High Sensitivity Research Articles

Large inter-individual variability in force-velocity profile changes in response to acute high-load resistance training.

While the acute effects of high-load resistance training on the force generating capacity of muscles have been widely examined, limited data exist on the relationship with the force-velocity profile (FV). Evidence suggests high sensitivity of the vertical FV profile to monitor changes in the muscle's mechanical properties according to the type of the exercise protocol. However, the interpretation of the findings seems not as straightforward. Therefore, the purpose of this study was to examine the effects of a high-load resistance training protocol on the muscle's mechanical properties during loaded jumps and on the vertical force-velocity profile (FV) in relation to maximal strength. 29 resistance-trained male (mean age±SD: 35.4 ± 7.8 years) and 29 female athletes (mean age±SD: 32.5 ± 7.0 years) participated in the study. Five-repetition maximum (5RM) in back squat, unloaded countermovement jump (CMJ) and FV profile were assessed. Loaded jumps were performed against 25, 50, 75, and 100 percent of body mass. Participants performed exercise protocols corresponding to their 5RM. Immediately after, unloaded CMJ and FV profile measurements were repeated. A significant decrease in CMJ height (∼5-6%) and in average power (∼4%) was recorded for both men and women. The FV profile did not change after the exercise protocol; however, there was a significant decrease in theoretical maximal power (from 4 to 5%) and in theoretical maximal velocity (∼3%). Maximal strength was not associated with the changes in FV profile. Findings suggest that an acute high-load exercise decreased vertical jump performance and maximal power output, but without a concomitant change in FV profile. The large interindividual variability in FV measures indicates a less straightforward connection of the applied exercise with the acute response in the FV profile, highlighting the complexity of the FV profile to monitor changes in response to an acute training load.

Superhydrophobic Highly Flexible Triple-Network Polyorganosiloxane-Based Aerogels for Thermal Insulation, Oil-Water Separation, and Strain/Pressure Sensing.

Polyvinylpolymethylsiloxane (PVPMS)/polydimethylsiloxane (PDMS) copolymer aerogels were synthesized via consecutive radical polymerization and cohydrolytic polycondensation of vinylmethyldimethoxysilane and dimethyldimethoxysilane, followed by supercritical drying or ambient pressure drying. The resultant PVPMS/PDMS copolymer aerogels exhibit a highly porous, tunable triple-network structure consisting of interlinked hydrocarbon polymers, PVPMS and PDMS. These aerogels display superhydrophobicity (151°), low density (109 mg cm-3), low thermal conductivity (29.8 mW m-1 K-1), and adjustable pore structure. The combination of good machinability, low thermal conductivity, excellent compressive elasticity and bending flexibility, and efficient organic solvent adsorption gives these aerogels broad application prospects in thermal insulation and oil-water separation. In addition, PVPMS/PDMS/carbon nanotube (CNT) composite aerogels were obtained by incorporating the conductive CNTs, followed by vacuum drying. The resultant PVPMS/PDMS/CNT composite aerogel exhibits high sensitivity with a broad pressure sensing range in strain and pressure sensing applications.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

Outcome After Self-Triage App Referral in Urgent Direct-to-Consumer Telemedicine Encounter.

Background: The quantification of self-triage effectiveness, guided by mobile applications, in urgent direct-to-consumer telemedicine (TM) encounters requires further investigation. The objective of this study was to evaluate the outcomes of referral guidance provided by a symptom-based self-management mobile application decision algorithm in the context of remote urgent care assessments. Methods: An observational retrospective single-center study was conducted from May 2022 to December 2023. The inclusion criteria encompassed individuals aged >18 years old, and those spontaneously seeking virtual emergency care through the EINSTEIN CONECTA application. Patients experiencing connectivity issues, preventing completion of the encounter, were excluded. The primary outcomes included the rate of patient concurrence with the algorithm's recommendation for seeking in-person emergency care and the referral rate to face-to-face assessment among cases evaluated through TM. The application's algorithm employs scientific evidence based on symptoms to recommend referrals to emergency departments (EDs). Results: Out of 88,834 patients connected to the TM Center, self-triage obviated the need for virtual physician assessment in 53,302 (60%) encounters. A total of 35,532 patients were remotely evaluated by 316 on-duty physicians, resulting in 1,125 ICD-coded diagnoses. Among these, 21,722 (61.1%) were initially advised by self-triage to visit the ED, with subsequent medical assessment leading to in-person referrals in 6,354 (29.3%) of the evaluations. Of the 13,810 patients recommended to continue with virtual care post-self-triage, 157 (1.1%) were referred for in-person assessment. Conclusions: Self-triage effectively reduced the need for physician encounters in approximately three-fifths of TM consultations. Despite being based on scientific evidence, symptom-based referral algorithms demonstrated high sensitivity but poor correlation with physician decision-making.

