BACKGROUND: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited. Notably, artificial intelligence (AI) technologies have significantly advanced drug discovery. This study aimed to discover RIPK3 inhibitor with AI assistance. METHODS: An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo . We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. Subsequently, these target candidates were validated using molecular simulations (molecular docking, absorption, distribution, metabolism, excretion, and toxicity [ADMET] prediction, and molecular dynamics simulations) and biological experiments (Western blotting and fluorescence staining) in vitro and in vivo . RESULTS: AI-driven drug screening techniques have the potential to greatly accelerate drug development. A compound called HG9-91-01, identified using AI methods, exerted neuroprotective effects in acute glaucoma. Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency, and HG9-91-01 showed a higher cell survival rate compared to the other candidates. Furthermore, HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model. It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis (apoptosis, pyroptosis, and necroptosis). Finally, we found that HG9-91-01 can regulate key proteins related to PANoptosis, indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis, pyroptosis, and necroptosis. CONCLUSION AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
Animals ; Glaucoma/metabolism* ; Neuroprotective Agents/pharmacology* ; Mice ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Artificial Intelligence ; Retinal Ganglion Cells/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation ; Mice, Inbred C57BL ; Male

Objective To screen the risk factors for hypoglycemia in adult intensive care unit(ICU)patients,construct a risk prediction model,and validate its predictive effect.Methods A retrospective study was conducted on adult critically ill patients admitted to the general ICU of Hospital of Chengdu University of Traditional Chinese Medicine from December 2023 to September 2024.Patients admitted from December 2023 to June 2024 served as the modeling group,and those from July to September 2024 as the validation group.A total of 928 patients were included,with 650 in the modeling group and 278 in the validation group.After literature review and expert consultation,27 potential risk factors for hypoglycemia in ICU patients were initially screened,and data were collected including general information[gender,age,acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,nutrition risk in critically ill(NUTRIC)score,mechanical ventilation status,hemodialysis status,enteral nutrition status],disease data(sepsis,liver disease history,kidney disease history,diabetes history,hypoglycemia history),blood glucose-related indicators[mean blood glucose,blood glucose coefficient of variation,insulin dosage,intravenous insulin titration use,inotropic drug use,insulin secretagogues(Sulfonylureas and Glinides),and combined use of hypoglycemic drugs(two or more)],and laboratory indicators[serum creatinine(SCr),blood urea nitrogen(BUN),serum albumin(Alb),alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(TBil),glomerular filtration rate(GFR)].The patients were divided into a hypoglycemia group and a non-hypoglycemia group based on the occurrence of hypoglycemia.Univariate analysis and binary Logistic regression analysis were used to identify influencing factors of hypoglycemia in adult ICU patients,and a nomogram prediction model was constructed.The area under the receiver operator characteristic curve(AUC)and calibration curves were employed to evaluate the discrimination and calibration of the model.Results The modeling cohort included 552 non-hypoglycemic patients and 98 hypoglycemic patients,with an ICU hypoglycemia incidence rate of 15.1％.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with renal disease history,diabetes history,hypoglycemia history,undergoing hemodialysis,using intravenous insulin titration,and combined use of hypoglycemic drugs,as well as lower blood glucose coefficient of variation,lower APACHEⅡ scores,and significantly elevated GFR(all P<0.05).Binary Logistic regression analysis was performed using the 9 variables with statistically significant differences in univariate analysis as independent variables and hypoglycemia occurrence as the dependent variable.The results indicated that a history of diabetes,a history of hypoglycemia,APACHEⅡ score,GFR,blood glucose coefficient of variation,and combined use of hypoglycemic drugs were independent risk factors for hypoglycemia in ICU patients[odds ratios(OR)were 1.761,2.095,1.048,0.990,1.029,and 1.975,respectively,and 95％confidence intervals(95％CI)were 1.052-2.949,1.220-3.600,1.022-1.074,0.982-0.997,1.013-1.046,and 1.145-3.408,respectively.The corresponding Pvalues were 0.031,0.007,0.000,0.009,<0.001,0.014].A nomogram prediction model for hypoglycemia in ICU patients was constructed using six independent predictors selected through binary logistic regression analysis.The ROC curve AUC for the modeling group was 0.884(95％CI 0.826-0.941,P=0.250),with a maximum Youden index of 0.713,sensitivity of 92.1％,and specificity of 79.2％.The validation cohort included 38 patients with hypoglycemia and 240 patients without hypoglycemia.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with a history of diabetes,a history of hypoglycemia,and combined use of hypoglycemic drugs,as well as lower APACHEⅡ scores and lower blood glucose coefficient of variation,with significantly increased GFR(all P<0.05).The ROC curve AUC for the validation cohort was 0.803(95％CI was 0.757-0.849,P=0.138),indicating high discriminatory ability.The predicted probability at the diagnostic cutoff point was P=0.138.The model's diagnostic threshold for predicted probability was P=0.138,while the optimal cut-off value based on the Youden index was 0.513,yielding a sensitivity of 76.5％and specificity of 74.8％,indicating predictive value for hypoglycemia in adult ICU patients.The mean absolute error(MAE)results for the modeling group and validation group were<0.05.The calibration curves of both the modeling and validation groups showed close alignment with the ideal curve,indicating excellent calibration performance of the model.Conclusion The constructed hypoglycemia risk prediction model for adult ICU patients has good predictive performance,which can quickly identify high-risk populations of hypoglycemia in ICU and provide reference for clinical preventive nursing.

