Due to patient compliance and convenience, oral medication is likely the most common and acceptable method of drug administration. However, traditional dosage forms such as tablets or capsules may lead to low drug bioavailability and poor therapeutic efficiency. Therefore, with advancements in material science and micro/nano manufacturing technology, various carriers have been developed to enhance drug absorption in the gastrointestinal tract. In this context, we initially discuss the key biological factors that hinder drug transport and absorption (including anatomical, physical, and biological factors). Building on this foundation, recent progress in both conventional and innovative oral drug delivery routes aimed at improving drug bioavailability and targeting is reviewed. Finally, we explore future prospects for oral drug delivery systems as well as potential challenges in clinical translation.

Objective:To investigate the association of systemic immune-inflammation index(SII)and serum cysta-tin C(CysC)with the severity of acute pulmonary embolism(APE),and their predictive value for prognosis.Meth-ods:A total of 181 patients who were first diagnosed with APE in Cangzhou People's Hospital between January 2018 and January 2023 were retrospectively selected.The severity of APE was determined according to risk stratification criteria for pulmonary embolism,and the patients were divided into low-risk group(n=67),middle-risk group(n=81)and high-risk group(n=33).General clinical data and venous blood neutrophils,platelet and lymphocyte counts,CysC and other indicators were collected,and SII was calculated according to the formula.The relevant in-dicators were compared among three groups,and their correlation with the severity of APE was analyzed by Spearman correlation analysis.According to the prognosis,all APE patients were divided into favorable outcome group(n=129)and unfavorable outcome group(n=52).The general clinical data were compared and multivariate Cox regression analysis was used to study the influencing factors of unfavorable outcome in APE patients.The re-ceiver operating characteristic curve(ROC)was drawn to evaluate the predictive value of SII,CysC and their com-bination for the prognosis of APE patients.Nomogram model for prognosis was constructed.Results:Compared with patients in low-risk group,those in the middle-risk group and the high-risk group had significantly higher levels of serum creatinine,CysC and uric acid(P＜0.05 or＜0.01).The SII in the high-risk group was significant-ly higher than those of middle-risk group and low-risk group(P＜0.001 all).Spearman correlation analysis showed that serum creatinine,CysC,uric acid and SII were significant positively correlated with the severity of APE(r=0.356,0.358,0.233,0.353,P＜0.01 all).Compared with patients in the favorable outcome group,those in the unfavorable outcome group had significantly higher levels of D-dimer,serum creatinine,CysC,uric acid and SII(P＜0.01 all).There was a statistically significant difference in the severity of APE between the two groups(P=0.001).Multivariate Cox regression analysis showed that CysC,SII,and middle or high risk of disease severity were independent risk factors for unfavorable outcome in APE patients(HR=1.001～14.453,P＜0.05 or＜0.01).ROC curve indicated that the AUC of SII,CysC and their combination in predicting unfavorable outcome of APE patients were 0.815(95％CI 0.749～0.881),0.747(95％CI 0.661～0.832)and 0.878(95％CI,0.821～0.936),respectively.The combined AUC of the two was significantly higher than those of SII and CysC alone(Z=-2.234,-3.500,P＜0.05 or＜0.01).Based on the above independent risk factors,the AUC values of the 1-year and 3-year unfavorable outcome nomogram models were 92.9 and 88.2,respectively.The calibration prediction curve and the ideal curve fitted well.The decision curve showed that the model had a good net benefit.Conclusion:SII and CysC are significant positively correlated with the severity of APE and they are independent risk factors for unfavor-able outcome of APE,and the combination of the two indicators has a good predictive value for the prognosis of APE.The nomogram constructed has good accuracy and practicability.

