High Sensitivity Rate Research Articles

Seizure Detection Devices

Goals of automated detection of epileptic seizures using wearable devices include objective documentation of seizures, prevention of sudden unexpected death in epilepsy (SUDEP) and seizure-related injuries, obviating both the unpredictability of seizures and potential social embarrassment, and finally to develop seizure-triggered on-demand therapies. Automated seizure detection devices are based on the analysis of EEG signals (scalp-EEG, subcutaneous EEG and intracranial EEG), of motor manifestations of seizures (surface EMG, accelerometry), and of physiologic autonomic changes caused by seizures (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). While the detection of generalized tonic-clonic and of focal to bilateral tonic-clonic seizures can be achieved with high sensitivity and low false alarm rates, the detection of focal seizures is still suboptimal, especially in the everyday ambulatory setting. Multimodal seizure detection devices in general provide better performance than devices based on single measurement parameters. Long-term use of seizure detection devices in home environments helps to improve the accuracy of seizure diaries and to reduce seizure-related injuries, while evidence for prevention of SUDEP is still lacking. Automated seizure detection devices are generally well accepted by patients and caregivers.

Prediction of surgical necessity in children with ureteropelvic junction obstruction using machine learning.

Hydronephrosis developing at the ureteropelvic junction due to obstruction poses clinical challenges as it has the potential to cause renal damage. This study aims to evaluate how well machine learning models such, as XGBClassifier and Logistic Regression can be used to predict the need for treatment in patients, with hydronephrosis resulting from ureteropelvic junction obstruction. Hydronephrosis was diagnosed in the medical records of patients from January 2015 to December 2020. These patients were classified into two groups: those who were not operated upon (n = 194) and those who had surgical procedures (n = 129). Details such as demographics, clinical presentations, and imaging findings were captured. XGBClassifier and Logistic Regression methods were employed to predict the requirement for an operation. The performance of the models was assessed based on ROC-AUC values, sensitivity, and specificity. The XGBClassifier algorithm gave the best prediction results with a ROC-AUC value of 0.977 and an accuracy rate of 95.4%. The Logistic Regression algorithm, on the other hand, offered the highest prediction during cross-validation. The presence of obstruction on scintigraphy, kidney size, anteroposterior diameter of the renal pelvic and parenchymal thickness observed in hydronephrotic kidney on USG have been identified as important predictive factors. In predicting the requirement for surgery in cases of hydronephrosis due to obstruction, machine learning algorithms have shown high accuracy and sensitivity rates. Consequently, clinical decision support systems based on these algorithms may lead to better care management of patients and more accurate projections concerning the need for surgical intervention. ESH/GOEK 2024/88-23/01/2024.

Preparation and application of porcine broadly neutralizing monoclonal antibodies in an immunoassay for efficiently detecting neutralizing antibodies against foot-and-mouth disease virus serotype O.

Neutralizing antibodies provide vital protection against foot-and-mouth disease virus (FMDV). The virus neutralization test (VNT) is a gold standard method for the detection of neutralizing antibodies. However, its application is limited due to the requirement for live virus and unsuitability for large-scale serological surveillance. In this study, a porcine broadly neutralizing monoclonal antibody (PO18-10) against FMDV was obtained from the heterologous sequentially vaccinated pig using single-B-cell antibody technology. A competitive enzyme-linked immunosorbent assay (C-ELISA) for detecting neutralizing antibodies against FMDV serotype O was developed using biotinylated PO18-10 as a detector antibody. The sensitivity and specificity of the assay were 100% and 99.55%, respectively, and the positive/negative coincidence rate with VNT was 94%, suggesting that C-ELISA based on natural host-derived monoclonal antibody (mAb) could be a promising tool to detect neutralizing antibodies against FMDV serotype O and evaluate the vaccine efficacy.IMPORTANCEFoot-and-mouth disease virus (FMDV) serotype O is one of the most prevalent serotypes in the world. The neutralizing antibody titers in primo-vaccinated animals are directly related to their level of protection against a virus challenge. The development of a safe, rapid, and accurate method for the detection of the neutralizing antibody is essential for the control and eradication of FMD. In this study, an inter-serotype broadly neutralizing monoclonal antibody PO18-10 was successfully produced using single-B-cell antibody technology from sequentially vaccinated pigs. A competitive ELISA based on this natural host-derived mAb for the detection of neutralizing antibodies against FMDV serotype O was developed and validated. The assay demonstrates high sensitivity, specificity, and coincidence rate with VNT, making it an alternative tool for confirming FMDV infection and evaluating the vaccine efficacy.