A sensitive methane gas sensor based on sagnac interferometer with a cryptophane-A film-coated birefringent photonic crystal fiber : design and FEM simulation

As being sensitive to methane gas, cryptophane-A is widely used in fiber optic methane gas sensors. In order to further improve the sensitivity, a Sagnac interference (SI) was constructed with a cryptophane-A film coated birefringent photonic crystal fiber (PCF) to realize the methane gas sensing. Cryptophane-A film absorbs the methane gas and as a result its refractive index decreases linearly with the increasing of methane gas concentration. Simulation results utilizing the finite element method demonstrate that sensitivity reaches 124.4 nm/% when the methane concentration ranges from 0% to 3.5%. The sensor is relatively simple to prepare and can achieve high sensitivity, which has potential application in the field of monitoring methane gas leakage.

An integrated smartphone-based electrochemical detection system for highly sensitive and on-site detection of chemical oxygen demand by copper-cobalt bimetallic oxide-modified electrode.

Aportable and integrated electrochemical detection system has been constructed for on-site and real-time detection of chemical oxygen demand (COD). The system mainly consists of four parts: (i) sensing electrode with a copper-cobalt bimetallic oxide (CuCoOx)-modified screen-printed electrode; (ii) an integrated electrochemical detector for the conversion, amplification, and transmission of weak signals; (iii) a smartphone installed with a self-developed Android application (APP) for issuing commands, receiving, and displaying detection results; and (iv) a 3D-printed microfluidic cell for the continuous input of water samples. Benefiting from the superior catalytic capability of CuCoOx, the developed system shows a high detection sensitivity with 0.335 μA/(mg/L) and a low detection limit of 5.957 mg/L for COD determination and possessing high anti-interference ability to chloride ions. Moreover, this system presents good consistency with the traditional dichromate method in COD detection of actual water samples. Due to the advantages of cost effectiveness, portability, and point-of-care testing, the system shows great potential for water quality monitoring, especially inresource-limited remote areas.

Application of Biosensors for the Detection of Mycotoxins for the Improvement of Food Safety

Mycotoxins, secondary metabolites synthesized by various filamentous fungi genera such as Aspergillus, Penicillium, Fusarium, Claviceps, and Alternaria, are potent toxic compounds. Their production is contingent upon specific environmental conditions during fungal growth. Arising as byproducts of fungal metabolic processes, mycotoxins exhibit significant toxicity, posing risks of acute or chronic health complications. Recognized as highly hazardous food contaminants, mycotoxins present a pervasive threat throughout the agricultural and food processing continuum, from plant cultivation to post-harvest stages. The imperative to adhere to principles of good agricultural and industrial practice is underscored to mitigate the risk of mycotoxin contamination in food production. In the domain of food safety, the rapid and efficient detection of mycotoxins holds paramount significance. This paper delineates conventional and commercial methodologies for mycotoxin detection in ensuring food safety, encompassing techniques like liquid chromatography, immunoassays, and test strips, with a significant emphasis on the role of electrochemiluminescence (ECL) biosensors, which are known for their high sensitivity and specificity. These are categorized into antibody-, and aptamer-based, as well as molecular imprinting methods. This paper examines the latest advancements in biosensors for mycotoxin testing, with a particular focus on their amplification strategies and operating mechanisms.

Telephone triage of chest pain in out-of-hours primary care: external validation of a symptom-based prediction rule to rule out acute coronary syndromes.

