Objective To clarify the evidence of the frequency and risk factors for falls in knee osteoarthritis(KOA)of adults by meta-analysis.Methods Computerized searches of the CNKI,VIP,Wanfang data,CBM,PubMed,Cochrane Library,Embase,Web of Science were conducted for literature on risk factors for falls in adults with KOA from the inception of the databases to August 2024.After literature screening,data extraction,and quality evaluation,RevMan 5.4 software was used for Meta-analysis.Results A total of 26 articles were involved.Meta-analysis result showed that the rate of falls was 29.0％.Factors associated with increased risk of falls included being female(OR=1.35),decreased lower limb muscle strength(OR=1.72),decreased knee flexion muscle strength(OR=7.05),decreased static posture stability(OR=1.28),opioid use(OR=1.79),antidepressant use(OR=1.69),frequent stair climbing(OR=7.58),combined neurological disease(OR=1.77),history of falls(OR=3.29)and fear of falling(OR=2.54).Conclusion The rate of falls of patients with KOA is high.The adults with KOA who are women,have lower muscle strength of lower limbs and knee flexion muscle strength,poorer static posture stability,use opioids,antidepressant,frequent stair climbing,combined neurological disorders,previous falls in the past year and fear of falls are at higher risk of falls.Healthcare professionals should dynamically assess and detect the risk of falls in the patients with KOA and adopt targeted,individualized interventions to prevent falls.

Objective To develop and validate an automated organoid image segmentation approach based on deep learning for addressing the issues of high misidentification rate,blurred boundary and poor generalization in current organoid segmentation,thereby facilitating researchers to monitor and analyze organoid growth more efficiently.Methods The wavelet scattering coefficient matrix and capsule convolution module were integrated into the U-Net architecture to construct the organoid image segmentation model OrgCapsU-Net which was trained and evaluated on 3 organoid image datasets from different tissue sources.Results Compared with current mainstream segmentation algorithms,OrgCapsU-Net could better distinguish organoid and impurity,and lead to smoother segmentation boundaries,achieving superior performance across 4 evaluation metrics on 3 datasets.Conclusion OrgCapsU-Net delivers excellent segmentation performance and can be applied to organoids from various tissue sources,showing strong potential for applications in the in vitro model establishment,high-throughput drug screening,and personalized medicine.

Objective To clarify the evidence of the frequency and risk factors for falls in knee osteoarthritis(KOA)of adults by meta-analysis.Methods Computerized searches of the CNKI,VIP,Wanfang data,CBM,PubMed,Cochrane Library,Embase,Web of Science were conducted for literature on risk factors for falls in adults with KOA from the inception of the databases to August 2024.After literature screening,data extraction,and quality evaluation,RevMan 5.4 software was used for Meta-analysis.Results A total of 26 articles were involved.Meta-analysis result showed that the rate of falls was 29.0％.Factors associated with increased risk of falls included being female(OR=1.35),decreased lower limb muscle strength(OR=1.72),decreased knee flexion muscle strength(OR=7.05),decreased static posture stability(OR=1.28),opioid use(OR=1.79),antidepressant use(OR=1.69),frequent stair climbing(OR=7.58),combined neurological disease(OR=1.77),history of falls(OR=3.29)and fear of falling(OR=2.54).Conclusion The rate of falls of patients with KOA is high.The adults with KOA who are women,have lower muscle strength of lower limbs and knee flexion muscle strength,poorer static posture stability,use opioids,antidepressant,frequent stair climbing,combined neurological disorders,previous falls in the past year and fear of falls are at higher risk of falls.Healthcare professionals should dynamically assess and detect the risk of falls in the patients with KOA and adopt targeted,individualized interventions to prevent falls.

