Screening Of Large Populations Research Articles

Anti-SARS-CoV-2 IgM Antibody Levels Measured by an In-House ELISA in a Convalescent Latin Population Persist over Time and Exhibit Neutralizing Capacity to Several Variants of Concern.

The coronavirus, SARS-CoV-2, is the causative agent for COVID-19, first registered in Wuhan, China and responsible for more than 6 million deaths worldwide. Currently, RT-PCR is the gold-standard method for diagnosing COVID-19. However, serological tests are needed for screening acute disease diagnosis and screening large populations during the COVID-19 outbreak. Herein, we described the development and validation of an in-house enzyme-linked immunosorbent assay (ELISA) for detecting the levels of anti-spike-1-RBD IgM antibody (CovIgM-ELISA) in well-defined serum/plasma panel for screening and identifying subjects infected with SARS-CoV-2 in a Latin population. In-house CovIgM-ELISA has the format of an indirect ELISA. It was optimized by checkerboard titration using recombinant SARS-CoV-2 spike-S1-RBD protein as an antigen. We found that, compared to the RT-PCR as the standard method, the in-house CovIgM-ELISA displayed sensitivities of 96.15% and 93.22% for samples collected up to 30 or 60 days after infection, respectively, as well as 95.59% specificity with 97.3% accuracy. The agreement kappa value (κ) of our CovIgM-ELISA was substantial when compared to RT-PCR (κ = 0.873) and the anti-SARS-CoV-2 IgM ELISA (InBios Int) (κ = 0.684). The IgM levels detected in the population positively correlated with the neutralizing activity against the wild-type, Alpha and Delta variants of concern, but failed to neutralize Omicron. These data indicate that our in-house CovIgM-ELISA is a compatible performing assay for the detection of SARS-CoV-2 infection.

S458 Impact of Personalized Patient Outreach on Multi-Target Stool DNA Test Adherence in a Large Colorectal Cancer Screening Population

S458 Impact of Personalized Patient Outreach on Multi-Target Stool DNA Test Adherence in a Large Colorectal Cancer Screening Population

Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.

Screening of Miscanthus Genotypes for Sustainable Production of Microcrystalline Cellulose and Cellulose Nanocrystals

Miscanthus spp. has been regarded as a promising industrial plant for the sustainable production of bio-based materials. To assess its potential for microcrystalline cellulose (MCC) and cellulose nanocrystals (CNCs) production, 50 representative clones of M. sinensis and M. floridulus were selected from a nationwide collection showcasing the extensive diversity of germplasm resources. Descriptive analysis indicates that the dry biomass weight of M. floridulus is advantageous whereas M. sinensis demonstrates higher MCC and CNCs yields as well as a smaller CNCs particle size. Correlation analyses indicated that MCC yield is solely influenced by the cellulose content whereas the yield of CNCs is affected by both the cellulose content and CrI. Comparative analyses of the chemical composition, physical features (degree of polymerization, crystalline index, particle size distribution and zeta potential), and scanning electron microscopy indicated that the MCC and CNCs extracted from M. sinensis and M. floridulus exhibited remarkable stability and quality. Additionally, the CNCs derived from M. sinensis and M. floridulus exhibited a distinctive ball-shaped structure. Notably, machine learning has demonstrated its efficacy and effectiveness in the high-throughput screening of large populations of Miscanthus spp. for predicting the yield of MCC and CNCs. Our results have also laid the theoretical foundation for the exploration, cultivation, and genetic breeding of M. sinensis and M. floridulus germplasm resources with the purpose of MCC and CNCs preparation.