Immunoproteomic analysis and identification of possible allergenic proteins in Artemisia annua pollen.

Artemisia annua (A. annua) is a wind-pollinated weed and a major allergen responsible for allergic respiratory diseases in Northern China. This study involved the separation of pollen proteins from A. annua utilizing SDS-PAGE and Coomassie Blue staining techniques. The effectiveness of extracting allergens from Artemisia annua pollen (AAP) were confirmed both in vivo and in vitro. A mouse model was established using A. annua pollen (AAP) extracts. In vitro, the interaction between allergenic proteins in the AAP extract and specific IgE antibodies present in patients' sera was analyzed, using IgE-immunoblotting and ELISA methods. Protein bands were subsequently analyzed by mass spectrometry. The recombinant Art an 3 (rArt an 3) protein was obtained via recombination, expression, and purification. Finally, the binding activity of rArt an 3 specific IgE was subsequently examined using Western blot and inhibition ELISA (iELISA). The electrophoretic profiles of AAP showed band patterns ranging from 10 to 70kDa, with the most prominet IgE-binding pollen proteins detected at approximately 12kDa. After stimulation of AAP, the sensitized group of mice exhibited significant allergic symptoms compared to the control group. Mass spectrometry analysis identified 7 proteins, including putative aldehyde oxidase Art an 7, Art v 1-like protein, Art an 3.0101 allergen precursor, Art an 3.0102 allergen precursor, Art an 3.0201 allergen precursor, lipid transfer protein 3, and non-specific lipid-transfer protein. The results of immunoblotting and iELISA showed that rArt an 3 could bind to IgE antibodies in the patient sera, and co-incubation of rArt an 3 with serum, the binding serum significantly inhibited IgE binding to the crude AAP. The Art an 3 is a key allergenic protein in AAP with a high IgE sensitization rate in the studied population sample. These findings enhance our understanding of the sensitization components of AAP and its sensitization characteristics within the Chinese population, thereby promoting the development of precise molecular diagnostics and therapeutic interventions.

Point-of-Care Potassium Measurement vs Artificial Intelligence-Enabled Electrocardiography for Hyperkalemia Detection.

Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence- enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared. To determine the accuracy of POC and ECG potassium measurements for hyperkalemia detection in patients with critical illness. This retrospective study involved intensive care patients in an academic medical center from October 2020 to September 2021. Patients who had 12-lead ECG, POC potassium measurement, and central laboratory potassium measurement within 1 hour were included. The POC potassium measurements were obtained from arterial blood gas analysis; ECG potassium measurements were calculated by a previously developed deep learning model. Hyperkalemia was defined as a central laboratory potassium measurement of 5.5 mEq/L or greater. Fifteen patients with hyperkalemia and 252 patients without hyperkalemia were included. The POC and ECG potassium measurements were available about 35 minutes earlier than central laboratory results. Correlation with central laboratory potassium measurement was better for POC testing than for ECG (mean absolute errors of 0.211 mEq/L and 0.684 mEq/L, respectively). For POC potassium measurement, area under the receiver operating characteristic curve (AUC) to detect hyperkalemia was 0.933, sensitivity was 73.3%, and specificity was 98.4%. For ECG potassium measurement, AUC was 0.884, sensitivity was 93.3%, and specificity was 63.5%. The ECG potassium measurement, with its high sensitivity and coverage rate, may be used initially and followed by POC potassium measurement for rapid detection of life-threatening hyperkalemia.

