Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

OBJECTIVE: To compare the short-term clinical efficacy and safety of closed reduction with Kirschner wire fixation versus open reduction with plate fixation for treating osteoporotic Colles' fractures in middle-aged and elderly patients. METHODS: Between January 2018 and January 2023, 119 patients with Colles fractures were retrospectively analyzed, including 39 males and 80 females, aged from 48 to 74 years old with an average of(60.58±6.71) years old. The time from injury to operation ranged 1 to 13 days with an average of (5.29±2.52) days. According to the surgical method, they were divided into Kirschner wire fixation group (Kirschner wire group) and plate internal fixation group (plate group). In Kirschner wire group, there were a total of 68 patients, comprising 21 males and 47 females. The average age was (61.15±6.24) years old, ranged from 49 to 74 years old. Among them, 41 cases involved the left side while 27 cases involved the right side. In the plate group, there were a total of 51 patients, including 18 males and 33 females. The average age was (59.78±5.71) years old ranged from 48 to 72 years old. Among them, there were 31 cases on the left side and 20 cases on the right side. The following parameters were recorded before and after the operation:operation time, intraoperative blood loss, hospitalization days, hospitalization expenses, postoperative complications, and radiographic parameters of distal radius (distal radius height, ulnar deviation angle, palmar tilt angle). The clinical efficacy was evaluated at 3 and 12 months after the operation using Gartland-Werley and disabilites of the arm shoulder and hand (DASH) scores. RESULTS: The patients in both groups were followed up for a duration from 12 to 19 months with an average of(13.32±2.02) months. The Kirschner wire group exhibited significantly shorter operation time compared to the plate group 27.91(13.00, 42.00) min vs 67.52(29.72, 105.32) min, Z=-8.74, P=0.00. Intraoperative blood loss was also significantly lower in the Kirschner wire group than in the plate group 3.24(1.08, 5.40) ml vs 21.91(17.38, 26.44) ml, Z=-9.31, P=0.00. Furthermore, patients in the Kirschner wire group had a shorter length of hospital stay compared to those in the plate group (8.38±2.63) days vs (11.40±2.78) days, t=-3.12, P=0.00. Additionally, hospitalization cost was significantly lower in the Kirschner wire group than in the plate group 10 111.29(6 738.98, 13 483.60) yuan vs 15 871.11(11 690.40, 20 051.82) yuan, Z=-5.62, P=0.00. The incidence of complications was 2 cases in the Kirschner wire group and 1 case in the plate group, with no statistically significant difference(P>0.05). At 3 months postoprative, the radial height of the Kirschner wire group was found to be significantly smaller than that of the plate group, with measurements of (11.45±1.69) mm and (12.11±1.78) mm respectively (t=-2.06, P=0.04). However, there were no statistically significant differences observed in ulnar deviation angle and palmar tilt angle between the two groups (P>0.05). The DASH score and Gartland-Werley score in the Kirschner group were significantly higher than those in the plate group at 3 months post-operation (19.10±9.89) vs (13.47±3.51), t=4.34, P=0.00;(11.15±3.61) vs (6.41±2.75), t=8.13, P=0.00). However, there was no significant difference between the two groups at 12 months post-operation (P>0.05). CONCLUSION Compared to plate internal fixation, closed reduction with Kirschner wire support fixation yields a slightly inferior recovery of radial height;however, there is no significant disparity in the functional score of the affected limb at 12 months post-operation. Nonetheless, this technique offers advantages such as shorter operation time, reduced intraoperative blood loss, decreased hospitalization duration, and lower cost.
Humans ; Female ; Male ; Middle Aged ; Aged ; Fracture Fixation, Internal/instrumentation* ; Bone Wires ; Bone Plates ; Retrospective Studies ; Colles' Fracture/surgery* ; Minimally Invasive Surgical Procedures/methods* ; Open Fracture Reduction/methods* ; Osteoporotic Fractures/surgery*