Increased glucosylsphingosine levels and Gaucher disease in GBA1-associated Parkinson's disease

IntroductionGaucher's disease (GD) is caused by biallelic mutations in the GBA1 gene, leading to reduced glucocerebrosidase (GCase) activity and substrate (glucosylceramide and glucosylsphingosine, GlcSph) accumulation. GBA1 variant carriers are at risk of Parkinson's disease (PD), but only those with biallelic mutations cross the threshold of GCase reduction, leading to substrate accumulation and GD. The link between GBA1 mutations, GD and PD is not fully understood. Here we aimed at reporting the results of a large PD population screening with dried blood spot tests for GD. MethodsWe measured GCase activity and GlcSph levels in 1344 PD patients with dried blood spot tests, and performed GBA1 genetic sequencing. ResultsWhile the GCase activity was reduced in GBA1-PD carriers compared to wild type PD, GlcSph was increased in GBA1-PD compared to GBA1-controls, regardless of the underlying type of GBA1 variant. 13.6 % and 0.4 % of PD patients had mono- or biallelic GBA1 mutations respectively. GCase deficiency, lipid accumulation and clinical manifestations of GD was detected in five PD patients with biallelic GBA1 mutations, of whom four had a risk combined with a GD causing variant. ConclusionsGlcSph appearing higher in PD may represent a reliable biomarker of the disease and deserves to be further investigated. This study highlights the importance of screening PD patients for possible underlying GD, which is a treatable condition that should not be missed. We diagnosed GD cases carrying a “risk” variant in one allele, which is an unprecedented finding deserving further investigation.

Is Concentrated Urine PCR Test Valuable to Detect Genital HPV Infection [ID 2683587

INTRODUCTION: Human papillomavirus (HPV) is the leading cause of cervical cancer and early detection is crucial for effective prevention. The standard HPV detection method, cervical smear PCR, entails discomfort and deters many women. Therefore, it is challenging to screen large populations for HPV from cervical smear samples. In this study, we investigated the sensitivity of detecting HPV by PCR in concentrated urine samples. METHODS: Urine and cervical smear samples were collected from 126 patients (ages 20–35) at Acibadem Altunizade Hospital. Urine samples were concentrated by a tool (MyMagiCon). The presence of HPV DNA in cervical smear and urine samples was determined by PCR. RESULTS: Among 126 patients, HPV was detected in 57 either in at least one of the sample types (cervical smear, urine, and concentrated urine). Among all HPV-positive patients, HPV was identified in 87.7% of swab samples, 86% of concentrated urine samples, and only 73.7% of unconcentrated urine samples. The χ2 test comparing cervical smear and concentrated urine samples yielded a Kappa value of 0.78 (95% confidence), indicating strong statistical correlation within this study. Additionally, PCR from concentrated urine samples, identified six HPV-positive samples that were HPV negative in cervical smear samples of the same patients. CONCLUSION: It was possible to identify HPV in concentrated urine samples by PCR with a sensitivity almost equal to cervical smear sampling. Since obtaining urine is much easier than obtaining cervical smear samples, this may enable easy screening of large populations for HPV. This may have a big effect in the prevention of cervical cancer.

Comparison of Novel Proteomic Expression Profiles for Radiation Exposure in Male and Female C57BL6 Mice.