Antibiotic-resistant Pseudomonas aeruginosa from abattoir and aquaculture environment in Abakaliki, Ebonyi State, southeast Nigeria

Background: Pseudomonas aeruginosa is frequently identified as the predominant bacterial pathogen in abattoir and aquaculture settings. In Ebonyi State, Nigeria there has been a lack of thorough investigation on the impact of the organism in the environment on public health. Therefore, it was necessary to investigate the occurrence of P. aeruginosa and determine its resistance characteristics to antimicrobial agents in selected abattoirs and aquaculture facilities in Abakaliki, Ebonyi State, southeast Nigeria.Methodology: Wastewater samples from randomly selected abattoirs (n=25) and aquaculture (n=25) sites in various locations in Abakaliki, Ebonyi State, Nigeria, were collected into sterile universal bottles and transported to the microbiology laboratory of Ebonyi State University, Abakaliki, for microbiological analysis. For heterotrophic colony count, measured in colony forming unit/ml (CFU/ml), to estimate the microbial load of the samples, a 1- in-10 dilution of the samples were prepared and cultured on nutrient agar, incubated at 37oC for 24 hours. Colonies from the culture plate were then sub-cultured on Pseudomonas agar and incubated aerobically at 37oC for 24 hours. Pseudomonas aeruginosa was phenotypically confirmed on the culture plate by conventional morphological characteristics and biochemical tests. Antibiotic susceptibility testing of the P. aeruginosa isolates was performed by the disk diffusion test and results interpreted using the Clinical and Laboratory Standards Institute (CLSI) zone diameter breakpoints. Multiple antibiotic resistance index (MARI) was calculated for each isolate.Results: The microbial load varied from 3.0±2.8 to 33.4±23.5 x 105 CFU/ml for abattoir samples and 0.00 to 26.0±2.8 x 105 CFU/ml for aquaculture samples. For the abattoir, wastewater samples from the butcher table had the highest frequency of P. aeruginosa (50.0%) isolation, followed by wastewater from the drainage (26.7%), while the lowest frequency was wash water (23.3%). For the aquaculture, wastewater from earth pond had a higher frequency (63.6%) of P. aeruginosa isolation than concrete pond (36.4%). The antibiotic susceptibility result showed that P. aeruginosa exhibited high resistance rate to amoxicillin-clavulanic acid (80.0%) and cefotaxime (80.0%). Additionally, the bacteria showed resistance rate of 50.0% to tobramycin. On the other hand, the isolates demonstrated high sensitivity rates of 90.0% to imipenem and cefepime, while sensitivity rates of 60.0% were observed for meropenem and ceftazidime. The multiple antibiotic resistance index (MARI) ranged from 0.2 to 0.7, with a mean MARI of 0.6.Conclusion: The results of this study highlight the importance of close monitoring of abattoir and aquaculture settings, as they may serve as major sources for the environmental dissemination of antibiotic resistant bacteria such as P. aeruginosa.

Application of semi-quantitative loop-mediated isothermal amplification for gene expression study in Expi293 cells

