ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

In a healthy human, the airway mucus forms a thin, protective liquid layer covering the surface of the respiratory tract. It comprises a complex blend of mucin, multiple antibacterial proteins, metabolic substances, water, and electrolytes. This mucus plays a pivotal role in the lungs' innate immune system by maintaining airway hydration and capturing airborne particles and pathogens. However, heightened mucus secretion in the airway can compromise ciliary clearance, obstruct the respiratory tract, and increase the risk of pathogen colonization and recurrent infections. Consequently, a thorough exploration of the mechanisms driving excessive airway mucus secretion is crucial for establishing a theoretical foundation for the eventual development of targeted drugs designed to reduce mucus production. Across a range of lung diseases, excessive airway mucus secretion manifests with unique characteristics and regulatory mechanisms, all intricately linked to mucin. This article provides a comprehensive overview of the characteristics and regulatory mechanisms associated with excessive airway mucus secretion in several prevalent lung diseases.
Humans ; Mucus/metabolism* ; Mucins/physiology* ; Lung Diseases/metabolism* ; Respiratory Mucosa/metabolism* ; Pulmonary Disease, Chronic Obstructive/physiopathology* ; Asthma/physiopathology* ; Cystic Fibrosis/physiopathology* ; Mucociliary Clearance/physiology*

Atherosclerosis caused by disorders of lipid metabolism is the main pathological basis of atherosclerotic cardiovascular disease.Statins are the cornerstone of lipid-modulating therapy for this type of disease,but in practice there are still some patients with suboptimal lipid management.Proprotein convertase subtilisin/kexin type 9(PCSK9)inhibitors have been gradually applied as a new class of lipid-modulating drugs for the treatment in patients with this type of disease,and recent studies have shown that in addition to regulating lipid metabolism,PCSK9 inhibitors also have potential anti-inflammatory and anti-platelet activation effects.This article sorts out the multiple pharmacological mechanisms of action of PCSK9 inhibitors and the current status of clinical research of PCSK9 inhibitors.Besides,it discusses the factors that may affect the efficacy of PCSK9 inhibitors,in order to provide a reference for the safe and rational medication of PCSK9 inhibitors.

AIM To investigate the effects and mechanism of Shenxiao Jiedu Tongluo Recipe on renal AIM 2-mediated pyroptosis of a golden hamster model of diabetic nephropathy(DN).METHODS Fifty male golden hamsters of SPF grade were randomly divided into the control group and the model group.The golden hamsters of the model group successfully developed into DN models by feeding of high glucose and high fat diet and intraperitoneal injection of STZ were further randomly assigned into the model group,the enagliflozin group(10 mg/kg),and the low-dose and the high-dose Shenxiao Jiedu Tongluo Recipe groups(12.8,25.6 g/kg)for 8 weeks gavage of the corresponding administration.The golden hamsters had their levels of fasting blood glucose,24 h-UTP,serum TC,LDL-C,Scr,and Sur detected by automatic biochemical analyzer;their serum SOD activity and MDA level detected by biochemical method;their serum levels of IL-1β,IL-18,and TNF-α detected by ELISA method;their pathomorphological changes of kidney tissue observed by HE and PAS staining;their protein expressions of ROS and γH2AX detected by immunofluorescence or immunohistochemistry;and their renal protein expressions of AIM 2,caspase-1 and GSDMD detected by Western blot and immunohistochemistry.RESULTS Compared with the control group,the model group showed atrophic glomeruli;enlarged glomerular capsule cavity;mesangial expansion;edema and necrosis in the dilated renal tubules;increased levels of fasting blood glucose,24 h-UTP,serum TC,LDL-C,Scr,Sur,IL-1β,IL-18,TNF-α,MDA and renal protein expressions of ROS,γH2AX,AIM2,caspase-1,GSDMD(P＜0.01);and decreased serum SOD activity(P＜0.01).Compared with the model group,the high-dose Shenxiao Jiedu Tongluo Recipe group and the enagliflozin group displayed improved renal histopathology,decreased levels of 24 h-UTP,serum TC,LDL-C,Scr,Sur,IL-1β,IL-18,TNF-α,MDA and renal protein expressions of ROS,γH2AX,AIM2,caspase-1,GSDMD(P＜0.05,P＜0.01);and increased serum SOD activity(P＜0.01).CONCLUSION Shenxiao Jiedu Tongluo Recipe can inhibit AIM 2-mediated cell death and alleviate renal inflammatory damage in golden hamsters by inhibiting their expression of ROS-dsDNA-AIM 2 signal pathway to attain reduction of their renal ROS level,DNA damage of renal intrinsic cells,and synthesis of AIM 2 inflammatory corpuscles as well.

The effect of porcine SIRT5 on replication of foot and mouth disease virus type O(FMDV-O)and the underlying regulatory mechanism were investigated.Western blot and RT-qPCR analyses were employed to monitor expression of endoge-nous SIRT5 in PK-15 cells infected with FMDV-O.Three pairs of SIRT5-specific siRNAs were synthesized.Changes to SIRT5 and FMDV-O protein and transcript levels,in addition to virus copy numbers,were measured by western blot and RT-qPCR analyses.PK-15 cells were transfected with a eukaryotic SIRT5 expression plasmid.Western blot and RT-qPCR analyses were used to explore the impact of SIRT5 overexpression on FMDV-O replication.Meanwhile,RT-qPCR analysis was used to detect the effect of SIRT5 overexpression on the mRNA expression levels of type I interferon-stimulated genes induced by SeV and FMDV-O.The results showed that expression of SIRT5 was up-regulated in PK-15 cells infected with FMDV-O and siRNA interfered with SIRT5 to inhibit FMDV-O replication.SIRT5 overexpression promoted FMDV-O replication.SIRT5 over-expression decreased mRNA expression levels of interferon-stimulated genes induced by SeV and FMDV-O.These results suggest that FMDV-O infection stimulated expression of SIRT5 in PK-15 cells,while SIRT5 promoted FMDV-O rep-lication by inhibiting production of type I interferon-stimula-ted genes.These findings provide a reference to further ex-plore the mechanism underlying the ability of porcine SIRT5 to promote FMDV-O replication.

