Sensitivity Rate Research Articles

The Characteristics of the Deformation of a Feeding Roller with Double V-Wings Honeycomb under Driven Modes with Different Rotation Rates and Axial Compressions

In this work, the roller-shaped double V-wing honeycomb (DVWH) structure was used as the object, which was designed for forest harvesting machines. The deformation characteristics of its rotation under a specific compression were studied during the feeding process for the steel feeding roller used in the traditional combined harvesting machine for trees, which is prone to damaging the wood. The compression rate (10%–15%) and rotational speed (100 mm/s–320 mm/s) were used to analyze the relationship between compression rate, rotational speed, and deformation characteristics. The deformation characteristics, characterized by equivalent stress, radial displacement sensitivity, and energy absorption efficiency, were analyzed. The results show that the deformation characteristics of the roller-shaped DVWH structure under driven mode were more sensitive to compression rate and less sensitive to changes in rotational speed. There was a positive correlation between the equivalent stress of the node and the compression rate. The peak equivalent stress of the outermost node at 10% and 15% compression rates were 111.9 MPa and 230.9 MPa. There was a negative correlation between radial displacement–compression sensitivity and compression rate, and a decrease in radial displacement–compression sensitivity from 10% to 15% compression rate at different nodes ranges from 5% to 22%. The feed performance efficiency (EF) grew from 8.3% to 12.7% for a 1% increase in compression rate.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Performance of Secure Door Access System Using Identification Numbers, Fingerprints, and Facial Recognition

Layered door security systems are increasingly essential in enhancing safety and convenience for people in their daily lives. Features such as ID number technology, fingerprints, and facial recognition can be employed in the security system. This study aims to develop and validate a system that authenticates the user’s identity as they pass through a doorway. The sensors utilized include of PN 532 as an ID number detection sensor, FPM10A as a fingerprint detection sensor, and ESP32CAM as a facial image detection sensor. The system is structured hierarchically and linked to the Internet of Things (IoT) in order to enable remote monitoring through the utilization of the Blynk application. This gadget employs a servo motor for door access control and incorporates an RFID module in its use. This study employs an Atmega328 microcontroller for the purpose of data processing. The PN532 test yielded a sensitivity rate of 100% and successfully detected and read RFID data from E-KTP chips. The testing of the FPM10A sensor demonstrated a sensitivity level of 90%. In addition, the testing of ESP32CAM demonstrates a sensitivity rate of 85%. Based on the test findings, the overall system achieved a tool performance score of 91.6%, indicating that the system is functioning effectively.

Erken Evre Karaciğer Fibrozisini Öngörmede Shear Wave Elastografinin Rolü

Aim: We examined the relationship between shear wave elastography (SWE) values and histopathological results in our study. Thus, we found the sensitivity of SWE in demonstrating early fibrosis. Materials and Methods: A total of consecutive 70 patients with chronic hepatitis B were prospectively evaluated. The patients included in fibrosis stages (F) 0, 1, 2, 3 and 4 according to Ishak scoring were examined with SWE. SWE measurements of F2, F3 and F4 patients who were found to have early stage fibrosis were compared with those of F0 and F1 patients. Results: The velocity+SD, and kPa+SD values in the group requiring treatment (F2, F3 and F4) were significantly higher than the group not requiring treatment (F0 and F1 )(p < 0.05). The sensitivity rate of the 1.85 cut-off value for velocity+SD was 53.8%, the positive prediction rate was 80.8%, the specificity rate was 83.3%, and the negative prediction rate was 58.1%. For kPa+SD, the cut-off value of 10.8 had a sensitivity rate of 51.3%, a positive prediction rate of 95.2%, a specificity of 96.7%, and a negative predictive rate of 60.4%. A significant correlation was observed between the fibrosis score and the kPa+SD distribution. Conclusion: SWE can differentiate the patients requiring treatment (F2, F3 and F4) from the patients not requiring treatment.

Non-navigated 2D intraoperative ultrasound: An unsophisticated surgical tool to achieve high standards of care in glioma surgery.

In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm3. Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 - 24/05/2023 - Retrospectively registered.

