OBJECTIVE: To precise classify sacral meningeal cysts, effective guide minimally invasive neurosurgery and postoperative personalized rehabilitation by multiple dimensions radiographic reconstruction MRI. METHODS: From March to December 2021, based on the original 3D-fast imaging employing steadystate acquisition (FIESTA) scanning sequence, 92 patients with sacral meningeal cysts were pre-operatively evaluated by multiple dimensional reconstruction MRI. The shape of nerve root and the leakage of cyst were reconstructed according to the direction of nerve root or leakage track showed on original MRI scans. Sacral canal cysts were accurately classified as including nerve root and without nerve root, so as to accurately design the incision of skin and formulate corresponding open range of the posterior wall of the sacral canal. Under the microscope intraoperation, the shape of the nerve roots inside cysts or leakage track of the cysts without nerve roots were verified and explored. After the reinforcement and shaping operation, several reexaminations of multiple dimensional reconstruction MRI were performed to understand the deformation of the nerve root and hydrops in the operation cavity, so as to formulate a persona-lized rehabilitation plan for the patients. RESULTS: Among the 92 patients with sacral mengingeal cyst, 58 (63.0%) cysts with nerve root cyst, 29 (31.5%) cysts without nerve root cyst, and 5 (5.4%) cysts with mixed sacral canal cyst. In 58 patients with nerve root cysts, the accuracy of preoperative clinical classification on MRI image reached 96.6% (56/58) through confirmation by operating microscope. Only 2 cases of large single cyst with nerve root on the head of cyst were mistaken for without nerve root type. In 29 patients with sacral cyst without nerve root, the accuracy of preoperative image reached 100% through confirmation by operating microscope. The accuracy of judging the internal nerve root and leakage of 12 cases with recurrent sacral cyst was also 100%. Two cases of delayed postoperative hydrops were found one month after operation. After rehabilitation treatment by moxibustion and bathing, the hydrops disappeared 4-6 months after operation. CONCLUSION Multiple dimensional reconstruction MRI can precisely make clinical classification of sacral meningeal cysts before operation, guide minimally invasive neurosurgery effectively, and improve the rehabilitation effect.
Humans ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Sacrum/surgery* ; Adult ; Middle Aged ; Imaging, Three-Dimensional/methods* ; Cysts/rehabilitation* ; Aged ; Adolescent ; Young Adult ; Spinal Nerve Roots/diagnostic imaging* ; Minimally Invasive Surgical Procedures ; Neurosurgical Procedures/methods*

One of the key components of adsorbents for blood purification is the microsphere adsorbent. Microsphere adsorbents should meet the following requirements: stable physical and chemical structures, easy for functional modification to endow the adsorbents with specific adsorption functions or characteristics, with good biocompatibility and with low non-specific adsorption, as well as with enough mechanical strength. Microsphere adsorbents prepared from polysaccharide bio-based materials fulfill the above requirements and have been widely used in the field of blood adsorption. In this article, adsorbents prepared from polysaccharide bio-based materials such as cellulose, agarose, alginate, as well as adsorbents prepared from the aforementioned materials and carbon materials and the application of the said bio-based adsorbents in blood adsorption is reviewed. The future development is also discussed, aiming to provide guidance and reference for the preparation, functional modification and application research of bio-based adsorbents for blood adsorption.
Microspheres ; Adsorption ; Humans

Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective Chronic atrophic gastritis(CAG)is often accompanied by intestinal flora disorder and intestinal symptoms,forming the phenomenon of"stomach disease involving intestine".This study explored the dynamic correlation between intestinal symptoms and qi-stagnation degree in patients with CAG qi-stagnation syndrome and analyzed the characteristics of gut microbiota from the perspective of"spleen-stomach system serving as the pivotal hub of qi movement"in TCM.Methods According to the syndrome element differentiation method,410 patients with CAG were divided into four groups:non-qi-stagnation group,mild qi-stagnation group,moderate qi-stagnation group and severe qi-stagnation group.Correlation analysis and 16S intestinal flora sequencing technology were used to analyze the correlation and differential flora between the degree of CAG qi-stagnation and intestinal symptoms.Results Patients with CAG qi-stagnation syndrome were often accompanied by intestinal symptoms such as frequent flatulence,poor defecation and alternating loose-constipated stools.The frequency of cases was significantly positively correlated with the degree of qi-stagnation"non-mild-moderate-severe"(P<0.05).There was a difference in the abundance of gut microbiota between the four groups of CAG qi-stagnation none,mild,moderate and severe.The relative abundance of Streptococcus,Subdoligranulum,Eubacterium_coprostanoligenes_group and Haemophilus was positively correlated with the degree of qi-stagnation.The relative abundance of Ruminococcus_torques_group and Butyricicoccus showed a negative correlation,and Haemophilus was statistically significant among the four groups(P<0.05).Conclusion This study can provide clinical evidence and micro-mechanism for the connotation of"gastrointestinal co-morbidities"and"different diseases with the same syndrome",which may open up new ideas for clinical diagnosis and treatment.