There is a need for point-of-care diagnostics for future mass casualty events involving radiation exposure. The development of radiation exposure and dose prediction algorithms for biodosimetry is needed for screening of large populations during these scenarios, and exploration of the potential effects which sex, age, genetic heterogeneity, and physiological comorbidities may have on the utility of biodosimetry diagnostics is needed. In the current study, proteomic profiling was used to examine sex-specific differences in age-matched C57BL6 mice on the blood proteome after radiation exposure, and the usefulness of development and application of biodosimetry algorithms using both male and female samples. Male and female mice between 9-11 weeks of age received a dose of total-body irradiation (TBI) of either 2, 4 or 8 Gy and plasma was collected at days 1, 3 and 7 postirradiation. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrated significant (FDR < 0.05 and |logFC| > 0.2) changes in expression after radiation exposure. All proteins were used for feature selection to build predictive models of radiation exposure using different sample and sex-specific cohorts. Both binary (prediction of any radiation exposure) and multidose (prediction of specific radiation dose) model series were developed using either female and male samples combined or only female or only male samples. The binary series (models 1, 2 and 3) and multidose series (models 4, 5 and 6) included female/male combined, female only and male only respectively. Detectable values were obtained for all ∼7,000 proteins included in the SomaScan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies in the binary model series was ∼100% at the model training level, and when tested with fresh samples, 97.9% for model 1 (female and male) and 100% for model 2 (female only) and model 3 (male only). When sex-specific models 2 and 3 were tested with the opposite sex, the overall predictive accuracy rate dropped to 62.5% for model 2 and remained 100% for model 3. The overall predictive accuracy rate in the multidose model series was 100% for all models at the model training level and, when tested with fresh samples, 83.3%, 75% and 83.3% for Multidose models 4-6, respectively. When sex-specific model 5 (female only) and model 6 (male only) were tested with the opposite sex, the overall predictive accuracy rate dropped to 52.1% and 68.8%, respectively. These models represent novel predictive panels of radiation-responsive proteomic biomarkers and illustrate the utility and necessity of considering sex-specific differences in development of radiation biodosimetry prediction algorithms. As sex-specific differences were observed in this study, and as use of point-of-care radiation diagnostics in future mass casualty settings will necessarily include persons of both sexes, consideration of sex-specific variation is essential to ensure these diagnostic tools have practical utility in the field.

On repeated diagnostic testing in screening for a medical condition: How often should the diagnostic test be repeated?

In screening large populations a diagnostic test is frequently used repeatedly. An example is screening for bowel cancer using the fecal occult blood test (FOBT) on several occasions such as at 3 or 6 days. The question that is addressed here is how often should we repeat a diagnostic test when screening for a specific medical condition. Sensitivity is often used as a performance measure of a diagnostic test and is considered here for the individual application of the diagnostic test as well as for the overall screening procedure. The latter can involve an increasingly large number of repeated applications, but how many are sufficient? We demonstrate the issues involved in answering this question using real data on bowel cancer at St Vincents Hospital in Sydney. As data are only available for those testing positive at least once, an appropriate modeling technique is developed on the basis of the zero-truncated binomial distribution which allows for population heterogeneity. The latter is modeled using discrete nonparametric maximum likelihood. If we wish to achieve an overall sensitivity of 90%, the FOBT should be repeated for 2 weeks instead of the 1 week that was used at the time of the survey. A simulation study also shows consistency in the sense that bias and standard deviation for the estimated sensitivity decrease with an increasing number of repeated occasions as well as with increasing samplesize.

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mortality. The interactive paradigm assisted by neural networks is considered to be an effective means for early lung cancer screening in large populations. However, some inherent characteristics of pulmonary nodules in high-resolution CT images, e.g., diverse shapes and sparse distribution over the lung fields, have been inducing inaccurate results. On the other hand, most existing methods with neural networks are dissatisfactory from a lack of transparency. In order to overcome these obstacles, a united framework is proposed, including the classification and feature visualization stages, to learn distinctive features and provide visual results. Specifically, a bilateral scheme is employed to synchronously extract and aggregate global-local features in the classification stage, where the global branch is constructed to perceive deep-level features and the local branch is built to focus on the refined details. Furthermore, an encoder is built to generate some features, and a decoder is constructed to simulate decision behavior, followed by the information bottleneck viewpoint to optimize the objective. Extensive experiments are performed to evaluate our framework on two publicly available datasets, namely, 1) the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and 2) the Lung and Colon Histopathological Image Dataset (LC25000). For instance, our framework achieves 92.98% accuracy and presents additional visualizations on the LIDC. The experiment results show that our framework can obtain outstanding performance and is effective to facilitate explainability. It also demonstrates that this united framework is a serviceable tool and further has the scalability to be introduced into clinical research.

A Novel Model Using Serum Thymidine Kinase 1 and Low-dose Computed Tomography Parameters to Predict Three-year Lung Cancer Risk in People with Pulmonary Nodules: A Retrospective Study.

