Clinical Characteristics and Fatality Risk Factors for Patients with Listeria monocytogenes Infection: A 12-Year Hospital-Based Study in Xi'an, China.

INTRODUCTION: Listeriosis is a severe food-borne disease caused by Listeria monocytogenes infection. The data of listeriosis in Xi'an population are limited. The aim of this study is to evaluate the clinical features and fatality risk factors for listeriosis in three tertiary-care hospitals in Xi'an, China METHODS: The characteristics of demographic data, underlying diseases, clinical manifestations, laboratory indicators, cranial imaging examination, antibiotics therapeutic schemes, and clinical outcomes were collected between 2011 and 2023. Logistic regression analysis was performed. RESULTS: Seventy-one etiologically confirmed listeriosis patients were enrolled, including 12 neonatal and 59 non-neonatal cases. The majority of neonatal listeriosis presented as preterm (50%) and fetal distress (75%). The main clinical manifestations of non-neonatal listeriosis included fever (88%), headache (32%), disorder of consciousness (25%), vomiting (17%), abdominal pain (12%), and convulsions (8%). The fatality rate in neonatal cases was higher than in non-neonatal listeriosis (42 vs. 17%). Although no deaths were reported in maternal listeriosis, only two of 23 patients had an uneventful obstetrical outcome. Five maternal listeriosis delivered culture-positive neonates, three of whom decreased within 1 week post-gestation due to severe complications. Twenty-eight cases were neurolisteriosis and 43 cases were bacteremia. Neurolisteriosis had a higher fatality rate compared with bacteremia listeriosis (36 vs. 12%). The main neuroradiological images were cerebral edema/hydrocephalus, intracranial infection, and cerebral hernia. Listeria monocytogenes showed extremely low resistance to ampicillin (two isolates) and penicillin (one isolate). The fatality risk factors were the involvement of the central nervous system, hyperbilirubinemia, and hyponatremia for all enrolled subjects. Hyperuricemia contributed to the elevation of fatality risk in non-neonatal listeriosis. CONCLUSIONS: When the patients suffered with symptoms of fever and central nervous system infection, they should be alert to the possibility of listeriosis. Early administration of ampicillin- or penicillin-based therapy might be beneficial for recovery of listeriosis.

A Novel Bacillus amyloliquefaciens Specifically Improving the Solubility and Antioxidant Activities of Edible Bird's Nest.

Edible bird's nest (EBN), a most highly priced and valuable foodstuff, contains high percentage of proteins and carbohydrates. However, proteins adhering to these carbohydrates make the EBN hard and tough, which need to be boiled as the bird's nest soup to make the Chinese cuisine. To overcome the hard and tough texture of EBN and improve the digestion degrees, the present study screened and identified a probiotic strain Bacillus amyloliquefaciens YZW02 from 5-year stored EBN sample completely solubilizing EBN for the first time. The 24-h B. amyloliquefaciens fermented EBN contained 20.30-21.48 mg/mL of the soluble protein contents with a recovery rate of 98-100%, DPPH radical scavenging rate of 84.76% and ABTS radical scavenging capacity of 41.05%. The mixed fermentation of B. amyloliquefaciens YZW02 and Bacillus natto BN1 were further applied to improve the low-MW peptide percentages and antioxidant activities. The mixed-fermentation of B. natto BN1 with 4-h cultured B. amyloliquefaciens YZW02 had the lowest percentage (82.23%) of >12-kDa proteins/peptides and highest percentages of 3-12 kDa, 1-3 kDa and 0.1-1 kDa peptides of 8.6% ± 0.08, 7.57% ± 0.09, 1.77% ± 0.05 and 0.73% ± 0.05, with the highest DPPH, ABTS and •OH scavenging capacity of 90.23%, 46.45% and 49.12%, respectively. These findings would provide an efficient strategy for improving the solubility and antioxidant activities of EBNs.

Disease burden of mental disorders among children and adolescents considering both co-morbidities and suicide in Northeastern China.

