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Fulltext Organ-Specific Analysis of Morus alba Using a Gel-Free/Label-Free Proteomic Technique

Zhu W, Zhong Z, Liu S, Yang B, Komatsu S, Ge Z, et al.

Int J Mol Sci, 2019 Jan 16;20(2).
PMID: 30654535 DOI: 10.3390/ijms20020365

Morus alba is an important medicinal plant that is used to treat human diseases. The leaf, branch, and root of Morus can be applied as antidiabetic, antioxidant, and anti-inflammatory medicines, respectively. To explore the molecular mechanisms underlying the various pharmacological functions within different parts of Morus, organ-specific proteomics were performed. Protein profiles of the Morus leaf, branch, and root were determined using a gel-free/label-free proteomic technique. In the Morus leaf, branch, and root, a total of 492, 414, and 355 proteins were identified, respectively, including 84 common proteins. In leaf, the main function was related to protein degradation, photosynthesis, and redox ascorbate/glutathione metabolism. In branch, the main function was related to protein synthesis/degradation, stress, and redox ascorbate/glutathione metabolism. In root, the main function was related to protein synthesis/degradation, stress, and cell wall. Additionally, organ-specific metabolites and antioxidant activities were analyzed. These results revealed that flavonoids were highly accumulated in Morus root compared with the branch and leaf. Accordingly, two root-specific proteins named chalcone flavanone isomerase and flavonoid 3,5-hydroxylase were accumulated in the flavonoid pathway. Consistent with this finding, the content of the total flavonoids was higher in root compared to those detected in branch and leaf. These results suggest that the flavonoids in Morus root might be responsible for its biological activity and the root is the main part for flavonoid biosynthesis in Morus.
Fulltext Electroactive Biomaterials for Facilitating Bone Defect Repair under Pathological Conditions

Heng BC, Bai Y, Li X, Lim LW, Li W, Ge Z, et al.

Adv Sci (Weinh), 2023 Jan;10(2):e2204502.
PMID: 36453574 DOI: 10.1002/advs.202204502

Bone degeneration associated with various diseases is increasing due to rapid aging, sedentary lifestyles, and unhealthy diets. Living bone tissue has bioelectric properties critical to bone remodeling, and bone degeneration under various pathological conditions results in significant changes to these bioelectric properties. There is growing interest in utilizing biomimetic electroactive biomaterials that recapitulate the natural electrophysiological microenvironment of healthy bone tissue to promote bone repair. This review first summarizes the etiology of degenerative bone conditions associated with various diseases such as type II diabetes, osteoporosis, periodontitis, osteoarthritis, rheumatoid arthritis, osteomyelitis, and metastatic osteolysis. Next, the diverse array of natural and synthetic electroactive biomaterials with therapeutic potential are discussed. Putative mechanistic pathways by which electroactive biomaterials can mitigate bone degeneration are critically examined, including the enhancement of osteogenesis and angiogenesis, suppression of inflammation and osteoclastogenesis, as well as their anti-bacterial effects. Finally, the limited research on utilization of electroactive biomaterials in the treatment of bone degeneration associated with the aforementioned diseases are examined. Previous studies have mostly focused on using electroactive biomaterials to treat bone traumatic injuries. It is hoped that this review will encourage more research efforts on the use of electroactive biomaterials for treating degenerative bone conditions.
Characterization of a New HIV-1 CRF01_AE/B Recombinant Virus Form Among Men Who Have Sex with Men in Shanghai, China

Ou W, Li K, Feng Y, Huang Q, Ge Z, Sun J, et al.

AIDS Res Hum Retroviruses, 2019 04;35(4):414-418.
PMID: 30229664 DOI: 10.1089/AID.2018.0197

To date, there are 16 types of CRF01_AE/B circulating recombinant forms identified, and most of them are distributed in Asian countries such as China, Malaysia, and Singapore. Previous HIV molecular epidemiological surveys showed that CRF01_AE (27.6%) and B (9.6%) subtypes are predominant strains in mainland of China. At the same time, the HIV-1 virus spreads faster in the men who have sex with men (MSM) population than in other risk groups. In Shanghai district, ∼66.0% of newly reported cases were infected through homosexual transmission. In this study, we report a novel recombinant strain of CRF01_AE/B. The near full-length genome phylogenetic tree showed that the strain clustered with the CRF01_AE reference sequence and placed in the peripheral position within the branch of the CRF01_AE strain. Subregional evolutionary results indicated that the CRF01_AE subtype was derived from cluster 4 of CRF01_AE, which is mainly distributed in northern China. The subtype B was correlated with the U.S./Europe B, which are widely prevalent in the Chinese MSM population. In recent years, a large number of recombinant forms between CRF01_AE and B strains are continuously emerging in China. Therefore, understanding the current epidemic recombinant forms will have significant implications for prevention and treatment of HIV/AIDS.
Sodium anilinide-cyclohexylamide pair: synthesis, characterization, and hydrogen storage properties

Jing Z, Yu Y, Chen R, Tan KC, He T, Wu A, et al.

Chem Commun (Camb), 2020 Jan 22.
PMID: 31967625 DOI: 10.1039/c9cc08593a

The lack of efficient hydrogen storage material is one of the bottlenecks for the large-scale implementation of hydrogen energy. Here, a series of new hydrogen storage materials, i.e., anilinide-cyclohexylamide pairs, are proposed via the metallation of an aniline-cyclohexylamine pair. DFT calculations show that the enthalpy change of hydrogen desorption (ΔHd) can be significantly tuned from 60.0 kJ per mol-H2 for the pristine aniline-cyclohexylamine pair to 42.2 kJ per mol-H2 for sodium anilinide-cyclohexylamide and 38.7 kJ per mol-H2 for potassium anilinide-cyclohexylamide, where an interesting correlation between the electronegativity of the metal and the ΔHd was observed. Experimentally, the sodium anilinide-cyclohexylamide pair was successfully synthesised with a theoretical hydrogen capacity of 4.9 wt%, and the hydrogenation and dehydrogenation cycle can be achieved at a relatively low temperature of 150 °C in the presence of commercial catalysts, in clear contrast to the pristine aniline-cyclohexylamine pair which undergoes dehydrogenation at elevated temperatures.
Fulltext Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy

Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, et al.

JAMA Neurol, 2022 Oct 01;79(10):986-996.
PMID: 36036923 DOI: 10.1001/jamaneurol.2022.2514

IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.
OBJECTIVE: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.
EXPOSURES: One of 7 antiseizure medications.
MAIN OUTCOMES AND MEASURES: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.
RESULTS: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.
CONCLUSIONS AND RELEVANCE: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.