AUC Of ROC Curve Research Articles

Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis

IntroductionAcute kidney injury (AKI) is notably prevalent after cardiac surgery for patients with active infective endocarditis. This study aims to create a machine learning model to predict AKI in this high-risk group, improving upon existing models by focusing specifically on endocarditis-related surgeries.MethodsWe analyzed medical records from 527 patients who underwent cardiac surgery for active infective endocarditis from January 2012 to December 2023. Feature selection was performed using LASSO regression. These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. The optimal model was selected based on ROC curve AUC. Model performance was assessed through discrimination, calibration, and clinical utility, with explanations provided by SHAP values.ResultsPost-surgical AKI was observed in 261 patients (49.53%). LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. The 15 most critical features were valve replacement, pre-operative hypertension, large vegetations, NYHA class, alcoholism, age, post-operative low cardiac output syndrome, TyG index, pre-operative creatinine clearance, cardiopulmonary bypass duration, intra-operative red blood cell transfusion, intra-operative urine output, pre-operative hemoglobin levels, and timing of surgery.ConclusionCompared to standard cardiac surgery, AKI occurs more frequently and with a more complex etiology in surgeries for active infective endocarditis. Machine learning models enable early prediction of post-surgical AKI, facilitating targeted perioperative optimization and risk stratification in this distinct patient group.

The predictive value of CK-MB on the curative effect of urokinase combined with LMWH calcium in AMI.

This study investigates the predictive value of CK-MB in treating acute myocardial infarction (AMI) with urokinase combined with low molecular weight heparin (LMWH) calcium. AMI patients treated at our hospital from April 2020 to August 2022 were included in this prospective, multi-center cohort study. Patients were randomly assigned to a urokinase group or a combined treatment group (urokinase plus LMWH). CK-MB levels were measured using ELISA, and adverse reactions were recorded. The accuracy of CK-MB as a predictor was evaluated via ROC curve analysis and AUC. Before treatment, CK-MB levels were similar in both groups (p > 0.05). Post-treatment, levels were significantly lower in the combined group (p < 0.05). The combined group had a higher proportion of mild CK-MB elevation and lower moderate/severe elevations (p < 0.05). Adverse reaction rates were similar (p > 0.05). The combined treatment group showed higher effectiveness (p < 0.05), with better sensitivity (94%) and specificity (82%) than the urokinase group (sensitivity 89%, specificity 78%). CK-MB is a valuable predictor for evaluating the efficacy of urokinase plus LMWH in AMI treatment, offering advantages in reducing CK-MB levels and improving outcomes.

Fake News Detection in Hausa Language Using Transfer Learning Method

Fake news poses a significant threat to societies worldwide, including in Hausa-speaking regions, where misinformation is rapidly disseminated via social media. The lack of NLP resources tailored to this language exacerbated the problem of fake news in the Hausa language. While extensive research has been conducted on counterfeit news detection in languages such as English, little attention has been paid to languages like Hausa, leaving a significant portion of the global population vulnerable to misinformation. Traditional machine-learning approaches often fail to perform well in low-resource settings due to insufficient training data and linguistic resources. This study aims to develop a robust model for detecting fake news in the Hausa language by leveraging transfer learning techniques with adaptive fine-tuning. A dataset of over 6,600 news articles, including both fake and truthful articles, was collected from various sources between January 2022 and December 2023. Cross-lingual transfer Learning (XLT) was employed to adapt pre- trained models for the low-resource Hausa language. The model was fine-tuned and evaluated using performance metrics such as accuracy, precision, recall, F-score, AUC-ROC, and PR curves. Results demonstrated a high accuracy rate in identifying fake news, with significant improvements in detecting misinformation within political and world news categories. This study addresses the gap in Hausa- language natural language processing (NLP) and contributes to the fight against misinformation in Nigeria. The findings are relevant for developing AI- driven tools to curb fake news dissemination in African languages.

The role of vitamin D: a promising pathway to combat neuropsychiatric lupus disorders.