BACKGROUND: Mammalian cell cultures play a key role in the pharmaceutical industry, requiring constant monitoring of the cell conditions during fermentation. In addition to monitoring of the physical-chemical parameters, it is important to evaluate the transcription state of cells, for which gene expression analysis is used. Currently, quantitative reverse transcription polymerase chain reaction (qPCR) is the dominant method. However, the loop-mediated isothermal amplification (LAMP) technique also attracts attention due to its high specificity, sensitivity and reaction rate. LAMP is becoming a promising tool for rapid analysis of gene expression, especially under the conditions of limited biological material or a large volume of samples. AIM: Development of a technique for semi-quantifying the expression level of target genes in human cell culture Expi293 using LAMP. MATERIALS AND METHODS: For LAMP, a recombinant large fragment of Bacillus stearothermophilus DNA polymerase (Bst-pol) was obtained, purified, and optimal reaction conditions were determined. SYBR Green I and LUCS13 were used as intercalating dyes. The amplification parameters for different concentrations of the enzyme and the dye were analyzed. Standard SYBR Green I kits were used for qPCR. Both methods were compared when analyzing the expression of IGF1, FGF2 and EIF3i genes in cell lines with an increased expression level of these genes. RESULTS: It has been shown that using LUCS13 dye provides the classic S-shape of the signal accumulation curve in LAMP, while using SYBR Green I dye causes artifacts. The optimal concentration of Bst-pol was 40 ng/µl. When comparing the two methods, it was found that LAMP has greater sensitivity, allows determining gene expression with an accuracy comparable to qPCR, demonstrating a shorter reaction time (up to 35 minutes). CONCLUSION: Although qPCR remains the main method for assessing the level of gene expression, LAMP offers a number of advantages that make it an attractive alternative for various biotechnological purposes. Due to its high speed, ease of execution and accessibility, as well as high sensitivity and specificity, LAMP is a valuable technique for rapid analysis of gene expression during cell culture monitoring.

Dual-cameras terahertz imaging with multi-kilohertz frame rates and high sensitivity via Rydberg-atom vapor

Abstract Terahertz (THz) imaging holds increasing importance across various scientific fields and practical applications due to its unique characteristics. However, achieving high sensitivity and high frame rates simultaneously remains a major challenge for most current THz imaging techniques. In this paper, we demonstrate an improved THz imaging system based on Rydberg-atom vapor, which converts THz waves into visible fluorescence. A high-sensitivity camera and a high-speed camera are used simultaneously to capture the visible fluorescence. Specifically, for a 0.55 THz source, a sensor with a minimum detectable power of 41.7 aW/μm² at 100 fps and 43 fW/μm² at 6000 fps has been achieved simultaneously, with an effective imaging area larger than 100 mm². To demonstrate the spectral properties, the measured spectrum of the emitted fluorescence is presented with a high resolution of 0.1 nm. The demonstrated system can promote the development of the Rydberg-atom vapor based THz imaging technique to practical THz imaging frontier applications.

CRP/Lymphocyte Ratio and CRP/Albumin Ratio for Prognosis of Hyperglycemia in Patients Tuberculosis with Diabetes Mellitus

The issue is complicated by the rise in Diabetes Mellitus (DM) cases, a known risk factor for pulmonary TB, with 15% of pulmonary TB patients having a history of DM. DM and chronic hyperglycemia impair immune function, leading to long-term inflammation and worsening TB prognosis. Diagnostic methods include blood culture, AFB examination, chest X-ray, and genotyping, but more accessible tests are needed. Inflammatory markers, particularly lymphocytes, play a crucial role in TB prognosis with DM. Lymphopenia indicates chronic inflammation and a shift to memory lymphocytes. CRP, an inflammatory marker, indicates chronic inflammation, and hypoalbuminemia in DM due to kidney damage further complicates TB prognosis. The ratios of CRP to lymphocytes (CLR) and CRP to albumin (CAR) are gaining attention to better illustrate TB prognosis with DM. This cross-sectional study analyzed CLR and CAR as prognostic indicators in 30 pulmonary TB patients with DM in Jambi City from May to June 2024. CRP was tested at Prodia Jambi Laboratory, while Albumin, Lymphocytes, and blood glucose levels were tested at Labkesda of Jambi Province. The results show a Comparison with each parameter based on treatment status. Both parameters show slightly higher average values in intensive (< 2 months), with an average of 5.55 for CLR and 1.82 for CAR, and there is a significant difference in CLR between intensive and follow-up treatment statuses, and ROC curve shows that CLR parameters have indicated high sensitivity and low false positive rate with AUC 0,692. A significant relationship is found between CLR and blood glucose levels with a p-value of 0.024 and an r-value of 0.411, indicating a moderately strong relationship where an increase in CRP-Lymphocyte ratio corresponds to an increase in blood glucose levels than each parameter.