Objective:To retrospectively analyze the clinical features and surgical efficacy of congenital cholesteatoma （CC） and acquired cholesteatoma （AC） in children. Methods:Clinical data of 169 children with middle ear cholesteatoma were reviewed in the Department of Otorhinolaryngology Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University from January 2010 to July 2020. The clinical characteristics, stages, surgical methods, and postoperative recurrence rates were analyzed and summarized. Results:The age distribution of enrolled children ranged from 2 to 14 years. The mean age of the CC group was （5.60±2.48） years compared with （6.45±2.48） years in the AC group, and the difference was statistically significant （P<0.05）. Preoperative hearing in the CC group was （40.06±13.52） dB HL, which was better than in the AC group at （48.40±13.84） dB HL （P<0.05）. The proportion of stage Ⅰ in the CC group was lower than that in the AC group according to EAONO/JOS staging （P<0.05）. The recurrence rate after primary surgery was 19.23% （10/52） in the CC group compared with 36.29% （45/124） in the AC group （P<0.05）. The mastoid retention rates after all operations were 28.85% （15/52） in the CC group and 5.65% （7/124） in the AC group （P<0.05）. Conclusion:Compared with congenital cholesteatoma, acquired cholesteatoma in children is more aggressive and has more complications, higher postoperative recurrence rate, and less possibility of mastoid retention. Early clinical detection and treatment are required, and canal wall-down tympanoplasty should be considered in surgery.
Humans ; Cholesteatoma, Middle Ear/congenital* ; Child ; Retrospective Studies ; Child, Preschool ; Adolescent ; Male ; Female ; Recurrence ; Cholesteatoma/congenital* ; Tympanoplasty ; Treatment Outcome

Objective To systematically evaluate the effects of total intravenous anesthesia and inhalational anesthesia on postoperative recovery in patients undergoing transsphenoidal pituitary tumor resection.Methods A comprehensive search was conducted in international biomedical databases including Ovid Medline,Embase,CINAHL(EBSCO),Cochrane Library,and Web of Science,from inception to July 4,2023.Additionally,ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform were searched for ongoing and completed trials.The randomized controlled trials(RCT)comparing total intravenous anesthesia and inhalational anesthesia in patients undergoing transsphenoidal surgery for pituitary tumors were included.The methodological quality of the included studies was evaluated by the Cochrane Collaboration tool.Relevant data were extracted and synthesized for analysis.Results A total of 327 records were identified,of which eight RCTs met the inclusion criteria.Four studies showed that the patients receiving desflurane or sevoflurane anesthesia experienced faster emergence from anesthesia than those receiving propofol.Two studies indicated that patients in the propofol group had lower levels of emergence agitation and a lower incidence of early postoperative nausea and vomiting.The results on postoperative cognitive function were inconsistent across studies.No differences were found between the groups in terms of postoperative complications or overall recovery quality during hospitalization.Conclusions Inhalational anesthesia appears to provide an advantage in promoting faster emergence following transsphenoidal pituitary surgery,whereas total intravenous anesthesia may contribute to smoother and more stable recovery.Further high-quality studies are needed to clarify the effects of different anesthetic techniques on both short- and long-term postoperative recovery.
Humans ; Anesthesia, Intravenous ; Pituitary Neoplasms/surgery* ; Anesthesia, Inhalation ; Randomized Controlled Trials as Topic ; Anesthesia Recovery Period ; Pituitary Gland/surgery* ; Postoperative Period

ObjectiveTo sum up the clinical features of 5α-reductase type 2 deficiency （5α-RD2） complicated by central precocious puberty （CPP）， and provide experience for clinicians. MethodsA retrospective review was conducted of child patients with 5α-RD2 followed up to puberty at a single tertiary pediatric center， in whom 6 cases developed CRP. Clinical characteristics and treatment history of them were analyzed. A literature review was also performed to investigate possible mechanisms underlying the co-occurrence of 5α-RD2 and CPP. ResultsThe median age at initial presentation was 5.55 years （IQR 3.50-7.20）. Common clinical features included micropenis and hypospadias. Median stretched penile length （SPL） was 2.25 cm （IQR 1.8-2.8）， with an SPL-SDS of -4.5 （IQR -3.1 to -5.8）. Median external masculinization score （EMS） and Prader scores were 8.5 （IQR 5.0-9.0） and 4.5 （IQR 3.0-5.0）， respectively. Pubertal onset occurred at a median age of 8.70 years （IQR 7.80-9.00）. Three patients were overweight or obese. Five had received 2.5% dihydrotestosterone （DHT） gel prior to pubertal onset， with a median cumulative dose of 205.5 mg/kg （IQR 72.0-660.3 mg/kg） with DHT gel therapy. Notably， one patient with normal body mass index （BMI） and no history of androgen or gonadotropin therapy also exhibited early pubertal onset at 8.4 years. ConclusionCPP in children with 5α-RD2 may be associated with prior dihydrotestosterone （DHT） gel therapy and elevated BMI； however， a potential intrinsic link to the underlying disorder cannot be excluded. Further studies are needed to elucidate the pathophysiological mechanisms.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.