Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

Graphene is a 2D material that has emerged as a versatile and advanced material for biosensing technology due to its large surface area, high conductivity, and biocompatibility. These properties make it well-suited for label-free detection of biomarkers with high sensitivity and accuracy, which is crucial for early diagnosis of various diseases, environmental monitoring, and food safety. This review highlights recent progress in graphene-based biosensor technologies, emphasizing key fabrication methods such as mechanical exfoliation, liquid phase exfoliation, chemical vapor deposition, electrochemical exfoliation, and microwave-assisted exfoliation, which are highly effective and suitable for generating graphene at an industry level. Furthermore, the study highlights characterization techniques such as Raman spectroscopy, optical spectroscopy, scanning electron microscope, transmission electron microscopy, and atomic force microscopy, which ensure quality and functionality of the graphene in biosensing applications. While hurdles like enhancing conductivity and achieving large-scale production remain, graphene-based biosensors offer a transformative approach to delivering precise and consistent results across various industries, paving the way for innovative solutions in diagnostics and monitoring systems.

Objective: The relationship between proton pump inhibitors (PPIs) and asthma is controversial. The goal of this study was to determine the association between PPI use in non-asthma subjects and their subsequent asthma prevalence. Design: nationalwide, population-based cohort study Methods: We conducted a nationwide, population-based retrospective cohort study using data from the National Health Insurance Research Database (NHIRD) of Taiwan from 1999–2013. We identified 24077 adult patients with PPI use for more than 3 months and 24077 controls matched by propensity score on a one-to-one ratio for age, gender, comorbidities (hypertension, hyperlipidemia, gastroesophageal reflux disease, allergic rhinitis, atopic dermatitis, peptic ulcer disease, diabetes, and sleep apnea syndrome), and medications (histamine 2 receptor antagonists [H2RA], non-steroidal anti-inflammatory drugs [NSAIDs], and acetaminophen). The cumulative asthma incidence for the two cohorts in the follow-up period was estimated with the Kaplan-Meier method and the difference was examined using the log-rank test. Multivariate Cox regression models were used to calculate the adjusted hazard ratios (HR). Results: The overall incidence of asthma was 1.58-fold greater in the PPI cohort than in the non-PPI cohort (13.3 versus 8.4 per 1000 person-years), with an adjusted HR of 1.76 (95% confidence interval [CI], 1.64–1.88). In patients without previous peptic ulcer disease, the adjusted HR of asthma associated with PPIs was higher than in the non-PPI group (1.95; 95% CI, 1.80–2.11). The risk of asthma due to PPI use was also more significant in patients not receiving H2RA (1.81; 95% CI, 1.66–1.96), NSAIDs (1.93; 95% CI, 1.73–2.15), and acetaminophen (1.88; 95% CI, 1.70–2.08). Conclusions: This population base study demonstrated that patients with long-duration PPI use are at a higher risk of developing asthma, regardless of age, gender, comorbidities, and medications.

Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) computer-aided diagnosis system can be developed for the automatic recognition of CT images of patients, creating a new form of intelligent medical service. For many years, lung cancer has been the leading cause of cancer-related deaths in Taiwan, with smoking and air pollution increasing the likelihood of developing the disease. The incidence of lung adenocarcinoma in never-smoking women has also increased significantly in recent years, resulting in an important public health problem. Early detection of lung cancer and prompt treatment can help reduce the mortality rate of patients with lung cancer. In this study, an improved 3D interpretable hierarchical semantic convolutional neural network named HSNet was developed and validated for the automatic diagnosis of lung cancer based on a collection of lung nodule images. The interpretable AI model proposed in this study, with different training strategies and adjustment of model parameters, such as cyclic learning rate and random weight averaging, demonstrated better diagnostic performance than the previous literature, with results of a four-fold cross-validation procedure showing calcification: 0.9873 ± 0.006, margin: 0.9207 ± 0.009, subtlety: 0.9026 ± 0.014, texture: 0.9685 ± 0.006, sphericity: 0.8652 ± 0.021, and malignancy: 0.9685 ± 0.006.

The objective was to compare the clinical efficacy of cefoperazone-sulbactam with piperacillin-tazobactam in the treatment of severe community-acquired pneumonia (SCAP). The retrospective study was conducted from March 1, 2018 to May 30, 2019. Clinical outcomes were compared for patients who received either cefoperazone-sulbactam or piperacillin-tazobactam in the treatment of SCAP. A total of 815 SCAP patients were enrolled. Among them, 343 received cefoperazone-sulbactam, and 472 received piperacillin-tazobactam. Patients who received cefoperazone-sulbactam presented with higher Charlson Comorbidity Index scores. (6.20 ± 2.77 vs 5.72 ± 2.61; P = .009). The clinical cure rates and effectiveness for patients receiving cefoperazone-sulbactam and piperacillin-tazobactam were 84.2% versus 80.3% (P = .367) and 85.4% versus 83.3% (P = .258), respectively. In addition, the overall mortality rate of the cefoperazone-sulbactam group was 16% (n = 55), which was also comparable to the piperacillin-tazobactam group (17.8%, n = 84, P = .572). The primary clinical outcomes for patients receiving cefoperazone-sulbactam were superior compared to those receiving piperacillin-tazobactam after adjusting disease severity status. The clinical efficacy of cefoperazone-sulbactam in the treatment of adult patients with SCAP is comparable to that of piperacillin-tazobactam. After adjusting for disease severity, cefoperazone-sulbactam tended to be superior to piperacillin-tazobactam.