Explore the vast range of titles available.

Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results. The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion. The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

Dopamine deficiency is mainly caused by apoptosis of dopaminergic nerve cells in the substantia nigra of the midbrain and the striatum and is an important pathologic basis of Parkinson’s disease (PD). Recent research has shown that dynamin-related protein 1 (Drp1)-mediated aberrant mitochondrial fission plays a crucial role in dopaminergic nerve cell apoptosis. However, the upstream regulatory mechanism remains unclear. Our study showed that Drp1 knockdown inhibited aberrant mitochondrial fission and apoptosis. Importantly, we found that ROCK1 was activated in an MPP + -induced PD cell model and that ROCK1 knockdown and the specific ROCK1 activation inhibitor Y-27632 blocked Drp1-mediated aberrant mitochondrial fission and apoptosis of dopaminergic nerve cells by suppressing Drp1 dephosphorylation/activation. Our in vivo study confirmed that Y-27632 significantly improved symptoms in a PD mouse model by inhibiting Drp1-mediated aberrant mitochondrial fission and apoptosis. Collectively, our findings suggest an important molecular mechanism of PD pathogenesis involving ROCK1-regulated dopaminergic nerve cell apoptosis via the activation of Drp1-induced aberrant mitochondrial fission. Parkinson’s disease: Looking back identifies a new target Researchers in China have revealed how a protein molecule plays an early part in the molecular steps that can lead to Parkinson’s disease, which is caused by the death of nerve cells that make the neurotransmitter dopamine. Disruption of mitochondria, the energy-generating bodies inside cells, was already known to lead to the death of dopamine-producing cells. Rong Zhang, Guobing Li and colleagues at The Second Affiliated Hospital of Army Medical University in Chongqing, China traced the chain of cause and effect back to a protein called ROCK-1. Using a mouse model of Parkinson’s disease, they found that ROCK-1 activates another protein previously shown to trigger the disruption of mitochondria. ROCK-1’s early role in the sequence might make it a suitable target for treatment using drugs that inhibit its activity.

IR‐783 is a kind of heptamethine cyanine dye that exhibits imaging, cancer targeting and anticancer properties. A previous study reported that its imaging and targeting properties were related to mitochondria. However, the molecular mechanism behind the anticancer activity of IR‐783 has not been well demonstrated. In this study, we showed that IR‐783 inhibits cell viability and induces mitochondrial apoptosis in human breast cancer cells. Exposure of MDA‐MB‐231 cells to IR‐783 resulted in the loss of mitochondrial membrane potential (MMP), adenosine triphosphate (ATP) depletion, mitochondrial permeability transition pore (mPTP) opening and cytochrome c (Cyto C) release. Furthermore, we found that IR‐783 induced dynamin‐related protein 1 (Drp1) translocation from the cytosol to the mitochondria, increased the expression of mitochondrial fission proteins mitochondrial fission factor (MFF) and fission‐1 (Fis1), and decreased the expression of mitochondrial fusion proteins mitofusin1 (Mfn1) and optic atrophy 1 (OPA1). Moreover, knockdown of Drp1 markedly blocked IR‐783‐mediated mitochondrial fission, loss of MMP, ATP depletion, mPTP opening and apoptosis. Our in vivo study confirmed that IR‐783 markedly inhibited tumour growth and induced apoptosis in an MDA‐MB‐231 xenograft model in association with the mitochondrial translocation of Drp1. Taken together, these findings suggest that IR‐783 induces apoptosis in human breast cancer cells by increasing Drp1‐mediated mitochondrial fission. Our study uncovered the molecular mechanism of the anti‐breast cancer effects of IR‐783 and provided novel perspectives for the application of IR‐783 in the treatment of breast cancer.

Triple-negative breast cancer (TNBC) has the high degree of malignancy and aggressiveness. There is no targeted therapy drug. Many studies have shown that RBC membrane-coated nanoparticles achieve biological camouflage. In addition, the RGD module in the iRGD mediates the penetration of the vector across the tumor blood vessels to the tumor tissue space. Therefore, we developed iRGD-RM-(DOX/MSNs) by preparing MSNs loaded with doxorubicin as the core, and coating the surface of the MSNs with iRGD-modified RBC membranes. iRGD-RM-(DOX/MSNs) were fabricated using physical extrusion. In addition, their physical and chemical characterization, hemolytic properties, in vivo acute toxicity and inflammatory response, in vitro and in vivo safety, and qualitative and quantitative cellular uptake by RAW 264.7 cells and MDA-MB-231 cells were evaluated and compared. Furthermore, we examined the antitumor efficacy of iRGD-RM-(DOX/MSN) nanoparticles in vitro and in vivo. iRGD-RM-(DOX/MSNs) have reasonable physical and chemical properties. iRGD-RM-(DOX/MSNs) escaped the phagocytosis of immune cells and achieved efficient targeting of nanoparticles at the tumor site. The nanoparticles showed excellent antitumor effects in vivo and in vitro. In this study, we successfully developed biomimetic iRGD-RM-(DOX/MSNs) that could effectively target tumors and provide a promising strategy for the effective treatment of TNBC.

