OBJECTIVE To establish a full-cycle management model for type 2 diabetes mellitus （T2DM） patients led by clinical pharmacists. METHODS Based on literature research， a basic framework and items of full-cycle management model led by clinical pharmacists were initially formulated. The Delphi method was adopted to conduct questionnaire inquiries among 26 experts to determine the specific implementation items of the model. The analytic hierarchy process （AHP） method was used to determine the weight values of items at all levels， and the reliability and validity of the model items were analyzed. RESULTS The recovery rates of the two rounds of expert consultation questionnaires were 86.67% and 100%， respectively， and the expert authority coefficient was 0.88. Kendall’s concordance coefficients of the tertiary-level items were 0.064 and 0.084， respectively， and the P values from the χ 2 tests were all less than 0.05； the consistent ratios of the judgment matrices for all levels of AHP model were all less than 0.1. The established full-cycle management led by clinical pharmacists comprised three primary-level items （pharmacy service pathway for T2DM patients during hospitalization， pharmacy management pathway for hypoglycemia in T2DM inpatients， and the pharmacy follow-up pathway for T2DM discharged patients， with weights of 0.098， 0.568 and 0.334， respectively）， twelve secondary-level items （e.g. pharmaceutical care during hospitalization for 1 to 2 days， admission assessment and education， with weights ranging from 0.143 to 0.333） and thirty-seven tertiary-level items （e.g. assessment of medication compliance， verification of the medication plan for discharge， with weights ranging from 0.068 to 0.750）. Cronbach’s α coefficients for primary-level items and the overall questionnaire were 0.762， 0.879， 0.928 and 0.951， respectively. The item-level and scale-level content validity indexes were 0.967 and 0.808， respectively. CONCLUSIONS A full-cycle management model for T2DM patients led by clinical pharmacists has been constructed successfully， demonstrating high scientificity and reliability.

Aconitines are a group of highly bioactive and toxic compounds found in aconitum species.[1] It is clinically used to treat rheumatism, rheumatoid arthritis, osteoarthritis, and cancer.[2] In folk medicine, aconitine is often soaked in white wine to make medicinal wine, which is used to treat rheumatism, joint pain, and other diseases.[3] Improper use of aconitine can cause adverse effects in the nervous system and digestive system, as well as cardiovascular dysfunction, especially arrhythmia.[4] Because of its high toxicity when it is improperly handled, aconitine has attracted widespread attention.

ObjectiveTo investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and to establish a predictive model. MethodsA total of 217 patients who were diagnosed with decompensated hepatitis C cirrhosis and were admitted to The Third People’s Hospital of Kunming l from January， 2019 to December， 2022 were enrolled， among whom 63 patients who were readmitted within at least 1 year and had no portal hypertension-related complications were enrolled as recompensation group， and 154 patients without recompensation were enrolled as control group. Related clinical data were collected， and univariate and multivariate analyses were performed for the factors that may affect the occurrence of recompensation. The independent-samples t test was used for comparison of normally distributed measurement data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed measurement data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. A binary Logistic regression analysis was used to investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and the receiver operating characteristic （ROC） curve was used to assess the predictive performance of the model. ResultsAmong the 217 patients with decompensated hepatitis C cirrhosis， 63 （29.03%） had recompensation. There were significant differences between the recompensation group and the control group in HIV history （χ²=4.566， P=0.034）， history of partial splenic embolism （χ²=6.687， P=0.014）， Child-Pugh classification （χ²=11.978， P=0.003）， grade of ascites （χ²=14.229， P<0.001）， albumin （t=4.063， P<0.001）， prealbumin （Z=-3.077， P=0.002）， high-density lipoprotein （t=2.854， P=0.011）， high-sensitivity C-reactive protein （Z=-2.447， P=0.014）， prothrombin time （Z=-2.441， P=0.015）， carcinoembryonic antigen （Z=-2.113， P=0.035）， alpha-fetoprotein （AFP） （Z=-2.063， P=0.039）， CA125 （Z=-2.270， P=0.023）， TT3 （Z=-3.304， P<0.001）， TT4 （Z=-2.221， P=0.026）， CD45⁺ （Z=-2.278， P=0.023）， interleukin-5 （Z=-2.845， P=0.004）， tumor necrosis factor-α （Z=-2.176， P=0.030）， and portal vein width （Z=-5.283， P=0.005）. The multivariate analysis showed that history of partial splenic embolism （odds ratio ［OR］=3.064， P=0.049）， HIV history （OR=0.195， P=0.027）， a small amount of ascites （OR=3.390， P=0.017）， AFP （OR=1.003， P=0.004）， and portal vein width （OR=0.600， P<0.001） were independent influencing factors for the occurrence of recompensation in patients with decompensated hepatitis C cirrhosis. The ROC curve analysis showed that HIV history， grade of ascites， history of partial splenic embolism， AFP， portal vein width， and the combined predictive model of these indices had an area under the ROC curve of 0.556， 0.641， 0.560， 0.589， 0.745， and 0.817， respectively. ConclusionFor patients with decompensated hepatitis C cirrhosis， those with a history of partial splenic embolism， a small amount of ascites， and an increase in AFP level are more likely to experience recompensation， while those with a history of HIV and an increase in portal vein width are less likely to experience recompensation.