Telephone triage is pivotal for evaluating the urgency of patient care, and in the Netherlands, the Netherlands Triage Standard (NTS) demonstrates moderate discrimination for chest pain. To address this, the Safety First Prediction Rule (SFPR) was developed to improve the safety of ruling out acute coronary syndrome (ACS) during telephone triage. We conducted an external validation of the SFPR using data from the TRACE study, a retrospective cohort study in out-of-hours primary care. We evaluated the diagnostic accuracy assessment for ACS, major adverse cardiovascular events (MACE), and major events within 6 weeks. Moreover, we compared its performance with that of the NTS algorithm. Among 1404 included patients (57.3% female, 6.8% ACS, 8.6% MACE), the SFPR demonstrated good discrimination for ACS (C-statistic: 0.79; 95%-CI: 0.75-0.83) and MACE (C-statistic: 0.79; 95%-CI: 0.0.76-0.82). Calibration was satisfactory, with overestimation observed in high-risk patients for ACS. The SFPR (risk threshold 2.5%) trended toward higher sensitivity (95.8% vs. 86.3%) and negative predictive value (99.3% vs. 97.6%) with a lower negative likelihood ratio (0.10 vs. 0.34) than the NTS algorithm. The SFPR proved robust for risk stratification in patients with acute chest pain seeking out-of-hours primary care in the Netherlands. Further prospective validation and implementation are warranted to refine and establish the rule's clinical utility.

Upcycling Humins via Esterification Reactions of Hydroxyl Groups: From Functional Powders to PLA Foams and Compatibilized Blends.

The valorization of humins side streams from bio-refineries holds significant economic and sustainability potential. One plausible strategy involves using them as building blocks to create new materials. However, in their natural state, humins pose conceptual challenges due to their high viscosity, processing difficulties, and temperature sensitivity. This article presents a synthetic strategy for modifying humins properties to make them thermally stable and processable. Employing a sequence of esterification reactions and varying the reagent steric length, we showcase the selective transformation of humins into thermally-stable fine powders and low-viscosity liquids. We further extend this approach by reacting humins with polyesters such as polylactic acids and polycaprolactone. In particular, we detail a one-pot single-step synthesis of micro-phase separated compatibilized blends of polylactic acid and humins capped with the polylactic acid arms. Processed via solution-casting, the obtained materials behave as high-strength thermoplastic elastomers having uniform foam morphologies and material characteristics superior to the pure polylactic acid. By varying the content of D-enantiomers, we demonstrate an additional possibility to manipulate the cellular structures of the foams. Finally, we provide a solution to the product circularity by reporting a dissolution recycling method.

Development and evaluation of rapid and accurate one-tube RPA-CRISPR-Cas12b-based detection of mcr-1 and tet(X4).

The emergence and quick spread of the plasmid-mediated tigecycline resistance gene tet(X4) and colistin resistance gene mcr-1 have posed a great threat to public health and raised global concerns. It is imperative to develop rapid and accurate detection systems for the onsite surveillance of mcr-1 and tet(X4). In this study, we developed one-tube recombinase polymerase amplification (RPA) and CRISPR-Cas12b integrated mcr-1 and tet(X4) detection systems. We identified mcr-1- and tet(X4)-conserved and -specific protospacers through a comprehensive BLAST search based on the NCBI nt database and used them for assembling the detection systems. Our developed one-tube RPA-CRISPR-Cas12b-based detection systems enabled the specific detection of mcr-1 and tet(X4) with a sensitivity of 6.25 and 9 copies within a detection time of ~ 55 and ~ 40min, respectively. The detection results using pork and associated environmental samples collected from retail markets demonstrated that our developed mcr-1 and tet(X4) detection systems could successfully monitor mcr-1 and tet(X4), respectively. Notably, mcr-1- and tet(X4)-positive strains were isolated from the positive samples, as revealed using the developed detection systems. Whole-genome sequencing of representative strains identified an mcr-1-carrying IncI2 plasmid and a tet(X4)-carrying IncFII plasmid, which are known as important vectors for mcr-1 and tet(X4) transmission, respectively. Taken together, our developed one-tube RPA-CRISPR-Cas12b-based mcr-1 and tet(X4) detection systems show promising potential for the onsite detection of mcr-1 and tet(X4). KEY POINTS: • One-tube RPA-CRISPR-Cas12b-based mcr-1 and tet(X4) detection systems were developed based on identified novel protospacers. • Both detection systems exhibited high sensitivity and specification with a sample-to-answer time of less than 1 h. • The detection systems show promising potential for onsite detection of mcr-1 and tet(X4).

