OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

OBJECTIVES

This study explored the interplay of three Filipino cultural values—hiya (modesty/self-restraint), pakikisama (conflict-avoidance dimension of social harmony), and pakikipag-ugnayan (professional engagement, characterized by vigor, dedication, and absorption)—among Generation Z nurses in Quezon City. Specifically, it assessed their levels, examined interrelationships through correlation and path analysis, and developed a framework to illustrate their influence on nursing practice. In the Filipino context, hiya encompasses both positive dimensions, such as conscientiousness, moral accountability, and social responsibility, and constraining dimensions, including excessive self-restraint, fear of criticism, and inhibition. Pakikisama, while fostering harmony, may discourage assertiveness and limit open communication. Pakikipag-ugnayan, conceptualized here as engagement, reflects dedication, vigor, and absorption, which are vital for performance, patient safety, and workforce retention.

A descriptive–correlational design was utilized to assess the relationships among hiya, pakikisama, and pakikipag-ugnayan. Data were gathered through a structured, self-administered questionnaire comprising validated scales for each construct, distributed in online formats to accommodate participants’ availability and work schedules. The purposive sample consisted of 115 Filipino Generation Z nurses engaged in direct patient care in various healthcare facilities in Quezon City. Data analysis involved descriptive statistics to summarize variable scores, Spearman’s rho to examine bivariate correlations, and path analysis to determine the direct and indirect effects among the variables. The results of the path analysis were used to develop a proposed framework illustrating the interplay of these cultural values in nursing practice.

Path analysis revealed distinct patterns in the interplay of hiya, pakikisama, and pakikipag-ugnayan among Generation Z nurses in Quezon City. Very high hiya scores (mean = 5.32) reflected both moral accountability and restraint-oriented tendencies. Despite these positive indicators, hiya showed a negative correlation with pakikipag-ugnayan, suggesting that its inhibiting side—marked by fear of criticism and avoidance of mistakes—may overshadow its constructive moral aspects. In contrast, the path analysis identified a moderate positive direct effect between hiya and pakikipag-ugnayan (β = 0.400, pCONCLUSION

Among Generation Z nurses, hiya demonstrates a dual role: while its ethical and conscientious aspects can strengthen pakikipag-ugnayan, its inhibiting side may suppress pakikipag-ugnayan. Pakikisama, operationalized as conflict avoidance and withholding dissent, emerged as a barrier to pakikipag-ugnayan by discouraging open communication. These findings informed the development of the Hiya–Pakikisama– Pakikipag-ugnayan Cultural Dynamics Framework, which illustrates how cultural values can simultaneously enable and constrain professional engagement. The framework has potential applications in nursing education and organizational practice, particularly in designing reflective learning, mentorship, and communication strategies that balance cultural sensitivity with assertiveness. Further validation across generations and healthcare settings is recommended to refine its applicability.

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

BACKGROUND: Concurrent chemo-radiotherapy (CCRT) is the standard treatment for locally advanced cervical cancer (LACC), but there are still many patients who suffer tumor recurrence. However, valuable predictors of treatment outcomes remain limited. This study aimed to assess the value of the serum immune biomarkers to predict the prognosis. METHODS: We reviewed cervical cancer patients treated with CCRT between January 2014 and May 2018 at Peking Union Medical College Hospital. The systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and lactate dehydrogenase (LDH) were calculated using blood samples. The relationship between immune markers and the treatment outcome was analyzed. The area under the receiver operating characteristic (ROC) curve was used to evaluate the predictive efficiency. The Cox proportional hazards model and log-rank were used to predict overall survival (OS) and disease-free survival (DFS). RESULTS: This study included 667 patients. Among them, 195 (29.2%) patients were defined as treatment failure, including 127 (19.0%) patients with pelvic failure, 94 (14.1%) distant failure, and 25 (3.7%) concurrent pelvic and distant failure. It revealed that the tumor stage, size, metastatic lymph nodes (MLNs), and serum immune biomarkers, such as SII, SIRI, and LDH, were significantly related to treatment outcomes. We demonstrated that the optimal cut-off of the SII, SIRI, and LDH were 970.4 × 10 9 /L, 1.3 × 10 9 /L, and 207.52 U/L, respectively. Importantly, this study presented that LDH level had the highest OR (OR = 4.2; 95% CI [2.3-10.8]). Furthermore, the OS and DFS for patients with pre-SII ≥970.5 × 10 9 /L were significantly worse than those with pre-SII <970.5 × 10 9 /L. Similarly, pre-SIRI ≥1.25 × 10 9 /L and pre-LDH ≥207.5 U/L were related to poor survival outcomes. CONCLUSIONS This study demonstrated that the baseline SII, SIRI, and LDH levels can be used to accurately and effectively predict the treatment outcomes after CCRT and long-term prognosis. Our results may offer additional prognostic information in clinical, which helps to detect the potential recurrent metastasis in time.
Humans ; Female ; Uterine Cervical Neoplasms/drug therapy* ; Middle Aged ; Adult ; Aged ; Chemoradiotherapy/methods* ; L-Lactate Dehydrogenase/blood* ; Treatment Outcome ; Disease-Free Survival ; Prognosis ; ROC Curve ; Biomarkers, Tumor/blood* ; Proportional Hazards Models