Intestinal ischemia-reperfusion(I/R)injury firstly causes damage to the intestine itself.After the intes-tinal barrier and function are impaired,bacteria,metabolites and endotoxins in the intestine will enter the portal system through the damaged intestinal mucosa,and then participate in the systemic microcirculatory system through the liver to act on the distal organs.This leads to further damage and induces reperfusion syndrome and multiple or-gan dysfunction syndromes(MODS),which can lead to death in severe cases.

With the rapid development of sequencing technologies, the detection of alternative polyadenylation (APA) in mammals has become more precise. APA precisely regulates gene expression by altering the length and position of the poly(A) tail, and is involved in various biological processes such as disease occurrence and embryonic development. The research on APA in mammals mainly focuses on the following aspects:(1) identifying APA based on transcriptome data and elucidating their characteristics; (2) investigating the relationship between APA and gene expression regulation to reveal its important role in life regulation;(3) exploring the intrinsic connections between APA and disease occurrence, embryonic development, differentiation, and other life processes to provide new perspectives and methods for disease diagnosis and treatment, as well as uncovering embryonic development regulatory mechanisms. In this review, the classification, mechanisms and functions of APA were elaborated in detail and the methods for APA identifying and APA data resources based on various transcriptome data were systematically summarized. Moreover, we epitomized and provided an outlook on research on APA, emphasizing the role of sequencing technologies in driving studies on APA in mammals. In the future, with the further development of sequencing technology, the regulatory mechanisms of APA in mammals will become clearer.