To evaluate neuropsychiatric manifestations in the pristane-induced lupus (PIL) model, as well as to evaluate immunoregulatory effects of vitamin D (vit-D) in the brain of mice with PIL. Eighty female BALB/c mice were divided into six groups with 90 (3 months) and 180 (6 months) days of experimentation: CO3, CO6 (controls), PIL3, PIL6 (pristane-induced lupus), VD3 and VD6 (PIL supplemented with 1,25-dihydroxyvitamin D). Forced-swim, elevated plus maze and Barnes maze were the behavioral tests performed. Expression of pVDR was assessed by immunofluorescence. Brain IgM and IgG deposits were evaluated by double staining fluorescence. Serum IL-6 and IFN-α1 were quantified by ELISA. AUC-ROC curve was also performed for immunoglobulins. PIL and VD showed depressive-like behavior in the forced-swim test and anxious-like behavior in the elevated plus maze test. PIL also presented cognitive and memory impairment in the Barnes maze test. Additionally, PIL and VD presented higher levels of serum IFN-α1, but not IL-6. Mice supplemented with vit-D had reduced IgM and IgG deposits and increased pVDR expression in the brain after 180 days. The AUC-ROC curve demonstrated high sensitivity and specificity for IgM and IgG in the brain. We observed neuropsychiatric manifestations in this model of systemic lupus erythematosus (SLE), strongly corroborating to PIL model being suitable as a neuropsychiatric lupus (NPSLE) model. Vit-D was able to reduce immunoglobulin deposits in the brain and influenced the levels of serum IL-6 in the animals assessed. Also, it improved memory, but it had no effect on depressive and anxious-like behavior.

Enhancing Diagnostic Accuracy of Fresh Vertebral Compression Fractures with Deep Learning Models.

Retrospective study. The study aimed to develop and authenticated a deep learning model based on X-ray images to accurately diagnose fresh thoracolumbar vertebral compression fractures. In clinical practice, diagnosing fresh vertebral compression fractures often requires MRI. However, due to the scarcity of MRI resources and the high time and economic costs involved, some patients may not receive timely diagnosis and treatment. Using a deep learning model combined with X-rays for diagnostic assistance could potentially serve as an alternative to MRI. In this study, the main collection included X-ray images suspected of thoracolumbar vertebral compression fractures from the municipal shared database between December 2012 and February 2024. Deep learning models were constructed using frameworks of EfficientNet, MobileNet, and MnasNet respectively. We conducted a preliminary evaluation of the deep learning model using the validation set. The diagnostic performance of the models was evaluated using metrics such as AUC value, accuracy, sensitivity, specificity, F1 score, precision, and ROC curve. Finally, the deep learning models were compared with evaluations from two spine surgeons of different experience levels on the control set. This study included a total of 3025 lateral X-ray images from 2224 patients. The dataset was divided into a training set of 2388 cases, a validation set of 482 cases, and a control set of 155 cases. In the validation set, the three groups of DL models had accuracies of 83.0%, 82.4%, and 82.2%, respectively. The AUC values were 0.861, 0.852, and 0.865, respectively. In the control set, the accuracies of the three groups of DL models were 78.1%, 78.1%, and 80.7%, respectively, all higher than spinal surgeons and significantly higher than junior spine surgeon. This study developed deep learning models for detecting fresh vertebral compression fractures, demonstrating high accuracy.

3D modelling-based left atrium geometry analysis to predict atrial fibrillation in patients with cryptogenic stroke

Abstract Introduction Atrial fibrillation (AF) is a high prevalence condition associated with high morbidity and mortality. Its early diagnosis allows to initiate several prophylactic treatments that have proved to improve its prognosis. 3D modeling techniques allow to design accurate representations of cardiac cavities from diverse source images. Purpose We evaluated the association between new geometrical measurements obtained by 3D echocardiographic models of the left atrium (LA) and the presence of underlying paroxysmal AF in patients with cryptogenic stroke (CS). Methods 3D LA volumes from a sample of consecutive patients aged ≥60 years old and admitted to our Stroke Unit with the diagnosis of CS from April 2019 to November 2020 were analyzed. 15 days ECG monitoring was performed after discharge to detect underlying paroxysmal AF. All 3D echo volumes were analyzed by a 3D modeling open-source application. From this analysis several LA geometry parameters were obtained: sphericity, elongation, flatness, major axis length, minor axis length, maximum 2D diameter (max2Ddiam) and surface-volume ratio (S/V). Functional atrial parameters (3D left atrial ejection fraction, LAEF) were also determined. Results 36 patients were analyzed. Mean age was 78 ± 7 years old. 19% were diabetic, 58% had high blood pressure and 14% of patients had history of ischemic cardiomyopathy. Mean body-mass index was 26. These baseline characteristics were equally distributed between groups. Baseline proBNP was significantly higher in patients developing atrial fibrillation (949 vs 275 pg/mL; p= 0,014.). Maximum 2D diameter (max2Ddiam) and Surface-volume ratio (S/V) were the best predictors of AF (AUC of ROC curves: 0.69; 0.49 – 0.88 and 0.63; 0.42 – 0.84, respectively). An optimal cut-off point of 39 mm was selected for max2Ddiam and of 0.20 for S/V. These values were included in a multivariate model adjusted by baseline proBNP. A Max2Ddiam &amp;lt;39 mm and a S/V ≥0.20 were both significantly associated with the presence of paroxysmal AF (OR 6.96; p= 0.048 and OR 12.22; p = 0.043, respectively). Conclusions New geometrical atrial parameters can be obtained from echo 3D models and might be able to predict AF in patients with CS. Further analysis and larger samples are required to better understand these findings.TableFigure