AIM: To delineate the specific mutation responsible for retinitis pigmentosa(RP)in a Yi pedigree, and to analyze the correlation of RHO gene mutation with clinical phenotype.METHODS:A comprehensive clinical evaluation was conducted on the proband diagnosed with RP and other familial members, complemented by a thorough ophthalmic examination. Peripheral blood samples were obtained from the proband and familial members, from which genomic DNA was extracte. Subsequent whole exome sequencing(WES)was employed to identify the variant genes in the proband. The identified variant gene was validated through Sanger sequencing, then an in-depth analysis of the mutation genes was carried out using genetic databases to ascertain the pathogenic mutation sites. Furthermore, an exhaustive analysis was performed to delineate the genotype and phenotype characteristics.RESULTS:The RP pedigree encompasses 5 generations with 42 members, including 19 males and 23 females. A total of 13 cases of RP were identified, consisting of 4 males and 9 females, which conforms to the autosomal dominant inheritance pattern. The clinical features of this family include an early onset age, rapid progression, and a more severe condition. The patients were found to have night blindness around 6 years old, representing the earliest reported case of night blindness in RP families. The retina was manifested by progressive osteocytoid pigmentation of the fundus, a reduced visual field, and significantly decreased or even vanished a and b amplitudes of ERG. The combined results of WES and Sanger sequencing indicated that the proband had a heterozygous missense mutation of the RHO gene c.1040C>T:p.P347L, where the 1 040 base C of cDNA was replaced by T, causing codon 347 to encode leucine instead of proline. Interestingly, this mutation has not been reported in the Chinese population.CONCLUSION:This study confirmed that the mutant gene of RP in a Yi nationality pedigree was RHO(c.1040C>T). This variant leads to the change of codon 347 from encoding proline to encoding leucine, resulting in a severe clinical phenotype among family members. This study provides a certain molecular, clinical, and genetic basis for genetic counseling and gene diagnosis of RHO.

