Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics.

The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.

[Identification of critical process parameters of Jinqing alcohol precipitation of Reduning Injection by big data].

Lonicerae Japonicae Flos and Artemisiae Annuae Herba(LA or Jinqing) alcohol precipitation has various process parameters and complex process mechanism, and is one of the key units for manufacturing Reduning Injection. In order to identify the critical process parameters(CPPs) affecting the weight of the extract produced from the alcohol precipitation process, 259 batches of historical production data from 2017 to 2018 were collected, with a total of 829 318 data points. These data showed characteristics of large data, such as a large data volume, a low value density, and diverse sources. The data cleaning and feature extraction were first performed, and 48 feature variables were selected. The original data points were reduced to 9 936. Then, a combination of Pearson correlation analysis and grey correlation analysis were used to screen out 15 potential critical process parameters(pCPPs). After that, the partial least squares(PLS) was used in prediction of the weight of the extract, proving that the performance of predictive model based on 15 pCMAs is equivalent to that of predictive model based on 48 feature variables. The variable importance in projection(VIP) index was used to identify 9 CPPs, including 2 alcohol precipitation supernatant volume parameters, 4 initial extract weight parameters and 3 added alcohol volume parameters. As a result, the number of data points was 1 863, accounting for 0.28% of the original data. The big data analysis approach from a holistic point of view can effectively increase the value density of the original data. The critical process parameters obtained can help to accurately describe the quality transfer mechanism of the Jinqing alcohol precipitation process.

[Predictive model for hygroscopicity of contents in Guizhi Fuling Capsules].

To control the risks of powder caking and capsule shell embrittlement of Guizhi Fuling Capsules, a predictive model for hygroscopicity of contents in Guizhi Fuling Capsules was built. A total of 90 batches of samples, including raw materials, intermediate powders and capsules, were collected during the manufacturing of Guizhi Fuling Capsules. According to the production sequence, 47 batches were used as the calibration set, and the properties of raw materials and the four intermediate powders were comprehensively characterized by the physical fingerprint. Then, the partial least squares(PLS) model was developed with the content hygroscopicity as the response variable. The variable importance in projection(VIP), variance inflation factor(VIF) and regression coefficients were used to screen out potential critical material attributes(pCMAs). As a result, five pCMAs from 54 physical parameters were screened out. Furthermore, different models were built by different combinations of pCMAs, and their predictive robustness of 43 batches was evaluated on the basis of the validation set. Finally, the tap density(D_c) of wet granules obtained from wet granulation and the angle of repose(α) of raw materials were identified as the critical material attributes(CMAs) affecting the hygroscopicity of the contents of Guizhi Fuling Capsules. The prediction model established with the two CMAs as independent variables had an average relative prediction error of 2.68% for samples in the validation set, indicating a good accuracy of prediction. This paper proved the feasibility of predictive modeling toward the control of critical quality attributes of Chinese medicine oral solid dosage(OSD). The combination of the continuous quality improvement, the industrial big data and the process modeling technique paved the way for the intelligent manufacturing of Chinese medicine oral solid preparations.

[Real time release testing of disintegration time of uncoated Tianshu Tablets].

In this paper, a real time release testing(RTRT) model for predicting the disintegration time of Tianshu tablets was established on the basis of the concept of quality by design(QbD), in order to improve the quality controllability of the production process. First, 49 batches of raw materials and intermediates were collected. Afterwards, the physical quality attributes of all materials were comprehensively characterized. The partial least square(PLS) regression model was established with the 72 physical quality attributes of raw materials and intermediates as input and the disintegration time(DT) of uncoated tablets as output. Then, the variable screening was carried out based on the variable importance in the projection(VIP) indexes. Moisture content of raw materials(%HR), tapped density of wet masses(D_c), hygroscopicity of dry granules(%H), moisture content of milling granules(%HR) and Carr's index of mixed granules(IC) were determined as the potential critical material attributes(pCMAs). According to the effects of interactions of pCMAs on the performance of the prediction model, it was finally determined that the wet masses' D_c and the dry granules'%H were critical material attributes(CMAs). A RTRT model of the disintegration time prediction was established as DT=34.09+2×D_c+3.59×%H-5.29×%H×D_c,with R~2 equaling to 0.901 7 and the adjusted R~2 equaling to 0.893 3. The average relative prediction error of validation set for the RTRT model was 3.69%. The control limits of the CMAs were determined as 0.55 g·cm~(-3)<D_c<0.63 g·cm~(-3) and 4.77<%H<7.59 according to the design space. The RTRT model of the disintegration time reflects the understanding of the process system, and lays a foundation for the implementation of intelligent control strategy of the key process of Tianshu Tablets.

Surgical repair of traumatic isolated inferior rectus muscle avulsion.

PURPOSE:: To report our experience in the management of avulsion of the inferior rectus muscle. METHODS:: We describe the approach we have used for surgical repair of isolated avulsion of the inferior rectus muscle in two patients who suffered orbital trauma. RESULTS:: In both cases, the proximal portion of the inferior rectus muscle was located successfully by the generated muscle force duction test and sutured back into its original insertion point. Infraduction was normal and orthotropia was present in all directions of gaze during 1 year of postoperative follow-up in both cases. These favorable outcomes are attributed in part to the inferior rectus muscle pulleys system and the natural history of this type of injury, which allows the lost proximal portion to be identified by generated muscle force duction test. CONCLUSION:: Generated muscle force duction test can be used to locate the proximal portion of a lost inferior rectus muscle and restore its function in patients with traumatic avulsion injury involving an extraocular muscle.

"Smart" Nanoparticles Enhance the Cytoplasmic Delivery of Anti-RhoC Silencing RNA and Inhibit the Migration and Invasion of Aggressive Breast Cancer Cells.

Rho-GTPases are small GTP-binding proteins that contribute to the epithelial-to-mesenchymal transition by regulating several cellular processes including organization of the actin cytoskeleton, cell motility, transcription, and cell proliferation. Overexpression of RhoC-GTPases (RhoC) in breast cancer has been implicated in poor disease prognosis due to increased cancer cells invasion, migration, and motility, which warranted its consideration as a therapeutic target for inhibiting breast cancer metastasis. Using silencing RNA (siRNA) molecules to knockdown RhoC expression is a promising approach to inhibit breast cancer metastases. However, transforming anti-RhoC siRNA molecules into a viable therapy remains a challenge due to the lack of a biocompatible carrier that can selectively deliver the RNA cargo into breast cancer cells. We report the use of a degradable, pH-sensitive, ß-cyclodextrin (ßCD)-based polymeric carrier that condenses anti-RhoC siRNA forming "smart" particles. These smart anti-RhoC particles were efficiently internalized, successfully escaped the endosome, and delivered the RNA cargo into the cytoplasm of SUM149 and MDA-MB-231 breast cancer cells. Our results show that anti-RhoC particles used at a low N/P ratio of 2.5/1 suppressed RhoC protein levels by 100% and 90% in SUM149 and MDA-MB-231 cells, respectively. Further, anti-RhoC particles inhibited the invasion, motility, and migration of SUM149 and MDA-MB-231 cells by 40-47%, 57-60%, and 61.5-73%, respectively. Smart particles encapsulating the scrambled siRNA sequence did not affect RhoC protein expression or the invasion, motility, and migration of SUM149 and MDA-MB-231 cells, which indicate the biocompatibility of the polymeric carrier and selectivity of the observed RhoC knockdown. These results collectively indicate the therapeutic potential of smart anti-RhoC particles in arresting the metastatic spread of breast cancer cells.