BACKGROUND: Few studies have assessed the burden of mental disorders among children and adolescents considering the impact of co-morbidities and suicide on disability adjusted life years (DALYs). METHODS: This was a multicenter cross-sectional study. Our survey data in Liaoning Province (LN) were used to estimate the burden of six mental disorders, supplemented with data from other investigative studies conducted in China to assess four other disorders. DALYs were derived from the sum of years lived with a disability (YLDs) adjusted for co-morbidities, and the years of life lost (YLLs) adjusted for suicide. The changes in DALYs, YLDs, and YLLs were compared with and without adjustment for co-morbidities and suicide. RESULTS: The DALYs rate of mental disorders among children and adolescents in LN decreased from 1579.6/105 to 1391.4/105, after adjusting for both co-morbidities and suicide (-11.9%). The DALYs rate for major depression, anxiety disorder, and conduct disorder (-80.8/105, -75.0/105 and -30.2/105, respectively) were the top three contributors to the DALYs reduction (-188.2/105). The YLDs decreased from 72724.8 to 62478.5 after co-morbidity adjustment (-17.8%), mainly due to the reduction by major depression (-35.3%) and attention deficit/hyperactivity disorder [ADHD] (-34.2%). The YLLs increased from 130 to 1697.8 after adjusting for suicides (+ 56.9% of all suicide YLLs), mainly due to the contribution of major depression (+ 32.4%) and anxiety disorder (+ 10.4%). Compared to GBD 2010, the estimated DALY rate for mental disorders in LN was to be about 80%, with the proportion of DALYs and DALY rates explained by major depressive disorder accounted for only approximately one-third (14.6% vs. 41.9% and 202.6 vs. 759.9, respectively). But the proportion and absolute level of DALY rates explained by anxiety disorders were approximately 2-fold higher (39.7% vs. 19.6% and 552.2 vs. 323.3, respectively). CONCLUSIONS: The DALYs of mental disorders among Chinese children and adolescents were approximately 80% of the global level, with anxiety disorders imposing about 2 times the global level. Co-morbidity and suicide must be adjusted when calculating DALYs.

A transthyretin-like protein acts downstream of miR397 and LACCASE to regulate grain yield in rice.

Increasing grain yield is a major goal of breeders due to the rising global demand for food. We previously reported that the miR397-LACCASE (OsLAC) module regulates brassinosteroid (BR) signaling and grain yield in rice (Oryza sativa). However, the precise roles of laccase enzymes in the BR pathway remain unclear. Here, we report that OsLAC controls grain yield by preventing the turnover of TRANSTHYRETIN-LIKE (OsTTL), a negative regulator of BR signaling. Overexpressing OsTTL decreased BR sensitivity in rice, while loss-of-function of OsTTL led to enhanced BR signaling and increased grain yield. OsLAC directly binds to OsTTL and regulates its phosphorylation-mediated turnover. The phosphorylation site Ser226 of OsTTL is essential for its ubiquitination and degradation. Overexpressing the dephosphorylation-mimic form of OsTTL (OsTTLS226A) resulted in more severe defects than did overexpressing OsTTL. These findings provide insight into the role of an ancient laccase in BR signaling and suggest that the OsLAC-OsTTL module could serve as a target for improving grain yield.

Music-evoked emotions classification using vision transformer in EEG signals.

Introduction: The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. Methods: In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Experiments: Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. Discussion: The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios.

The effect of music training on students' mathematics and physics development at middle schools in China: A longitudinal study.

As a descriptive-inferential study, this research aimed at revealing the relationship between music training and academic development with the Chinese middle school students' academic performance of mathematics and physics skills. The participants of this study consisted of the students from two different middle schools located at two cities in Shandong province, China. From each school 250 students were selected, and the statistics was used to analyze both the academic performance of the students and the data obtained from the scale designed by the authors. The research results show that the non-music students outperformed music students on both mathematics and physics development. In addition, music training did not contribute to the academic achievement independently but rather integrated with several factors like parents' education and out-of-school engagement. The findings suggest the positive influence on non-musical cognitive learning, and it has potential implications for the Chinese middle school education.