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

OBJECTIVE: To investigate the diagnostic efficacy of ^99mTc-MDP three-phase bone scintigraphy (TPBS) combined with C-reactive protein (CRP) for periprosthetic joint infection (PJI). METHODS: The clinical data of 198 patients who underwent revision surgery of artificial joint between January 2017 and January 2024 and received TPBS examination before surgery were retrospectively analyzed. There were 77 males and 121 females with an average age of 63.74 years ranging from 24 to 92 years. There were 90 cases of hip arthroplasty and 108 cases of knee arthroplasty. PJI was diagnosed according to the 2013 American Musculoskeletal Infection Society (MSIS) standard diagnostic criteria. The sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predict value (PPV) were calculated. The receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of the three methods, and the area under curve (AUC) was used to evaluate the diagnostic performance. RESULTS: According to the 2013 MSIS criteria, 116 cases were diagnosed as PJI, and the remaining 82 cases were aseptic loosening. The cases of PJI diagnosed by TPBS, CRP, and TPBS-CRP were 125, 109, and 137 respectively, and the cases of aseptic loosening were 73, 89, and 61 respectively. The sensitivity, accuracy, NPV, and PPV of TPBS-CRP combination in the diagnosis of PJI were higher than those of TPBS and CRP, but the specificity was lower than that of TPBS and CRP. ROC curve analysis further showed that the AUC value of TPBS-CRP combination was better than that of TPBS and CRP. The severity of bone defect and the duration of symptoms in patients with false positive TPBS diagnosis were worse than those in patients with true negative TPBS diagnosis (P<0.05), but there was no significant difference in the survival time of prosthesis between the two groups (P>0.05). Among the patients diagnosed with PJI by TPBS, CRP, and TPBS-CRP, 49, 35, and 54 patients had received antibiotic treatment 2 weeks before diagnosis, respectively. There was no significant difference in the diagnostic accuracy of TPBS and TPBS-CRP before diagnosis between patients treated with and without antibiotics and those not treated (P>0.05). The diagnostic accuracy of antibiotic therapy before CRP diagnosis was significantly lower than that of untreated patients (P<0.05). CONCLUSION TPBS and CRP have limited specificity in differentiating PJI from aseptic loosening. The TPBS-CRP combination diagnostic method can synergize the local bone metabolic characteristics and systemic inflammatory response to achieve higher diagnostic accuracy, but caution should be exercised in patients with severe bone defects and longer symptom duration.
Humans ; Prosthesis-Related Infections/blood* ; Middle Aged ; Male ; Female ; Aged ; C-Reactive Protein/metabolism* ; Retrospective Studies ; Adult ; Radionuclide Imaging/methods* ; Arthroplasty, Replacement, Knee/adverse effects* ; Aged, 80 and over ; Technetium Tc 99m Medronate ; Arthroplasty, Replacement, Hip/adverse effects* ; Sensitivity and Specificity ; Knee Prosthesis/adverse effects* ; ROC Curve ; Reoperation ; Radiopharmaceuticals ; Young Adult

OBJECTIVE: To compare the diagnostic accuracy of supraspinatus muscle outlet X-ray film, oblique sagittal multislice helical CT (MSCT), and oblique sagittal MRI in the diagnosis of subacromial impingement syndrome (SIS). METHODS: A retrospective analysis was conducted on the imaging data of 106 patients diagnosed with SIS between January 2023 and December 2024. The cohort consisted of 32 males and 74 females, with ages ranging from 43 to 70 years (mean, 60.19 years). All patients underwent supraspinatus muscle outlet X-ray film, MSCT, and MRI scans, with MSCT further subjected to three-dimensional reconstruction. Two experienced radiologists independently evaluated the acromion morphology in each imaging modality using the Bigliani classification system. Inter-observer reliability was assessed via Kappa statistics. The CT three-dimensional reconstructions were used as the "gold standard". The overall consistency, Kappa values, sensitivity, and specificity of the three imaging modalities were calculated. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was computed. RESULTS: The inter-observer reliability for supraspinatus muscle outlet X-ray film, oblique sagittal MSCT, and oblique sagittal MRI was moderate, with Kappa values of 0.62, 0.63, and 0.55, respectively. When compared to the CT three-dimensional reconstructions as the "gold standard", the overall consistency was 88.7% (94/106), 62.3% (66/106), and 58.5% (62/106), respectively. The supraspinatus muscle outlet X-ray film showed excellent consistency (Kappa=0.77), whereas the consistency of MSCT and MRI was lower (Kappa=0.34 and 0.29, respectively). In terms of diagnostic sensitivity and specificity, the supraspinatus muscle outlet X-ray film outperformed oblique sagittal MSCT and oblique sagittal MRI in distinguishing various acromion types. ROC analysis demonstrated that the AUC for the supraspinatus muscle outlet X-ray film was consistently higher than for oblique sagittal MSCT and oblique sagittal MRI, with the highest diagnostic performance observed for type Ⅲ hooked acromion (AUC=0.939). CONCLUSION Supraspinatus muscle outlet X-ray film provides the highest diagnostic accuracy for acromion classification in SIS patients, particularly in identifying type Ⅲ hooked acromion, which is strongly associated with SIS. Given its superior sensitivity and consistency, it should be considered the primary screening tool. MSCT and MRI serve as valuable supplementary modalities for complex cases and preoperative evaluation.
Humans ; Middle Aged ; Male ; Female ; Shoulder Impingement Syndrome/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Retrospective Studies ; Aged ; Adult ; Imaging, Three-Dimensional ; Sensitivity and Specificity ; Tomography, Spiral Computed/methods* ; Multidetector Computed Tomography/methods* ; Reproducibility of Results

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential