Low-cost Tool Research Articles

Usefulness of Atherogenic Indices for Predicting High Values of Avoidable Lost Life Years Heart Age in 139,634 Spanish Workers

Background: Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide, accounting for one-third of all global deaths. The World Health Organization (WHO) asserts that prevention is the most effective strategy to combat CVD, emphasizing the need for non-invasive, low-cost tools to identify individuals at high risk of CVD. Atherogenic indices and heart age (HA) are valuable tools for assessing cardiovascular risk (CVR). The aim of our study was to evaluate the association between atherogenic indices and HA. Methods: A cross-sectional study was conducted involving 139,634 Spanish workers to determine the association between three atherogenic indices and HA. ROC curves were employed to identify the cut-off values for the various atherogenic indices used to estimate high HA. The cut-off points, along with their sensitivity, specificity, and Youden index, were determined, and the area under the curve (AUC) was calculated. Results: As the values of the atherogenic indices increased, so did the risk of having elevated avoidable lost life years (ALLY) HA. In the ROC curve analysis, the AUC with the best results corresponded to the total cholesterol/HDL-c atherogenic index, with an AUC of 0.803 in females and 0.790 in males. The LDL-c/HDL-c atherogenic index showed an AUC of 0.780 in women and 0.750 in men, with Youden indices around 0.4. When analyzing the AUC of the atherogenic index for triglycerides/HDL-c, the results were 0.760 in women and 0.746 in men. Conclusions: Atherogenic indices and HA show a close relationship, with an increase in these indices leading to a rise in HA values. Raising patient awareness that as their CVR levels increase, so does their HA may be useful in achieving some benefit in reducing CVR.

Developing and validating a self-assessment tool for assessing confidence of nurse-midwives against competency domains of the International Confederation of Midwives, in India.

While midwives are recognized as primary care-providers for maternal and new-born care in many parts of the world, India is transitioning to professional midwifery. The pathway to midwifery in India has been through integrated nursing and midwifery education. Since 2018, India has introduced an 18-month post nursing midwifery education programme. To establish a baseline for midwives' competence and measure progress, there is a need for a quick, easy-to-administer and low-cost tool that can be used at scale to guide programme efforts. This paper describes the process of validation and testing of a tool to assess the confidence of practicing nurse-midwives as a proxy indicator for competence against the seven competency domains of the International Confederation of Midwives (2013). A total of 2198 nurse-midwives, providing maternal and new-born services at the time of the study, from 442 public and private hospitals across six states in India, responded to a self-administered questionnaire. The tool is psychometrically sound and can potentially be used in low-middle-income countries to measure confidence of midwives and nurse-midwives against international competence standards. It is suitable for quick large-scale programmatic assessments within a short time period, providing evidence to inform midwifery strengthening initiatives. The tool can be contextualized to suit specific country contexts. Since it was tested in India, where a distinct cadre of midwives is not yet established and nurse-midwives provide maternity care, the tool can be easily adapted for use in other countries in the South East Asia Region with similar contexts.

Discovery and analysis of the relationship between organic components in exhaled breath and bronchiectasis

The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy (ACC) of breath biomarkers (BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning (ML) method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without nontuberculous mycobacterium (NTM),P. aeruginosa(P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. The top ten BBs based ML model achieved an area under the curve of 0.940, sensitivity of 90.7%, specificity of 85%, and ACC of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection,P.aisolation, the number of involved lobes, and three lung functional parameters in BE patients. BBs-based BE detection model showed good ACC for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for BE screening and personalized management.

Effect of slope on water run-off and soil vulnerability in an unglaciated sub-watershed: a case study of conservation practice siting

For maximization of soil and water quality benefits, the right agricultural conservation practices (CPs) should be practiced for the right place. The Agricultural Conservation Planning Framework (ACPF) is a tool that helps decision makers achieve precise conservation within a target watershed using rapid, low-cost Geographic Information System-based tools. ACPF takes the information on landscape characteristics and combines it with user input to identify locations susceptible to soil loss and water run-off and suggest potential CP locations. Suggested CPs may further be analyzed for urgency, installation cost, and effectiveness. In this study, ACPF was applied to analyze soil and water vulnerability within a small unglaciated sub-watershed in southwest Wisconsin and identify potential CP practices and recommended placement sites. To promote environmental sustainability in the agriculture sector, land managers are tasked with implementing CPs, but often lack adequate information on the most effective practice and placement strategy. Field-scale siting of CPs was achieved using high-resolution terrain derivatives and accurate simulation of the stream network. Slope disaggregation among soil loss vulnerability categories was also conducted. Greatest sources of vulnerability were associated with slope and erodibility factors, with average slope within cropped fields being 6% and erodibility being as high as 43%. Fields classified as critical for conservation had a mean slope of 7.4%. Suggested contour buffer strip (CBS) placement per field varied with slope, with steeper fields having denser placement. The results suggested that 3% of cropland could be removed from production if the recommended placement of CBS was implemented. A further 0.2% of the sub-watershed area would go to grassed waterways. The results support implementation of effective CPs based on local conditions and act as a tool to help reveal if existing practices in the sub-watershed are of a size and extent comparable to the predicted potential.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder.