Validation and Reliability of the Dyslexia Adult Checklist in Screening for Dyslexia.

Dyslexia is a language-based neurobiological and developmental learning disability marked by inaccurate and disfluent word recognition, poor decoding, and difficulty spelling. Individuals can be diagnosed with and experience symptoms of dyslexia throughout their lifespan. Screening tools such as the Dyslexia Adult Checklist allow individuals to self-evaluate common risk factors of dyslexia prior to or in lieu of obtaining costly and timely psychoeducational assessments. Although widely available online, the Dyslexia Adult Checklist has yet to be validated. The purpose of this study was to validate this Checklist in a sample of adults with and without dyslexia using both univariate and multivariate statistical approaches. We hypothesised that the Dyslexia Adult Checklist would accurately distinguish between individuals with a self-reported diagnosis of dyslexia (n = 200) and a control group (n = 200), as measured by total scores on the screening tool. Results from our sample found the Dyslexia Adult Checklist to be valid (Cronbach's α = 0.86), and reliable (sensitivity = 76%-91.5%, specificity = 80%-88%). Compared to the originally proposed cut-off score of 45, given the higher sensitivity rate and negative predictive value, we recommend researchers and clinicians use a cut-off score of 40 to indicate possible mild to severe symptoms of dyslexia when using the Dyslexia Adult Checklist.

ISOLATION AND CHARACTERIZATION OF ESCHERICHIA COLI IN UTI PATIENTS OF DISTRICT D.I.KHAN AND TO PROBE THEIR ANTIBIOTIC RESISTANCE

Background: Globally urinary tract infections are present predominantly and mostly UTIs are present or noticed in the developing countries like Pakistan, Bangladesh etc. There are multiple factors in spread of UTIs like microbes, taking certain medicines, sexual activities, poor hygiene, not taking enough water etc. Objectives: The prevalence of UTIs and development of resistance against the antibiotics is due to unjustified use of antibiotics. Methodology: We performed a study in a remount area of Khyber Pakhtunkhwa i,e Dera Ismail Khan. We conducted a research study consist of a three months duration. Hundred (100) urine samples were collected from the patients who visited District Head Quarter (DHQ) teaching hospital Dera Ismail Khan. Patients having different age groups were included in study, Results: After processing samples, the number of samples having E.coli i,e positive samples were 73 in number, which were confirmed on the basis of different tests. The prevalence of UTIs due to E. coli organism was noticed increased in female patients (69 %) as compared to male patients and its prevalence was 31%.When the isolated E. coli strain were tested against different antibiotics, showed that those strains which were isolated from urine samples of the male patients of age group 61 to 80 years showed higher sensitivity against PTZ, NF, FOS, AK, IMP, and SCF, while in female high rate of sensitivity against PTZ, NF, FOS, AK, IMP, and SCF antibiotics was observed against E. coli strains which were isolated from the patients having age from 21 to 40 years Likewise E.coli isolates were sensitive against Imipenem, Peracillin + Tazobactam, Cefoperazone + sulbactam, Fosfomycin, Amikacin, Nitrofurantoin, Peracillin and Tambactam irrespective of age of the patients from which bacteria were isolated. Conclusion: Furthermore, present study also revealed, that the E. coli has developed resistance against commonly used antibiotics which is a serious concern to worry about and serious steps should be taken by the authorities to stop frequent use of antibiotics without proper prescription to avoid this issue.

Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.