Objective To screen the risk factors for hypoglycemia in adult intensive care unit(ICU)patients,construct a risk prediction model,and validate its predictive effect.Methods A retrospective study was conducted on adult critically ill patients admitted to the general ICU of Hospital of Chengdu University of Traditional Chinese Medicine from December 2023 to September 2024.Patients admitted from December 2023 to June 2024 served as the modeling group,and those from July to September 2024 as the validation group.A total of 928 patients were included,with 650 in the modeling group and 278 in the validation group.After literature review and expert consultation,27 potential risk factors for hypoglycemia in ICU patients were initially screened,and data were collected including general information[gender,age,acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,nutrition risk in critically ill(NUTRIC)score,mechanical ventilation status,hemodialysis status,enteral nutrition status],disease data(sepsis,liver disease history,kidney disease history,diabetes history,hypoglycemia history),blood glucose-related indicators[mean blood glucose,blood glucose coefficient of variation,insulin dosage,intravenous insulin titration use,inotropic drug use,insulin secretagogues(Sulfonylureas and Glinides),and combined use of hypoglycemic drugs(two or more)],and laboratory indicators[serum creatinine(SCr),blood urea nitrogen(BUN),serum albumin(Alb),alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(TBil),glomerular filtration rate(GFR)].The patients were divided into a hypoglycemia group and a non-hypoglycemia group based on the occurrence of hypoglycemia.Univariate analysis and binary Logistic regression analysis were used to identify influencing factors of hypoglycemia in adult ICU patients,and a nomogram prediction model was constructed.The area under the receiver operator characteristic curve(AUC)and calibration curves were employed to evaluate the discrimination and calibration of the model.Results The modeling cohort included 552 non-hypoglycemic patients and 98 hypoglycemic patients,with an ICU hypoglycemia incidence rate of 15.1％.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with renal disease history,diabetes history,hypoglycemia history,undergoing hemodialysis,using intravenous insulin titration,and combined use of hypoglycemic drugs,as well as lower blood glucose coefficient of variation,lower APACHEⅡ scores,and significantly elevated GFR(all P<0.05).Binary Logistic regression analysis was performed using the 9 variables with statistically significant differences in univariate analysis as independent variables and hypoglycemia occurrence as the dependent variable.The results indicated that a history of diabetes,a history of hypoglycemia,APACHEⅡ score,GFR,blood glucose coefficient of variation,and combined use of hypoglycemic drugs were independent risk factors for hypoglycemia in ICU patients[odds ratios(OR)were 1.761,2.095,1.048,0.990,1.029,and 1.975,respectively,and 95％confidence intervals(95％CI)were 1.052-2.949,1.220-3.600,1.022-1.074,0.982-0.997,1.013-1.046,and 1.145-3.408,respectively.The corresponding Pvalues were 0.031,0.007,0.000,0.009,<0.001,0.014].A nomogram prediction model for hypoglycemia in ICU patients was constructed using six independent predictors selected through binary logistic regression analysis.The ROC curve AUC for the modeling group was 0.884(95％CI 0.826-0.941,P=0.250),with a maximum Youden index of 0.713,sensitivity of 92.1％,and specificity of 79.2％.The validation cohort included 38 patients with hypoglycemia and 240 patients without hypoglycemia.Compared with the hypoglycemia group,the non-hypoglycemia group showed significantly lower proportions of patients with a history of diabetes,a history of hypoglycemia,and combined use of hypoglycemic drugs,as well as lower APACHEⅡ scores and lower blood glucose coefficient of variation,with significantly increased GFR(all P<0.05).The ROC curve AUC for the validation cohort was 0.803(95％CI was 0.757-0.849,P=0.138),indicating high discriminatory ability.The predicted probability at the diagnostic cutoff point was P=0.138.The model's diagnostic threshold for predicted probability was P=0.138,while the optimal cut-off value based on the Youden index was 0.513,yielding a sensitivity of 76.5％and specificity of 74.8％,indicating predictive value for hypoglycemia in adult ICU patients.The mean absolute error(MAE)results for the modeling group and validation group were<0.05.The calibration curves of both the modeling and validation groups showed close alignment with the ideal curve,indicating excellent calibration performance of the model.Conclusion The constructed hypoglycemia risk prediction model for adult ICU patients has good predictive performance,which can quickly identify high-risk populations of hypoglycemia in ICU and provide reference for clinical preventive nursing.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To explore the targets and pathways of Cynanchum wilfordii for treatment of ulcerative colitis(UC).Methods UPLC-QE-MS was used to identify the components of Cynanchum wilfordii ethanol extract,and their targets were screened using public databases for construction of the core protein-protein interaction(PPI)network and GO and KEGG enrichment analyses.Forty male C57 mice were randomized into normal control group,model group,mesalazine group and Cynanchum wilfordii group(n=10),and in the latter 3 groups,mouse UC models were established by treatment with 2.5%DSS and the latter 2 groups drug interventions by gavage.The therapeutic effect was evaluated by recording body weight changes and DAI score.Pathological changes of the colon tissue were observed with HE and AB-PAS staining,and JAK2 and STAT3 protein expressions were detected with Western blotting.The metabolites and metabolic pathways were identified by metabonomics analysis.Results We identified 240 chemical components in Cynanchum wilfordii alcoholic extracts,including 19 steroids.A total of 177 Cynanchum wilfordii targets,5406 UC genes,and 117 intersection genes were obtained.JAK2 and STAT3 were the core targets and significantly enriched in lipid and atherosclerosis pathways.Cynanchum wilfordii treatment significantly increased the body weight and decreased DAI score of UC mice(P<0.05),alleviated intestinal pathologies,and decreased JAK2 and STAT3 protein expressions in the colon tissues.Most of the 83 intersecting differential metabolites between the control,model and Cynanchum wilfordii groups were identified as glycerophospholipids,arachidonic acid,and amino acids involving glycerophospholipid metabolism and other pathways.Correlation analysis suggested that the core targets of Cynanchum wilfordii for UC participated in regulation of the metabolites.Conclusion Cynanchum wilfordii alleviates lipid and amino acid metabolism disorders to lessen UC in mice by regulating the core targets including JAK2 and STAT3 and the levels of endogenous metabolites.

