Diagnostic Accuracy Research Articles

Meta-analysis of the GALAD model for diagnosing primary hepatocellular carcinoma.

Ever since the GALAD (gender-age-Lens culinaris agglutinin-reactive alpha-fetoprotein-alpha-fetoprotein-des-gamma-carboxy prothrombin) logistic regression model was established to diagnose hepatocellular carcinoma (HCC), there has been no high-level evidence that evaluates and summarizes it. This meta-analysis was performed to assess the diagnostic ability of the GALAD model. The following databases were systematically searched for original diagnostic studies on HCC: PubMed, Embase, Medline, the Web of Science, Cochrane Library, China National Knowledge Infrastructure Wanfang (China), Wiper and the Chinese BioMedical Literature Database. After screening the search results according to our criteria, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the methodologic qualities, and statistical software were used to output the statistics. Ultimately, 10 studies were included and analyzed. The results revealed the pooled sensitivity and specificity of the GALAD model to be 0.86 (95% confidence interval [CI]: 0.82, 0.90) and 0.90 (95% CI: 0.87, 0.92), respectively, for all-stage HCC. The area under the curve (AUC) was 0.94. For early-stage HCC, the pooled sensitivity and specificity of the GALAD model were 0.83 (95% CI: 0.78, 0.87) and 0.81 (95% CI: 0.78, 0.83), respectively. The AUC was 0.90. This meta-analysis confirmed that the GALAD model has excellent diagnostic performance for early-stage and all-stage HCC and can maintain high sensitivity and specificity in early-stage HCC. Therefore, the GALAD model is qualified for screening early-stage canceration from chronic liver disease.

ACUTE RHABDOMYOLYSIS IN A JUVENILE WITH DENGUE FEVER: A RARE MANIFESTATION BUT BEWARE

Myalgia is a common symptom in patients with Dengue fever. However, acute rhabdomyolysis is a rare manifestation. The precise mechanism of Dengue-associated rhabdomyolysis has not been determined, it is thought likely to be caused by myotoxic cytokines released in response to viral infection, such as TNF and IFN-α. Here we report a case of a 13-year-old boy with Dengue fever, presenting with red urine on day 5 of illness. Together with a decreased platelet count (26 G/L), we initially suspected hematuria but the urinalysis showed no red blood cells. We then thought of rhabdomyolysis causing myoglobinuria, so we ordered diagnostic tests with the following results: CK 1353 U/L, GOT 361 U/L, GPT 90 U/L. The patient was given intravenous fluids and oral oresol to control urine output. Urine color faded and blood CK concentration returned to normal after 13 days. Rhabdomyolysis is rare in dengue fever but requires proper diagnosis and management to prevent acute kidney injury - a life-threatening complication.

Evaluating Diagnostic Algorithms for Heparin-Induced Thrombocytopenia using Two Combined Automated Rapid Immunoassays.

Heparin-induced thrombocytopenia (HIT) is an autoimmune disorder caused by antibodies against platelet factor 4 (PF4) and heparin complexes. Rapid immunoassays (IAs) for detection of these antibodies mark a milestone in HIT diagnosis, despite a higher false-positive rate compared with functional platelet-activation assays. However, combining different rapid IAs may help to improve their diagnostic specificity. Here, we compared the individual performance of the latex immunoturbidimetric assay (LIA; HemosIL HIT-Ab [PF4-H]; sensitivity 91.7%, specificity 68.4%) and chemiluminescence immunoassay (CLIA; HemosIL AcuStarHIT-Ab [PF4-H]; sensitivity 92.4%, specificity 85.8%) with their combined performance using two unique diagnostic algorithms in a single prospective cohort of suspected HIT patients. Using the simultaneous algorithm adapted from Warkentin et al, the combined LIA-CLIA had a sensitivity of 99.0% and specificity of 64.3%. The sequential algorithm adapted from Rittener-Ruff et al was applied in two theoretical scenarios to reflect real-world circumstances in diagnostic laboratories where access to clinical information is limited: (1) assuming all patients had an intermediate 4Ts score and (2) assuming all patients had a high 4Ts score. This algorithm correctly predicted HIT in 94.5% (high 4Ts) and 96.0% (intermediate 4Ts) and excluded HIT in 82.6% (high 4Ts) and 80.1% (intermediate 4Ts) of patients in either scenario, respectively. Although both combined algorithms improved diagnostic performance of individual IAs, the simultaneous algorithm showed fewer false predictions (7.9%) than the sequential algorithm (intermediate 4Ts: 37.6% and high 4Ts: 41.5%) and proved more practical as it does not rely on physician evaluations. Our findings highlight the importance of accounting for clinician and interlaboratory variability when evaluating diagnostic tests for HIT.

Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis.

Fractional flow reserve (FFR) is the ratio of blood pressure measured distal to a stenosis and pressure proximal to a stenosis. FFR can be estimated noninvasively using computed tomography (CT) although the usefulness of this technique remains controversial. This meta-analysis evaluated the agreement of FFR estimated by CT (FFR-CT) with invasively measured FFR. The study also evaluated the diagnostic accuracy of FFR-CT, defined as the ability of FFR-CT to classify lesions as hemodynamically significant (invasive FFR ≤0.8) or insignificant (invasive FFR >0.8). Forty-three studies reporting on 7291 blood vessels from 5236 patients were included. A moderate positive linear relationship between FFR-CT and invasively measured FFR was observed (Spearman correlation coefficient: 0.67). Agreement between the 2 measures increased as invasively measured FFR values approached 1. The overall diagnostic accuracy, sensitivity and specificity of FFR-CT were 82.2%, 80.9%, and 83.1%, respectively. Diagnostic accuracy of 90% could be demonstrated for FFR-CT values >0.90 and <0.49. The diagnostic accuracy of off-site tools was 79.4% and the diagnostic accuracy of on-site tools was 84.1%. The agreement between FFR-CT and invasive FFR is moderate although agreement is highest in vessels with FFR-CT >0.9. Diagnostic accuracy varies widely with FFR-CT value but is above 90% for FFR-CT values >0.90 and <0.49. Furthermore, on-site and off-site tools have similar performance. Ultimately, FFR-CT may be a useful adjunct to CT coronary angiography as a gatekeeper for invasive coronary angiogram.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

The performance of the node reporting and data system 1.0 (Node-RADS) and DWI-MRI in staging patients with cervical carcinoma according to the new FIGO classification (2018).

To evaluate the diagnostic accuracy of the Node-RADS score and the utility of apparent diffusion coefficient (ADC) values in predicting metastatic lymph nodes (LNs) involvement in cervical cancer (CC) patients using magnetic resonance imaging (MRI). The applicability of the Node RADS score across three readers with different years of experience in pelvic imaging was also assessed. Among 140 patients, 68 underwent staging MRI, neoadjuvant chemotherapy and radical surgery, forming the study cohort. Node-RADS scores of the main pelvic stations were retrospectively determined to assess LN metastatic likelihood and compared with the histological findings. Mean ADC, relative ADC (rADC), and correct ADC (cADC) values of LNs classified as Node-RADS ≥ 3 were measured and compared with histological reports, considered as gold standard. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and accuracy were calculated for different Node-RADS thresholds. Node RADS ≥ 3 showed a sensitivity of 92.8% and specificity of 72.5%. Node RADS ≥ 4 yielded a sensitivity of 71.4% and specificity of 100%, while Node RADS 5 yielded 42.9% and 100%, respectively. The diagnostic performance of mean ADC, cADC and rADC values from 78 LNs with Node-RADS score ≥ 3 was assessed, with ADC demonstrating the highest area under the curve (AUC 0.820), compared to cADC and rADC values. The Node-RADS score provides a standardized LNs assessment, enhancing diagnostic accuracy in CC patients. Its ease of use and high inter-observer concordance support its clinical utility. ADC measurement of LNs shows promise as an additional tool for optimizing patient diagnostic evaluation.

Facilitators of and Barriers to the Use of a Digital Self-Management Service for Diagnostic Testing: Focus Group Study With Potential Users.

Health care lags in digital transformation, despite the potential of technology to improve the well-being of individuals. The COVID-19 pandemic has accelerated the uptake of technology in health care and increased individuals' willingness to perform self-management using technology. A web-based service, Directlab Online, provides consumers with direct digital access to diagnostic test packages, which can digitally support the self-management of health. This study aims to identify the facilitators, barriers, and needs of Directlab Online, a self-management service for web-based access to diagnostic testing. A qualitative method was used from a potential user's perspective. The needs and future needs for, facilitators of, and barriers to the use of Directlab Online were evaluated. Semistructured focus group meetings were conducted in 2022. Two focus groups were focused on sexually transmitted infection test packages and 2 were focused on prevention test packages. Data analysis was performed according to the principles of the Framework Method. The Consolidated Framework for Implementation Research was used to categorize the facilitators and barriers. In total, 19 participants, with a mean age of 34.32 (SD 14.70) years, participated in the focus groups. Important barriers were a lack of privacy information, too much and difficult information, and a commercial appearance. Important facilitators were the right amount of information, the right kind of tests, and the involvement of a health care professional. The need for a service such as Directlab Online was to ensure its availability for users' health and to maintain their health. According to the participants, facilitators and barriers were comprehension of the information, the goal of the website, and the overall appearance of the service. Although the service was developed in cocreation with health care professionals and users, the needs did not align. The users preferred understandable and adequate, but not excessive, information. In addition, they preferred other types of tests to be available on the service. For future research, it would be beneficial to focus on cocreation between the involved medical professionals and users to develop, improve, and implement a service such as Directlab Online.