This study was designed to develop a model of serum thymidine kinase 1 protein (STK1p) concentration in combination with low-dose computed tomography (LDCT) to predict the risk of benign pulmonary nodules progressing into lung cancer within three years in a large screening population. The study included a retrospective cohort of 6,841 individuals aged > 30 years who had LDCT-detected pulmonary nodules, but no cancer history or baseline cancer. The outcome was a lung cancer diagnosis recorded within three years after the first detection of pulmonary nodules. The adaptive least absolute shrinkage and selection operator was used to select candidate predictors and fit a logistic model. The model was validated internally by examining discrimination (area under the receiver operating characteristic curve (AUC), calibration (calibration plot)) and net benefit. A web application was developed based on the model. The results showed that the proportion of incident lung cancer cases was 0.79% (n=52). Predictors selected for the model were STK1p and three LDCT parameters (nodule size, type, and count). The AUC of the model was 0.91 (95% confidence interval (CI): 0.86, 0.96). The model had satisfactory discrimination at internal validation (AUC: 0.90 (0.84, 0.96)) and in subgroups (AUC=0.69-0.93). The high-risk group identified by the model exhibited a significantly higher three-year lung cancer risk than the low-risk group (odds ratio (OR): 66.03 (95% CI: 30.49, 162.98)). We concluded that the novel model of STK1p and LDCT parameters together can be used to accurately predict the three-year risk of lung cancer in people with pulmonary nodules.

ASSESSMENT OF THE ACCURACY OF THE RAPID TEST FOR THE DIAGNOSIS OF HELICOBACTER PYLORI IN PATIENTS THAT DIDN'T UNDERGO PREVIOUS ERADICATION THERAPY AND WHO WENT THROUGH ENDOSCOPY.

Helicobacter pylori infection is widely spread globally and is known to cause potentially serious diseases. Several diagnostic methods exist to identify and treat carriers of this bacterium. Serological tests for the diagnosis of infection are based on the detection of antibodies immunoglobulin G against H. pylori, a non-invasive, inexpensive, and easy-to-perform option. This research aims to ascertain the accuracy of an immunochromatographic serological test to verify the feasibility of using this method in patients who have not undergone previous eradication therapy. Rapid tests and questionnaires were applied to 53 patients that underwent upper digestive endoscopy with research for H. pylori between the period of September and October 2021. The results were compared with histopathology. In the rapid tests, seven positive and 46 negative results were obtained. When compared with the gold stan-dard, the following values were described: sensitivity 54.5%, specificity 97.6%, positive predictive value 85.7%, and negative predictive value 89.1%. In the present study, the immunochromatographic serological tests had an accuracy close to the values found in other similar studies. Therefore, it may be concluded that the rapid serological test remains a reasonable choice for screening large populations due to its low cost and ease of application, especially in those individuals who have not undergone previous treatment. • Helicobacter pylori infection can cause potentially serious diseases. • Serological tests are based on the detection of antibodies immunoglobulin G against Helicobacter pylori. • Serological tests for the diagnosis of Helicobacter pylori infection are low cost tools and have easy application. • Rapid serological test is a reasonable choice for screening large populations.

Evaluation of 'Normal' Cognitive Functions and Correlation With MRI Volumetry: Towards a Definition of Vascular Cognitive Impairment.