Introduction: Nirmatrelvir/ritonavir (Paxlovid) is an effective antiviral drug for treating coronavirus disease 2019 (COVID-19) in adults. However, Paxlovid treatment of children, especially those who are under 12 years and with severe underlying diseases, is rare. Case report: A three-year-old COVID-19 patient (weighing 14.5 kg) was infected by the Omicron variant (BA.5.2) after undergoing allogeneic hematopoietic stem cell transplantation. The patient had severe bilateral pneumonia along with recurrent fever. The patient was administered 150 mg of nirmatrelvir plus 50 mg of ritonavir twice daily for 5 days, starting 27 days after the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive test. Clinical manifestations and chest computed tomography improved considerably after the treatment. The real time reverse transcriptase polymerase chain reaction (RT-PCR) cycle threshold values increased from 23.92 to 38.40 in the case of ORF 1-ab gene, and from 22.22 to 36.28 in the case of N gene. Only a mild increase in serum urea nitrogen (10.10 mmol/L), alanine transaminase (ALT, 65 IU/L), and aspartate transaminase (AST, 68.5 IU/L) was observed. Conclusions: Paxlovid can effectively inhibit the replication of SARS-CoV-2 and help in improving the clinical manifestations in pediatric COVID-19 patients. Our study provided novel information on Paxlovid treatment in very young children.

Summary Pseudomonas aeruginosa is an important opportunistic human pathogen, which raises a worldwide concern for its increasing resistance. Nonthermal plasma, which is also called cold atmospheric plasma (CAP), is an alternative therapeutic approach for clinical infectious diseases. However, the bacterial factors that affect CAP treatment remain unclear. The sterilization effect of a portable CAP device on different P. aeruginosa strains was investigated in this study. Results revealed that CAP can directly or indirectly kill P. aeruginosa in a time‐dependent manner. Scanning electron microscopy and transmission electron microscope showed negligible surface changes between CAP‐treated and untreated P. aeruginosa cells. However, cell leakage occurred during the CAP process with increased bacterial lactate dehydrogenase release. More importantly, pigmentation of the P. aeruginosa culture was remarkably reduced after CAP treatment. Further mechanical exploration was performed by utilizing mutants with loss of functional genes involved in pyocyanin biosynthesis, including P. aeruginosa PAO1 strain‐derived phzA1::Tn, phzA2::Tn, ΔphzA1/ΔphzA2, phzM::Tn and phzS::Tn, as well as corresponding gene deletion mutants based on clinical PA1 isolate. The results indicated that pyocyanin and its intermediate 5‐methyl phenazine‐1‐carboxylic acid (5‐Me‐PCA) play important roles in P. aeruginosa resistance to CAP treatment. The unique enzymes, such as PhzM in the pyocyanin biosynthetic pathway, could be novel targets for the therapeutic strategy design to control the growing P. aeruginosa infections. Cold atmospheric plasma (CAP) produced by a portable CAP device can directly or indirectly kill P. aeruginosa in a time‐dependent manner. Importantly, pigmentation of the P. aeruginosa culture was remarkably reduced after CAP treatment. Further mechanical exploration based on pigment mutants indicated that pyocyanin and its intermediate 5‐methyl phenazine‐1‐carboxylic acid (5‐Me‐PCA) play important roles in P. aeruginosa resistance to CAP treatment.

Objective: Adding selexipag to the combined treatment of endothelin receptor antagonists (ERA) and phosphodiesterase 5 inhibitor (PDE5i) reduces the risk of clinical worsening events in patients with pulmonary arterial hypertension (PAH) but at a considerably higher cost. This study evaluated the cost-effectiveness of adding selexipag to the combined treatment of ERA and PDE5i in patients with PAH from a Chinese healthcare system perspective. Methods: A Markov model was developed to assess costs and quality-adjusted life years (QALYs) of macitentan + tadalafil + selexipag vs. macitentan + tadalafil for the treatment of PAH. Markov states included WHO Functional Class (FC) (I–IV) and death. Transition probabilities were based on data from the TRITON trial. Mortality rates, costs, and utilities were obtained from published literature and public databases. Results: In the base case analysis, compared with macitentan + tadalafil, selexipag + macitentan + tadalafil increased costs ( $357,807.588 vs. $ 116,534.543, respectively) and QALYs (7.234 QALYs vs. 6.666 QALYs, respectively). The resulting incremental cost-effectiveness ratio was$424,746.070 per QALY, which was higher than the willingness-to-pay (WTP) of $ 38,223.339 per QALY. The results were most sensitive to HR for mortality of patients with FC IV relative to the general population, discount rate, and the cost of selexipag. The probability was greater than 50% for the selexipag + macitentan + tadalafil only if the WTP was more significant than $426,019.200 per QALY. Conclusion: In China, adding selexipag may not be cost-effective for patients with PAH who failed to control their condition after combined treatment of ERA and PDE5i. Results of the analysis can aid discussions on the value and position of selexipag for the combined treatment of PAH.