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research. We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD). Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition). The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

IRodd (intelligent-road damage detection) for real-time infrastructure preservation in detection, classification, calculation, and visualization

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in addition, existing automated identification methods lack scalability, mobile deployability, and field-test validity. To address these bottlenecks, a mosquito image database with fresh wild-caught specimens using basic smartphones is introduced, and we present a novel CNN-based architecture, VectorBrain, designed for identifying the species, sex, and abdomen status of a mosquito concurrently while being efficient and lightweight in computation and size. Overall, our proposed approach achieves 94.44±2% accuracy with a macro-averaged F1 score of 94.10±2% for the species classification, 97.66±1% accuracy with a macro-averaged F1 score of 96.17±1% for the sex classification, and 82.20±3.1% accuracy with a macro-averaged F1 score of 81.17±3% for the abdominal status classification. VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information

The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy.
Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners.
Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion.
Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model.
Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

How motivations and costs affect financial knowledge learning? Evidence from a large-scale FinTech experiment in China

Utilizing the launch of mobile wealth management services in China in 2013 as a shock to financial education, we analyze a panel of 16,440 households through a difference-in-differences strategy to examine how learning motivations and costs affect financial knowledge learning. Results show that using mobile services encourages risky asset holdings and enhances the self-discipline to learn, which, coupled with low-cost and personalized learning tools provided through the services, increases financial knowledge. We contribute by utilizing a large-scale experiment beyond the classroom to show the role of learning motivations and costs in financial knowledge learning, therefore help clear the controversy on the effectiveness of financial education.

A clinical application of gait quality patterns in osteoarthritis

AimTo investigate whether a smartphone-based gait analysis tool can reliably output gait quality parameters that can be cross-analyzed to establish individual & disease-based changes in gait quality patterns. MethodsA cross-sectional study made up of a 48-patients undergoing disability certification at the “Dr. José Castro Villagrana” or the “Dr. David Fragoso Lizalde” Health Centers in Mexico City, Mexico. Their sensorimotor performance was evaluated through an in-house smartphone/IMU based digital tool. Gait was analyzed by means of frequency analysis of the acceleration of the body mass measured at the sternum. A composite gait quality score was determined through principal component analysis based primarily on the explainability and uniformity of gait. Quality independence against demographic variables (age & weight) was tested through ANCOVA. The association between gait quality and gait parameters was analyzed by using multiple linear regression. ResultsA multiple regression model developed with a limited set of gait quality parameters successfully predicted gait smoothness with a 97.05 % accuracy with a mean square error of 0.085 between predicted and actual quality scores. The model demonstrates different predictive capacities across disease groups, with Osteoarthrosis + Osteoporosis having the highest R2 at 0.98 (p < 0.001) and Coxarthrosis having the lowest explained R2 at 0.79 (p < 0.001). ConclusionsThe assessment of gait quality, in family medicine, with low-cost digital tools is an area of opportunity yet to be explored. This tool can potentially disrupt the current disability workflow between primary and specialty care to have an objective method of assessing gait within a clinical consult. Individual patient-level benchmarking can give us insights into the patient's disease status, develop practical intervention strategies, and control the cost and quality of medical care by predicting an individualized course of disability or rehabilitation. Further studies are needed to validate digital gait assessments as clinical decision support tools for day-to-day clinical operations. MeSHGait Analysis, Smartphone, Primary Health Care, Osteoarthrosis

A low-cost digital first aid tool to reduce psychological distress in refugees: a multi-country randomized controlled trial of Self-Help Online in the first months after the invasion of Ukraine