Serum eosinophil-derived neurotoxin: a new promising biomarker for cow's milk allergy diagnosis.

Cow's Milk Allergy (CMA) diagnosis is often a challenge due to the non-specific nature of symptoms and lack of a confirmatory diagnostic test. To our knowledge no previous studies investigated serum Eosinophil-Derived Neurotoxin (sEDN) in CMA. So, we aimed to assess the role of sEDN in CMA diagnosis. Forty-five infants with CMA were compared to 45 infants with functional gastrointestinal disorders (FGIDs) and 45 healthy controls. For all participants, Cow's Milk-related Symptom Score (CoMiSS) was documented, and sEDN level with hematological parameters were measured before starting elimination diet. Receiver operation characteristic (ROC) curve identified sEDN > 14 ng/mL and CoMiSS > 9 as the optimal cut-off points to discriminate CMA from other groups with sensitivity 86.67%, 97.78% and specificity 60.00%, 78.89% respectively. Additionally, absolute neutrophil count (ANC) showed the highest sensitivity and specificity (80.0% and 78.89%) among hematological parameters. Although CoMiSS and ANC showed a significant positive correlation with sEDN in CMA group, CoMiSS was the only significant predictor for sEDN in multivariate linear regression. sEDN showed high sensitivity in discriminating infants with and without CMA. Therefore, it is suggested as a potential biomarker for CMA diagnosis. Also, ANC should be closely monitored in these infants. CMA presents with high heterogeneity, which complicates the diagnosis especially non-IgE-mediated and mixed types. So, oral food challenge continues to be the gold standard for its diagnosis. ROC curve identified CoMiSS > 9 as the best cut-off point to identify CMA. However, CoMiSS is a good awareness tool for CMA but not a diagnostic tool. sEDN level was significantly higher in infants with CMA with a good diagnostic performance in differentiating them than those without CMA. So, it is suggested as a potential biomarker for CMA diagnosis. ANC could have a role in CMA diagnosis and differentiating it from FGIDs.

Serum (-2)proPSA/freePSAratio, (-2)proPSA/freePSA density, prostate health index, and prostate health index density as clues to reveal postoperative clinically significant prostate cancer in men with prostate-specific antigen 2-10 ng/mL.

There is a strong clinical need to fill the gap of identifying clinically significant prostate cancer (csPCa) in men with prostate-specific antigen(PSA) gray zone values. Promising, but not definitive results have been obtained using PSA derivatives such as prostate health index (PHI) and PHI density (PHID) and the percentage (-2)proPSA/free PSA(%p2PSA/fPSA). Thus, this study aimed to compare the diagnostic value of PHI, PHID, %proPSA/fPSA, and (-2)proPSA/freePSA density (-2pPSA/fPSAD) for csPCa in the patients with PSAwithin 2-10 ng/mL. Serum samples and clinicopathological features were prospectively collected from 142 patients who underwent robot-assisted radical prostatectomy between September 2021 and December 2023. According to the inclusion criteria, the patients with total PSA within 2 and 10 ng/mL and negative or suspicious digital rectal examination were enrolled. We used two different classifications for csPCa: 1) patients with Gleason score (GS) ≥ 7(4 + 3) and 2) patients with GS ≥ 7(3 + 4). The receiver operating characteristic curves and the area under the curve (AUC)values were used to assess the diagnostic performance. Of the 142 men included, 116 (82%) patients were diagnosed with csPCa as GS ≥ 3 + 4 and 107 (75%) defined as csPCa as GS ≥ 7(4 + 3), respectively. We found that p2PSA/fPSA, p2PSA/fPSAD, PHI, and PHID were significantly higher in csPCa classified as GS ≥ 7(3 + 4) as well as GS ≥ 7(4 + 3), with p-values 0.027, 0.054, 0.0016, and 0.0027, respectively. AUCs of the analyzed variables were higher when used to predict csPCa as GS ≥ 6 compared to csPCa as GS ≥7(4 + 3), with an AUC equal, respectively, to 0.679 (95% CI: 0.571-0.786), 0.685 (95% CI: 0.571-0.799), 0.737 (95% CI: 0.639-0.836), and 0.736 (95% CI: 0.630-0.841) in the first subgroup and with an AUC equal, respectively, to 0.653 (95% CI: 0.552-0.754), 0.665 (95% CI: 0.560-0.770), 0.668 (95% CI: 0.568-0.769), and 0.670 (95% CI: 0.567-0.773) in the second, respectively. Both PHID and p2PSA/fPSADallowed improvement in the diagnostic accuracy with respect to PHI and p2PSA/fPSA ratio, however the differences were not statistically significant (p = 0.409, 0.180 for csPCa as G ≥ Gleason grade (GG) 2 and 0.558 and 0.087 for csPCa as G ≥ GG3, respectively). We found that PHI, PHID, p2PSA/fPSA ratio, and p2PSA/fPSADshowed higher sensitivity, specificity, and positive predictive valuewhen used to predict csPCa as GG ≥ 2, whereas negative predictive valueof all four parameters was higher when used to predict GG ≥ 3. In men with a PSA level between 2 and 10 ng/mL, PHI and PHID, p2PSA/fPSA, and p2PSA/fPSAD showed good diagnostic performance for postoperative csPCa. However, PHID and p2PSA/fPSAD had a small advantage over PHI which needs to be further investigated for the reduction of unnecessary surgical interventions. This finding suggests that it could be a promising biomarker for making the treatment-decision strategy.

