A case of hepatic paragonimiasis was misdiagnosed as hepatocellular carcinoma with rupture and haemorrhage.

Paragonimiasis is a disease caused by parasitic infections that mainly involve the lungs. However, it can also produce ectopic infections, such as when the parasites invade the liver, brain and subcutaneous tissue, which then cause different symptoms. This current case report describes a 55-year-old male patient with hepatic paragonimiasis that was misdiagnosed as liver cancer with rupture and haemorrhage. The initial computed tomography findings suggested ruptured liver cancer. The patient underwent laparoscopic right hemihepatectomy. Postoperative pathological analysis resulted in a diagnosis of hepatic paragonimiasis. The patient recovered well postoperatively and was treated with 25 mg/kg praziquantel orally three times a day for 3 days after discharge with good efficacy. In this present case, the rupture and haemorrhage of the liver mass made it difficult for the treating physicians to consider hepatic paragonimiasis, which lead to the initial misdiagnosis of this patient. Although paragonimiasis is very rare, medical staff should be vigilant and have a comprehensive understanding of the different diseases that can cause liver masses so that misdiagnosis can be avoided.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models.

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics.

BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. © 2019 Society of Chemical Industry.