Artificial intelligence to assist the echocardiographic identification of transthyretin cardiac amyloidosis

Abstract Introduction Transthyretin cardiac amyloidosis (ATTR-CM) is a severe disease, and treatments are most effective when initiated at early stage. Echocardiography is the main tool for screening patients with suspected ATTR-CM or populations at high risk of ATTR-CM. Suspicion of ATTR-CM triggers a dedicated confirmatory evaluation. Echocardiographic diagnosis is however difficult to implement in a non-expert cardiologist workflow. An algorithm to assist the identification of potential cases would be helpful to assist clinicians. Objective To develop an algorithm using artificial intelligence (AI) to assist the identification of potential ATTR-CM cases using routine transthoracic echocardiography. Methods In this non-interventional, monocentric case-control study, a dedicated database, comprising confirmed ATTR-CM patients and a control group, was built. We trained and validated a deep learning model based on Convolutional Neural Network from transthoracic echocardiography video loops. Results A total of 4394 patients were included in the database: 208 in the ATTR-CM group (76% males, mean age 69±10 years old, 90% hereditary ATTR-CM with NAC I score in 77%) and 4186 in the control group (59% male, mean age 62±16 years). After training the model to identify potential ATTR-CM, the ROC curve AUC was 0.97, accuracy was 94.7%, precision was 87.3%, and recall 84.4%. After selecting a subgroup of 130 pairs of matched ATTR-CM/controls based on age, sex, and wall thickness, the algorithm performance remained robust (AUC 0.99, accuracy 99.4%, precision 100%, recall 98.3%). Conclusion and Perspectives: Diagnosis of ATTR-CM by echocardiography can be assisted by an algorithm using AI. The algorithm remains effective in distinguishing ATTR-CM from other causes of left ventricular hypertrophy. External validation of this tool is underway.ROC curveAssisted classification

Cutting-edge approaches for judging surface water dynamics in semi-arid environments: Integrating landsat 8 OLI/TIRS and HYDROSAM model

This research addresses the critical need to assess surface water dynamics in semi-arid regions of Andhra Pradesh, India, by using advanced Spectral Indices for hydrological applications and methodologies. It aims to answer how remote sensing data from Landsat 8 OLI/TIRS can be effectively used to monitor and classify surface water bodies. The study applies indices such as Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Thermal Index (NDTI), and Water Ratio Index (WRI). Additionally, the Weighted Composite Index (WCI) and Normalized Composite Index (NCI) are integrated with Principal Component Analysis (PCA) for multi-criteria decision making. The HYDROSAM model successfully classifies and maps various categories of surface water bodies, including non-water features (53.62%), urban water zones (37.89%), seasonal water bodies (4.32%), transitional zones (2.17%), permanent water bodies (1.05%), and river bodies (0.95%). The resultant map was validated using the AUC-ROC curve, achieving an AUC of 0.820, indicating a high level of accuracy. This methodology provides a nuanced understanding of water resource distribution and availability in the region. The findings demonstrate the robustness and reliability of the HYDROSAM model in accurately assessing surface water characteristics, thereby providing critical insights for informed water resource management, strategic land-use planning, and effective ecological conservation. This innovative methodology not only fosters sustainable water resource management in semi-arid regions but also sets a precedent for advancing research in similar ecosystems globally.