Establishment of a digital electrocardiogram telemonitoring system and remote consultation service.

The use of telemonitoring electrocardiogram (ECG) improves the detection rate of various arrhythmias as it offers an extended recording time and does not impose activity restrictions. However, many community hospitals lack the resources to conduct this test due to a lack of cardiac function specialists and antiquated screening equipment. To establish a digital ECG telemonitoring system and remote consultation service between tertiary hospitals and community medical service stations to improve diagnosis and treatment effectiveness. We used the PI ECG telemonitoring data acquisition system, with a personal digital assistant (PDA) or computer serving as the platform for ECG acquisition, storage, display, printing, and transmission. We introduced this system to standardize the storage and transmission of ECG telemonitoring data, and ensure accurate transmission, reproducibility, and preservation of data. The implementation of the PI ECG telemonitoring data acquisition system enabled the sharing of remote ambulatory electrocardiography data between Beijing Shijitan Hospital and its affiliated community hospitals. This has resulted in reduced waiting times for patients to receive reports (from 0.3 to 1 hour, to 0.1 or less), shortened consultation times (from 2 hours to 0.1 hour), and improved patient satisfaction with consultations (> 20% of the patients reported they were highly satisfied). The system successfully combined the goals of intelligence, real-time synchronization, and efficiency, revolutionizing ECG telemonitoring diagnosis in the network era. Sharing digital telematics information between tertiary and community hospitals enables a tiered approach to diagnosis and treatment and allows for more efficient, accurate, and convenient remote diagnostics.

Comparison of a portable, pneumotach flow-sensor–based spirometer (Spirofy™) with the vitalograph alpha Touch™ spirometer in evaluating lung function in healthy individuals, asthmatics, and COPD patients—a randomized, crossover study

BackgroundSpirofy™ is India’s first portable, pneumotach flow-sensor-based digital spirometer developed to diagnose asthma and chronic obstructive pulmonary disease (COPD). In this study, we compared the performance of the Spirofy™ device with that of the Vitalograph Alpha Touch™ spirometer in measuring the lung capacities of healthy individuals, asthmatics, and COPD patients. We also assessed the inter-device variability between two Spirofy™ devices.MethodsIn a randomized, three-way crossover, open-label study, we measured the differences in forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) between the Spirofy™ and Vitalograph Alpha Touch™ spirometers. A proportion of the FEV1/FVC ratio distribution of < 0.7 was used to compare the diagnostic accuracies of the Spirofy™ with Vitalograph™ Alpha Touch™ spirometers.ResultsNinety subjects participated in this study. The mean ± SD FVC values obtained from the Spirofy™ 1, Spirofy™ 2, and Vitalograph Alpha Touch™ devices were 2.60 ± 1.05 L, 2.64 ± 1.04 L, and 2.67 ± 1.04 L, respectively. The mean ± SD FEV1 values obtained from the Spirofy™ 1, Spirofy™ 2, and Vitalograph Alpha Touch™ devices were 1.87 ± 0.92 (L), 1.88 ± 0.92 (L), and 1.93 ± 0.93 (L), respectively. A significant positive correlation was found between the FVC and FEV1 values recorded by Vitalograph Alpha Touch™, Spirofy™ 1, and Spirofy™ 2. As compared to Vitalograph Alpha Touch™, the Spirofy™ device showed good sensitivity (97%), specificity (90%), and overall accuracy (93.3%) at an FEV1/FVC ratio < 0.7. No inter-device variability was observed between the two Spirofy™ devices.ConclusionSpirofy™ is a portable and easy-to-use device and is as accurate as the standard Vitalograph Alpha Touch™ spirometer for the diagnosis of COPD and asthma.Trial registrationCTRI/2021/09/036492 (Clinical Trials Registry - India)

Performance of thyroid fine needle aspiration biopsy in a low- and middle-income country.