Objective To develop and validate an automated organoid image segmentation approach based on deep learning for addressing the issues of high misidentification rate,blurred boundary and poor generalization in current organoid segmentation,thereby facilitating researchers to monitor and analyze organoid growth more efficiently.Methods The wavelet scattering coefficient matrix and capsule convolution module were integrated into the U-Net architecture to construct the organoid image segmentation model OrgCapsU-Net which was trained and evaluated on 3 organoid image datasets from different tissue sources.Results Compared with current mainstream segmentation algorithms,OrgCapsU-Net could better distinguish organoid and impurity,and lead to smoother segmentation boundaries,achieving superior performance across 4 evaluation metrics on 3 datasets.Conclusion OrgCapsU-Net delivers excellent segmentation performance and can be applied to organoids from various tissue sources,showing strong potential for applications in the in vitro model establishment,high-throughput drug screening,and personalized medicine.

Objective:To explore the switch time of noninvasive-invasive mechanical ventilation sequential treatment for acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and effectively reduce the rate of tracheal intubation.Methods:A retrospective study was performed on patients with AECOPD, who underwent mechanical ventilation in emergency resuscitation room and admitted to department of respiration of Kaifeng Central Hospital Emergency Center from July 2014 to March 2019. The patients who used noninvasive mechanical ventilation (NIV) were included in NIV group (118 cases), and those who used invasive positive pressure ventilation (IPPV) were included in IPPV group (52 cases). The usage of breathing machine time, hospital days and hospital mortality were compared between the two groups. Clinical indicators such as age, gender, body temperature, respiratory rate, body mass index (BMI), mean arterial pressure (MAP), oxygenation index (PaO 2/FiO 2), respiratory index (RI), pH value, D-dimer, hemoglobin (HB), albumin, blood lactate (Lac), brain natriuretic peptide (BNP), C-reactive protein (CRP), procalcitonin (PCT), serum creatinine (SCr), white blood cell count (WBC), Glasgow coma scale (GCS), sputum excretion drainage were collected. The factors influencing the failure of NIV were analyzed by Logistic stepwise regression analysis. The receiver operating characteristic (ROC) curve was used to test the value of the NIV failure risk prediction model. Results:There was no significant difference in total mechanical ventilation time and hospital mortality between NIV group and IPPV group (hours: 65.6±11.11 vs. 66.9±12.1, 6.8% vs. 9.6%, both P > 0.05), but the hospital time in group NIV was significantly shorter than that in IPPV group (days: 12.3±2.1 vs. 14.2±2.5, P < 0.05). In NIV group, 101 cases completed NIV continuously, 17 cases of NIV failure turned to IPPV, and the failure rate of NIV was 14.4%. There were statistically significant differences in gender, PaO 2/FiO 2, RI, pH value, D-dimer, PCT, WBC, Lac, sputum excretion drainage and GCS score between NIV failure patients and NIV success patients. Logistic regression analysis showed that RI, pH value, WBC and sputum excretion drainage were independent risk factors for NIV failure [RI: odds ratio ( OR) = 3.879, 95% confidence interval (95% CI) was 1.258-11.963, P = 0.018; pH value: OR = 3.316, 95% CI was 1.270-8.660, P = 0.014; WBC: OR = 3.684, 95% CI was 1.172-11.581, P = 0.026; sputum excretion drainage: OR = 0.125, 95% CI was 0.042-0.366, P = 0.000]. The NIV failure risk prediction model based on the above independent risk factors had a good goodness of fit ( χ2 = 9.02, P = 0.34). ROC curve analysis showed that the NIV failure risk prediction model had a high predictive value for the patients with AECOPD [the area under ROC curve (AUC) was 0.818±0.051, 95% CI was 0.718-0.918, P = 0.000]. Conclusions:If patients with AECOPD have relative contraindications of NIV but still insist on using NIV, further risk stratification of NIV failure is needed. For those with RI, pH value, WBC abnormalities and sputum excretion drainage, the risk of choosing NIV is significantly increased. We need to pay more attention to the change of the condition and switch to IPPV in time to avoid exacerbation of the condition.