Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique.

Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treatment, and control of hypertension. In recent years, face video based non-contact blood pressure prediction is a promising research topic. Interestingly, face diagnosis has been an important part of traditional Chinese medicine (TCM) for thousands of years. TCM practitioners observe some typical regions of the face to determine the health status of the Zang Fu organs (i.e., heart). However, the effectiveness of face diagnosis theory in conjunction with computer vision analysis techniques to predict blood pressure is unclear. We proposed an artificial intelligence framework for predicting blood pressure using deep convolutional neural networks in this study. First, we extracted pulse wave signals through 652 facial videos. Then, we trained and compared nine artificial neural networks and chose the best performed prediction model, with an overall true predict rate of 90%. We also investigated the impact of face reflex regions selection on blood pressure prediction model, and the five face regions outperformed. Our high effectiveness and stability framework may provide an objective and convenient computer-aided blood pressure prediction method for hypertension screening and disease prevention.

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis.

Background: Radiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC. Methods: Original studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression. Results: We identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased. Conclusion: Radiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.

Recent advances in small molecular near-infrared fluorescence probes for a targeted diagnosis of the Alzheimer disease.

Nowadays, it is still quite challenging to achieve an early diagnosis of the Alzheimer disease (AD) in clinics. The burgeoning near-infrared fluorescence (NIRF) imaging fulfills the requirements for a precise diagnosis with good sensitivity and a high signal-to-background ratio and offers opportunities for the efficient AD diagnosis. As the pathogenesis of AD is quite complex, there is an ongoing exploration of advanced probes to specifically target AD biomarkers (e.g., amyloid-ß (Aß) plaques, neurofibrillary tangles, viscosity, peroxynitrite (ONOO-), reactive oxygen species, and methylglyoxal). To this end, a great number of small molecular fluorescent probes with good water solubility, blood-brain barrier crossing capability, and ease in tuning photophysical and biological properties have been studied for the AD diagnosis. Herein, we systematically update the progress of NIRF AD probes in the last three years. The special focus is on the mechanisms for the targeted diagnosis and the relationship between the structure and properties of the probes. Importantly, NIRF probes with complementary functions such as dual-responsiveness and multimodal imaging and even therapeutics are discussed. Moreover, the challenges and perspectives of the AD probes are briefly elucidated. We hope that this review provides guidance for researchers and expedites the preclinical and clinical study of the NIRF AD probes.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation.

ETHNOPHARMACOLOGICAL RELEVANCE: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. AIM OF THE STUDY: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. MATERIALS AND METHODS: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. RESULTS: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. CONCLUSIONS: Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark.

Tongue diagnosis plays a pivotal role in traditional Chinese medicine (TCM) for thousands of years. As one of the most important tongue characteristics, tooth-marked tongue is related to spleen deficiency and can greatly contribute to the symptoms differentiation and treatment selection. Yet, the tooth-marked tongue recognition for TCM practitioners is subjective and challenging. Most of the previous studies have concentrated on subjectively selected features of the tooth-marked region and gained accuracy under 80%. In the present study, we proposed an artificial intelligence framework using deep convolutional neural network (CNN) for the recognition of tooth-marked tongue. First, we constructed relatively large datasets with 1548 tongue images captured by different equipments. Then, we used ResNet34 CNN architecture to extract features and perform classifications. The overall accuracy of the models was over 90%. Interestingly, the models can be successfully generalized to images captured by other devices with different illuminations. The good effectiveness and generalization of our framework may provide objective and convenient computer-aided tongue diagnostic method on tracking disease progression and evaluating pharmacological effect from a informatics perspective.

A machine learning-driven study indicates emodin improves cardiac hypertrophy by modulation of mitochondrial SIRT3 signaling.

Cardiac hypertrophy (CH) is an enormous risk factor in the process of heart failure development, however, there is still lack of effective treatment for CH. Mitochondrial protection is an effective way against CH. Rheum palmatum L. (rhubarb) has been used to treat chronic heart diseases such as heart failure, especially to inhibit cardiac compensatory enlargement. The aim of this study was to explore the pharmacodynamic component of rhubarb and reveal its pharmacological effects and targets in the treatment of CH. Based on network pharmacology and machine learning approach, ingredients of rhubarb and targets for CH were extracted and surflex docking was conducted for obtaining the optimal ingredient-target combination(s) and emodin-SIRT3 was identified for further functional analysis. Transverse aortic constriction or isoproterenol induced CH mice and phenylephrine injured cardiomyocytes were used to verify the mitochondria protection effect and CH improvement of emodin in vivo and in vitro by modulation of mitochondrial SIRT3 signaling. The results showed that emodin could block agonist-induced and pressure overload-mediated CH. Emodin prevented mitochondrial dysfunction and its underlying mechanism was attributed to the activation of SIRT3, but the effect was not obvious with the presence of SIRT3 inhibitors (3-TYP)/SIRT3 siRNA. Furthermore, PGC-1ɑ was involved in the process of emodin regulating SIRT3 signaling pathway as an upstream target. Our findings clarified the main material basis and mechanism of rhubarb in the treatment of CH. Emodin, as the major ingredient of rhubarb, has therapeutic potential for CH through mitochondrial protection due to the modulation of SIRT3 signaling.

Different Survival Benefits of Chinese Medicine for Pancreatic Cancer: How to Choose?

OBJECTIVE: To assess the efficacy of Chinese medicine (CM) on patients with pancreatic cancer (PC) in a retrospective population-based study. METHODS: Between January 1, 2013, and August 30, 2016, according to whether received Western medicine treatment, the patients were included into either integrative medicine (IM) group or CM group. All enrolled patients were orally administrated with Gexia Zhuyu Decoction () or Liujun Ermu Decoction () by syndrome differentiation, twice a day, last for at least 2 months. The primary end point was overall survival (OS). RESULTS: A total of 174 patients with PC were enrolled in this study. In stage I/II, the median OS was 20.5 months in the IM group [95% confidence interval (CI), 12.499 to 28.501] and 11.17 months in the CM group (95% CI, 5.160 to 17.180, P=0.015). The 1- and 2-year survival rates for the two groups were 47.0%, 40.0% and 21.0%, 21.0%, respectively. In stage III/IV, median OS was 13.53 months (95% CI, 8.665 to 18.395) in the IM group versus 6.4 months (95% CI, 0.00 to 15.682) in the CM group, respectively (P=0.32). The 1- and 2-year survival rate for the IM and CM groups were 27.0%, 7.0% and 20.0%, 2.0%, respectively. CONCLUSIONS: Intervention of CM contributes to the different survival benefits for PC in different stages. Multimodality treatment might be a promising strategy for PC patients in early stage. While, in advanced stage, CM might be an alternative candidate for PC patients.

Polyureas from diamines and carbon dioxide: synthesis, structures and properties.

Polyureas were synthesized from diamines and carbon dioxide in the absence of any catalyst or solvent, analogous to the synthesis of urea from condensation of ammonia with carbon dioxide. The method used carbon dioxide as a carbonyl source to substitute highly toxic isocyanates for the synthesis of polyureas. FTIR and DFT calculations confirmed that strong bidentate hydrogen bonds were formed between urea motifs, and XRD patterns showed that the PUas were highly crystalline and formed a network structure through hydrogen bonds, which served as physical cross-links. The long chain PUas presented a microphase separated morphology as characterized by SAXS and showed a high melting temperature above 200 °C. The PUas showed high resistance to solvents and excellent thermal stability, which benefitted from their special network structures. The PUas synthesized by this method are a new kind of functional material and could serve some areas where their analogues with similar functional groups could not be applied.