Multicenter study of seasonal and regional airborne allergens in Chinese preschoolers with allergic rhinitis

This study is nationwide multicenter epidemiological research, aimed at investigating the distribution changes and seasonal patterns of various airborne allergens among preschool children with allergic rhinitis (AR) in different regions of China, and analyzing the clinical correlation between sensitization to various airborne allergens and AR symptoms in children. Information on children was collected through standard questionnaires, and total IgE (tIgE) and specific IgE (sIgE) for 11 inhalant allergens were tested. The results showed that dust mites are the primary allergens for preschool AR children (39%). Among pollen allergens, Amb a had the highest positivity rate (8.1%), followed by Art v (7.8%). The sensitization rates for two mites peaked in May (46.9% and 40.6%). Art v peaked in August (21.5%), while Amb a had peaks in May (12.7%) and August (17.8%). The sensitization peaks for various tree pollens mainly occurred in August. In the Eastern monsoon region, the sensitization rate to mites was significantly higher than in the Northwest arid and semi-arid regions; whereas, for pollen allergens, the sensitization rates to Amb a, Pla a, Pin a, Pop d, and Bet v were significantly higher in the Northwest arid and semi-arid regions than in the Eastern monsoon region. The correlation among various tree pollens, specifically between Pla a, Pin r, Pop d, and Bet v was strong (0.63 ~ 0.79), with a cross-overlapping percentage of 53.9%. Children with multiple pollen sensitizations had higher cumulative nasal symptom scores than those negative for pollen (P < 0.01). Children with only pollen sensitization had higher cumulative rhinitis symptom scores than the all-negative group (P < 0.0001) and the mite-only sensitization group [P < 0.05], while the mite-only sensitization group also had higher scores than the all-negative group [P < 0.05], and the group sensitized to both pollen and mites had lower scores than the pollen-only group [P < 0.05]. This study indicates that sensitization to mites and grass pollens exhibits significant regional differences, with grass pollen allergies primarily occurring in autumn, sensitization to pollens in general exhibits a pronounced seasonal pattern. Moreover, pollen sensitization aggravates nasal and ocular symptoms in AR children.

Optimizing SH-SY5Y cell culture: exploring the beneficial effects of an alternative media supplement on cell proliferation and viability

In the quest to unravel the mysteries of neurological diseases, comprehending the underlying mechanisms is supreme. The SH-SY5Y human neuroblastoma cell line serves as a crucial tool in this endeavor; however, the cells are known for its sensitivity and slow proliferation rates. Typically, this cell line is cultured with 10% Fetal Bovine Serum (FBS) supplement. Nu-Serum (NuS), a low-protein alternative to FBS, is promising to advance cell culture practices. Herein, we evaluated the substitution of NuS for FBS to test the hypothesis that an alternative serum supplement can aid and promote SH-SY5Y cell proliferation and differentiation. Our findings revealed that the NuS-supplemented group exhibited a notable increase in adhered cells compared to both the FBS and serum-free (SF) groups. Importantly, cell viability remained high in both sera treated groups, with the NuS-supplemented cells displaying significantly larger cell sizes compared to the SF-treated group. Furthermore, cell proliferation rates were higher in the NuS-treated group, and neuroblast-like morphology was observed earlier than FBS group. Notably, both FBS and NuS supported the differentiation of these cells into mature neurons. Our data supports NuS as an alternative for SH-SY5Y cell culture, with the potential to elevate the quality of research in the neuroscience field.

VentRa: distinguishing frontotemporal dementia from psychiatric disorders.

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

The effects of synthesis route and LaB6-doping on FeCoNiCrTi coating performance: Morphology, microstructure, microhardness and corrosion resistance