Breast Cancer Detection using CNN

Breast cancer stands as a leading cause of cancer- related fatalities worldwide. Assessing cancer accurately through eosin-stained images remains a complex task, often resulting in discrepancies among medical professionals while reaching a con- clusive diagnosis. To streamline this intricate process, Computer- Aided Diagnosis (CAD) systems present a promising avenue, aiming to reduce costs and enhance efficiency. Traditional clas- sification methods hinge on problem-specific feature extraction, rooted in domain knowledge. However, addressing the multitude of challenges posed by these feature-centric techniques has led to the emergence of deep learning methods as viable alternatives. Here, we propose a novel approach employing Convolutional Neural Networks (CNNs) for the classification of hematoxylin and eosin-stained breast biopsy images. Our method categorizes images into four distinct groups: normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma. Additionally, it per- forms a binary classification distinguishing carcinoma from non- carcinoma cases. The meticulously designed network architecture facilitates information extraction across multiple scales, encom- passing both individual nuclei and overall tissue organization. This design choice enables seamless integration of our proposed system with wholeslide histology images. Notably, our method achieves a commendable accuracy of 77.8four-class classification and demonstrates a high sensitivity of 95.6% in identifying cancer cases.

Validity of a smartwatch for detecting atrial fibrillation in patients after heart valve surgery: a prospective observational study

Objectives Atrial fibrillation (AF) is a common early arrhythmia after heart valve surgery that limits physical activity. We aimed to evaluate the criterion validity of the Apple Watch Series 5 single-lead electrocardiogram (ECG) for detecting AF in patients after heart valve surgery. Design We enrolled 105 patients from the University Hospital of North Norway, of whom 93 completed the study. All patients underwent single-lead ECG using the smartwatch three times or more daily on the second to third or third to fourth postoperative day. These results were compared with continuous 2–4 days ECG telemetry monitoring and a 12-lead ECG on the third postoperative day. Results On comparing the Apple Watch ECGs with the ECG monitoring, the sensitivity and specificity to detect AF were 91% (75, 100) and 96% (91, 99), respectively. The accuracy was 95% (91, 99). On comparing Apple Watch ECG with a 12-lead ECG, the sensitivity was 71% (62, 100) and the specificity was 92% (92, 100). Conclusion The Apple smartwatch single-lead ECG has high sensitivity and specificity, and might be a useful tool for detecting AF in patients after heart valve surgery.

Maillard Reaction Inspired Microexplosion toward Fast Synthesis of Two-Dimensional Mesoporous Tin Oxides for Efficient Chemiresistive Gas Sensing.