Introduction The 6 categories of the Bethesda System for Reporting Thyroid Cytology (TBSRTC) with associated risk of malignancy (ROM) provide evidence-based clinical management guidelines. This study aimed to determine the ROM and accuracy of FNAB in South Africa (SA). Methods Thyroid specimens from 3 pathology laboratories registered between January 2015 and December 2019 were considered for inclusion. ROM was obtained per TBSRTC category by cytohistological correlation and dividing the total number of specimens with malignant histology by the total number of cases operated. Accuracy was calculated based on the Bethesda category and eventual malignant histology. Results Seventeen thousand seven hundred and seventy-three histology and 4 791 cytology cases were identified. Of the 4 791 cytology cases, 931 (19%) underwent surgery. More than a third (333, 35.8%) of cases were confirmed as malignant following histological assessment, with the majority being benign (584, 62.7%). The ROM for the non-diagnostic and benign categories was 24.3% and 20.5%. The highest ROM was for category VI (91.5%), followed by categories V (69.5%), IV (51.9%) and III (38.8%). Thyroid FNAB had a sensitivity of 73%, specificity of 74%, and overall accuracy of 74%. Conclusion Bethesda categories II and IV have a relatively higher ROM in SA compared to findings from other developed countries. The diagnostic accuracy of thyroid FNAB in SA and the high rate of non-diagnostic diagnoses (38%) require further investigation. A national thyroid registry could provide location-specific data to aid the implementation of appropriate local policies and national guidelines for practicing thyroid surgeons.

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis.

To evaluate the performance of machine learning models in predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) and magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before January 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. A total of 1690 patients from 24 studies were included. The meta-analysis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0.89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 4 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies conducted in other countries. Machine learning has promising potential in predicting tumor response to nCRT in patients with locally advanced rectal cancer. Together with clinical information, machine-learning based models may bring us closer toward precision medicine. Compared to traditional machine learning models, deep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine-learning based models may bring us closer toward precision medicine.

A Novel Objective Pathologic Criterion for Isolated Hypoganglionosis.

Isolated hypoganglionosis (IHG) is histologically characterized by small numbers of myenteric ganglion cells and small myenteric ganglia; however, no numerical diagnostic criteria for IHG have been established. Therefore, this study aimed to develop quantitative pathologic criteria for IHG. We evaluated 160 resected intestinal tissue specimens from 29 pediatric autopsies and 10 IHG cases. These specimens were obtained from the jejunum, ileum, ascending colon, transverse colon, and rectum. Morphologic features of the myenteric ganglion cells and myenteric ganglia were quantified and analyzed in digitized HuC/HuD-immunostained and CD56-immunostained sections, respectively. Quantitative criteria were developed with a scoring system that used parameters with the area under the receiver operating characteristic curve (AUC) values >0.7 and sensitivity and specificity exceeding 70%. The selected parameters were the number of myenteric ganglion cells per cm and the number of myenteric ganglia with an area >2500µm2per cm. The score for each parameter ranged from -1 to 2, and the total score of the scoring system ranged from -2 to 4. With a cutoff value of ≥2 (AUC, 0.98; 95% CI: 0.96-1.00), the scoring system had a sensitivity of 96% (95% CI: 0.82-1.00) and a specificity of 99% (95% CI: 0.95-1.00). We devised a novel pathologic criterion based on the quantification of the number of myenteric ganglion cells and ganglia. Furthermore, this criterion showed high diagnostic accuracy and could lead to a definitive diagnosis of IHG in clinical practice.

Improved accuracy of spectral-domain optical coherence tomography and optical coherence tomography angiography for monitoring myopic macular neovascularisation activity

Background/aimsTo evaluate the diagnostic accuracy of spectral-domain optical coherence tomography (SD OCT) combined with OCT angiography (OCTA) for myopic myopic macular neovascularisation (MNV) activity.MethodsBoth eyes of patients with myopic MNV...