This study utilized a chiral liquid chromatography-mass spectrometry (LC-MS)-guided isolation strategy to accurately capture angular-type pyranocoumarins (APs) in Peucedani Radix (Chinese name: Qianhu). Sixteen APs were successfully purified from the ethyl acetate extract of Peucedani Radix through deploying various techniques such as silica gel, ODS, Sephadex LH-20, and achiral (chiral) semi-preparative liquid chromatography. After extensive structural measurements, such as ¹H and ¹³C NMR spectroscopy, their structures were identified as (3′S)-3′-(2-methyl-butyroyl)-4′-oxo-3′,4′-dihydroseselin (1A), (3′R)-3′-(2-methyl-butyroyl)-4′-oxo-3′,4′-dihydroseselin (1B), (3′S)-3′-isovaleryl-4′-oxo-lomatin (2A), (3′R)-3′-isovaleryl-4′-oxo-lomatin (2B), (3′S)-3′-angeloyloxy-4′-oxo-3′,4′-dihydroseselin (3A), (3′R)-3′-angeloyloxy-4′-oxo-3′,4′-dihydroseselin (3B), (3′S,4′S)-praeruptorin B (4A), (3′R,4′R)-praeruptorin B (4B), (3′S,4′S)-praeruptorin E (5A), (3′R,4′R)-praeruptorin E (5B), 3′-isovaleryl-4′-angeloyl-cis-khellactone (6), 3′-angeloyl-4′-(2-methyl-butyroyl)-cis-khellactone (7), (3′S,4′S)-praeruptorin A (8A), (3′R,4′R)-praeruptorin A (8B), (3′S,4′S)-khellactone (9A), and (3′R,4′R)-khellactone (9B), respectively. Thereof, compounds 1A and 1B were new compounds, while compound 2A represents a new configuration for a known planar structure. Compounds 2A and 2B were isolated for the first time from Peucedani Radix. Above all, chiral LC-MS-guided isolation strategy is advantageous at rapid capturing new compounds from herbal medicines, providing an effective means for the separation of novel structures, especially new enantiomers.

Objective To explore the targets and pathways of Cynanchum wilfordii for treatment of ulcerative colitis(UC).Methods UPLC-QE-MS was used to identify the components of Cynanchum wilfordii ethanol extract,and their targets were screened using public databases for construction of the core protein-protein interaction(PPI)network and GO and KEGG enrichment analyses.Forty male C57 mice were randomized into normal control group,model group,mesalazine group and Cynanchum wilfordii group(n=10),and in the latter 3 groups,mouse UC models were established by treatment with 2.5%DSS and the latter 2 groups drug interventions by gavage.The therapeutic effect was evaluated by recording body weight changes and DAI score.Pathological changes of the colon tissue were observed with HE and AB-PAS staining,and JAK2 and STAT3 protein expressions were detected with Western blotting.The metabolites and metabolic pathways were identified by metabonomics analysis.Results We identified 240 chemical components in Cynanchum wilfordii alcoholic extracts,including 19 steroids.A total of 177 Cynanchum wilfordii targets,5406 UC genes,and 117 intersection genes were obtained.JAK2 and STAT3 were the core targets and significantly enriched in lipid and atherosclerosis pathways.Cynanchum wilfordii treatment significantly increased the body weight and decreased DAI score of UC mice(P<0.05),alleviated intestinal pathologies,and decreased JAK2 and STAT3 protein expressions in the colon tissues.Most of the 83 intersecting differential metabolites between the control,model and Cynanchum wilfordii groups were identified as glycerophospholipids,arachidonic acid,and amino acids involving glycerophospholipid metabolism and other pathways.Correlation analysis suggested that the core targets of Cynanchum wilfordii for UC participated in regulation of the metabolites.Conclusion Cynanchum wilfordii alleviates lipid and amino acid metabolism disorders to lessen UC in mice by regulating the core targets including JAK2 and STAT3 and the levels of endogenous metabolites.

The paper introduces professor ZHUANG Li-xing's clinical experience in treatment of dyskinesia of Parkinson's disease with acupuncture at triple-acupoint prescription. In pathogenesis, dyskinesia of Parkinson's disease refers to yang deficiency and disturbing wind. In treatment, acupuncture focuses on warming yang, promoting the circulation of the governor vessel, regulating the spirit and stopping trembling; and Baihui (GV 20), Suliao (GV 25) and Dingchanxue (Extra) are selected to be "trembling relief needling". In combination with Jin's three needling, named "three-trembling needling" "three-governor-vessel needling" and "three-spasm needling", the triple-acupoint prescription is composed. To ensure the favorable therapeutic effect, this prescription is modified according to the symptoms and the specific techniques of acupuncture are combined such as conducting qi, harmonizing yin and yang, and manipulating gently for reinforcing and reducing.
Humans ; Acupuncture Points ; Parkinson Disease/therapy* ; Acupuncture Therapy/methods* ; Acupuncture ; Dyskinesias