Two-dimensional (2D) mesoporous transition metal oxides are highly desired in various applications, but their fast and low-cost synthesis remains a great challenge. Herein, a Maillard reaction inspired microexplosion approach is applied to rapidly synthesize ultrathin 2D mesoporous tin oxide (mSnO2). During the microexplosion between granular ammonia nitrate with melanoidin at high temperature, the organic species can be carbonized and expanded rapidly due to the instantaneous release of gases, thus producing ultrathin carbonaceous templates with rich functional groups to effectively anchor SnO2 nanoparticles on the surface. The subsequent removal of carbonaceous templates via calcination in air results in the formation of 2D mSnO2 due to the confinement effect of the templates. Pd nanoparticles are controllably deposited on the surface of 2D mSnO2 via in situ reduction, forming ultrathin 2D Pd/mSnO2 nanocomposites with thicknesses of 6-8 nm. Owing to the unique 2D mesoporous structure with rich oxygen defects and highly exposed metal-metal oxide interfaces, 2D Pd/mSnO2 exhibits excellent sensing performance toward acetone with high sensitivity, a short response time, and good selectivity under low working temperature (100 °C). This fast and convenient microexplosion synthesis strategy opens up the possibility of constructing 2D porous functional materials for various applications including high-performance gas sensors.

Progress of circulating tumor DNA methylation for gastric cancer screening and management

Circulating tumor DNA (ctDNA) is cell-free DNA released by tumors or circulating tumor cells, containing abundant tumor-specific information that can serve as biomarkers for cancer early screening, monitoring, prognosis, and prediction of treatment response. This is particularly attractive in the field of gastric cancer, where high-quality screening, monitoring, and prediction methods are currently lacking. Gastric cancer exhibits significant tumor heterogeneity, with large differences in genetic and epigenetic characteristics among different subgroups. Methylated ctDNA has high sensitivity and specificity, which can help clarify tumor genotyping and facilitate the formulation of precise diagnostic and therapeutic strategies. Furthermore, numerous studies have confirmed the unique advantages of methylated DNA in predicting treatment response, adjuvant therapy, and drug resistance assessment, which may be used in the future to enhance the efficacy of chemotherapy regimens and improve patient chemotherapeutic response, and even treat multidrug resistance. However, there are several challenges associated with methylated ctDNA, such as low sensitivity and specificity at single-target sites, limited association between some gastric cancer subtypes and ctDNA, off-target risks, and the lack of large-scale and high-quality clinical research evidence. This review mainly summarizes current research on the methylation status of ctDNA in gastric cancer and connects these findings to early screening, recurrence monitoring, and potential treatment opportunities for gastric cancer. With advances in technology and the deepening of interdisciplinary research, ctDNA detection will reveal more disease information and become an essential foundation for gastric cancer research and precision medicine treatment.

Handwritten Based Alzheimer Disease Prediction from One Dimensional Datasets Using Deep Learning

Because to their high cost, sensitivity, and difficulty in completing surgeries, brain-related disorders are among the most challenging conditions. On the other hand, since the procedure's outcomes could be negative, the operation itself does not have to succeed. Alzheimer's disease, which affects adults and causes varied degrees of memory loss and knowledge forgetfulness, is one of the most prevalent brain diseases. based on the state of each patient. For these reasons, using user-handwritten datasets to categorise memory loss and determine the patient's evaluation of Alzheimer's disease at every given level is crucial. This work offers a novel method for predicting Alzheimer's disease by using advanced deep learning techniques on handwritten data. Alzheimer's is a degenerative. Alzheimer's disease, a neurological condition that progresses and necessitates prompt diagnosis and appropriate treatment. Traditional diagnostic techniques are mostly based on clinical evaluations and imaging, which are frequently inaccessible and expensive. This study investigates the unrealized potential of handwritten data as a special kind of Alzheimer's disease prediction. The dataset provides a broad picture of cognitive impairments by containing handwritten samples collected from individuals in varying states of cognition. Using deep learning architectures, such as the multi-layer perceptron method, the suggested model takes use of the temporal dependencies found in sequential handwritten patterns. These designs show promise for ensuring the temporal characteristics of handwritten data are captured with subtle features. The handwritten input is converted into a format suitable for deep learning using feature extraction techniques, which helps with efficient model training. A thorough assessment of the model's performance is conducted using common metrics including specificity, sensitivity, and accuracy. The goal is to determine whether the model can correctly forecast Alzheimer's disease based just on the unique features present in handwritten samples.