Armed conflicts increase distress levels among affected populations, particularly impacting refugees who often face barriers to accessing psychological support. We evaluate an online version of a previously tested in-person and endorsed for online adaptation by the WHO Self-Help Plus (SH+) program among Ukrainian refugees dispersed across 17 countries, internally displaced and not displaced Ukrainians. This is the first randomized controlled trial to test an online psychological intervention simultaneously on refugees, internally displaced, and non-displaced conflict-affected populations. This study is an online two-arm, individually randomized controlled trial among participants above 18 years old in Ukraine or EU countries who were randomly assigned to receive either the Self-Help Online (SHO) intervention and passive informational resource or the passive informational resource alone. We recruited 652 participants starting the program on July 7th, 2022. The analysis focused on 292 participants who completed the final survey one week after the end of the program. Results indicated significant distress reduction among refugees (β −2.16, 95% CI −4.17 to −0.16; p=0.03; d −0.47) but not among internally displaced in Ukraine (β 0.56, 95% CI −1.1 to 2.99; p=0.17; d 0.2) or non-displaced participants in Ukraine (β 0.2, 95% CI −0.95 to 1.35; p=0.73; d 0.08). The effect size in stress reduction for refugees was comparable to other similar interventions but with lower average costs. The average cost per participant was €11, with €46.16 for each benefiting (refugee) participant, suggesting cost-effectiveness for scale-up. These findings suggest that Self-Help Online is an effective psychological intervention for reducing stress among geographically dispersed refugees at a low cost. We also find that the online delivery format of psychological interventions is feasible for internally displaced and non-displaced conflict-affected populations.

Deposit of Red Blood Cells at low concentrations in evaporating droplets is dominated by a central edge growth

Evaporation of blood droplets and diluted blood samples is a topic of intensive research, as it is considered a potential low-cost diagnostic tool. So far, samples with a volume fraction down to a few percent of red blood cells have been studied, and these were reportedly dominated by a “coffee-ring” deposit. In this study, samples with lower volume fractions were used to investigate the growth of the evaporative deposit from sessile droplets in more detail. We observed that blood samples and salt solutions with less than 1% volume fraction of red blood cells are dominated by a central deposit. We characterized the growth process of this central deposit by evaporating elongated drops and determined that it is consistent with the Kardar-Parisi-Zhang process in the presence of quenched disorder. Our results showed a sensitivity of the deposit size to fibrinogen concentration and the shape of red blood cells, suggesting that this parameter could be developed into a new and cost-effective clinical marker for inflammation and red blood cell deformation.

Automated image clarity detection for the improvement of colposcopy imaging with multiple devices

The proportion of women dying from cervical cancer in middle- and low-income countries is over 60%, twice that of their high-income counterparts. A primary screening strategy to eliminate this burden is cervix visualization and application of 3–5% acetic acid, inducing contrast in potential lesions. Recently, machine learning tools have emerged to aid visual diagnosis. As low-cost visualization tools expand, it is important to maximize image quality at the time of the exam or of images used in algorithms. Objective: We present the use of an object detection algorithm, the YOLOv5 model, to localize the cervix and describe blur within a multi-device image database. Methods: We took advantage of the Fourier domain to provide pseudo-labeling of training and testing images. A YOLOv5 model was trained using Pocket Colposcope, Mobile ODT EVA, and standard of care digital colposcope images. Results: When tested on all devices, this model achieved a mean average precision score, sensitivity, and specificity of 0.9, 0.89, and 0.89, respectively. Mobile ODT EVA and Pocket Colposcope hold out sets yielded mAP scores of 0.81 and 0.83, respectively, reflecting the generalizability of the algorithm. Compared to physician annotation, it yielded an accuracy of 0.72. Conclusion: This method provides an informed quantitative, generalizable analysis of captured images that is highly concordant with expert annotation. Significance: This quality control framework can assist in the standardization of colposcopy workflow, data acquisition, and image analysis and in doing so increase the availability of usable positive images for the development of deep learning algorithms.

Ethynylbenzaldehydes as novel reaction-based “turn-on” fluorescent probes for primary amine detection in solution, vapor, food, proteins, and live cells

Amine detection is of great importance as excess volatile amine exposure and increased concentration of biogenic amines in human body can cause adverse effects to human health. Thus, the development of easy-accessible and efficient fluorescent probes for amine detection is of great importance. In this study, we are the first to establish a cascade reaction sequence featuring imine formation followed by 6-endo-dig cyclization reaction for the development of sensitive and selective “turn-on” fluorescent probes toward primary amines by using the ortho-ethynylbenzaldehydes (EBAs) as the reaction site. The corresponding isoquinolinium products generated give a significant red-shifted absorbance change and highly intense “turn-on” fluorescence. Nine EBAs (EBA-1a-1e, EBA-2–5) were designed and synthesized to study the structure-photophysical property relationship (SPPR) for selective primary amine sensing. Our EBAs possessed a highly sensitive response toward primary amines in aqueous conditions within 30 min (LOD as low as 0.45 µM) with tunable absorption (338–430 nm) and emission (410–535 nm), large Stokes shift (up to 115 nm), and high fluorescence quantum yield (up to 0.86). EBA-loaded paper strips were prepared as a portable low cost tool for amine detection in solution, vapor, and food. Fluorogenic protein labelling and in-gel fluorescent imaging were efficiently achieved by using EBAs in which the fluorescence signal was obviously observed by naked eyes under 365 nm UV lamp. Moreover, the favorable membrane permeabilization, low to moderate cytotoxicity, and “turn-on” fluorescent response of EBA-1a-1e and EBA-4–5 allow wash-free live cell imaging. Interestingly, EBA-1a-1e and EBA-4 showed high selectivity to fluorescent imaging of mitochondria.

