OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

OBJECTIVES: To characterize fine particulate matter (PM _2.5)-bound polycyclic aromatic hydrocarbons (PAHs) emitted from different cooking fumes and their exposure routes and assess their health-associated impact to provide a reference for health risk prevention from PAH exposure across different age and sex groups. METHODS: Sixteen PM _2.5-bound PAHs emitted from 11 cooking styles were analyzed using GC-MS/MS. The health hazards of these PAHs in the Handan City population (stratified by age and sex) were predicted using the incremental lifetime cancer risk ( ILCR) model. The respiratory deposition doses ( RDDs) of the PAHs in children and adults were calculated using the PM _2.5 deposition rates in the upper airway, tracheobronchial, and alveolar regions. RESULTS: The total concentrations of PM _2.5-bound PAHs ranged from 61.10 to 403.80 ng/m ³. Regardless of cooking styles, the ILCR _total values for adults (1.23 × 10 ^-6 to 3.70 × 10 ^-6) and older adults (1.28 × 10 ^-6 to 3.88 × 10 ^-6) exceeded the acceptable limit of 1.00 × 10 ^-6. With increasing age, the ILCR _total value first declined and then increased, varying substantially among the population groups. Cancer risk exhibited particularly high sensitivity to short exposure to barbecue-derived PAHs under equivalent body weights. Furthermore, barbecue, Sichuan and Hunan cuisine, Chinese cuisine, and Chinese fast food were associated with higher RDDs for both adults and children. CONCLUSION ILCR _total values exceeded the acceptable limit for both females and males of adults, with all cooking styles showing a potentially high cancer risk. Our findings serve as an important reference for refining regulatory strategies related to catering emissions and mitigating health risks associated with cooking styles.
Humans ; Polycyclic Aromatic Hydrocarbons/analysis* ; Cooking/methods* ; Male ; Female ; Particulate Matter/analysis* ; Adult ; Child ; Middle Aged ; Air Pollutants/analysis* ; Adolescent ; Air Pollution, Indoor/analysis* ; Young Adult ; Child, Preschool ; Aged ; China ; Inhalation Exposure ; Age Factors ; Sex Factors ; Cities ; Infant

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Coronavirus,a major pathogen infecting humans,mammals,and birds,causes primarily respiratory diseases af-ter infecting humans.Seven coronaviruses have been found to infect humans and subsequently cause varying degrees of respira-tory symptoms.From 2019 to 2023,millions of people died from severe acute respiratory syndrome coronavirus 2 infections,and the virus continues to mutate.Therefore,drug screening research must urgently be expanded to develop more effective,broad-spectrum anti-coronavirus drugs.In-depth research has indicated that the coronavirus 3C-like protease and RNA polymer-ase are necessary for viral reproduction and are highly conserved among strains,and consequently are anti-virus targets of great interest.This article summarizes the enzymes encoded by coronaviruses and drug screening methods,to provide a reference for coronavirus prevention and control.

Coronavirus,a major pathogen infecting humans,mammals,and birds,causes primarily respiratory diseases af-ter infecting humans.Seven coronaviruses have been found to infect humans and subsequently cause varying degrees of respira-tory symptoms.From 2019 to 2023,millions of people died from severe acute respiratory syndrome coronavirus 2 infections,and the virus continues to mutate.Therefore,drug screening research must urgently be expanded to develop more effective,broad-spectrum anti-coronavirus drugs.In-depth research has indicated that the coronavirus 3C-like protease and RNA polymer-ase are necessary for viral reproduction and are highly conserved among strains,and consequently are anti-virus targets of great interest.This article summarizes the enzymes encoded by coronaviruses and drug screening methods,to provide a reference for coronavirus prevention and control.

AIM To study the chemical constituents from flower buds of Magnolia biondii Pamp.and their in vitro acetylcholinesterase inhibitory activities.METHODS The 50% acetone extract from the flower buds of M.biondii was isolated and purified by Diaion HP-20,Toyopearl HW-40C,ODS and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The in vitro acetylcholinesterase inhibitory activities of these compounds were determined according to previous method established by research group.RESULTS Seventeen compounds were isolated and identified as crassifolioside(1),magnoloside B(2),rutin(3),isoquercitrin(4),quercetin(5),northalifoline(6),cordysinin B(7),thymidine(8),indazole(9),dihydrodehydrodiconiferyl alcohol(10),aesculetin(11),C-veratroylglycol(12),3,4-dihydroxyphenylethanol(13),3-methoxy-4-hydroxyphenylethanol(14),3,4-dihydroxybenzoic acid(15),2,4,6-trimethoxyphenol(16),syringic acid(17).CONCLUSION Compounds 1-17 are isolated from this plant for the first time,none of which show acetylcholinesterase inhibitory activities at the concentration of 20 μmol/L.

AIM To study the secondary metabolites from the endophytic fungi Candida sp.of Berberis atrocarpa Schneid.METHODS The ethyl acetate fraction and petroleum ether fraction from the secondary metabolites of Candida sp.fermentation extract were separated and purified by silica gel,Sephadex LH-20 and preparative liquid chromatography,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Eighteen compounds were isolated and identified as 1-phenyl-1,2-ethanediol(1),4-hydroxyphenethyl alcohol(2),4-hydroxybenzoic acid(3),4-hydroxyphenylacetic acid(4),3-hydroxyphenylacetic acid(5),3-methylsulfinyl propionic acid(6),phenylacetic acid(7),(S)-N-nitroso-1-amino-p-hydroxy phenylethanol(8),2-phenylacetamide(9),p-hydroxybenzaldehyde(10),ethyl 2-(4-hydroxyphenyl)acetate(11),dibutyl phthalate(12),5,5'-dimethoxybiphenyl-2,2'-diol(13),3-indolealdehyde(14),N-acetyl-L-phenylalanine(15),9-hydroxy-10E,12Z-octadecadienoic acid(16),9-hydroxy-10E,12E-octadecadienoic acid(17),(6E)-5-methylene-6-tetradecenoic acid(18).CONCLUSION Compounds 1,3-8 and 10-18 are isolated from Candida sp for the first time.