Introduction It is important to establish criteria to define vascular cognitive impairment (VCI) in India as VCI is an image-based diagnosis and magnetic resonance imaging (MRI) changes resulting from age with prevalent vascular risk factors may confound MRI interpretation. The objective of this study was to establish normative community data for MRI volumetryincluding white matter hyperintensity volume (WMHV), correlated with age-stratified cognitive scores and vascular risk factors (VRFs), in adults aged 40 years and above. Methods We screened 2651 individuals without known neurological morbidity, living in Mumbai and nearby rural areas, using validated Marathi translations of Kolkata Cognitive Battery (KCB) and geriatric depression score (GDS). We stratified 1961 persons with GDS ≤9 by age and cognitive score, and randomly selected 10% from each subgroup for MRI brain volumetry. Crude volumes were standardized to reflect percentage of intracranial volume. Results MRI volumetry studies were done in 199 individuals (F/M = 90/109; 73 with body mass index (BMI) ≥25; 44 hypertensives; 29 diabetics; mean cognitive score 76.3). Both grey and white matter volumes decreased with increasing age. WMHV increased with age and hypertension. Grey matter volume (GMV) decreased with increasing WMHV. Positive predictors of cognition included standardized hippocampal volume (HCV), urban living, education, and BMI, while WMHV and age were negative predictors. Urban dwellers had higher cognitive scores than rural, and, paradoxically, smaller HCV. Conclusion In this study of MRI volumetry correlated with age, cognitive scores and VRFs, increasing age and WMHV predicted lower cognitive scores, whereas urban living and hippocampal volume predicted higher scores. Age and WMHV also correlated with decreasing GMV. Further study is warranted into sociodemographic and biological factors that mutually influence cognition and brain volumes, including nutritional and endocrine factors, especially at lower cognitive score bands. In this study, at the lower KCB score bins, the lack of laboratory data pertaining to nutritional and endocrine deficiencies is a drawback that reflects the logistical limitations of screening large populations at the community level.Our volumetric data which is age and cognition stratified, and takes into account the vascular risk factors associated, nevertheless constitutesimportant baseline data for the Indian population. Our findings couldpossibly contribute to the formulation of baseline criteria for defining VCI inIndia and could help in early diagnosis and control of cognitive decline and its key risk factors.

Preliminary Evaluation Salivary Biomarkers in Patients with Oral Potentially Malignant Disorders (OPMD): A Case-Control Study.

Oral potentially malignant disorders (OPMD) are lesions associated with an increased risk of transformation (MT) into cancer. A study was made of the salivary levels of adenosine deaminase (ADA), ferritin (FRR) and total proteins (TP) in healthy individuals and in patients with oral potentially malignant disorders (OPMD), assessing the potential role of saliva as a diagnostic tool. A total of 91 subjects participated in the study, divided into two groups-59 patients with OPMD (oral leukoplakia or oral lichen planus) and 32 healthy controls-with measurements being made of salivary ADA, ferritin (FRR) and total proteins (TP). There were no significant differences in salivary mean ADA between the OPMD group 0.85 ± 2.18 UI/I and the controls 0.71 ± 1.72 UI/I (p = 0.934), though the levels of both FRR mean OPMD, 12.66 ± 10.50 (µg/L), versus control, 7.19 ± 4.44 (p = 0.001), and TP, 23.41 ± 17, versus control, 14.15 ± 15.19, were significantly higher in the OPMD group (p = 0.001). Patients with oral lichen planus showed significant differences in terms of FRR (p = 0.009) and TP (p = 0.003). The ferritin in LPO with a cut-off point of 8.5C showed a sensitivity and specificity of 54.3% and 82.3, respectively. The area under the curve (AUC) was 0.69 (95% confidence interval (95% CI): 0.58-0.82; p = 0.003). Ferritin and total proteins may constitute potential salivary biomarkers for oral lichen planus, though further studies are still needed in this field. In addition, saliva testing is a reliable and noninvasive diagnostic tool and appears to be a reliable strategy offering an interesting alternative for the screening of large populations.

Direct cord blood LAMP colorimetric phenol red assay for detecting α0-thalassemia (SEA deletion); the validation and post-natal screening in Thailand