9303 Evaluation of The Consistency of Thyroid Indicators Tests in Minimally Invasive Peripheral Blood and Venous Blood

Abstract Disclosure: Z. Zhu: None. Y. Huang: None. Z. Cao: None. L. Zhang: None. D. Wang: None. S. Wang: None. J. Zhang: None. Y. Gao: None. Y. Zhang: None. Background The detection of thyroid indicators is of high demand in clinical and epidemiological screenings. Compared with existing venous blood tests, peripheral fingertip blood tests are less invasive and have a lower blood collection volume, which is the basis for point-of-care tests of thyroid indicators. Objective To explore the feasibility of minimally invasive and micro blood collection tests for thyroid disease (TD) patients by evaluating the consistency of thyroid indicators and TD diagnostic efficiency between fingertip blood tests and venous blood tests. Method 63 subjects with or without thyroid dysfunction were consecutively recruited. All subjects underwent serum, plasma and fingertip blood (same day collection) measurements for TSH and TT4 detection via the chemiluminescence method and for TPOAb detection via the electrochemiluminescence immunoassay method. The fingertip blood was centrifuged and diluted after EDTA anticoagulation. Bland–Altman plot, Passing–Bablok regression, and weighted kappa test were used for consistency test; AUC of ROC curve, sensitivity and specificity were used for authenticity evaluation among the three collection and testing methods. Results 43 subjects with euthyroidism and 20 subjects with thyroid dysfunction were included; including 29 (46.03%) females, the average age was 50.98±14.76 years. Bland-Altman plots showed more than 90% of the subjects had TSH and TT4 differences within the 95% limits of agreement between peripheral blood (PB) and venous blood (including serum and plasma). Passing–Bablok regression analyses revealed strong correlations between TSH levels in PB and serum (r=0.888) and plasma (r=0.894) (p&amp;lt;0.05); for TT4, there was a strong correlation between PB and serum (r=0.746), and there was also a moderate correlation between PB and plasma (r=0.664) (p&amp;lt;0.05 for both). Weighted kappa tests were performed for low, normal and high TSH values, indicating strong consistency between PB and serum and between PB and plasma, with coefficients of 0.901 and 0.881, respectively (P&amp;lt;0.001); for TT4, the coefficients between PB and serum, PB and plasma were 0.541 and 0.585 (P&amp;lt;0.001), indicating moderate consistency. Regardless of TPOAb positivity or negativity, the consistency among three blood sample sources was 100%. For euthyroidism (n=43), hyperthyroidism (n=6), subclinical hyperthyroidism (n=9), hypothyroidism (n=1), and subclinical hypothyroidism (n=4), the diagnostic sensitivities of PB were 97.06%, 83.33%, 87.50%, 100%, and 75.00%, respectively; the specificities were 94.74%, 97.87%, 95.74%, 98.11%, and 100.00%, respectively. The AUC of PB ROC curve for determining thyroid dysfunction was 0.944 (95% CI 0.867-1.000). Conclusion PB tests of thyroid indicators has high consistency with venous blood tests, offers sufficient diagnostic efficiency for TD, and is a feasible minimally invasive detection method. Presentation: 6/1/2024

7583 Evaluation of The Consistency of Thyroid Indicators Tests in Minimally Invasive Peripheral Blood and Venous Blood

Abstract Disclosure: Z. Zhu: None. Y. Huang: None. Z. Cao: None. L. Zhang: None. D. Wang: None. S. Wang: None. J. Zhang: None. Y. Gao: None. Y. Zhang: None. Background: The detection of thyroid indicators is of high demand in clinical and epidemiological screenings. Compared with existing venous blood tests, peripheral fingertip blood tests are less invasive and have a lower blood collection volume, which is the basis for point-of-care tests of thyroid indicators. Objective To explore the feasibility of minimally invasive and micro blood collection tests for thyroid disease (TD) patients by evaluating the consistency of thyroid indicators and TD diagnostic efficiency between fingertip blood tests and venous blood tests. Method 63 subjects with or without thyroid dysfunction were consecutively recruited. All subjects underwent serum, plasma and fingertip blood (same day collection) measurements for TSH and TT4 detection via the chemiluminescence method and for TPOAb detection via the electrochemiluminescence immunoassay method. The fingertip blood was centrifuged and diluted after EDTA anticoagulation. Bland–Altman plot, Passing–Bablok regression, and weighted kappa test were used for consistency test; AUC of ROC curve, sensitivity and specificity were used for authenticity evaluation among the three collection and testing methods. Results 43 subjects with euthyroidism and 20 subjects with thyroid dysfunction were included; including 29 (46.03%) females, the average age was 50.98±14.76 years. Bland-Altman plots showed more than 90% of the subjects had TSH and TT4 differences within the 95% limits of agreement between peripheral blood (PB) and venous blood (including serum and plasma). Passing–Bablok regression analyses revealed strong correlations between TSH levels in PB and serum (r=0.888) and plasma (r=0.894) (p&amp;lt;0.05); for TT4, there was a strong correlation between PB and serum (r=0.746), and there was also a moderate correlation between PB and plasma (r=0.664) (p&amp;lt;0.05 for both). Weighted kappa tests were performed for low, normal and high TSH values, indicating strong consistency between PB and serum and between PB and plasma, with coefficients of 0.901 and 0.881, respectively (P&amp;lt;0.001); for TT4, the coefficients between PB and serum, PB and plasma were 0.541 and 0.585 (P&amp;lt;0.001), indicating moderate consistency. Regardless of TPOAb positivity or negativity, the consistency among three blood sample sources was 100%. For euthyroidism (n=43), hyperthyroidism (n=6), subclinical hyperthyroidism (n=9), hypothyroidism (n=1), and subclinical hypothyroidism (n=4), the diagnostic sensitivities of PB were 97.06%, 83.33%, 87.50%, 100%, and 75.00%, respectively; the specificities were 94.74%, 97.87%, 95.74%, 98.11%, and 100.00%, respectively. The AUC of PB ROC curve for determining thyroid dysfunction was 0.944 (95% CI 0.867-1.000). Conclusion PB tests of thyroid indicators has high consistency with venous blood tests, offers sufficient diagnostic efficiency for TD, and is a feasible minimally invasive detection method. Presentation: 6/1/2024

