Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To investigate the correlation between membranous urethral length (MUL) and early urinary continence recovery after robot-assisted radical prostatectomy (RARP).Methods:A retrospective analysis was conducted on 71 prostate cancer patients who underwent RARP by a single surgeon at the PLA General Hospital between January 2020 and December 2023. Patient characteristics included: age of (65.32±6.04) years, BMI (25.21 ± 2.59) kg/m 2, prostate volume 32.41 (24.75, 44.40) ml, PSA 11.67 (8.22, 22.66) ng/ml. Gleason score [6/7/8/9-10: 15 (21.2%)/29 (40.8%)/16 (22.5%)/11 (15.5%)], Clinical stage [cT 1/cT 2/cT 3: 4 (5.6%)/61 (85.9%)/6 (8.5%)]. Measured MUL using multiparametric prostate MRI, median MUL was 13.25 (10.41-14.99) mm. Neurovascular bundle (NVB) preservation in 13 (18.3%) cases. Patients were grouped based on continence recovery at 1 and 3 months post-catheter removal. Age, BMI, prostate volume, PSA, Gleason score, clinical stage, NVB preservation, pathological stage, catheter indwelling time, and MUL were compared between groups. Multivariate analysis identified independent predictors of continence recovery. Results:All 71 surgeries were successful, pathological stage [pT 2/pT 3-4: 47 (66.2%)/24 (33.8%)], and catheter indwelling time 2.7 (2.0, 3.0) weeks. Follow-up data at 2 months were available for 71 patients, at 1 month, 42 patients achieved continence (continence group) and 29 had incontinence (incontinence group).No significant differences were observed between continence and incontinence groups in age [(64.93±6.48)years vs. (65.79±5.89) years], BMI [(26.26±2.52)kg/m 2 vs. (24.52±2.42) kg/m 2], prostate volume [32.00 (24.12, 41.11)ml vs. 33.00 (25.27, 47.97) ml], PSA [12.55 (8.31, 24.00) ng/ml vs. 11.30 (7.92, 20.65) ng/ml], Gleason score [6/7/8/9-10: 6 (14.2%)/18 (42.9%)/12 (28.6%)/6 (14.3%) vs. 9 (31.0%)/11 (37.9%)/4 (13.8%)/5 (17.3%)], clinical stage [cT 1/cT 2/cT 3: 2 (4.8%)/35 (83.3%)/5 (11.9%) vs. 2 (6.9%)/26 (89.7%)/1 (3.4%)], NVB preservation [7 (16.7%) vs. 6 (20.7%)], pathological stage [pT 2/pT 3-4: 27 (64.3%)/15 (35.7%) vs. 20 (69.0%)/9 (31.0%)], or catheter indwelling time [2.6(2.0, 3.0) weeks vs. 2.9 (2.0, 3.4) weeks]. However, MUL was significantly longer in the continence group [13.77 (11.70, 15.32) mm vs. 10.32 (9.65, 13.57) mm, P<0.01]. Follow-up data at 3 months were available for 69 patients, At 3 months, 61 patients achieved continence (continence group) and 8 remained incontinent (incontinence group). No significant differences were observed in age [(64.89±6.25)years vs. (68.13±4.09) years], BMI [(25.34±2.64)kg/m 2 vs. (24.36±2.49) kg/m 2], prostate volume [32.41 (24.44, 44.16)ml vs. 36.13 (27.48, 48.26) ml], PSA [12.50 (8.28, 22.76)ng/ml vs. 13.34 (5.88, 23.39) ng/ml], Gleason score [6/7/8/9-10: 12 (19.7%)/25 (41.0%)/14 (23.0%)/10 (16.3%) vs. 3 (37.5%)/3 (37.5%)/2 (25.0%)/0], clinical stage [cT 1/cT 2/cT 3: 3 (4.9%)/52 (85.2%)/6 (9.8%) vs. 1 (12.5%)/7 (87.5%)/0], NVB preservation [9 (14.8%) vs. 3 (37.5%)], pathological stage [pT 2/pT 3-4: 41 (67.2%)/20 (32.8%) vs. 5 (62.5%)/9 (31.0%)], or catheter indwelling time [2.7(2.0, 3.0)weeks vs. 3.0 (2.3, 3.7) weeks]. MUL remained significantly longer in the continence group [13.57 (10.57, 15.10)mm vs. 10.12 (9.36, 10.42) mm, P=0.002]. Multivariate logistic regression incorporating age, BMI, prostate volume, MUL, NVB preservation, and catheter indwelling time identified MUL as an independent protective factor for continence recovery at both 1 month [ OR=0.62, 95 CI 0.49-0.79, P<0.01] and 3 months [ OR=0.61, 95 CI 0.41-0.92, P=0.017]. Conclusions:MUL is independently associated with early urinary continence recovery after RARP, serving as a protective predictor at both 1 and 3 months after catheter removal.