Post-natal or newborn screening for thalassemia and hemoglobinopathies is useful for genetic counseling and managing thalassemia in children. We characterized thalassemia genotypes in newborns from the eastern part of Thailand. The results demonstrated a high heterogeneity of thalassemia and hemoglobinopathies with seventeen genotypes. We focused on α0- thalassemia (Southeast Asian [SEA] deletion) in this study. We developed and validated the loop-mediated isothermal amplification (LAMP) colorimetric assay for detecting α0- thalassemia (SEA deletion) using simple direct cord blood sampling compared to genomic DNA. A total of 160 cord blood samples were evaluated with the LAMP assay. The sensitivity and specificity of the LAMP colorimetric assay for α0-thalassemia (SEA deletion) using direct cord blood showed 100% (6/6 x 100) and 98.05% (151/154 x 100) whereas, genomic DNA showed 100% (6/6 x 100) and 100% (154/154 x 100), respectively. Moreover, we demonstrated other simple screening tools for α0-thalassemia with %Hb Bart’s, MCV, and MCH values and found that these parameters were not diagnostic in our samples. The direct cord blood with colorimetric LAMP assay is simple, rapid, and does not require a post-LAMP step compared to conventional PCR. These techniques could be applied in post-natal or large population screening for α0-thalassemia (SEA deletion). Finally, this could support early prevention of complications, early management, genetic counseling for α-thalassemia disease in children, or a long-term prevention and control program of severe thalassemia in Thailand.

A-374 Analytical Performance Assessment and Comparison of Point of Care Testing (POCT) Devices for Blood Lipids Measurements

Abstract Background Cardiovascular diseases (CVDs) are the leading cause of death in the U.S. and worldwide. According to the World Health Organization (WHO), worldwide, 85% of CVD deaths are due to heart disease and strokes, and over 75% occur in low- and middle-income countries (LMICs). Early detection and treatment are key to preventing the consequences of CVD. Point-of-care testing (POCT) devices offer alternatives to screening large populations for CVD risk. Although POCT are increasingly used for screening, only few information is available about their accuracy and reliability. Methods Eight POCT devices for blood lipids that previously successfully participated in the CDC CVD Biomarker Standardization Programs were used in this preliminary study. The analytical performance for all eight devices was assessed using split sample comparisons to the CDC reference measurement procedures for measurements performed in whole blood (WB) and serum. Specifically, the accuracy of total cholesterol (TC) was determined in WB and serum. The accuracy of high-density lipoprotein-cholesterol (HDL-c), and low-density lipoprotein-cholesterol (LDL-c) was determined in serum. The precision of TC, HDL-c, LDL-c, and triglycerides (TG) was determined in WB and serum. Results The reference values of the specimens from 20 donors ranged between 154–279 mg/dL, 36.9–109 mg/dL, and 71.1–182 mg/dL for TC, HDL-c, and LDL-c in serum, and between 145–210 mg/dL for TC in WB. Preliminary assessments found that the mean bias for each instrument ranged from -7.86%–13.82% and from -11.90%–3.24% for TC in WB and serum, respectively. For serum HDL-c samples, the mean bias ranged from −6.81%–15.43%. The mean bias for serum LDL-c samples ranged from −22.40% to 3.08%. For three devices, approximately 30% of the WB measurements and approximately 50% of the serum measurements were reported as outside of the measurement range. The mean imprecision for each instrument ranged from 2.2%–13.75% and from 1.88%–11.76% for TC in WB and serum, respectively. For HDL-c, the mean imprecision ranged from 5.36%–23.21% and from 2.30% to 26.69% in WB and serum, respectively. The mean imprecision ranged from 4.82%–20.34% and from 3.06%–20.54% for LDL-c in WB and serum, respectively. For TG, the mean imprecision ranged from 5.78%–23.91% and from 1.71%–11.02% in WB and serum, respectively. Conclusion These preliminary findings about the analytical performance of POCT devices indicate notable differences in accuracy and precision for lipids measured in WB and serum, with serum measurements being more likely within performance requirements than WB measurements. Some devices appear to be more likely to report values outside the measurement range even though reference values are within the measurement range of the device. Further studies to verify these preliminary findings with more individual donor samples are underway.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