Levels of peripheral blood routine, biochemical and coagulation parameters in patients with hemorrhagic fever with renal syndrome and their relationship with prognosis: an observational cohort study.

BACKGROUND: Hantaan virus (HTNV), Seoul virus (SEOV) and Puumala virus (PUUV) are major serotypes of the Hantavirus, which can cause hemorrhagic fever with renal syndrome (HFRS). The pathophysiology of HFRS in humans is complex and the determinants associated with mortality, especially the coagulation and fibrinolysis disorders, are still not been fully elucidated. Severe patients usually manifest multiple complications except for acute kidney injury (AKI). The aim of this study was to observe the levels of peripheral blood routine, biochemical and coagulation parameters during the early stage, so as to find independent risk factors closely related to the prognosis, which may provide theoretical basis for targeted treatment and evaluation. METHODS: A total of 395 HFRS patients from December 2015 to December 2018 were retrospectively enrolled. According to prognosis, they were divided into a survival group (n = 368) and a death group (n = 27). The peripheral blood routine, biochemical and coagulation parameters were compared between the two groups on admission. The relationship between the parameters mentioned above and prognosis was analyzed, and the dynamic changes of the coagulation and fibrinolysis parameters during the first week after admission were further observed. RESULTS: In addition to AKI, liver injury was also common among the enrolled patients. Patients in the death group manifested higher levels of white blood cell counts (WBC) on admission. 27.30% (107/392) of the patients enrolled presented with disseminated intravascular coagulation (DIC) on admission and DIC is more common in the death group; The death patients manifested longer prothrombin time (PT) and activated partial thromboplastin time (APTT), higher D-dimer and fibrinogen degradation product (FDP), and lower levels of platelets (PLT) and fibrinogen (Fib) compared with those of the survival patients. The proportion of D-dimer and FDP abnormalities are higher than PT, APTT and Fib. Prolonged PT, low level of Fib and elevated total bilirubin (TBIL) on admission were considered as independent risk factors for prognosis (death). CONCLUSIONS: Detection of PT, Fib and TBIL on admission is necessary, which might be benefit to early predicting prognosis. It is also important to pay attention to the dynamic coagulation disorders and hyperfibrinolysis during the early stage in the severe HFRS patients.

MicroRNA397 promotes rice flowering by regulating the photorespiration pathway.

The precise timing of flowering plays a pivotal role in ensuring successful plant reproduction and seed production. This process is intricately governed by complex genetic networks that integrate internal and external signals. This study delved into the regulatory function of microRNA397 (miR397) and its target gene LACCASE-15 (OsLAC15) in modulating flowering traits in rice (Oryza sativa). Overexpression of miR397 led to earlier heading dates, decreased number of leaves on the main stem, and accelerated differentiation of the spikelet meristem. Conversely, overexpression of OsLAC15 resulted in delayed flowering and prolonged vegetative growth. Through biochemical and physiological assays, we uncovered that miR397-OsLAC15 had a profound impact on carbohydrate accumulation and photosynthetic assimilation, consequently enhancing the photosynthetic intensity in miR397-overexpressing rice plants. Notably, we identified that OsLAC15 is at least partially localized within the peroxisome organelle, where it regulates the photorespiration pathway. Moreover, we observed that a high CO2 concentration could rescue the late flowering phenotype in OsLAC15-overexpressing plants. These findings shed valuable insights into the regulatory mechanisms of miR397-OsLAC15 in rice flowering and provided potential strategies for developing crop varieties with early flowering and high-yield traits through genetic breeding.

Effects of elevated temperature and copper exposure on the physiological state of the coral Galaxea fascicularis.