Comparison of three LAMP protocols for the simultaneous detection of DNA from species that produce cystic echinococcosis

Cystic echinococcosis (CE) is a zoonotic parasitic disease caused by species of the Echinococcus granulosus sensu lato complex. Different types of canids may act as definitive hosts by eating raw viscera infected with fertile hydatid cysts. The intermediate host (mainly ungulates) and humans acquire the infection through the fecal oral route (i.e. egg ingestion). Globally, more than 1 million people are affected by CE, causing a loss of 1–3 million disability-adjusted life years (DALYs) and a financial burden of US$ 3 billion annually. Loop mediated isothermal amplification (LAMP) protocols promise to be a useful tool to detect DNA, providing a low cost and thermocycle-free methodology. Given that surveillance for CE can be performed in feces from canids or other environmental matrixes contaminated with eggs, the characteristics of a LAMP protocol would favor implementation in endemic areas with basic resources. Herein, we compared three LAMP protocols for the simultaneous detection of E. granulosus s.l. species that cause CE. This comparation was carried with DNA obtained from different stages of E. granulosus s.l. Two of these are newly developed protocols that showed good analytical sensitivity and specificity. In both cases, the use of malachite green dye to directly visualize the test result was possible. From these two new LAMP protocols, one had better values for the detection of DNA from different types of E. granulosus s.l. DNA samples. Therefore, through this study, we provide a low-cost new tool for DNA detection of E. granulosus s.l. in poorly equipped laboratories from endemic areas.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a “diagnostic toolkit” consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, “black box” nature of the algorithms, and data storage/transfer concerns.

Homemade isothermal amplification-initiated Cas14a assay for rapid quantitative detection of aquatic RNA virus gene with no PAM

The red-spotted grouper nervous necrosis virus (RGNNV) is the most widely distributed nervous necrosis virus (NNV) and causes a significant risk to aquatic food safety. Though a few CRISPR-based aquatic pathogen detection methods have been reported recently, the absence of the PAM site of RGNNV specific gene RNA2 impedes its CRISPR-based diagnostic. Herein, we developed a rapid and accurate homemade isothermal amplification-initiated Cas14a assay (HiaCas14a) for RGNNV detection. The introduction of the PAM site into RT-LAMP specific amplicons of RGNNV RNA2 unlocked its PAM restriction. Furthermore, fish genomic RNA purified by magnetic beads and polymerases used in RT-LAMP prepared by ourselves obviously gave the system an enhanced convenience and a reduced cost. HiaCas14a detected 63.4 aM RNA with excellent specificity and sensitivity quantitatively, and it demonstrated a high accuracy of field sample validation results compared with RT-qPCR. The method provided an accurate, low-cost, and sensitive tool for aquatic pathogen detection in resource-poor areas.

Review of voice biomarkers in the screening of neurodegenerative diseases

Abstract Neurodegenerative diseases significantly impact patients and their families, making early identification crucial for improving patients’ quality of life and reducing care burdens. Current screening methods for neurodegenerative diseases, such as dementia and mild cognitive impairment, still rely on subjective assessments or expensive techniques like invasive cerebrospinal fluid analysis and magnetic resonance imaging. These factors make early identification challenging. Voice biomarkers present a promising alternative as convenient, non-invasive, and low-cost screening tools. With the application and development of artificial intelligence and big data, the prediction and screening of neurodegenerative diseases based on voice data have become a research focus. This article reviews the progress in voice biomarkers for neurodegenerative diseases screening and classification. It summarizes relevant studies on both single and multimodal data, identifies existing challenges, and suggests future research directions to enhance the application of voice biomarkers in neurodegenerative disease contexts.

Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients.

Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested. Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques. Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs. These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening. ClinicalTrials.gov Identifier: NCT06034730.