In this study, we aim to determine if machine learning can reduce manual smear review (MSR) rates while meeting or exceeding the performance of traditional MSR criteria. 9938 automated CBCs with paired MSRs were performed on samples from rhesus and cynomolgus macaques. The definition of a positive (abnormal) smear was determined. Two expert-derived MSR criteria were created: criteria adapted from published, standardized human laboratory criteria (Adapted International Consensus Guidelines[aICG]) and internally generated criteria (Center Consensus Guidelines[CCG]). An ensemble machine learning model was trained on an independent subset of the data to optimize the balanced accuracy of classification, a combined measure of sensitivity and specificity. The resulting machine learning model and the two expert-derived MSR criteria were applied to a test dataset, and their performance compared. aICG criteria demonstrated high sensitivity (80.8%) and MSR rate (74.2%) while CCG criteria demonstrated lower sensitivity (57.1%) and MSR rate (36.1%). The machine learning model integrated with CCG criteria had a superior combination of both sensitivity (76.8%) and MSR rate (45.1%) achieving a false negative rate of 1.6%. Machine learning in combination with expert-derived criteria can optimize the selection of samples for MSR thus decreasing MSR rates and labor efforts required for CBC performance.

Performance Characteristics of a New Generation 148-cm Axial Field-of-View uMI Panorama GS PET/CT System with Extended NEMA NU 2-2018 and EARL Standards.

The uMI Panorama GS PET/CT system is a new long-axial-field-of-view scanner featuring high sensitivity, time-of-flight (TOF) resolution, spatial resolution, and count rate performance. The aim of this study is to assess the PET system on the basis of the National Electrical Manufacturers Association (NEMA) NU 2-2018 and European Association of Nuclear Medicine Research Limited (EARL) standards. Methods: Spatial resolution, count rate performance, sensitivity, accuracy, image quality, TOF resolution, and coregistration accuracy were evaluated following the NEMA NU 2-2018 standard. Additional experiments included energy resolution, 200-cm-long line sources for sensitivity, a 175-cm-long scatter phantom for count rate and TOF resolution, as well as the compliance with the EARL guideline. Moreover, an 18F-FDG PET patient study was reconstructed with various frame durations. Results: The PET system achieved sub-3-mm transaxial and axial spatial resolutions at a 1-cm radial offset. The sensitivities with the 70-cm-long and 200-cm-long line sources were observed to be 176.3 and 90.8 kcps/MBq, respectively, at the center of the field of view. The noise-equivalent count rates (NECRs) of the 70-cm-long and 175-cm-long scatter phantoms were measured to be 3.35 Mcps at 57.57 kBq/mL and 2.24 Mcps at 33.27 kBq/mL, respectively. The TOF resolutions for both phantoms were approximately 189 ps at 5.3 kBq/mL and lower than 200 ps below the NECR peaks. The absolute count rate errors of all 34 acquisitions were less than 3% below the NECR peak for the 70-cm-long scatter phantom. With the standard NEMA image quality phantom experiment, the contrast recovery coefficient varied from 68.17% to 94.20% and the background variabilities were all below 2%. The maximum PET/CT coregistration error was 1.33 mm. Regarding EARL compliance, the gaussian filter of 5-mm full width at half maximum could produce acceptable images. The patient data demonstrate visually satisfactory image quality with short frames (less than 1 min). Conclusion: The uMI Panorama GS exhibits spatial resolution and TOF resolution similar to those of the uMI Panorama system (35-cm axial field of view), despite the extended axial field of view. The 148-cm axial coverage, sub-200-ps TOF resolution, high sensitivity, and count rate performances are expected to yield superior image quality and offer new opportunities for various clinical applications.

Study on chemical kinetics of plasma-assisted energetic material combustion

Abstract The combustion kinetic mechanism of C4H8N8O8(HMX) is established, with 30 components and 94 reactions. To account for the effects of plasma ignition and assisted combustion, the model incorporates reactions involving H free radicals at varying mole fractions. The thermodynamic parameters of products and combustion pathways of HMX are analysed by using Chemkin. The result of sensitivity analysis shows that the reaction of N2O+H=OH+N2 has a great influence on the ignition delay time. H and OH radicals play a positive role in ignition, while OH has higher activity than H. The sensitivity analysis of OH shows that N2O+H=OH+N2 has high sensitivity and highest reaction rate. As the mole fraction of H in the reaction chamber increases, the generation rate of OH increases. The reaction rate at 0.2 mole concentration of H is twice higher than 0.1 mole concentration. The increase of OH mole fraction increases the reactivity and decreases the ignition delay time.

