Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study.

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS: Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION: The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.

Prognostic Prediction Using the Clinical Data and Ultrasomics-Based Model in Acute Respiratory Distress Syndrome (ARDS) Combined with Acute Kidney Injury (AKI).

Objective: A model was constructed based on clinical and ultrasomics features to predict the prognosis of patients in the respiratory intensive unit (RICU) who had acute respiratory distress syndrome (ARDS) combined with acute kidney injury (AKI). AKI ensues after ARDS in RICU ordinarily. The prognostic prediction tool was further developed on this basis. Methods: We collected clinical and ultrasonic data from 145 patients who had ARDS combined with AKI and received continuous renal replacement therapy (CRRT) in the RICU of Xiangyang Hospital of Traditional Chinese Medicine from March 2016 to November 2019. The patients were divided into the survival group (n = 51) and the death group (n = 94), depending on the treatment outcome. The training set (n = 102) and the testing set (n = 43) were established based on patient data. The clinical and ultrasomics features and the CRRT parameters were compared between the two groups. The influence factors of death were analyzed by logistic regression, and four predictive models were established. The predictive performance of 4 models was compared using the R Software 4.1.3. The decision curve analysis graphs were drawn using the R language to determine the net benefit of each. Result: Univariate analysis was conducted in the training set. The following risk factors for poor prognosis were identified: age, concurrent cancers, sequential organ failure assessment score (SOFA), number of organ dysfunctions, positive cumulative fluid balance at 72 h, time from ICU admission to CRRT, mean arterial pressure, oxygenation index, and gray-level size zone matrix, GLSZM (SumEntropy.239/SmallDependenceHighGrayLevelEmphasis.314/Maximum.327/Variance.338) (P < 0.05). Four models were built based on the above factors: clinical model, CRRT model, ultrasomics-based model, and combination model. Comparison using the MedCalc software indicated that the best predictive performance achieved with the combination model. The decision curve analysis also suggested that the combination model had the highest net benefit. Similar results were reported after validation on the testing set. Conclusion: The prognosis of ARDS patients combined with AKI is usually poor. The combination model based on clinical and ultrasomics features had the highest predictive performance. This model can be used to improve the clinical outcome and prognosis.

Effects of neoadjuvant chemotherapy on minimum alveolar concentration values of sevoflurane and desflurane in patients with hepatocellular carcinoma complicated with jaundice.

The effects of neoadjuvant chemotherapy on the minimum alveolar concentration (MAC) values of sevoflurane and desflurane in patients with hepatocellular carcinoma (HCC) complicated with jaundice were investigated. Eighty patients with HCC complicated with jaundice were selected. Forty patients underwent the neoadjuvant chemotherapy and were grouped into the desflurane group (Group D) and the sevoflurane group (Group S). Patients in all chemotherapy groups received 2 cycles of chemotherapy prior to surgery and underwent surgical treatment 3 weeks after chemotherapy. The remaining 40 patients in the control group were divided into the desflurane group (Group C1) and the sevoflurane group (Group C2). Changes in MAP, HR and BIS at different time points before and after anesthesia induction and skin incision were compared among the groups. Results showed that there were no significant differences in MAP, HR and BIS before anesthesia induction (T0) (P>0.05); at each time point from T1 to T6, MAP, HR and BIS of Group D were significantly lower than those of Group C1 (P>0.05). Furthermore, MAP, HR and BIS of Group S were significantly lower than those of Group C2 (P>0.05). The MACMean of sevoflurane and desflurane were compared among all patient groups using the mean method. MACMean values of Group D were significantly lower than those of Group C1 (P<0.05). Notably, MACDixon values of sevoflurane and desflurane were compared among all patient groups using the Dixon method and the differences were statistically significant (P<0.05). Logistic regression analyses were conducted, respectively, which revealed that the MAC of sevoflurane and desflurane were associated with whether patients received the neoadjuvant chemotherapy. MACLog of sevoflurane and desflurane were decreased in patients receiving the neoadjuvant chemotherapy. The results suggested that neoadjuvant chemotherapy can reduce MAC values of sevoflurane and desflurane in HCC patients complicated with jaundice and may improve these patients' sensitivity to sevoflurane and desflurane.

Detecting endotoxin with a flow cytometry-based magnetic aptasensor.

Endotoxin, which is also known as lipopolysaccharide (LPS), is a marker for intruding gram-negative pathogens. It is essential to detect endotoxin quickly and sensitively in a complex milieu. A new flow cytometry (FCM)-based magnetic aptasensor assay that employs two endotoxin-binding aptamers and magnetic beads has been developed to detect endotoxin. The endotoxin-conjugated sandwich complex on magnetic beads was observed by scanning confocal laser microscopy. The resulting magnetic aptasensor rapidly detected (<1 min) endotoxin within a broad dynamic detection range of 10(-8) to 10(0)mg/ml in the presence of bovine serum albumin (BSA), RNA, sucrose, and glucose, which are most likely to coexist with endotoxin in the majority of biological liquids. Only 2 µl of magnetic aptasensor was required to quantify the endotoxin solution. Furthermore, the magnetic aptasensor could be regenerated seven times and still presented an outstanding response to the endotoxin solution. Therefore, the magnetic aptasensor exhibited high sensitivity, selectivity, and reproducibility, thereby serving as a powerful tool for the quality control and high-throughput detection of endotoxin in the food and pharmaceutical industries.

[Death of acute pulmonary embolism after multiple maxillofacial fractures operation: a case report].

Acute pulmonary embolism after surgery of multiple maxillofacial fractures is rare. This paper reported a case of multiple maxillofacial fractures who experienced acute pulmonary embolism after operation and died.