The co-occurrence of elevated seawater temperature and local stressors (heavy metal contamination) affects the ecophysiology of phototrophic species, and represents a risk to the environmental quality of coral reefs. Therefore, we investigated the effects of both Cu alone and Cu in combination with elevated temperature (ET) on the physiology of the coral Galaxea fascicularis, and measured the parameters related to the photo-physiology and oxidative state. G.fascicularis is one of the dominant coral species in the South China Sea which exhibits strong adaptability to environmental stress. We exposed the common coral species G.fascicularis to a series of environmentally relevant concentrations of Cu at 29 °C (normal temperature, NT) and 32 °C (elevated temperature, ET) for 96 h. Single polyps were used in the experiments, which reduced individual variability when compared to the coral colonies. The results suggested that: i) Cu or ET had significant negative effects on the actual operating ability of photosystem Ⅱ (PSII), but not on the maximal chlorophyll fluorescence in darkness (Fv/Fm). ii) Symbiodiniaceae density was significantly reduced by high Cu concentrations, for Cu-NT and Cu-ET, a high concentration of Cu (40 µg/L) significantly impacted Symbiodiniaceae density, causing a 75.4% and 81.0% decrease, respectively. iii) the content of malondialdehyde (MDA) in coral tissues increased significantly under Cu-ET. iv) a certain range of copper concentration (25-30 µg/L) increased the pigment content of the Symbiodiniacea. Our results indicated that the combined stressors of Cu and ET made the coral tissue sloughed, caused the coral tissue damaged by lipid oxidation, reduced the photosynthetic capacity of the Symbiodiniacea, and led to the excretion of Symbiodiniacea.

Cascade electrocatalysis via AgCu single-atom alloy and Ag nanoparticles in CO2 electroreduction toward multicarbon products.

Electrocatalytic CO2 reduction into value-added multicarbon products offers a means to close the anthropogenic carbon cycle using renewable electricity. However, the unsatisfactory catalytic selectivity for multicarbon products severely hinders the practical application of this technology. In this paper, we report a cascade AgCu single-atom and nanoparticle electrocatalyst, in which Ag nanoparticles produce CO and AgCu single-atom alloys promote C-C coupling kinetics. As a result, a Faradaic efficiency (FE) of 94 ± 4% toward multicarbon products is achieved with the as-prepared AgCu single-atom and nanoparticle catalyst under ~720 mA cm-2 working current density at -0.65 V in a flow cell with alkaline electrolyte. Density functional theory calculations further demonstrate that the high multicarbon product selectivity results from cooperation between AgCu single-atom alloys and Ag nanoparticles, wherein the Ag single-atom doping of Cu nanoparticles increases the adsorption energy of *CO on Cu sites due to the asymmetric bonding of the Cu atom to the adjacent Ag atom with a compressive strain.

Creating Order in Ultrastable Phosphonate Metal-Organic Frameworks via Isolable Hydrogen-Bonded Intermediates.

The stability presented by trivalent metal-organic frameworks (MOFs) makes them an attractive class of materials. With phosphonate-based ligands, crystallization is a challenge, as there are significantly more binding motifs that can be adopted due to the extra oxygen tether compared to carboxylate counterparts and the self-assembly processes are less reversible. Despite this, we have reported charge-assisted hydrogen-bonded metal-organic frameworks (HMOFs) consisting of [Cr(H2O)6]3+ and phosphonate ligands, which were crystallographically characterized. We sought to use these HMOFs as a crystalline intermediate to synthesize ordered Cr(III)-phosphonate MOFs. This can be done by dehydrating the HMOF to remove the aquo ligands around the Cr(III) center, forcing metal-phosphonate coordination. Herein, a new porous HMOF, H-CALF-50, is synthesized and then dehydrated to yield the MOF CALF-50. CALF-50 is ordered, although it is not single crystalline. It does, however, have exceptional stability, maintaining crystallinity and surface area after boiling in water for 3 weeks and soaking in 14.5 M H3PO4 for 24 h and 9 M HCl for 72 h. Computational methods are used to study the HMOF to MOF transformation and give insight into the nature of the structure and the degree of heterogeneity.