Case Report On Rare Co-Occurrence of Complicated Acute Appendicitis and Ovarian Cyst Rupture

Acute appendicitis is the most common surgical emergency, and is also the most common cause of non-gynecological pelvic pain. Female patients presenting with abdominal pain in the presence of an underlying gynecological pathology such as ectopic pregnancy, threatened abortion, ovarian cyst, tubo-ovarian abscess or pelvic inflammatory disease can mimic acute appendicitis, potentially complicate the clinical picture and affect the diagnostic accuracy. Open or laparoscopic surgery is the mainstay of management for Complicated Appendicitis. Our patient was operated having signs and symptoms of pelvic peritonitis, with intraoperative finding of pelvic cavity blood with right ruptured ovarian simple cyst. She also had significant appendiceal abscess contained by omentum, terminal ilium, cecum and sigmoid colon with eaten up appendix in the abscess. This is a rare co-occurrence of appendiceal abscess with ruptured ovarian cyst, only one previous published case report of acute appendicitis with ruptured ovarian cyst.

Controlled attenuation parameter (CAP): the clinical value based on MRI-PDFF in children with obesity.

Controlled attenuation parameter (CAP) is a noninvasive and quantitative method to evaluate hepatic steatosis, which is not well evaluated in children. The aim ofthis study was to examine the diagnostic value of CAP forhepatic steatosis in children with obesity based on MR proton density fat fraction (PDFF). About 108 pediatric patients with nonalcoholic fatty liver disease (NAFLD) who were assessed for PDFF, CAP, and other laboratory results were enrolled. In this study, pediatric patients were separated for the obese group (n=80) and the severe obese group (n=28). Hepatic steatosis grades (0-3) were classified according to PDFF using cutoff values of 6.4 , 17.4, and 22.1 %. There are significant differences in CAP between the obese and severe obese groups (p<0.05). CAP showed a good correlation with PDFF in pediatric patients with NAFLDfor diagnosing hepatic steatosis using a cutoff valueof 265 dB/m (p<0.001). Meanwhile, ALT significantly outperforms CAP in receiver-operating curve (ROC) analysis for diagnosing hepatic steatosis grades. The diagnostic accuracy of CAP for steatosis is 77.8 %, and the diagnostic accuracy of ALT for steatosis is 83.3 %. While CAP holds promise as a diagnostic tool for pediatric NAFLD, its diagnostic performance warrants some caution. The potential of CAP is evident; however, ALTemerges as a simpler and more accurate measure for detecting hepatic steatosis in children. Further research is essential to determine the optimal role of CAP in pediatric NAFLD diagnosis and management.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

HSLE: A Hybrid Ensemble Classifier for Prediction of Heart Disease

Background: Detecting heart disease in a timely manner is vital for preventing its progression, as it is the primary cause of death across the globe. Machine learning has the potential to enhance diagnostic accuracy and enable better clinical decision-making. A machine learningpowered hybrid system for diagnosing heart disease may provide a better optimal solution for heart disease prediction. Objective: The overarching objectives include accuracy improvement, enhanced classification reliability, and the development of high-performance prediction models for heart disease. These objectives indicate a commitment to advancing methodologies and models in the field of machine learning and data science, particularly within the domain of healthcare and disease prediction. Method: The proposed system was developed using the Cleveland dataset that was preprocessed and analyzed using Recursive Feature Elimination with Cross-Validation (RFECV) and Least Absolute Shrinkage and Selection Operator (LASSO) feature extraction techniques. Further, a hybrid feature selection approach using RFECV and K-Best has been proposed for feature selection. Eight machine learning classifiers such as Multilayer Perceptron (MLP), Random Forest (RF), Extreme Gradient Boosting (XGB), K-Nearest Neighbours (KNN), Extra Tree (ET), Support Vector Machine (SVC), Adaboost, Decision Tree (DT) were utilized, and the performance of the system was measured in terms of various metrics. Result: The results showed that the proposed HSLE algorithm with hybrid feature selection led to the highest overall accuracy of 98.76%. Conclusion: As mentioned, the main cause of adult death worldwide is chronic disease. Early detection can stop the condition from getting worse. Our research presents an innovative hybrid machine-learning approach designed to forecast heart disease.