Exposure of cattle to tick-borne encephalitis virus in the historical endemic zone in north-eastern France

BackgroundTick-borne encephalitis (TBE) is a severe human neuroinfection caused by TBE virus (TBEV). TBEV is transmitted by tick bites and by the consumption of unpasteurized dairy products from infected asymptomatic ruminants. In France, several food-borne transmission events have been reported since 2020, raising the question of the level of exposure of domestic ungulates to TBEV. In this study, our objectives were (i) to estimate TBEV seroprevalence and quantify antibodies titres in cattle in the historical endemic area of TBEV in France using the micro virus neutralisation test (MNT) and (ii) to compare the performance of two veterinary cELISA kits with MNT for detecting anti-TBEV antibodies in cattle in various epidemiological contexts. A total of 344 cattle sera from four grid cells of 100 km² in Alsace-Lorraine (endemic region) and 84 from western France, assumed to be TBEV-free, were investigated.ResultsIn Alsace-Lorraine, cattle were exposed to the virus with an overall estimated seroprevalence of 57.6% (95% CI: 52.1–62.8%, n = 344), varying locally from 29.9% (95% CI: 21.0–40.0%) to 92.1% (95% CI: 84.5–96.8%). Seroprevalence did not increase with age, with one- to three-year-old cattle being as highly exposed as older ones, suggesting a short-life duration of antibodies. The proportion of sera with MNT titres lower than 1:40 per grid cell decreased with increased seroprevalence. Both cELISA kits showed high specificity (> 90%) and low sensitivity (less than 78.1%) compared with MNT. Sensitivity was lower for sera with neutralising antibodies titres below 1:40, suggesting that sensitivity of these tests varied with local virus circulation intensity.ConclusionsOur results highlight that cattle were highly exposed to TBEV. Screening strategy and serological tests should be carefully chosen according to the purpose of the serological study and with regard to the limitations of each method.

Fundus- Based Glaucoma Detection- Machine Learning A Design Approach

Glaucoma is a leading cause of irreversible blindness worldwide, characterized by the progressive degeneration of the optic nerve, often associated with increased intraocular pressure. This paper proposes a machine learning-based design approach for the detection of glaucoma using fundus images. Our approach leverages advanced image processing techniques and machine learning algorithms to identify glaucomatous features in fundus images. The system comprises several key components: pre-processing of fundus images to enhance quality, feature extraction to identify relevant glaucomatous markers, and classification using machine learning models to differentiate between healthy and glaucomatous eyes. The proposed model achieved high accuracy, sensitivity, and specificity in detecting glaucoma, demonstrating its potential as a reliable and non-invasive diagnostic tool. Our findings highlight the efficacy of machine learning in medical imaging applications and underscore the importance of automated systems in assisting ophthalmologists in early glaucoma detection

Highly sensitive extraction-free saliva-based molecular assay for rapid diagnosis of SARS-CoV-2.

Three years after the SARS-CoV-2 outbreak, there are no molecular tests that combine low-cost and straightforward sample preparation, effective sample handling, minimal reagent and disposable requirements, high sensitivity, and high throughput required for mass screening. Existing rapid molecular techniques typically sacrifice certain requirements to meet others. Yet, localized outbreaks of novel viral diseases happen daily in different parts of the world. In this context, respiratory diseases are of specific importance, as they are frequently airborne and highly contagious, with the potential for a rapid global spread. The widely accepted opinion is that another pandemic is just a question of time. To ensure that the containment efforts for the upcoming "disease X" are successful, introducing rapid, high-throughput, and highly sensitive diagnostic methods for detecting and identifying pathogens is critical. A few months into the pandemic, saliva was suggested as a diagnostic matrix for SARS-CoV-2 detection. The collection of saliva does not require swabs and is minimally invasive. In particular, extraction-free saliva-based assays require fewer reagents and disposables, and therefore are faster and cheaper, offering an appealing alternative for low-income countries. Unfortunately, current extraction-free saliva-based detection methods, such as direct RT-qPCR or isothermal amplification, have either low sensitivity or low throughput. Therefore, we believe that the presented highly sensitive ht-OMBi platform and the extraction-free saliva-based molecular assay can become an essential tool in the infectious disease monitoring toolbox.