7771 Surveillance Role in Differentiated Thyroid Cancer Through Ultrasensitive Thyroglobulin IRMA with High Negative Prediction Value

Abstract Disclosure: J. Jang: None. H. Kim: None. S. Ha: None. K. Jung: None. G. Jung: None. S. Cho: None. D. Park: None. G. Cheon: None. Y. Park: None. TSH-stimulated serum thyroglobulin (s-Tg) is a well-established marker for monitoring differentiated thyroid cancer (DTC) following remnant ablation therapy. The 2nd-generation immunoradiometric assay for Tg (ultrasensitive Tg, uTg) was developed to detect Tg with higher sensitivity compared with the 1st generation (conventional Tg, cTg). This study aimed to compare efficacy of both methods, cTg and uTg, to determine whether unstimulated Tg (us-Tg) could predict s-Tg levels. Paired serum samples were collected from 274 DTC patients before (us-Tg) and after (s-Tg) TSH stimulation for 1st radioiodine therapy after surgery (1st Tg) or both 1st therapy and 2nd therapy/diagnostic test (1st and 2nd Tg, respectively). Both cTg (Dynotest Tg-plus, BRAHMS Gmbh, Germany, LOD 0.1 ng/mL and functional sensitivity (FS) 0.2 ng/mL) and uTg (RIAKEY, Shinjin Medics, South Korea; LOD 0.01 ng/mL and FS 0.06 ng/mL) were measured. For analysis, samples with inadequate TSH stimulation (&amp;lt;30 μIU/mL) or with anti-thyroglobulin antibody levels &amp;gt; 60 U/mL were excluded. Ultimately, the included samples consisted of 296 for us-Tg and 352 for s-Tg.. Among these, paired samples for us-Tg and s-Tg were available in 260 cases (189 for 1st Tg and 71 for 2nd Tg). Comparing all Tg values, cTg and uTg levels had a correlation (Intraclass correlation coefficient r=0.752, P &amp;lt; 0.001), with no significant difference between us-Tg (r=0.803, P &amp;lt; 0.001) and s-Tg (r= 0.752, P &amp;lt; 0.001). The optimal cut-off level for predicting s-cTg at 1 ng/mL was 0.12 ng/mL in uTg or 0.16 ng/mL in cTg tests using AUC-ROC curve analysis. Considering the FS for cTg, diagnostic performance was assessed with a cut-off level of 0.2 ng/mL for cTg and 0.16 ng/mL for uTg. For s-cTg positivity as ≥ 1 ng/mL, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of uTg were 72.1%, 59.8%. 52.0%, and 78.0%, respectively, while for cTg, they were 33.3%, 95.3%, 78.4%, and 73.9%, respectively. Similar trends were observed for s-cTg ≥ 2 ng/mL (69.0%, 71.4%, 35.0%, and 91.2% for uTg, respectively; 49.0%, 93.8%, 64.9%, and 88.7% for cTg, respectively). In essence, uTg demonstrated higher sensitivity than cTg, while having lower specificity and PPV, with a similar NPV. Of the 71 patients with paired 2nd Tg levels, 7 (9.8%) had biochemical disease (BCD) by s-cTg (1 ng/mL), whereas only 1 (1.4%) showed BCD using us-cTg ≥ 0.2 ng/mL. Conversely, 40 (56.0%) were classified as BCD using us-uTg ≥ 0.16 ng/mL. During the 6.48 years of follow-up, 4 patients developed structural recurrence; all had positive us-Tg levels with uTg tests, while only 1 patient demonstrated positivity with cTg tests. Although, the ultrasensitive Tg is sensitive in predicting positive stimulated Tg and recurrence, the specificity is very low resulting high frequency of BCD cases without clinical significance. Therefore, the clinical utility of uTg appears to be limited. Presentation: 6/2/2024