Identification of emotions evoked by music via spatial-temporal transformer in multi-channel EEG signals.

Introduction: Emotion plays a vital role in understanding activities and associations. Due to being non-invasive, many experts have employed EEG signals as a reliable technique for emotion recognition. Identifying emotions from multi-channel EEG signals is evolving into a crucial task for diagnosing emotional disorders in neuroscience. One challenge with automated emotion recognition in EEG signals is to extract and select the discriminating features to classify different emotions accurately. Methods: In this study, we proposed a novel Transformer model for identifying emotions from multi-channel EEG signals. Note that we directly fed the raw EEG signal into the proposed Transformer, which aims at eliminating the issues caused by the local receptive fields in the convolutional neural networks. The presented deep learning model consists of two separate channels to address the spatial and temporal information in the EEG signals, respectively. Results: In the experiments, we first collected the EEG recordings from 20 subjects during listening to music. Experimental results of the proposed approach for binary emotion classification (positive and negative) and ternary emotion classification (positive, negative, and neutral) indicated the accuracy of 97.3 and 97.1%, respectively. We conducted comparison experiments on the same dataset using the proposed method and state-of-the-art techniques. Moreover, we achieved a promising outcome in comparison with these approaches. Discussion: Due to the performance of the proposed approach, it can be a potentially valuable instrument for human-computer interface system.

Epileptic EEG Classification via Graph Transformer Network.

Deep learning-based epileptic seizure recognition via electroencephalogram signals has shown considerable potential for clinical practice. Although deep learning algorithms can enhance epilepsy identification accuracy compared with classical machine learning techniques, classifying epileptic activities based on the association between multichannel signals in electroencephalogram recordings is still challenging in automated seizure classification from electroencephalogram signals. Furthermore, the performance of generalization is hardly maintained by the fact that existing deep learning models were constructed using just one architecture. This study focuses on addressing this challenge using a hybrid framework. Alternatively put, a hybrid deep learning model, which is based on the ground-breaking graph neural network and transformer architectures, was proposed. The proposed deep architecture consists of a graph model to discover the inner relationship between multichannel signals and a transformer to reveal the heterogeneous associations between the channels. To evaluate the performance of the proposed approach, the comparison experiments were conducted on a publicly available dataset between the state-of-the-art algorithms and ours. Experimental results demonstrate that the proposed method is a potentially valuable instrument for epoch-based epileptic EEG classification.

A Molecular Insight into the Dehydration of a Metal-Organic Framework and its Impact on the CO2 Capture.

Metal-organic frameworks (MOFs) are porous material formed by the self-assembly of metallic ligands and organic linkers. They are a good candidate for CO2 gas capture because they have large surface areas and the metal or linker can be tuned to improve CO2 uptake. In the quest for water and acid stable MOFs, a phosphonate-based organic linker has recently been designed by Glavinovic et al. (Chem. Eur. J. 2022, 28, e202200874). By combining ionic calcium nodes, water and methanol molecules, they formed a microporous network, CALF-37. This network has been shown to be robust and can maintain its pore shape even in absence of water molecules or by the inclusion of gas molecules, such as CO2 . The network can be heated to release the water and methanol molecules and form a dehydrated MOF, which retains its shape with the imprinted pore within. Herein, we perform molecular dynamics (MD) simulations in order to provide insight into the CO2 capture and sequestration ability of the CALF-37. We model the dehydration of the inactivated MOF (HCALF-37) in the absence and in the presence of methanol molecules by progressively withdrawing water molecules from the MOF networks. We determine the crystal structure of the intermediate states from HCALF-37 to CALF-37 and shed light on the critical role of water molecules in the mediation of metal-linker bonds. Our calculations also reveal that the favorable interactions between the CO2 molecules and the aromatic core of the linkers and metallic ions are responsible for the efficient sequestration of the gas in the CALF-37.