Background. The prevalence of diabetes increases year by year, posing a severe threat to human health. Current treatments are difficult to prevent the progression of diabetes and its complications. It is imperative to carry out individualized treatment of diabetes, but current diagnostic methods are difficult to specify an individualized treatment plan. Objective. Clarify the distribution law of tongue features of the diabetic population, and provide the diagnostic basis for individualized treatment of traditional Chinese medicine (TCM) in the treatment of diabetes. Methods. We use the TFDA-1 tongue diagnosis instrument to collect tongue images of people with diabetes and accurately calculate the color features, texture features, and tongue coating ratio features through the Tongue Diagnosis Analysis System (TDAS). Then, we used K-means and Self-organizing Maps (SOM) networks to analyze the distribution of tongue features in diabetic people. Statistical analysis of TDAS features was used to identify differences between clusters. Results. The silhouette coefficient of the K-means clustering result is 0.194, and the silhouette coefficient of the SOM clustering result is 0.127. SOM Cluster 3 and Cluster 4 are derived from K-means Cluster 1, and the intersections account for (76.7% 97.5%) and (22.3% and 70.4%), respectively. K-means Cluster 2 and SOM Cluster 1 are highly overlapping, and the intersection accounts for the ratios of 66.9% and 95.0%. K-means Cluster 3 and SOM Cluster 2 are highly overlaid, and the intersection ratio is 94.1% and 82.1%. For the clustering results of K-means, TB-a and TC-a of Cluster 3 are the highest (P<0.001), TB-a of Cluster 2 is the lowest (P<0.001), and TB-a of Cluster 1 is between Cluster 2 and Cluster 3 (P<0.001). Cluster 1 has the highest TB-b and TC-b (P<0.001), Cluster 2 has the lowest TB-b and TC-b (P<0.001), and TB-b and TC-b of Cluster 3 are between Cluster 1 and Cluster 2 (P<0.001). Cluster 1 has the highest TB-ASM and TC-ASM (P<0.001), Cluster 3 has the lowest TB-ASM and TC-ASM (P<0.001), and TB-ASM and TC-ASM of Cluster 2 are between the Cluster 1 and Cluster 3 (P<0.001). CON, ENT, and MEAN show the opposite trend. Cluster 2 had the highest Per-all (P<0.001). SOM divides K-means Cluster 1 into two categories. There is almost no difference in texture features between Cluster 3 and Cluster 4 in the SOM clustering results. Cluster 3’s TB-L, TC-L, and Per-all are lower than Cluster 4 (P<0.001), Cluster 3’s TB-a, TC-a, TB-b, TC-b, and Per-part are higher than Cluster 4 (P<0.001). Conclusions. The precise tongue image features calculated by TDAS are the basis for characterizing the disease state of diabetic people. Unsupervised learning technology combined with statistical analysis is an important means to discover subtle changes in the tongue features of diabetic people. The machine vision analysis method based on unsupervised machine learning technology realizes the classification of the diabetic population based on fine tongue features. It provides a diagnostic basis for the designated diabetes TCM treatment plan.

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. Hence, new anti-liver cancer treatment strategies need to be urgently developed. Coptisine is a natural alkaloid extracted from rhizoma coptidis which exhibits anticancer activity in various preclinical models, including liver cancer. However, the molecular mechanisms underlying the anti-liver cancer effects of coptisine remains unclear. We used flow cytometry to assess the binding of coptisine to 67LR expressed on the surface of SMMC7721, HepG2, LO2 and H9 cells. Then SMMC7721, HepG2 and BEL7402 cells, belonging to the HCC cell lines, were treated with coptisine. The cell viability was detected using a cell counting kit-8 assay. Apoptosis was evaluated using flow cytometry and transferase-mediated dUTP nick-end labeling (TUNEL) assay. Apoptotic-related proteins and tumor death receptor 67-kDa laminin receptor (67LR) were detected using Western blot analysis. The cyclic guanosine 3',5'-monophosphate (cGMP) concentration was determined using enzyme-linked immunosorbent assay. sh67LR lentivirus, anti67LR antibody, and cGMP inhibitor NS2028 were used to determine how a 67LR/cGMP signaling pathway regulated coptisine-induced apoptosis. Tumor growth inhibited by coptisine was confirmed in a SMMC7721 cell xenograft mouse model. Coptisine selectively exhibited cell viability in human hepatoma cells but not in normal human hepatocyte cell line LO2 cells. Coptisine promoted SMMC7721 and HepG2 cell apoptosis by increasing 67LR activity. Both 67LR antibody and sh67LR treatment blocked coptisine-induced apoptosis and inhibition of cell viability. Coptisine upregulated the expression of cGMP. Moreover, cGMP inhibitor NS2028 significantly decreased coptisine-induced apoptosis and inhibition of cell viability. experiments confirmed that coptisine could significantly suppress the tumor growth and induce apoptosis in SMMC7721 xenografts through a 67LR/cGMP pathway. Coptisine-mediated 67LR activation may be a new therapeutic strategy for treating hepatic malignancy.