In this work, the FeCoNiCrTi HEA coatings were fabricated on the Ti6Al4V surface via laser cladding technology. The effects of synthesis routes (different energy density, Led) and LaB6-doping on the single-track morphology, phase distribution, microstructure, microhardness and corrosion resistance of HEA coatings were comprehensively studied. Interestingly, the regulation of Led and addition of LaB6 significantly reduced the crack sensitivity and dilution rate of the coating. On the one hand, the optimized coating contributed to fine grain size when Led was 73.33 J/mm2. On the other hand, the in-situ generation of TiC, TiB2 and TiC/TiB2 composite structure played an important role in achieving the more excellent propertiy in LaB6/FeCoNiCrTi composite coating. Furthermore, the microhardness and corrosion resistance of the coatings were significantly improved owing to the ceramic phases and fine grain strengthening. Specifically, the average microhardness coating was up to 593 HV0.2. The corrosion current density (Icorr) of the coating with a Led of 73.33 J/mm2 was 4.796 × 10−6 A·cm−2, the carrier density (ND) and thickness (d) of the passivation film were 1.556 × 1022 cm−2 and 3.244 nm, respectively, indicating the best corrosion resistance in HEA coatings. In terms of LaB6/FeCoNiCrTi coating, the Icorr, ND and d were 2.400 × 10−6 A·cm−2, 1.053 × 1022 cm−2 and 4.739 nm, respectively. Consequently, the doping of LaB6 further enhanced the corrosion resistance of HEA coating.

The molecular detection of carbapenem markers with a two-levels amplification screening protocol: epidemiological and resistome insights.

Carbapenem-resistance is a challenging healthcare concern and require specific stewardship programs. Monitoring workflows include the identification from surveillance samples, such as rectal swabs. Although culture assays represent the gold standard, data report a significant effectiveness in detecting carbapenemases genes directly from rectal swabs. The aim of this study was to evaluate the REALQUALITY Carba-Screen kit (AB ANALITICA, Padova, Italy) in detecting carbapenemases genes directly from rectal swabs, also comparing its effectiveness to culture assays results. A next-generation sequencing (NGS) was performed to investigate the positive samples about resistance markers and sequence type (ST). A number of 136 rectal swabs were collected from the University Hospital Policlinico of Catania critical wards. The samples simultaneously underwent culture and molecular assays (REALQUALITY Carba-Screen kit). The molecular method included two-steps. The first step (1 h and 6 min) rapidly excluded negative samples, while the second one (1 h and 6 min) included only positive samples for a resistance confirmation. All the positive culture samples underwent NGS analysis. Statistical evaluations demonstrated high sensitivity (100%) and detection rates (92.6%) for the REALQUALITY Carba-Screen kit, which mostly correlated to the standard workflow. All the culture positive results matched the positive molecular results, which were mainly confirmed by the NGS resistome analysis. The identified ST appeared to be diversified and different from the clinically significative strains of the same setting, furnishing interesting epidemiological evidence. The molecular detection allowed a coordinate approach in a high-prevalence multi-drug-resistance area. The rapid identification with a multi-step procedure accelerated the infection control procedures, while the preliminary negative results reduced the overtreatment episodes. The molecular method efficacy was confirmed through the NGS. In conclusion, the molecular screening could initially lead to a more conservative approach, which may be reevaluated after a culture result about the microorganisms' identification and susceptibility profile.

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

Increasing non-cosmetic exposure and sensitization to isothiazolinones require action for prevention: Review.

Frequent use of methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) and MI in cosmetic products has been the main cause of widespread sensitization and allergic contact dermatitis to these preservatives (biocides). Their use in non-cosmetic products is also an important source of sensitization. Less is known about sensitization rates and use of benzisothiazolinone (BIT), octylisothiazolinone (OIT), and dichlorooctylisothiazolinone (DCOIT), which have never been permitted in cosmetic products in Europe. BIT and OIT have occasionally been routinely patch-tested. These preservatives are often used together in chemical products and articles. In this study, we review the occurrence of contact allergy to MI, BIT, OIT, and DCOIT over time, based on concomitant patch testing in large studies, and case reports. We review EU legislations, and we discuss the role of industry, regulators, and dermatology in prevention of sensitization and protection of health. The frequency of contact allergy to MI, BIT, and OIT has increased. The frequency of contact allergy to DCOIT is not known because it has seldom been patch-tested. Label information on isothiazolinones in chemical products and articles, irrespective of concentration, is required for assessment of relevance, information to patients, and avoidance of exposure and allergic contact dermatitis.