BACKGROUND: Splenomegaly has historically been assessed on imaging using potentially inaccurate linear measurements. Prior work tested a deep-learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE: To apply the deep-learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS: This retrospective study included a primary (screening) sample of 8901 patients (mean age, 56±10 years; 4235 men, 4666 women) who underwent CT colonoscopy (n=7736) or renal-donor CT (n=1165) from April 2004 to January 2017, and a secondary sample of 104 patients (mean age, 56±8 years; 62 men, 42 women) with end-stage liver disease (ESLD) who underwent pre-liver transplant CT from January 2011 to May 2013. The automated deep-learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly using weight-based volumetric thresholds in the secondary sample was determined. RESULTS: In the primary sample, both observers confirmed splenectomy in 20 patients with automated splenic volume of zero; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low (<50 cc) volume, 49 patients with high (>600 cc) volume, and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding zero or error values), the mean automated splenic volume was 216±100 ml. Weight-based volumetric threshold (in milliliters) for splenomegaly was defined as 3.0*(weight[kg])+127; for weight >125 kg, splenomegaly threshold was constant (503 ml). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100% at true-craniocaudal length of 13 cm, and 78% and 88% for maximum-3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. Mean automated splenic volume in the 103 remaining patients was 796±457 ml; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION: We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT: The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

Enhancing Crop Improvement through Synergistic Integration of Advanced Plant Breeding and Proximal Remote Sensing Techniques: A Review

Accelerating crop improvement with enhanced adaptability to changing climatic conditions and meeting the ever-increasing global food demand requires urgent action. To achieve this, we must employ advanced molecular breeding techniques, such as marker-assisted selection, marker-assisted backcrossing, genomic selection, genome editing, and targeted mutation. However, these approaches demand the screening of large populations to identify potential genes and genotypes. Unfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding. Traditional phenotyping methods, reliant on trained experts, are slow, expensive, labour-intensive, subjective, and often require destructive sampling. Proximal remote sensing technologies, including RGB imaging, thermal imaging, hyperspectral imaging, multispectral imaging, and fluorescence imaging, offer a non-destructive and rapid collection of detailed phenotypic data, providing valuable insights into various plant traits at different growth stages. High-throughput phenotyping platforms, such as Conveyor-Type Indoor, Benchtop-Type Indoor, Unmanned Aerial Platform (UAP), and Manned Aerial Platform (MAP), utilize a combination of the aforementioned remote sensing technologies. This review article aims to explore the integration of proximal remote sensing and molecular breeding approaches, showcasing how this synergistic approach can expedite crop improvement efforts. By emphasizing the benefits, challenges, and future prospects of this integrative approach, we hope to pave the way for sustainable and productive agriculture, ensuring food security in the face of changing environmental conditions.

Pooling of six respiratory samples for the detection of SARS-CoV-2: A validation and cost study in a cohort in Lima, Peru

BackgroundThe continuous evolution of the SARS-CoV-2 pandemic has led to a high demand for diagnostic testing and major shortages in testing materials, especially in low- and middle-income countries. As an alternative to testing individual samples, pooling of respiratory samples has been suggested. Previous studies have assessed performance of pooling, mainly using nasopharyngeal samples for the detection of SARS-CoV-2, but few studies have examined the performance of pooling the more practical nasal swabs or saliva samples. ObjectiveTo evaluate the sensitivity, specificity, and potential cost reduction of pooling of nasal swab (NS) and saliva (SL) samples for detection of SARS-CoV-2 in a community-based cohort study in Lima, Peru. Study designA prospective cohort study was conducted in a community setting in San Juan de Lurigancho, Lima-Peru. NS and SL samples were collected from 132 participants twice-a-week for a 2-month period. Pools of 2 to 12 samples of the same type, from participants of the same household, were tested by RT-PCR. After pooled testing, all individual samples from positive pools and all individual samples from randomly chosen negative pools were evaluated. For assessment of diagnostic performance, pool testing results were compared with results from individual testing, which served as reference, and concordance in pooled and individual test detections was evaluated. Laboratory costs for both types of samples and testing were compared. ResultsA total of 2008 NS and 2002 SL samples were collected from 132 study participants. We tested 329 NS and 333 SL pools. The mean pool size for NS and SL pools was 6.22 (SD = 0.92) and 6.39 (SD = 1.71), respectively. Using individual testing as reference, NS pooling of 6 had a sensitivity and specificity of 94% and 100%, respectively, with kappa of 0.97 (CI 95%: 0.93–1.00). The corresponding values for SL pooling of 6 were 83%, 100%, and 0.90 (CI 95%: 0.83–0.97). Compared with individual testing, pooling resulted in a cost reduction of 74.8% for NS and 72.4% for SL samples. ConclusionsPooling easy-to-collect respiratory samples, especially NS, demonstrated very high diagnostic performance for detection of SARS-CoV-2 with substantial cost savings. This approach could be considered in large population screening programs, especially in LMIC.