Journal homepage: http://ijai.iaescore.com Detecting fraudulent financial statement under imbalanced data using neural network

In this paper a novel approach for detecting fraudulent financial statements by employing a combination of neural networks and synthetic minority over sampling technique (SMOTE) is introduced. This approach is designed to tackle the problem of imbalanced datasets prevalent in fraudulent cases, which if left unaddressed will hinder the model to accurately identify fraud. Three neural network models, each representing different fraud predictors as the input layer: 28 inputs raw financial data; 14 inputs financial ratios data; and 42 inputs combination both raw financial and financial ratios data are developed. Experimental validation using established research datasets is conducted to assess the performance of the proposed method. Performance metrics, namely area under the curve (AUC), precision, and sensitivity, are used for evaluation, comparing the proposed model against existing benchmark models found in literature. Results indicate that the proposed model achieves an AUC score of 70.6% and a precision score of 2.89%, in comparable to the existing models, with a sensitivity score of 83% outperforming all counterparts. The high sensitivity rate of the proposed model underscores its practical utility for auditors and regulators, as it minimizes the risk of false negatives, thereby enhancing confidence in fraud detection.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Prevalence of inhaled allergen sensitization among patients with suspected allergic diseases in Sichuan province: 3-year data from a single center.

Allergic diseases are becoming increasingly common, and they are a threat to people's health. Exploring the distribution characteristics of allergen-specific immunoglobulin E (sIgE) in people from Sichuan province, southwest China, can provide clinical epidemiological data. This study enrolled 12,204 consecutive patients with suspected allergies from May 2018 to May 2021. Among the patients, 4206 were diagnosed with allergic rhinitis or asthma. The Rayto Lumiray 1600 detection system and sIgE and total IgE (tIgE) detection kits were used to measure the levels of nine common sIgE and tIgE. sIgE ≥ 0.35 IU/mL was considered positive. The sensitization rate of D1 (Dermatophagoides pteronyssinus) is the highest (22.97%), followed by D2 (Dermatophagoides farinae) (23.33%); M3 (Aspergillus fumigatus) has the lowest positive rate (1.5%). E5 (Canis familiaris) and I6 (Blatella germanica) exhibited relatively high sensitization rates (8.64% and 12.35%, respectively). Allergen sensitization was significantly higher in men than in women. Moreover, 90.3% of sensitization samples from D1 were combined with at least one of the other eight allergens. Similarly, a positive correlation was demonstrated between D1, D2, and E1 (Felis domesticus). In addition, patients with allergic rhinitis (91.88% and 91.54%, respectively), allergic asthma (72.44% and 74.01%, respectively), and allergic rhinitis with asthma (92.12% and 93.22%, respectively) were mainly sensitized to D1 and D2. D1, D2, I6, and E5 are the main allergens. Moreover, D1 and D2 are the main allergens in patients with respiratory allergic diseases. Research focusing on the distribution of allergens and the characteristics of hypersensitivity in different people helps prevent, diagnose, and treat allergic diseases.

A Cascade Activation Probe with Double-Enhanced Near-Infrared Imaging for Monitoring Peroxynitrite Fluctuations in Vivo.

