity with poor prognosis. The dysregulation of Notch signaling pathway has been implicated in the OSCC tumorigenesis. However, the clinical implication of NOTCH1 mutation status in OSCC remains unelucidated. We extracted the NOTCH1 gene mutations from a whole exome sequencing dataset of 168 frozen OSCC tumor specimens and validated these NOTCH1 gene mutations by Sanger sequencing. We also assessed these NOTCH1 gene mutations and its pathological significance in our OSCC tumor tissues using immunohistochemistry. Univariate and multivariate analyses were also used to determine whether the association between NOTCH1 mutation status and prognostic factors was independent of other parameters. In this study, we have identified 44 (26.19 %) NOTCH1 gene mutations from a whole-exome sequencing of 168 OSCC formalin-fixed, paraffin-embedded (FFPE) tissue specimen. These mutations distributed in different NOTCH1 function domains, including the EGF-like repeats region, negative regulatory region, and Ankyrin repeats region. The immunohistochemical staining analysis revealed that NOTCH1 expression was increased in oral cancer tissues. In addition, of the 43 OSCC tumors with NOTCH1 mutations, we observed that the majority were negative for NOTCH1 intracellular domain 1 (NICD1) staining (76.74 %), and 10 tumors were positive for NICD1 staining (23.26 %). In conclusion, our study suggested that NOTCH1 expression is associated with the progression of OSCC. We also demonstrated that presence of a mutated NOTCH1 gene will help prognostic stratification in OSCC when combined with other clinicopathologic parameters.