B-350 The application of MALDI-TOF imaging to biomarker discovery in a mouse model of papillomavirus associated tumorigenesis

Abstract Background Human papillomavirus (HPV) can cause infection and dysplasia in cutaneous epithelium of immunodeficient patients. An animal model has been developed to study HPV-associated tumorigenesis using Nude mouse (Foxn1nu) and mouse papillomavirus (MmuPV1). This study applies MALDI-TOF imaging to compare the expression pattern of small molecules between viral-infected dysplastic mouse skin and mock-infected control tissue. Three sets of molecules showing distinct patterns are identified in correlation with the upregulation of an enzyme in related biosynthetic pathway. Methods Nude mice were wounded on tail and inoculated with MmPV1 suspension (1×109 viral DNA genomes per infected site) or PBS 24 hours later for treatment and control group, respectively. Tail skins were harvested 4 weeks post-infection and processed for cryosectioning. Tissue sections were analyzed using timsTOF flex (Bruker) under negative ion mode with 9-aminoacridine as the matrix. Viral infection (MmuPV1-L1 antigen) and enzyme expression (3'-phosphoadenosine 5'-phosphosulfate synthetase 1, PAPSS1) were assessed by immunofluorescence on adjacent sections. Results Three peaks demonstrated distinct distribution patterns in mouse skin. One peak at m/Z = 465 was identified as cholesterol sulfate (CS) by tandem MS and comparing with the standard. CS was enriched in keratinocytes, hair follicles, and blood vessels in normal skins, and it increased in dysplastic skins with enhanced angiogenesis. The combined peaks at m/Z = 327 &amp; 329 were tentatively identified as docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA). These long chain polyunsaturated fatty acids (LC-PUFAs) were present in the dermis as foci partially co-localized with CS, and they also increased in virus-infected skins. The peak at m/Z = 426 may represent a combination of ADP and APS. Although these two isobars cannot be separated by tandem MS, APS was revealed by the presence of sulfur isotopes at expected natural abundance (34S and 32S). They were present at a low level in the epidermis of normal skins but significantly increased after virus infection. APS and ADP were closely associated with the proliferating cells, and the signal intensity also increased with the degree of tissue dysplasia. As a single factor, APS &amp; ADP demonstrated a fair differential power for MmuPV1-induced dysplastic skin and normal control (AUC of ROC curve = 0.75). The bifunctional enzyme PAPSS1 catalyzes the conversion of ATP to 3’-phosphonato-5’-adenyl sulfate (PAPS) via APS as an intermediate. Interestingly, immunofluorescence showed the expression of PAPSS1 also increased significantly with the severity of tissue dysplasia and MmuPV1 infection. Conclusions MALDI-TOF imaging reveals three sets of molecules with distinct patterns in mouse skin, and their levels are increased during MmuPV1-induced epithelium dysplasia. As a precursor of the common sulfate donor (PAPS), increased APS and ADP in the epidermis of virus-infected skins indicate an upregulation of sulfation pathway and adenine metabolism. This finding is supported by enhanced expression of enzyme PAPSS1 and an increased sulfation reaction product, CS. In addition, DHA and DPA are also increased in the dermis of virus-infected skins, implying a disturbance in LC-PUFA metabolism. This study demonstrates combined use of MALDI-TOF imaging and immunostaining in the search of novel biomarkers of HPV-associated tumorigenesis.