Soluble programmed cell death 1 ligand 1(PD-L1)in the serum includes exosomal,mi-crovesical and secreted forms of PD-L1.Previous studies have shown that the level of exosomal PD-L1 in the serum significantly correlated with the prognosis of various cancers.However,current analysis detects all forms of PD-L1 in the serum as a whole,without distinguishing exosomal PD-L1 from other forms.In this study,a specific detection method for exosomal PD-L1 was established based on surface plasmon res-onance.This method first captures PD-L1 by antibody recognition and immobilizes it on the surface of the detection chip.Then,α-hemolysin was recruited to form multiple oligomers on the exosomal membrane.This method quantifies the content of exosomal PD-L1 by monitoring the signal change during the binding process of α-hemolysin,effectively reducing background noises and amplifying the signal.The linear range before signal amplification with α-hemolysin was 0.035-2.208 pg/mL,and after signal amplifica-tion,it was 0.004-0.552 pg/mL.Methodological validation showed that this method has good specifici-ty,sensitivity,and repeatability,and has certain clinical application prospects.

Objective To understand the distribution and antimicrobial resistance changes of bacteria isolated from pleural and peritoneal effusion in Hunan Province,and provide reference for correct clinical diagnosis and rational antimicrobial use.Methods Data reported by member units of Hunan Provincial Antimicrobial Resistance Survei-llance System from 2012 to 2021 were collected.Bacteria antimicrobial resistance surveillance method was imple-mented according to technical scheme of China Antimicrobial Resistance Surveillance System(CARSS),and WHO-NET 5.6 software was used to analyze the data of bacteria isolated from pleural and peritoneal effusion as well as antimicrobial susceptibility testing results.Results From 2012 to 2021,a total of 28 934 bacterial strains were iso-lated from specimens of pleural and peritoneal effusions from member units of Hunan Provincial Antimicrobial Re-sistance Surveillance System,with 5 752 strains from pleural effusion and 23 182 from peritoneal effusion.The top five bacteria isolated from pleural effusion were Escherichia coli(n=907,15.8％),Staphylococcus aureus(n=535,9.3％),Klebsiella pneumoniae(n=369,6.4％),Staphylococcus epidermidis(n=452,7.9％),and Staphy-lococcus haemolyticus(n=285,5.0％).The detection rate of methicillin-resistant Staphylococcus aureus(MR-SA)from pleural effusion was 24.3％-39.2％,and that of methicillin-resistant coagulase negative Staphylococcus(MRCNS)was 58.8％-77.1％.The top five bacteria isolated from peritoneal effusion were Escherichia coli(n=8 264,35.6％),Klebsiella pneumoniae(n=2 074,9.0％),Enterococcus faecium(n=1 458,6.3％),Staphylo-coccus epidermidis(n=1 383,6.0％),and Pseudomonas aeruginosa(n=1 152,5.0％).The detection rate of MRSA from peritoneal effusion was 22.1％-52.4％,which presented a decreasing trend(P=0.004).The detec-tion rate of MRCNS was 60.4％-79.4％.The resistance rates of Enterobacterales from peritoneal effusion to ce-fazolin,cefuroxime,ceftriaxone and cefepime all showed decreasing trends(all P＜0.05).Vancomycin-,linezo-lid-,and teicoplanin-resistant Staphylococcus strains were not found in pleural and peritoneal effusions.The resis-tance rates of Enterococcus faecium to most tested antimicrobial agents were higher than those of Enterococcus fae-calis.The resistance rates of Enterobacterales to imipenem and meropenem were ≤8.5％.The resistance rates of non-fermentative Gram-negative bacilli to imipenem and meropenem were ≤43.3％.Conclusion The data structure of Hunan Antimicrobial Resistance Surveillance System for pleural and peritoneal effusions from 2012 to 2021 is relatively complete.The constituent and antimicrobial susceptibility of isolated pathogenic bacteria vary in different years.

From January 2019 to December 2021, overweight and obese children who visited in health outpatient Center of Hunan Children's Hospital were studied to explore and analyze the rate, related factors and patterns of multimorbidity of overweight and obesity-related diseases in children in Hunan Province. Univariate and multivariate logistic regression models were used to analyze the multimorbidity-related factors of overweight and obesity-related diseases in children. Association rules (apriori algorithm) were used to explore the multimorbidity patterns of overweight and obesity-related diseases in children. A total of 725 overweight and obese children were included in this study. The multimorbidity rate of overweight and obesity-related diseases in children was 46.07% (334/725). Age, waist circumference, the frequency of food consumption such as hamburgers and fries and adding meals before bedtime were multimorbidity-related factors of overweight and obesity-related diseases in children. The multimorbidity associated with nonalcoholic fatty liver disease (NAFLD) was relatively common. The patterns with the top three support degrees were "NAFLD+dyslipidemia","NAFLD+hypertension" and "NAFLD+hyperuricemia". The patterns with the top three confidence and elevation degrees were "Hypertension+dyslipidemia => NAFLD","Hyperuricemia => NAFLD" and "NAFLD+hypertension => dyslipidemia".
Child ; Humans ; Overweight/complications* ; Non-alcoholic Fatty Liver Disease ; Pediatric Obesity/epidemiology* ; Hyperuricemia ; Multimorbidity ; Hypertension/epidemiology* ; Dyslipidemias ; Body Mass Index ; Risk Factors