In view of the characteristics of H5N6 subtype avian influenza virus(AIV)that it has both high pathogenicity and the risk of cross-species transmission,posing a serious threat to the poultry farming industry and public health security,in order to effectively prevent and control the spread of H5N6 avian influenza,a rapid,sensitive and specific detection technolo-gy was established in this study.The specific monoclonal antibodies against the neuraminidase N6 protein of avian influenza A virus subtype H5N6 were obtained through hybridoma and monoclonal antibody technology.These antibodies were coupled and labeled with carboxyl-functionalized fluorescent quantum dots,along with previously prepared specific antibodies against the hemagglutinin H5 protein.A rapid fluorescence immunochromatographic detection method for the H5N6 subtype of avian influ-enza virus was established according to the principle of double-antibody sandwich immunochromatography.This method a-chieved a detection sensitivity of 1 ng/mL for recombinant hemagglutinin H5 subtype protein and 0.1 ng/mL for recombinant neuraminidase N6 subtype protein.Moreover,the method exhibited no cross-reactivity with other influenza subtypes or patho-gens,such as Newcastle disease(ND),infectious bronchitis(IB),and infectious laryngotracheitis(ILT),thus demonstrating good specificity.The method effectively identified the highly pathogenic avian influenza virus H5 subtype and directly distin-guished the H5N6 subtype with good accuracy.The fluorescent quantum dot immunochromatographic typing detection method established herein met the sensitivity,specificity,and accuracy requirements for H5N6 subtype detection,and can be further used for rapid detection of the H5 and H5N6 subtypes of avian influenza virus.

In view of the characteristics of H5N6 subtype avian influenza virus(AIV)that it has both high pathogenicity and the risk of cross-species transmission,posing a serious threat to the poultry farming industry and public health security,in order to effectively prevent and control the spread of H5N6 avian influenza,a rapid,sensitive and specific detection technolo-gy was established in this study.The specific monoclonal antibodies against the neuraminidase N6 protein of avian influenza A virus subtype H5N6 were obtained through hybridoma and monoclonal antibody technology.These antibodies were coupled and labeled with carboxyl-functionalized fluorescent quantum dots,along with previously prepared specific antibodies against the hemagglutinin H5 protein.A rapid fluorescence immunochromatographic detection method for the H5N6 subtype of avian influ-enza virus was established according to the principle of double-antibody sandwich immunochromatography.This method a-chieved a detection sensitivity of 1 ng/mL for recombinant hemagglutinin H5 subtype protein and 0.1 ng/mL for recombinant neuraminidase N6 subtype protein.Moreover,the method exhibited no cross-reactivity with other influenza subtypes or patho-gens,such as Newcastle disease(ND),infectious bronchitis(IB),and infectious laryngotracheitis(ILT),thus demonstrating good specificity.The method effectively identified the highly pathogenic avian influenza virus H5 subtype and directly distin-guished the H5N6 subtype with good accuracy.The fluorescent quantum dot immunochromatographic typing detection method established herein met the sensitivity,specificity,and accuracy requirements for H5N6 subtype detection,and can be further used for rapid detection of the H5 and H5N6 subtypes of avian influenza virus.

Objective:To investigate the association of systemic immune-inflammation index(SII)and serum cysta-tin C(CysC)with the severity of acute pulmonary embolism(APE),and their predictive value for prognosis.Meth-ods:A total of 181 patients who were first diagnosed with APE in Cangzhou People's Hospital between January 2018 and January 2023 were retrospectively selected.The severity of APE was determined according to risk stratification criteria for pulmonary embolism,and the patients were divided into low-risk group(n=67),middle-risk group(n=81)and high-risk group(n=33).General clinical data and venous blood neutrophils,platelet and lymphocyte counts,CysC and other indicators were collected,and SII was calculated according to the formula.The relevant in-dicators were compared among three groups,and their correlation with the severity of APE was analyzed by Spearman correlation analysis.According to the prognosis,all APE patients were divided into favorable outcome group(n=129)and unfavorable outcome group(n=52).The general clinical data were compared and multivariate Cox regression analysis was used to study the influencing factors of unfavorable outcome in APE patients.The re-ceiver operating characteristic curve(ROC)was drawn to evaluate the predictive value of SII,CysC and their com-bination for the prognosis of APE patients.Nomogram model for prognosis was constructed.Results:Compared with patients in low-risk group,those in the middle-risk group and the high-risk group had significantly higher levels of serum creatinine,CysC and uric acid(P＜0.05 or＜0.01).The SII in the high-risk group was significant-ly higher than those of middle-risk group and low-risk group(P＜0.001 all).Spearman correlation analysis showed that serum creatinine,CysC,uric acid and SII were significant positively correlated with the severity of APE(r=0.356,0.358,0.233,0.353,P＜0.01 all).Compared with patients in the favorable outcome group,those in the unfavorable outcome group had significantly higher levels of D-dimer,serum creatinine,CysC,uric acid and SII(P＜0.01 all).There was a statistically significant difference in the severity of APE between the two groups(P=0.001).Multivariate Cox regression analysis showed that CysC,SII,and middle or high risk of disease severity were independent risk factors for unfavorable outcome in APE patients(HR=1.001～14.453,P＜0.05 or＜0.01).ROC curve indicated that the AUC of SII,CysC and their combination in predicting unfavorable outcome of APE patients were 0.815(95％CI 0.749～0.881),0.747(95％CI 0.661～0.832)and 0.878(95％CI,0.821～0.936),respectively.The combined AUC of the two was significantly higher than those of SII and CysC alone(Z=-2.234,-3.500,P＜0.05 or＜0.01).Based on the above independent risk factors,the AUC values of the 1-year and 3-year unfavorable outcome nomogram models were 92.9 and 88.2,respectively.The calibration prediction curve and the ideal curve fitted well.The decision curve showed that the model had a good net benefit.Conclusion:SII and CysC are significant positively correlated with the severity of APE and they are independent risk factors for unfavor-able outcome of APE,and the combination of the two indicators has a good predictive value for the prognosis of APE.The nomogram constructed has good accuracy and practicability.