Objective To investigate and analyze the various reasons that affect the accuracy of ICD-9-CM-3 classifica-tion coding,identify key factors,and propose improvement strategies to enhance the accuracy and standardization level of surgical operation coding.Methods Using case analysis method,various factors affecting the accurate coding of ICD-9-CM-3 were sys-tematically listed and analyzed in detail.Through specific examples,this article analyzes the non-standard behavior of clinical physicians in writing surgical operation names,as well as the problems of coders relying on doctors to write,ignoring coding rules,and not fully reading medical records and surgical records during the coding process.It further explores how these factors lead to surgical classification errors.Results The main reasons affecting the accuracy of ICD-9-CM-3 coding include:lack of standardization in writing surgical operation names by clinical physicians,and failure to provide detailed descriptions of key ele-ments of the surgery;The coder overly relied on the doctor's written content during the coding process,failed to strictly follow the coding rules,and did not fully and deeply read and analyze medical records and surgical records,resulting in errors and devia-tions in surgical classification.Conclusion Each component of the surgical procedure name is an important factor affecting the accuracy of coding.Ensuring the completeness and accuracy of surgical operation names is crucial for improving the precision of ICD-9-CM-3 coding.In order to improve the quality of coding,clinical physicians need to enhance writing standards,while cod-ers need to strengthen their professional knowledge learning,strictly abide by coding rules,and comprehensively and meticulously review medical records and surgical records to achieve precise classification and coding of surgical operations.

Objective Chronic atrophic gastritis(CAG)is often accompanied by intestinal flora disorder and intestinal symptoms,forming the phenomenon of"stomach disease involving intestine".This study explored the dynamic correlation between intestinal symptoms and qi-stagnation degree in patients with CAG qi-stagnation syndrome and analyzed the characteristics of gut microbiota from the perspective of"spleen-stomach system serving as the pivotal hub of qi movement"in TCM.Methods According to the syndrome element differentiation method,410 patients with CAG were divided into four groups:non-qi-stagnation group,mild qi-stagnation group,moderate qi-stagnation group and severe qi-stagnation group.Correlation analysis and 16S intestinal flora sequencing technology were used to analyze the correlation and differential flora between the degree of CAG qi-stagnation and intestinal symptoms.Results Patients with CAG qi-stagnation syndrome were often accompanied by intestinal symptoms such as frequent flatulence,poor defecation and alternating loose-constipated stools.The frequency of cases was significantly positively correlated with the degree of qi-stagnation"non-mild-moderate-severe"(P<0.05).There was a difference in the abundance of gut microbiota between the four groups of CAG qi-stagnation none,mild,moderate and severe.The relative abundance of Streptococcus,Subdoligranulum,Eubacterium_coprostanoligenes_group and Haemophilus was positively correlated with the degree of qi-stagnation.The relative abundance of Ruminococcus_torques_group and Butyricicoccus showed a negative correlation,and Haemophilus was statistically significant among the four groups(P<0.05).Conclusion This study can provide clinical evidence and micro-mechanism for the connotation of"gastrointestinal co-morbidities"and"different diseases with the same syndrome",which may open up new ideas for clinical diagnosis and treatment.

Objective To investigate and analyze the various reasons that affect the accuracy of ICD-9-CM-3 classifica-tion coding,identify key factors,and propose improvement strategies to enhance the accuracy and standardization level of surgical operation coding.Methods Using case analysis method,various factors affecting the accurate coding of ICD-9-CM-3 were sys-tematically listed and analyzed in detail.Through specific examples,this article analyzes the non-standard behavior of clinical physicians in writing surgical operation names,as well as the problems of coders relying on doctors to write,ignoring coding rules,and not fully reading medical records and surgical records during the coding process.It further explores how these factors lead to surgical classification errors.Results The main reasons affecting the accuracy of ICD-9-CM-3 coding include:lack of standardization in writing surgical operation names by clinical physicians,and failure to provide detailed descriptions of key ele-ments of the surgery;The coder overly relied on the doctor's written content during the coding process,failed to strictly follow the coding rules,and did not fully and deeply read and analyze medical records and surgical records,resulting in errors and devia-tions in surgical classification.Conclusion Each component of the surgical procedure name is an important factor affecting the accuracy of coding.Ensuring the completeness and accuracy of surgical operation names is crucial for improving the precision of ICD-9-CM-3 coding.In order to improve the quality of coding,clinical physicians need to enhance writing standards,while cod-ers need to strengthen their professional knowledge learning,strictly abide by coding rules,and comprehensively and meticulously review medical records and surgical records to achieve precise classification and coding of surgical operations.

Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.