Prediction of hemorrhage rate after tonsil surgery in children based on random forest model

Objective:Hemorrhage after tonsil surgery in children is a serious and potentially life-threatening complication. The purpose of this study was to establish a risk warning model for hemorrhage after tonsil surgery in children through a national multi-center retrospective study, providing a basis for hierarchical management after tonsil surgery in children. Methods:Stratified sampling was performed on 8 854 children who underwent tonsillectomy under general anesthesia from 15 research centers in different provinces from January 15, 2022 to May 15, 2023. The sample size of this study was 2 724 cases, including 1 096 males and 1 628 females. Children were divided into bleeding and non-bleeding groups according to whether or not they had bleeding after surgery. The random forest algorithm was used to build a risk warning model. By continuously exploring the optimized model, the accuracy of predicting the postoperative bleeding rate of tonsils in children was improved, and the prediction effectiveness of the model was verified by ten-fold cross-validation. Results:Among 2 724 children, 117 had postoperative bleeding after tonsillectomy, with a bleeding rate of 4.30%. The model constructed by the random forest algorithm for the training set was verified in the test set, and the obtained prediction accuracy was 98.72%, the recall rate was 78.95%, and the area under the ROC curve AUC was 0.96. Conclusion:Although the recall rate of the random forest model needs to be improved, the overall accuracy is quite excellent. It can effectively avoid misjudging positive cases as negative cases. It is a useful tool that can be used to predict the postoperative bleeding rate of tonsils and clinical medical decision-making, laying a good foundation for subsequent optimization and improvement.

The use of a clinical calculator to determine the rate of development of chronic kidney disease in elderly patients with type 2 diabetes mellitus

Aim – to develop an applied prognostic calculator for the rate of progression of CKD in elderly patients with type 2 diabetes, which makes it possible to identify a group of high rate of GFR reduction in conditions of routine outpatient admission. Material and methods. 69 clinical indicators were studied, the interrelationships and significance of differences in parameters in the groups identified according to the original diagnostic parameter, the glomerular filtration rate reduction index with a threshold value of 3.83 ml/min/1.73 m2 per year, above which the rate of progression of CKD was considered high. Results. By using regression analysis, significant factors for the prognostic calculator were identified: the duration of diabetes and insulin therapy, concomitant diagnosis of obesity, pulse in the ankle artery, severe stage of polyneuropathy, risk group IV of hypertension, treatment with sulfonylureas, the number of antihypertensive drugs taken. When evaluating the information capacity and predictive ability of the calculator, the area under the AUC ROC curve was 0.89 (0.80; 0.99) p 0.001, which characterizes the quality of the diagnostic technique as high. Conclusion. The presented calculator gives the doctor the opportunity to identify a group of patients with the risk of rapid progression of CKD directly on an outpatient basis.

Enhancing alzheimer’s diagnosis through optimized brain lesion classification in MRI with attention-driven grid feature fusion

This paper explores cognitive interface technology, aiming to tackle current challenges and shed light on the prospects of brain-computer interfaces (BCIs). It provides a comprehensive examination of their transformative impact on medical technology and patient well-being. Specifically, this study contributes to addressing challenges in classifying brain lesion images arising from the complex nature of lesions and limitations of traditional deep learning approaches. It introduces advanced feature fusion models that leverage deep learning algorithms, including the African vulture optimization (AVO) algorithm. These models integrate informative features from multiple pre-trained networks and employ innovative fusion techniques, including the attention-driven grid feature fusion (ADGFF) model. The ADGFF model incorporates an attention mechanism based on the optimized weights obtained using AVO. The objective is to improve the overall accuracy by providing fine-grained control over different regions of interest in the input image through a grid-based technique. This grid-based technique divides the image into vertical and horizontal grids, simplifying the exemplar feature generation process without compromising performance. Experimental results demonstrate that the proposed feature fusion strategies consistently outperform individual pre-trained models in terms of accuracy, sensitivity, specificity, and F1-score. The optimized feature fusion strategies, particularly the GRU-ADGFF model, further enhance classification performance, outperforming CNN and RNN classifiers. The learning progress analysis shows convergence, indicating the effectiveness of the feature fusion strategies in capturing lesion patterns. AUC-ROC curves highlight the superior discriminatory capabilities of the ADGFF-AVO strategy. Five-fold cross-validation is employed to assess the performance of the proposed models, demonstrating their accuracy, and few other accuracy-based measures. The GRU-ADGFF model optimized with AVO consistently achieves high accuracy, sensitivity, and AUC values, demonstrating its effectiveness and generalization capability. The GRU-ADGFF model also outperforms the majority voting ensemble technique in terms of accuracy and discriminative ability. Additionally, execution time analysis reveals good scalability and resource utilization of the proposed models. The Friedman rank test confirms significant differences in classifier performance, with the GRU-ADGFF model emerging as the top-performing method across different feature fusion strategies and optimization algorithms.