AIM To optimize the extraction process for uracil,hypoxanthine,xanthine,uridine,thymine,inosine,guanosine and 2'-deoxyguanosine from Pheretima guillelmi(Michaelsen),and to determine their contents.METHODS With solid-liquid ratio,ultrasonic time and ultrasonic temperature as influencing factors,contents of hypoxanthine and total nucleosides as evaluation indices,the extraction process was optimized by orthogonal test.HPLC was adopted in the content determination of varioud nucleosides,the analysis was performed on a 30℃thermostatic Agilent C18 column(4.6 mm×250 mm,5 μm),with the mobile phase comprising of methanol-water flowing at 1 mL/min in a gradient elution manner,and the detection wavelength was set at 260 nm.RESULTS The optimal conditions were determined to be 1∶250 for solid-liquid ratio,60 min for ultrasonic time,and 60℃for ultrasonic temperature.Eight nucleosides showed good linear relationships within their own ranges(R2＞0.999 0),whose average recoveries were 99.11%-103.27%with the RSDs of 0.85%-2.89%.CONCLUSION This stable and reliable method can be used for the extraction and content determination of nucleosides from P.guillelmi.

Objective:To analyze and compare the differences of expression profiles between RPL and normal adipose tissue by transcriptome sequencing(RNA-Seq),then identify the key genes related to prognosis and explore their potential mechanisms.Methods:Tumor tissues and normal adipose tissues of patients with RPL were collected for RNA-Seq,and then the differentially ex-pressed genes were analyzed by GO and KEGG enrichment analysis.Based on TCGA,the high-risk genes related to prognosis were screened and verified by Kaplan-Meier curve and receiver operat-ing characteristic(ROC)curve.Results:Compared with normal adipose tissue,279 differentially expressed genes were simultaneously up-regulated in RPL tissues,which were mainly enriched in immune response and PPAR signaling pathway.Combined with TCGA,7 stable prognostic high risk genes were identified and the overall survival rate of the high risk group was significantly lower than that of the low risk group(P＜0.05).Conclusion:KCNQ5,RBPJ and some other genes may be re-lated to the poor prognosis of RPL patients.The analysis of the mechanism of these genes in RPL is expected to provide new evidence for the formulation of diagnosis and treatment strategies for RPL patients.

Natural products are important sources for the discovery of anti-tumor drugs. Evodiamine is the main alkaloid component of the traditional Chinese herb Wu-Chu-Yu, and it has weak antitumor activity. In recent years, a number of highly active antitumor candidates have been discovered with a significant progress. This article reviews the research progress of evodiamine-based antitumor drug design strategies, in order to provide reference for the development of new drugs with natural products as leads.

Parkinson's disease (PD) is a chronic neurodegenerative disease. At present, levodopa and other drugs are mainly used for dopamine supplementation therapy. However, the absorption of levodopa in the gastrointestinal tract is unstable and its half-life is short, and long-term use of levodopa will lead to the end-of-dose deterioration, dyskinesia, the "ON-OFF" phenomenon and other symptoms. Therefore, new preparations need to be developed to improve drug efficacy, reduce side effects or improve compliance of patients. Based on the above clinical needs, this review briefly introduced the preparation modification strategies for the treatment of PD through case analysis, in order to provide references for the research and development of related preparations.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.