Analgesic and Anti-Inflammatory Activities of Methanol Extract of Ficus pumila L. in Mice

This study investigated possible analgesic and anti-inflammatory mechanisms of the methanol extract of Ficus pumila (FPMeOH). Analgesic effects were evaluated in two models including acetic acid-induced writhing response and formalin-induced paw licking. The results showed FPMeOH decreased writhing response in the acetic acid assay and licking time in the formalin test. The anti-inflammatory effect was evaluated by λ-carrageenan-induced mouse paw edema and histopathological analyses. FPMeOH significantly decreased the volume of paw edema induced by λ-carrageenan. Histopathologically, FPMeOH abated the level of tissue destruction and swelling of the edema paws. This study indicated anti-inflammatory mechanism of FPMeOH may be due to declined levels of NO and MDA in the edema paw through increasing the activities of SOD, GPx, and GRd in the liver. Additionally, FPMeOH also decreased the level of inflammatory mediators such as IL-1β, TNF-α, and COX-2. HPLC fingerprint was established and the contents of three active ingredients, rutin, luteolin, and apigenin, were quantitatively determined. This study provided evidence for the classical treatment of Ficus pumila in inflammatory diseases

Improved Breath Phase and Continuous Adventitious Sound Detection in Lung and Tracheal Sound Using Mixed Set Training and Domain Adaptation

Previously, we established a lung sound database, HF_Lung_V2 and proposed convolutional bidirectional gated recurrent unit (CNN-BiGRU) models with adequate ability for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound detection in the lung sound. In this study, we proceeded to build a tracheal sound database, HF_Tracheal_V1, containing 11107 of 15-second tracheal sound recordings, 23087 inhalation labels, 16728 exhalation labels, and 6874 CAS labels. The tracheal sound in HF_Tracheal_V1 and the lung sound in HF_Lung_V2 were either combined or used alone to train the CNN-BiGRU models for respective lung and tracheal sound analysis. Different training strategies were investigated and compared: (1) using full training (training from scratch) to train the lung sound models using lung sound alone and train the tracheal sound models using tracheal sound alone, (2) using a mixed set that contains both the lung and tracheal sound to train the models, and (3) using domain adaptation that finetuned the pre-trained lung sound models with the tracheal sound data and vice versa. Results showed that the models trained only by lung sound performed poorly in the tracheal sound analysis and vice versa. However, the mixed set training and domain adaptation can improve the performance of exhalation and CAS detection in the lung sound, and inhalation, exhalation, and CAS detection in the tracheal sound compared to positive controls (lung models trained only by lung sound and vice versa). Especially, a model derived from the mixed set training prevails in the situation of killing two birds with one stone.Comment: To be submitted, 31 pages, 6 figures, 5 table

Bacteremic pneumonia caused by Nocardia veterana in an HIV-infected patient

SummaryDisseminated Nocardia veterana infection has rarely been reported. We describe the first reported case of N. veterana bacteremic pneumonia in an HIV-infected patient. The isolate was confirmed by 16S rRNA sequencing analysis. The patient initially responded well to trimethoprim–sulfamethoxazole treatment (minimum inhibitory concentration 0.25μg/ml), but died of ventilator-associated pneumonia