A lightweight dynamic dual-damped wavelet-based convolutional neural network for interpretable bearing fault diagnosis

Wavelet-based convolutional neural networks (CNNS) have attracted widespread attention because they can improve the interpretability of intelligent fault diagnosis methods. However, the fault feature representation capability of typical wavelet-based convolution kernel frameworks must be strengthened to improve the diagnostic accuracy of complex faults. In the meantime, the large number of network parameters leads to high computational costs. To address these issues, a lightweight wavelet-based dynamic CNN, which comprises a dual-damping wavelet-based dynamic CNN (DWDC) block and a discrete wavelet transformation (DWT) enhancement (DWTE) block, is put forward. In the DWDC block, a wavelet convolution layer is initially designed, where a dual-damping wavelet is used as the kernel function to improve the match of the convolution kernel with fault impulses. Subsequently, a dynamic convolution layer with multiple parallel small-size convolutional kernels is designed to screen the fault features instead of a multilayer network structure, thereby greatly reducing the number of network parameters. Finally, the DWTE block is constructed by combining the DWT and residual dense block, and it can mine more fault information from the previously extracted features. The experiments on the variable speed bearing dataset, locomotive bearing dataset with constant speed and the Case Western Reserve University dataset prove that the proposed approach outperforms five classical CNN models and six advanced wavelet-based CNN models. In addition, it can effectively solve the issue of data imbalance because of its powerful feature extraction capability.

Minimally Invasive with Maximal Yield: A Narrative Review of Current Practices in Mediastinal Lymph Node Staging in Non-Small Cell Lung Cancer.

Background: Lung cancer remains the leading cause of cancer deaths in the United States despite declining incidence and improved outcomes because of advancements in early detection and development of novel therapies. Accurate mediastinal lymph node staging is crucial for determining prognosis and guiding treatment decisions, particularly for non-small cell lung cancer (NSCLC). Materials and Methods: A systematic search of PubMed was conducted to identify English language articles published between January 2010 and January 2024 focusing on preoperative lymph node staging in adults with NSCLC. Case series, observational studies, randomized trials, guidelines, narrative reviews, systematic reviews, and meta-analyses were included. Results: Various imaging modalities, surgical and nonsurgical procedures for mediastinal lymph node staging were reviewed, including positron emission tomography with computed tomography, cervical mediastinoscopy, video-assisted cervical mediastinoscopy, anterior mediastinotomy, video-assisted thoracoscopy, endobronchial ultrasound-guided fine needle aspiration (EBUS-FNA), transesophageal endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), and computed tomography-guided percutaneous lymph node biopsy. EBUS-FNA emerged as the preferred initial staging procedure because of its high sensitivity and low complication rate. Combining it with other procedures or confirmatory testing may be helpful in determining appropriate treatment. Conclusions: Although cervical mediastinoscopy remains a valuable confirmatory procedure in select cases, its role as a first-line staging modality is diminishing with the widespread adoption of EBUS-FNA and EUS-FNA. The combination of EBUS-FNA and EUS-FNA allows access to nearly all mediastinal lymph node stations with high diagnostic accuracy. Future research may further refine the selection criteria for invasive mediastinal staging procedures, ultimately optimizing patient outcomes in the management of NSCLC.

Role of Ayurvedic Herbs in the Management of Celiac Disease

Celiac disease or gluten-sensitive enteropathy is an autoimmune hypersensitivity disorder affecting the small intestine. It occurs due to an inappropriate immune reaction to gluten, which results in inflammation and damage to the small intestine. One of the most prevalent illnesses, celiac disease, is brought on by both environmental (gluten) and genetic causes of human leukocyte antigen (HLA) and non-HLA genes. The prevalence of celiac disease has been estimated to approximate 0.5%-1% in different parts of the world. Due to improved physician awareness and expertise, as well as the widespread use of extremely sensitive and precise diagnostic tests for celiac disease, the prevalence of celiac disease has considerably grown in the past 30 years. While older children have either limited or unusual symptoms, only a small percentage of celiac patients have the classic signs of the condition like chronic or intermittent diarrhea, failure to thrive, weight loss, delayed puberty, short stature, nausea, vomiting, chronic abdominal pain, abdominal distension, chronic constipation. Early detection of celiac disease is crucial to preventing long-term consequences. According to Ayurveda, some symptoms of celiac disease found in Grahani Dosha. Grahani is described as an Agni Adhisthana by most of Acharya. Mandagini is the root cause of Ama Dosha, and it is a crucial factor for the manifestation of most diseases. The treatment approach of celiac disease in Ayurveda medicinal science involves the intake of various preparations of some specific medicinal plants like Kutaja (Holarrhena antidysenterica) Pippali, (Piper longum Linn.), Chitraka, (Plumbago zeylanica) Guduchi, (Tinospora cordifolia) Ashwagandha, (Withania somnifera), Haridra (Curcuma longa Linn.), etc which have Agni Deepana and Ama Pachana properties. These illness-specific medicinal herbs help normalize digestive fire and evict "Ama" toxins from the body channels, which pacify the vitiated Agni and maintain homeostasis among vitiated Doshas.