Evaluation of measuring vomer-palate diameters at 11-13 + 6 weeks of gestation and its diagnostic value for cleft lip and palate / 中华超声影像学杂志

Objective:To investigate the reference range of the length and thickness of the fetal vomer-palate diameters at 11-13 + 6 weeks, and their role in the diagnosis of cleft lip and palate(CLP). Methods:From May 2020 to August 2021, 1 559 pregnant women who underwent ultrasound examination at 11-13 + 6 weeks in Guangdong Women and Children Hospital were selected, and the fetal vomer-palate in the median sagittal plane of the face was observed. The length and thickness diameters of the fetal were measured separately to establish the reference value range of normal fetal.The reference range was compared with the vomer-palate data of fetuses with confirmed CLP. Results:The 1 518 normal fetuses were divided into 11-13 + 6 weeks, 12-12 + 6 weeks and 13-13 + 6 weeks. The reference values of the long diameter of fetal vomer-palatine were 4.3-5.9 mm, 5.0-6.8 mm, 5.4-7.7 mm, and the reference values of the thick diameter were 2.0-2.9 mm, 2.2-3.4 mm, and 2.5-3.8 mm, respectively. The length and thickness of the fetal vomer-palatine were significantly positively correlated with the Crown-rump length ( rs=0.733, 0.634; all P<0.001). In the 1 559 fetals, 25 cases were diagnosed and confirmed with CLP, and the vomer-palate thickness diameters were smaller than the reference values in all cases, meanwhile, the vomer-palate length diameters of 22(88.0)% cases were smaller than the reference values. Conclusions:The reference range of fetal vomer-palate length and thickness at 11-13 + 6 weeks of gestation is valuable for the screening of fetal CLP.

Multi-label classification of retinal disease via a novel vision transformer model.

Introduction: The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously. Methods: This study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function. Results and discussion: The experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.

Virtual Reality and Internet of Things-Based Music Online Learning via the Graph Neural Network.

Virtual reality and the Internet of Things have shown their capability in a variety of tasks. However, their availability in online learning remains an unresolved issue. To bridge this gap, we propose a virtual reality and Internet of Things-based pipeline for online music learning. The one graph network is used to generate an automated evaluation of learning performance which traditionally was given by the teachers. To be specific, a graph neural network-based algorithm is employed to identify the real-time status of each student within an online class. In the proposed algorithm, the characteristics of each student collected from the multisensors deployed on their bodies are taken as the input feature for each node in the presented graph neural network. With the adoption of convolutional layers and dense layers as well as the similarity between each pair of students, the proposed approach can predict the future circumstance of the entire class. To evaluate the performance of our work, comparison experiments between several state-of-the-art algorithms and the proposed algorithm were conducted. The result from the experiments demonstrated that the graph neural network-based algorithm achieved competitive performance (sensitivity 91.24%, specificity 93.58%, and accuracy 89.79%) over the state-of-the-art.

Spatial Enhanced Pattern Through Graph Convolutional Neural Network for Epileptic EEG Identification.

Feature extraction is an essential procedure in the detection and recognition of epilepsy, especially for clinical applications. As a type of multichannel signal, the association between all of the channels in EEG samples can be further utilized. To implement the classification of epileptic seizures from the nonseizures in EEG samples, one graph convolutional neural network (GCNN)-based framework is proposed for capturing the spatial enhanced pattern of multichannel signals to characterize the behavior of EEG activity, which is capable of visualizing the salient regions in each sequence of EEG samples. Meanwhile, the presented GCNN could be exploited to discriminate normal, ictal and interictal EEGs as a novel classifier. To evaluate the proposed approach, comparison experiments were conducted between state-of-the-art techniques and ours. From the experimental results, we found that for ictal and interictal EEG signal discrimination, the presented approach can achieve a sensitivity of 98.33%, specificity of 99.19% and accuracy of 98.38%.