Diagnosis of α-thalassaemia by colorimetric gap loop mediated isothermal amplification

α-Thalassaemia is an inherited haemoglobin disorder that results from the defective synthesis of α-globin protein. Couples whom both carry the α-thalassaemia 1 gene are at risk of having a foetus with the most severe thalassaemia, Hb Bart’s hydrops fetalis, with a risk of maternal mortality. However, haematological parameters alone cannot distinguish between a α-thalassaemia 1 carrier and a homozygous α-thalassaemia 2, in which one α-globin gene has been deleted on each chromosome. A rapid and accurate molecular detection assay is essential for prevention of the disease in populations where α-thalassaemia 1 is common. Multiplex Gap-PCR analysis is widely used for diagnosis of α-thalassaemia. However, the technique requires a thermocycler and post-amplification processing, which limits its application in primary care or in rural areas in developing countries. Loop mediated isothermal amplification (LAMP) amplifies target DNA at a constant temperature and does not require a thermocycler. This study developed a colorimetric Gap-LAMP using malachite green to allow naked eye visualization of two deletional α-thalassaemia 1 commonly found in Asian populations, the Southeast Asian type (--SEA) and the Thai type (--THAI) deletions. The Gap-LAMP was performed on DNA samples from 410 individuals carrying various α-thalassaemia gene defects with 100% concordance with conventional Gap-PCR analysis. This method eliminates post-amplification processing or the use of expensive sophisticated equipment and allows screening large populations for the prevention and control of α-thalassaemia.

An observational cohort study of interstitial lung abnormalities (ILAs) in a large Japanese health screening population (Kumamoto ILA study in Japan: KILA-J)

BackgroundInterstitial lung abnormalities (ILAs) are subtle or mild parenchymal abnormalities observed in more than 5% of the lungs on computed tomography (CT) scans in patients in whom interstitial lung disease was not previously clinically suspected and is considered. ILA is considered to be partly undeveloped stages of idiopathic pulmonary fibrosis (IPF) or progressive pulmonary fibrosis (PPF). This study aims to clarify the frequency of subsequent IPF or PPF diagnosis, the natural course from the preclinical status of the diseases, and the course after commencing treatment.MethodsThis is an ongoing, prospective, multicentre observational cohort study of patients with ILA referred from general health screening facilities with more than 70,000 annual attendances. Up to 500 participants will be enrolled annually over 3 years, with 5-year assessments every six months. Treatment intervention including anti-fibrotic agents will be introduced in disease progression cases. The primary outcome is the frequency of subsequent IPF or PPF diagnoses. Additionally, secondary and further endpoints are associated with the efficacy of early therapeutic interventions in cases involving disease progression, including quantitative assessment by artificial intelligence.DiscussionThis is the first prospective, multicentre, observational study to clarify (i) the aetiological data of patients with ILA from the largest general health check-up population, (ii) the natural course of IPF or PPF from the asymptomatic stage, and (iii) the effects and outcomes of early therapeutic intervention including anti-fibrotic agents for progressive cases of ILA. The results of this study could significantly impact the clinical practice and treatment strategy for progressive fibrosing interstitial lung diseases.Trial registration numberUMIN000045149.