Economic analysis of world cities using improved deep shallow learning network with intelligent shell game optimization

An economic analysis is required to determine the financial status of every city throughout the world The economic growth of a city depends on multiple factors like health, finances, transport, industry, and so on. Therefore, it is necessary to have a user-friendly factor for handling the economic data regarding the financial evaluation outcomes The main motive behind this research work is to tackle the issue of low prediction accuracy of the conventional economic and financial growth trend assumption model by developing a model using the latest deep network technology. The data regarding the economic status of a city is collected from standard online sources. The collected data are given to the preprocessing for economic prediction in any city throughout the world phase. After that the optimal attributes from the preprocessed data are extracted with the help of a newly suggested Accuracy-based Shell Game Optimization (ASGO) algorithm Consequently, the chosen optimal attributes are given as input to the final prediction stage. The economy prediction of a city is done using the Optimized and Deep Shallow Learning Network (ODSLN). The parameters in the ODSLN are tuned using the same ASGO algorithm. This helps in enhancing the prediction functionality of the deployed model over large dimensional data. The developed model is validated with standard performance metrics against other conventional prediction models. Throughout the result analysis, the developed model attains a 94% accuracy rate and 93% sensitivity rate which is much better than the existing approaches. The efficiency of the suggested deep learning-based economic prediction model is evaluated against the recently developed model based on several performance measures.

Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs

PurposeTo develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs. DesignMulti-center retrospective study Subjects851 fundus photographs from 235 children (age <18 years) with pseudopapilledema and true papilledema MethodsFour pediatric neuro-ophthalmologists at four different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tri-branch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared to two masked human expert pediatric neuro-ophthalmologists, who performed the same classification task. Main Outcome MeasuresAccuracy, sensitivity, and specificity of the AI model compared to human experts. ResultsThe area under receiver operating curve (AUC) of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model’s accuracy was significantly higher than human experts on the cross validation set (p<0.002), and the model’s sensitivity was significantly higher on the external test set (p<0.0002). The specificity of the AI model and human experts was similar (56.4-67.3%). Moreover, the AI model was significantly more sensitive at detecting mild papilledema than human experts, whereas AI and humans performed similarly on photographs of moderate-to-severe papilledema. On review of the external test set, only one child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema. ConclusionsWhen classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved over 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions.

Prevalence of aeroallergen sensitization in children in Northern China

Objective This study aimed to analyze the sensitization rate of different aeroallergens in children of different age, sex, and disease groups, describe the changing trend of different aeroallergens in different ages, and analyze the sensitization risk factors for asthma. Methods Children (<18 years old) with suspected atopic diseases who visited the Department of Allergy of Children’s Hospital Affiliated to Capital Institute of Pediatrics and underwent a skin prick test (SPT) were retrospectively enrolled from January 2019 to November 2021. Results A total of 5465 patients (3514 boys, 1951 girls; mean age, 7 ± 3 years) were enrolled. Of them, 3703 patients (67.8%) were sensitized to at least one aeroallergen. Before 4 years of age, mold was the most prevalent aeroallergen (103/380 [27.1%]), whereas after 4 years of age, weed pollen was the most prevalent aeroallergen. After 6 years of age, tree pollen became the second most prevalent aeroallergen. After 12 years of age, the sensitization rate of indoor aeroallergens was lower than that of outdoor aeroallergens. Logistic regression showed that sensitization to mold (odds ratio [OR]:1.4, 95% confidence interval (CI): 1.2–1.7, p < 0.001), animal dander (OR: 1.6, 95% CI: 1.4–1.9, p < 0.001), and polysensitization (OR: 1.4, 95% CI: 1.0–1.8, p = 0.038) were potential sensitization risk factors for asthma. Conclusions Mold is an important allergen in early life. Different kinds of allergens affect different age groups. Patients who are sensitized to mold or animal dander or experience polysensitization should be carefully monitored for asthma risk.

Lycorine and homolycorine derivatives for chemo-sensitizing resistant human ovarian adenocarcinoma cells