AIM: To delineate the specific mutation responsible for retinitis pigmentosa(RP)in a Yi pedigree, and to analyze the correlation of RHO gene mutation with clinical phenotype.METHODS:A comprehensive clinical evaluation was conducted on the proband diagnosed with RP and other familial members, complemented by a thorough ophthalmic examination. Peripheral blood samples were obtained from the proband and familial members, from which genomic DNA was extracte. Subsequent whole exome sequencing(WES)was employed to identify the variant genes in the proband. The identified variant gene was validated through Sanger sequencing, then an in-depth analysis of the mutation genes was carried out using genetic databases to ascertain the pathogenic mutation sites. Furthermore, an exhaustive analysis was performed to delineate the genotype and phenotype characteristics.RESULTS:The RP pedigree encompasses 5 generations with 42 members, including 19 males and 23 females. A total of 13 cases of RP were identified, consisting of 4 males and 9 females, which conforms to the autosomal dominant inheritance pattern. The clinical features of this family include an early onset age, rapid progression, and a more severe condition. The patients were found to have night blindness around 6 years old, representing the earliest reported case of night blindness in RP families. The retina was manifested by progressive osteocytoid pigmentation of the fundus, a reduced visual field, and significantly decreased or even vanished a and b amplitudes of ERG. The combined results of WES and Sanger sequencing indicated that the proband had a heterozygous missense mutation of the RHO gene c.1040C>T:p.P347L, where the 1 040 base C of cDNA was replaced by T, causing codon 347 to encode leucine instead of proline. Interestingly, this mutation has not been reported in the Chinese population.CONCLUSION:This study confirmed that the mutant gene of RP in a Yi nationality pedigree was RHO(c.1040C>T). This variant leads to the change of codon 347 from encoding proline to encoding leucine, resulting in a severe clinical phenotype among family members. This study provides a certain molecular, clinical, and genetic basis for genetic counseling and gene diagnosis of RHO.

[摘 要] 目的：探究中性粒细胞/淋巴细胞比值（NLR）、血小板/淋巴细胞比值（PLR）、淋巴细胞/单核细胞比值（LMR）及预后营养指数（PNI）对免疫检查点抑制剂（ICI）联合抗血管生成药物治疗晚期肝细胞癌（HCC）患者疗效及预后的预测价值。方法:收集2020年1月至2023年12月间在大理大学第一附属医院收治的使用ICI联合抗血管生成药物治疗的85例晚期HCC患者的一般临床资料、治疗前患者的NLR、PLR、LMR、PNI、临床疗效及预后。采用受试者工作特征（ROC）曲线计算NLR、PLR、LMR、PNI的最佳截断值，以此将患者分成高、低2组。使用Kaplan-Meier法绘制生存曲线，采用Cox比例风险回归模型进行单因素、多因素分析各指标对患者OS的影响。结果:低NLR组、低PLR组、高LMR组、高PNI组患者治疗有效率均分别高于高NLR组、高PLR组、低LMR组和低PNI组（均P < 0.05）。低NLR组、低PLR组患者OS率均分别显著高于高NLR组、高PLR组（均P < 0.05）；高LMR组、高PNI组患者OS率均分别显著高于低LMR组和低PNI组（均P < 0.05）。NLR ≥ 1.94、男性、乙型肝炎病毒感染是晚期HCC患者预后的独立危险因素（均P < 0.05）。结论: NLR ≥ 1.94、男性、乙型肝炎病毒阳性是影响患者疗效、预后的危险因素，NLR ≥ 1.94评估晚期HCC患者免疫联合靶向的疗效及预后有一定的临床价值。

Epidermal growth factor receptor tyrosine kinase inhibitors （EGFR-TKIs） are targeted drugs for the treatment of advanced non-small cell lung cancer （NSCLC）， but long-term use inevitably leads to drug resistance. Resistance to EGFR-TKIs can alter the tumor microenvironment， and patients with NSCLC resistant to EGFR-TKIs can regain the benefits of immune checkpoint inhibitors （ICIs）， but the changes in the tumor microenvironment are complex and the efficacy is unclear. This article reviews the clinical studies of ICIs in the treatment of EGFR-TKIs-resistant NSCLC， and finds that for patients with EGFR-TKIs-resistant NSCLC， the efficacy of ICIs as a single agent is unclear， and other relevant biomarkers need to be found to screen the beneficiary population. ICIs+EGFR-TKIs have potential toxicity and are not recommended for clinical use. There is controversy about the efficacy of ICIs+chemotherapy， and it is recommended to use it cautiously in clinical practice. ICIs+anti-vascular endothelial growth factor （VEGF） drug therapy has a synergistic effect， but may increase the incidence of adverse events. ICIs+chemotherapy+anti- VEGF drug have a synergistic effect and the incidence of adverse events is similar to that of chemotherapy. New ICIs such as lymphocyte activating gene 3 inhibitors are still in the clinical research stage or preclinical research stage， but they may be a new promising treatment.

