A Study on Benjamin Hobson’s Contribution to the Translation of Western Medicine in Modern China

Benjamin Hobson is a British medical missionary who came to China in the Qing Dynasty. Living in China for nearly twenty years, Hobson had quite a few translation works published, and he was not only the first one who systematically translated various kinds of medicine theories into Chinese but also the pioneer of creating medical terms in Chinese. This paper first attempts to make a thorough inquiry into Hobson’s medical translation practice and his views on translation, and then points out that Hobson’s major contributions to the translation of western medicine in modern China are that his medical translation promoted the popularization of western medicine in China, that the publication of Treatise on Physiology set a milestone of translating medicine works for modern China to learn from the West, and that his compilation of A Medical Vocabulary in English and Chinese laid the foundation for the Chinese translation of modern medicine terminology.

Insulin resistance in NSCLC: unraveling the link between development, diagnosis, and treatment

Lung cancer is responsible for the highest number of cancer-related deaths, with non-small cell lung cancer (NSCLC) being the most prevalent subtype. A critical aspect of managing lung cancer is reducing morbidity and mortality rates among NSCLC patients. Identifying high-risk factors for lung cancer and facilitating early diagnosis are invaluable in achieving this objective. Recent research has highlighted the association between insulin resistance and the development of NSCLC, further emphasizing its significance in the context of lung cancer. It has been discovered that improving insulin resistance can potentially inhibit the progression of lung cancer. Consequently, this paper aims to delve into the occurrence of insulin resistance, the mechanisms underlying its involvement in lung cancer development, as well as its potential value in predicting, assessing, and treating lung cancer

Initial Serum Magnesium Level Is Associated with Mortality Risk in Traumatic Brain Injury Patients

Background: Electrolyte disorder is prevalent in traumatic brain injury (TBI) patients. This study is designed to explore the association between initial serum magnesium levels and mortality of TBI patients. Methods: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were screened for this study. Logistic regression analysis was used to explore risk factors for mortality of included TBI patients. The restricted cubic spline (RCS) was applied to fit the correlation between initial serum magnesium level and mortality of TBI. Results: The 30-day mortality of included TBI patients was 17.0%. Patients with first-tertile and third-tertile serum magnesium levels had higher mortality than those of the second tertile. Univariate regression analysis showed that the serum magnesium level was not associated with mortality. Unadjusted RCS indicated the relationship between serum magnesium level mortality was U-shaped. After adjusting confounding effects, multivariate regression analysis presented that serum magnesium level was positively associated with mortality. Conclusion: TBI patients with abnormally low or high levels of serum magnesium both have a higher incidence of mortality. At the same time, a higher initial serum magnesium level is independently associated with mortality in TBI patients. Physicians should pay attention to the clinical management of TBI patients, especially those with higher serum magnesium levels

Hypomagnesemia Is Associated with the Acute Kidney Injury in Traumatic Brain Injury Patients: A Pilot Study

Background: Acute kidney injury (AKI) commonly develops among traumatic brain injury (TBI) patients and causes poorer outcomes. We perform this study to explore the relationship between serum magnesium and the risk of AKI among TBI. Methods: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were eligible for this research. The restricted cubic spline (RCS) was utilized to fit the correlation between serum magnesium level and the AKI. Univariate and subsequent multivariate logistic regression analysis were utilized to explore risk factors of AKI and confirmed the correlation between serum magnesium and AKI. Results: The incidence of AKI in included TBI was 21.0%. The RCS showed that the correlation between magnesium level and risk of AKI was U-shaped. Compared with patients whose magnesium level was between 1.5 and 2.0 mg/dL, those with a magnesium level of 2.0 mg/dL had a higher incidence of AKI. Multivariate logistic regression confirmed age, chronic renal disease, ISS, serum creatinine, vasopressor, mechanical ventilation, and serum magnesium <1.5 mg/dL were independently related with the AKI in TBI. Conclusion: Abnormal low serum magnesium level is correlated with AKI development in TBI patients. Physicians should pay attention on renal function of TBI patients especially those with hypomagnesemia

Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms

Background: Acute respiratory distress syndrome (ARDS) commonly develops in traumatic brain injury (TBI) patients and is a risk factor for poor prognosis. We designed this study to evaluate the performance of several machine learning algorithms for predicting ARDS in TBI patients. Methods: TBI patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. ARDS was identified according to the Berlin definition. Included TBI patients were divided into the training cohort and the validation cohort with a ratio of 7:3. Several machine learning algorithms were utilized to develop predictive models with five-fold cross validation for ARDS including extreme gradient boosting, light gradient boosting machine, Random Forest, adaptive boosting, complement naïve Bayes, and support vector machine. The performance of machine learning algorithms were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. Results: 649 TBI patients from the MIMIC-III database were included with an ARDS incidence of 49.5%. The random forest performed the best in predicting ARDS in the training cohort with an AUC of 1.000. The XGBoost and AdaBoost ranked the second and the third with an AUC of 0.989 and 0.815 in the training cohort. The random forest still performed the best in predicting ARDS in the validation cohort with an AUC of 0.652. AdaBoost and XGBoost ranked the second and the third with an AUC of 0.631 and 0.620 in the validation cohort. Several mutual top features in the random forest and AdaBoost were discovered including age, initial systolic blood pressure and heart rate, Abbreviated Injury Score chest, white blood cells, platelets, and international normalized ratio. Conclusions: The random forest and AdaBoost based models have stable and good performance for predicting ARDS in TBI patients. These models could help clinicians to evaluate the risk of ARDS in early stages after TBI and consequently adjust treatment decisions

A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods

ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition

Direct Interaction between Selenoprotein P and Tubulin

Selenium (Se), an essential trace element for human health, mainly exerts its biological function via selenoproteins. Among the 25 selenoproteins identified in human, selenoprotein P (SelP) is the only one that contains multiple selenocysteines (Sec) in the sequence, and has been suggested to function as a Se transporter. Upon feeding a selenium-deficient diet, mice lacking SelP develop severe neurological dysfunction and exhibit widespread brainstem neurodegeneration, indicating an important role of SelP in normal brain function. To further elucidate the function of SelP in the brain, SelP was screened by the yeast two-hybrid system from a human fetal brain cDNA library for interactive proteins. Our results demonstrated that SelP interacts with tubulin, alpha 1a (TUBA1A). The interaction between SelP and tubulin was verified by fluorescence resonance energy transfer (FRET) and co-immunoprecipitation (co-IP) assays. We further found that SelP interacts with the C-terminus of tubulin by its His-rich domain, as demonstrated by FRET and Isothermal Titration Calorimetry (ITC) assays. The implications of the interaction between SelP and tubulin in the brain and in Alzheimer’s disease are discussed

The complete mitochondrial genome and phylogenetic analysis of Neorhodomela munita

Neorhodomela munita (Perestenko) Masuda 1982 is distributed in the coastal areas of Shandong and Liaoning in China, and also in Japan. In this study, the complete nucleotide sequence of the circular mitochondrial DNA of the red alga Neorhodomela munita has been determined. The complete mitochondrial DNA sequence of Neorhodomela munita was 25,318 bp in length with an overall GC content of 25.1% and encoded 23 protein-coding genes, two ribosomal RNAs and 24 transfer RNAs. Phylogenetic tree showed that Neorhodomela munita clustered together with Choreocolax polysiphoniae. The phylogenetic analysis may provide a better understanding of the evolution of the Rhodophyta species