BackgroundMultidrug resistance is the major obstacle to cancer chemotherapy. Modulation of P-glycoprotein and drug combination approaches have been considered important strategies to overcome drug resistance. PurposeAiming at generating a small library of Amaryllidaceae-type alkaloids to overcome drug resistance, two major alkaloids, isolated from Pancratium maritimum, lycorine (1), and 2α-10bα-dihydroxy-9-O-demethylhomolycorine (2), were derivatized, giving rise to nineteen derivatives (3 – 21). MethodsThe main chemical transformation of lycorine resulted from the cleavage of ring E of the diacetylated lycorine derivative (3) to obtain compounds that have carbamate and amine functions (5 - 16), while acylation of compound 2 provided derivatives 17 – 21. Compounds 1 - 21 were evaluated for their effects on cytotoxicity, and drug resistance reversal, using resistant human ovarian carcinoma cells (HOC/ADR), overexpressing P-glycoprotein (P-gp/ABCB1), as model. ResultsExcluding lycorine (1) (IC50 values of 1.2- 2.5 µM), the compounds were not cytotoxic or showed moderate/weak cytotoxicity. Chemo-sensitization assays were performed by studying the in vitro interaction between the compounds and the anticancer drug doxorubicin. Most of the compounds have shown synergistic interactions with doxorubicin. Compounds 5, 6, 9 – 14, bearing both carbamate and aromatic amine moieties, were found to have the highest sensitization rate, reducing the dose of doxorubicin 5–35 times, highlighting their potential to reverse drug resistance in combination chemotherapy. Selected compounds (4 – 6, 9 – 14, and 21), able of re-sensitizing resistant cancer cells, were further evaluated as P-gp inhibitors. Compound 11, which has a para‑methoxy-N-methylbenzylamine moiety, was the strongest inhibitor. In the ATPase assay, compounds 9–11 and 13 behaved as verapamil, suggesting competitive inhibition of P-gp. At the same time, none of these compounds affected P-gp expression at the mRNA or protein level. ConclusionsThis study provided evidence of the potential of Amaryllidaceae alkaloids as lead candidates for the development of MDR reversal agents.

Rapid antigen testing for SARS-CoV-2 by lateral flow assay: A field evaluation of self- and professional testing at UK community testing sites

BackgroundThe advent of lateral flow devices (LFDs) for SARS-CoV-2 detection enabled widespread use of rapid self-tests during the pandemic. While self-testing using LFDs is now common, whether self-testing provides comparable performance to professional testing was a key question that remained important for pandemic planning. MethodsThree prospective multi-centre studies were conducted to compare the performance of self- and professional testing using LFDs. Participants tested themselves or were tested by trained (professional) testers at community testing sites in the UK. Corresponding qRT-PCR test results served as reference standard. The performance of Innova, Orient Gene and SureScreen LFDs by users (self) and professional testers was assessed in terms of sensitivity, specificity, and kit failure (void) rates. Impact of age, sex and symptom status was analysed using logistic regression modelling. Results16,617 participants provided paired tests, of which 15,418 were included in the analysis. Self-testing with Innova, Orient Gene or SureScreen LFDs achieved sensitivities of 50 %, 53 % or 72 %, respectively, compared to qRT-PCR. Self and professional LFD testing showed no statistically different sensitivity with respect to corresponding qRT-PCR testing. Specificity was consistently equal to or higher than 99 %. Sex and age had no or only marginal impact on LFD performance while sensitivity was significantly higher for symptomatic individuals. Sensitivity of LFDs increased strongly to up to 90 % with higher levels of viral RNA measured by qRT-PCR. ConclusionsOur results support SARS-CoV-2 self-testing with LFDs, especially for the detection of individuals whose qRT-PCR tests showed high viral concentrations.

A Novel Magnetic β-Cyclodextrin-Modified Graphene Oxide and Chitosan Composite as an Adsorbent for Trace Extraction of Four Bisphenol Pollutants from Environmental Water Samples and Food Samples.

In this study, a novel functionalized magnetic composite (MNCGC) for magnetic solid-phase extraction of bisphenols from environmental and food samples was developed, featuring a multistep synthesis with Fe3O4, chitosan, graphene oxide, and β-cyclodextrin, crosslinked by glutaraldehyde. Characterization confirmed its advantageous morphology, intact crystal structure of the magnetic core, specific surface area, and magnetization, enabling efficient adsorption and separation via an external magnetic field. The optimized MSPE-HPLC-FLD method demonstrated excellent sensitivity, linearity, and recovery rates exceeding 80% for bisphenol pollutants, validating the method's effectiveness in enriching and detecting trace levels of bisphenols in complex matrices. This approach offers a new avenue for analyzing multiple bisphenol residues, with successful application to environmental water and food samples, showing high recovery rates.

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing.

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.