Epidermal growth factor receptor tyrosine kinase inhibitors （EGFR-TKIs） are targeted drugs for the treatment of advanced non-small cell lung cancer （NSCLC）， but long-term use inevitably leads to drug resistance. Resistance to EGFR-TKIs can alter the tumor microenvironment， and patients with NSCLC resistant to EGFR-TKIs can regain the benefits of immune checkpoint inhibitors （ICIs）， but the changes in the tumor microenvironment are complex and the efficacy is unclear. This article reviews the clinical studies of ICIs in the treatment of EGFR-TKIs-resistant NSCLC， and finds that for patients with EGFR-TKIs-resistant NSCLC， the efficacy of ICIs as a single agent is unclear， and other relevant biomarkers need to be found to screen the beneficiary population. ICIs+EGFR-TKIs have potential toxicity and are not recommended for clinical use. There is controversy about the efficacy of ICIs+chemotherapy， and it is recommended to use it cautiously in clinical practice. ICIs+anti-vascular endothelial growth factor （VEGF） drug therapy has a synergistic effect， but may increase the incidence of adverse events. ICIs+chemotherapy+anti- VEGF drug have a synergistic effect and the incidence of adverse events is similar to that of chemotherapy. New ICIs such as lymphocyte activating gene 3 inhibitors are still in the clinical research stage or preclinical research stage， but they may be a new promising treatment.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective To investigate the factors affecting the distribution of Pomacea and project the trends in the spread of suitable distribution areas of Pomacea in 2050 and 2070 in Dali Bai Autonomous Prefecture, so as to provide insights into Pomacea control in the prefecture. Methods The longitudes and latitudes of Pomacea sampling sites were captured based on Pomacea field survey data in 12 cities (counties) of Dali Bai Autonomous Prefecture from 2023 to 2024. A total of 19 climatic factors (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest month, mean temperature of the coldest month, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter) and representative concentration pathways (RCPs) were retrieved from the world climate database (www.worldclim.org). All climatic variables were employed to create a maximum entropy (MaxEnt) model. The predictive accuracy of the model was assessed with the area under the receiver operating characteristic (ROC) curve (AUC), and the contributions of these 19 climatic factors to the distribution of Pomacea were analyzed in Dali Bai Autonomous Prefecture using Jackknife test. In addition, the suitable distribution areas of Pomacea were predicted with the MaxEnt model in Dali Bai Autonomous Prefecture in 2024 and in 2050 and 2070 under RCP4.5. Results Data pertaining to 91 Pomacea sampling sites were captured. ROC analysis revealed the MaxEnt model had an AUC value of 0.885 ± 0.088 for predicting the suitable distribution areas of Pomacea in Dali Bai Autonomous Prefecture. Of the 19 climatic factors, the maximum temperature of the warmest month had the highest contribution to the distribution of Pomacea in Dali Bai Autonomous Prefecture, followed by mean temperature of the driest quarter, mean temperature of the wettest quarter and minimum temperature of the coldest month. The suitable distribution area of Pomacea was predicted to be 14 555.69 km² in Dali Bai Autonomous Prefecture in 2024, and would expand gradually to the southeastern part of the prefecture in the future due to climatic factors. The suitable distribution areas of Pomacea were projected to expand to 21 475.61 km² in 2050 and 25 782.52 km² in 2070 in Dali Bai Autonomous Prefecture, respectively. Conclusions Temperature is an important contributor to the distribution of Pomacea in Dali Bai Autonomous Prefecture, and the suitable distribution area of Pomacea will gradually expand to the southeastern part of the prefecture in 2050 and 2070.