Monitoring peroxynitrite (ONOO-) fluctuations is particularly important for assessing pathological progression and oxidative damage due to their crucial role in maintaining the redox balance of organisms. However, due to the lack of efficient tools for differentially monitoring ONOO- fluctuations at different concentration ranges in vivo, the precise detection of endogenous ONOO- fluctuations under pathological conditions in living systems remains challenging. Herein, we rationally designed a double-enhanced emission cascade activatable near-infrared (NIR) fluorescent probe (B-TCF) for the measurement of ONOO-, which consists of a borate ester response group and a malononitrile hemicyanine fluorophore. Especially, after sequential oxidative hydrolysis of the borate ester group and xanthene skeleton, B-TCF exhibited a sequentially double-enhanced NIR emission response at 776 and 625 nm for different ONOO- concentration ranges. Moreover, B-TCF revealed excellent and promising performance for ONOO- in terms of high selectivity, sensitivity, and reaction rate (k = 28.2 M-1 s-1). Motivated by the two-step emission signal enhancement and large wavelength shift in the NIR region, B-TCF enabled discriminative imaging of ONOO- with the low and high concentrations in living cells. Importantly, B-TCF was successfully applied for assessing the pathological progression of isoniazid and acetaminophen-induced liver damage in vivo by detecting the endogenous different ONOO- levels. Overall, this study not only demonstrates the first double-enhanced emission cascade activatable NIR fluorescent probe for measuring the dynamic variation of ONOO- in related diseases but also shows great potential as an effective molecular tool for evaluating the various stages of drug-induced liver damage.

Performance evaluation of a small-animal PET scanner with 213 mm axis using NEMA NU 4-2008.

Long-axis positron emission tomography (PET) has emerged as one of the recent research directions in PET due to its ability to significantly enhance sensitivity and counting performance for low-dose imaging, rapid imaging, and whole-body dynamicimaging. The PET system presented in this study is a long-axis animal PET based on lutetium-yttrium orthosilicate and silicon photomultiplier, designed for whole-body imaging in rats. It features a diameter of 143 mm and an axial length of 213.3 mm. This study evaluated the performance of this PET system in accordance with the National Electrical Manufacturers Association (NEMA) NU 4-2008standards. The performance evaluation was conducted according to the NEMA NU 4-2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction (SF) and image quality. In addition, a rat imaging study was conducted to assess the imaging capability of this PETsystem. The average energy resolution of the PET system was 12.87%, the average coincidence timing resolution was 751 ps. The FWHM of spatial resolution reconstructed by filtered back projection and 3D-OSEM-PSF algorithm at 5 mm radial offset from the axial center were 1.65 and 0.88 mm. The peak absolute sensitivity measured by a point source at the center of the field of view was evaluated as 6.71% (361-661keV) and 10.31% (250-750 keV). For the mouse-like phantom, the SF was 11.0% and the peak noise equivalent counting rate (NECR) was 1193 kcps at 94.2 MBq (2.54 mCi). For the rat-like phantom, the SF was 26.8% and the NECR was 682.5 kcps at 78.6 MBq (2.12 mCi). The performance measurement results demonstrate that this PET system exhibits high sensitivity and count rate performance, making it potential for high-quality whole-body dynamic imaging ofrats.

A New Epileptic Seizure Prediction Framework Based on Electroencephalography Signals

This research seeks to evaluate how effectively seizures can be predicted and managed in epilepsy using a specialized deep learning model based on Long Short-Term Memory (LSTM) neural networks. The model leverages non-invasive scalp electroencephalography (EEG) recordings for predicting seizures. To develop and assess the proposed LSTM neural network model, a comprehensive dataset was gathered. The model emphasizes achieving high sensitivity and reducing false alarms to improve its real-time applicability. The evaluation involved various metrics to measure accuracy, sensitivity, and rates of false positives and false negatives. The effectiveness of the proposed LSTM neural network model was outstanding, with accuracy rates ranging from 99.07% to 99.95%. Notably, the sensitivity score of 1 confirmed precise prediction for all seizure cases. The model demonstrated minimal false positive and false negative rates, highlighting its reliability in predicting seizures. This study emphasizes the promising potential of the proposed LSTM neural network model in providing advanced warning for seizures. The high accuracy and sensitivity rates suggest its usefulness in enabling timely preventive measures for patients, ultimately reducing the occurrence of seizures. This innovative approach holds significance in enhancing the overall management and quality of life for individuals dealing with epilepsy.