Objective To investigate the effect of exogenous luteinizing hormone (LH) on vitrification granulosa cells and its mecha-nisms. Methods The granulosa cells of 4-week-old preadolescent mice were selected and divided into the fresh group (without vitrification procedure),the vitrification group (treated according to vitrification procedure) and the LH group (treated with 0.3 IU/L of LH intervention on the basis of the vitrification group). Granulocyte count was performed,and Realtime PCR and Western blot were used to detect the expression changes of Foxl2,PI3K,AKT and mTOR mRNAs and proteins in each group. Results Compared with the vitrification group,the total number of granulosa cells in the LH group was significantly increased (P<0.05),and the expression of Foxl2,PI3K,AKT and mTOR mRNAs and proteins were elevated (P<0.05). Conclusion The intevention of exogenous LH is beneficial for the survival of vitrifi-cation granulosa cells,and the mechanism may be related to the activation of PI3K/AKT/mTOR pathway.

The scaphoid bone is one of the important bone of hand,which is frequently injured and difficult to treat in clinical practice.Therefore,it is very important to deeply study the microstructure and biomechanical characteristics of the scaphoid bone for understanding its injury mechanism and optimizing treatment scheme.Microcomputed tomography(micro-CT)provides high-resolution imaging of bone tissue,while finite element analysis can help to simulate the stress distribution and behavioral patterns of the scaphoid bone under various physiological and pathological states.The high-resolution three-dimensional image of the scaphoid bone obtained by micro-CT technology can be used to construct finite element models of real anatomical structure of the scaphoid bone,thus achieving accurate simulation of the mechanical properties of the scaphoid bone.The fusion of these two advanced technologies provides a new perspective for revealing the structural and functional relationships and injury mechanism of the scaphoid bone.Therefore,this paper reviews the anatomical characteristics of the scaphoid bone and its biomechanical behavior in different states,emphasizing the specific applications and advantages of micro-CT and finite element analysis techniques in the study of the scaphoid bone.By summarizing the research findings in recent years,this paper provides novel scientific basis and methods for the diagnosis,treatment,and prevention of scaphoid bone-related disorders.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective To investigate the effect of exogenous luteinizing hormone (LH) on vitrification granulosa cells and its mecha-nisms. Methods The granulosa cells of 4-week-old preadolescent mice were selected and divided into the fresh group (without vitrification procedure),the vitrification group (treated according to vitrification procedure) and the LH group (treated with 0.3 IU/L of LH intervention on the basis of the vitrification group). Granulocyte count was performed,and Realtime PCR and Western blot were used to detect the expression changes of Foxl2,PI3K,AKT and mTOR mRNAs and proteins in each group. Results Compared with the vitrification group,the total number of granulosa cells in the LH group was significantly increased (P<0.05),and the expression of Foxl2,PI3K,AKT and mTOR mRNAs and proteins were elevated (P<0.05). Conclusion The intevention of exogenous LH is beneficial for the survival of vitrifi-cation granulosa cells,and the mechanism may be related to the activation of PI3K/AKT/mTOR pathway.

The scaphoid bone is one of the important bone of hand,which is frequently injured and difficult to treat in clinical practice.Therefore,it is very important to deeply study the microstructure and biomechanical characteristics of the scaphoid bone for understanding its injury mechanism and optimizing treatment scheme.Microcomputed tomography(micro-CT)provides high-resolution imaging of bone tissue,while finite element analysis can help to simulate the stress distribution and behavioral patterns of the scaphoid bone under various physiological and pathological states.The high-resolution three-dimensional image of the scaphoid bone obtained by micro-CT technology can be used to construct finite element models of real anatomical structure of the scaphoid bone,thus achieving accurate simulation of the mechanical properties of the scaphoid bone.The fusion of these two advanced technologies provides a new perspective for revealing the structural and functional relationships and injury mechanism of the scaphoid bone.Therefore,this paper reviews the anatomical characteristics of the scaphoid bone and its biomechanical behavior in different states,emphasizing the specific applications and advantages of micro-CT and finite element analysis techniques in the study of the scaphoid bone.By summarizing the research findings in recent years,this paper provides novel scientific basis and methods for the diagnosis,treatment,and prevention of scaphoid bone-related disorders.