Support Vector Machine based Data Hacking Prediction using PMU Data

As global reliance on power systems grows due to increasing energy demands and modern consumption patterns, maintaining the stability and reliability of the power grid has become crucial. Power systems are complex and nonlinear, and their operations are continuously evolving, making it difficult and expensive to ensure stability. Traditionally, power systems are designed to handle a single outage at a time. However, recent years have seen several significant blackouts, each originating from a single failure, which have been extensively reported. These reports are vital for mitigating operational risks by strengthening systems against identified high-risk scenarios. While extensive research has been conducted on these blackouts, cyber- attacks introduce a new dimension of risk. The advent of Phasor Measurement Units (PMUs) has enabled centralized monitoring of power system data, allowing for more effective fault and cyber-attack detection.This paper proposes a machine learning-based approach to detecting cyber-attacks using PMU data. Given the complexity and volume of power system data, traditional mathematical and statistical methods are challenging to implement. Instead, a Support Vector Classification (SVC) algorithm is used for binary classification, distinguishing between 'attack' and 'normal' states. The algorithm is trained on PMU data and evaluated using metrics such as the AUC-ROC curve and confusion matrix, achieving an 82% AUC- ROC score, demonstrating its effectiveness in identifying cyber- attacks.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images.

Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound. The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification. The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (area under the ROC curve AUC = 0.81 - 0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84-0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well. The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC. The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

Multidimensional-Based Prediction of Pressure Ulcers Development and Severity in Hospitalized Frail Oldest Old: A Retrospective Study.

In recent times, growing uncertainty has emerged regarding the effectiveness of standard pressure ulcer (PU) risk assessment tools, which are suspected to be no better than clinical judgment, especially in the frail and comorbid elderly population. This study aimed to identify the primary clinical predictive variables for PU development and severity in hospitalized older adults, utilizing a multidimensional frailty assessment, and compare them with the Braden scale. The population consisted of 316 patients, admitted to the Geriatric Unit and Transitional Care of San Bartolomeo Hospital in Sarzana (Italy) during the period 21/02/22-01/07/22. The collected information included both anamnestic and laboratory data. A comprehensive geriatric assessment was performed, including also anthropometric and physical performance measurements. Multivariate logistic analysis was used, both in a binary classification test and in the subsequent ordinal classification test of severity levels. The final performance of the model was assessed by ROC curve estimation and AUC comparison with the Braden scale. Within the population, 152 subjects (48%) developed PU at different levels of severity. The results showed that age, Braden scale (subscales of mobility and friction/shear), Barthel scale, Mini Nutritional Assessment, hemoglobin, and albumin are predictors associated with the development of PU (AUC 85%). The result is an improvement over the use of the Braden scale alone (AUC 75%). Regarding the identification of predictive factors for PU severity, 4AT also emerges as potentially relevant. Assessing the subject's nutritional status, physical performance, and functional autonomies enables the effective integration of the Braden scale in identifying patients most susceptible to developing PU. Our findings support the integration of a comprehensive set of methodologically robust frailty determinants into traditional risk assessment tools. This integration reflects the mutual interplay between patients' frailty, skin frailty, and PU development in very old hospitalized patients.

Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

Bagging Nearest Neighbor and its Enhancement for Machinery Predictive Maintenance

K-nearest Neighbor is a simple algorithm in Machine learning for such a prediction classification task which plays in valuable aspects of understanding big data. However, this algorithm sometimes does a lacking job of classification tasks for many different dataset characteristics. Therefore, this study will adopt enhancement methods to create a better performance of the nearest-neighbor model. Thus, this study focused on nearest neighbor enhancement to do a binary classification task from the extremely unbalanced dataset of a machine failure problem. Firstly, this study will create new features from the machinery dataset through the feature engineering processes and transform the chosen numerical features with standardization steps as the proper scaling. Then, the modified under-sampling method will be given which will reduce the amount of the majority class to 4.75 times that of the minority class. Next is the applied grid-search tuning which will find the right parameter combinations for the nearest-neighbor model being applied. Furthermore, the previous pre-processing steps will be combined with an additional bagging method. Finally, the resulting bagged KNN will present a 0.971 rate of accuracy, 0.555 rate of precision, 0.781 rate of recall, 0.649 rate of f1-score, 0.95 auc of ROC curve, and 0.702 auc of precision-recall curve.