Individual Samples Research Articles

Improvement of magnetic properties and hardness by alloying Mo to a FeCrCo alloy

In most of the technologies used for improving material properties, the enhancement of some properties usually leads to the degradation of others. In this study, we demonstrated that we were able to simultaneously improve hardness, coercivity, and remanence by alloying an FeCrCo alloy with Mo by engineering its α → α1 + α2 spinodal decomposition. We prepared six Mo-alloyed samples and subjected them to the appropriate aging protocols. As aging progressed, hardness increased from 272 to 447 HV and coercivity increased from 47 to 592 Oe in the alloyed samples. Remanence gradually increased from 52 to 98 Am2/kg, and then decreased to 82 Am2/kg in the final stage of the aging process. By adding Mo to six individual FeCrCo samples aged to different stages, we increased the hardness of these samples by 3 %, 8 %, 7 %, 8 %, 5 %, and 10 %, respectively. At the same time, we increased their coercivity by 74 %, 13 %, 19 %, 4 %, 14 %, and 9 % respectively and their remanence by 82 %, 63 %, 4 %, 4 %, 9 %, and 13 %, respectively. We used data drawn from scanning transmission electron microscopy, first-principles calculations, and magnetic force microscopy in order to elucidate the reasons behind the observed enhancements of magnetic properties in the FeCrCoMo alloy.

Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework.

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.

Evaluation of comparative effect between aluminum hydroxide gel and montanide (ISA 70) in potency and protection of locally prepared rabbit hemorrhagic disease virus 2 (RHDV2) vaccines in rabbits

BackgroundRabbit hemorrhagic disease (RHD) is an acute infectious disease that damages the rabbit industry by producing significant mortality rates in young and adult rabbits. RHD is better controlled by vaccination.ObjectiveThe current study's goal was to prepare and evaluate the immuno-enhancing effect of montanide ISA70 and aluminum hydroxide (Al(OH)3) gel incorporated within the inactivated RHDV2 vaccine and assess the vaccine's protective efficacy against the homologous and heterologous local RHDV2 strains in rabbits.MethodsInactivated RHDV vaccines were prepared using Montanide ISA70 oil or Al(OH)3 gel adjuvants and submitted to sterility, safety, and potency tests. 200 rabbits were equally divided into 4 groups: G1 (control), G2 (vaccinated with gel-incorporated vaccine), G3 (vaccinated with montanide-incorporated vaccine), and G4 (vaccinated with gel- and montanide-incorporated vaccines). Individual blood samples were collected from one week to six months from all groups. The vaccine's potency was measured by the HI test and protection percentage post challenge.ResultsData revealed slightly increasing HI titer means reaching the 1st peak at 4 weeks post-vaccination (7.33, 7.67, and 7.33 log2 in the 2nd, 3rd, and 4th groups, respectively), then slightly decreasing and peaked again, giving 9.33 log2 for the2nd group at 3 months post-vaccination (MPV), 10.67 log2 for 3rd the group, and 10.33 log2 for the 4th group at 5 months post-vaccination. Titer gradually decreased but remained protective. The protection rate ranged from 80–100% and 80–90% for homologous and heterologous local RHDV2 vaccines, respectively, within 3 weeks and 6 months post-challenge. The montanide oil RHDV2 vaccine induced better protection than the aluminum gel RHDV2 vaccine.ConclusionThe results demonstrated evidence of cross-protection between RHDV2 strains. The oil emulsion vaccine induced higher and longer-lasting antibody titers than those obtained with the RHDV2 aluminum gel vaccine.

Metagenomic analysis of colonic tissue and stool microbiome in patients with colorectal cancer in a South Asian population

BackgroundThe gut microbiome is thought to play an important role in the development of colorectal cancer (CRC). However, as the gut microbiome varies widely based on diet, we sought to investigate the gut microbiome changes in patients with CRC in a South Asian population.MethodsThe gut microbiome was assessed by 16s metagenomic sequencing targeting the V4 hypervariable region of the bacterial 16S rRNA in stool samples (n = 112) and colonic tissue (n = 36) in 112 individuals. The cohort comprised of individuals with CRC (n = 24), premalignant lesions (n = 10), healthy individuals (n = 50) and in those with diabetes (n = 28).ResultsOverall, the relative abundances of genus Fusobacterium (p < 0.001), Acinetobacter (p < 0.001), Escherichia-Shigella (p < 0.05) were significantly higher in gut tissue, while Romboutsia (p < 0.01) and Prevotella (p < 0.05) were significantly higher in stool samples. Bacteroides and Fusobacterium were the most abundant genera found in stool samples in patients with CRC. Patients with pre-malignant lesions had significantly high abundances of Christensenellaceae, Enterobacteriaceae, Mollicutes and Ruminococcaceae (p < 0.001) compared to patients with CRC, and healthy individuals. Romboutsia was significantly more abundant (p < 0.01) in stool samples in healthy individuals compared to those with CRC and diabetes.ConclusionDespite marked differences in the Sri Lankan diet compared to the typical Western diet, Bacteroides and Fusobacterium species were the most abundant in those with CRC, with Prevotella species, being most abundant in many individuals. We believe these results pave the way for possible dietary interventions for prevention of CRC in the South Asian population.

Identifying patterns of co-occurring chronic conditions preceding dementia: An unsupervised machine learning approach using health administrative data

ObjectivesIndividual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia. ApproachUsing linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death. ResultsWe identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]). ConclusionsWe found significant associations between machine learning-derived clusters of chronic conditions and dementia. ImplicationsUnsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.

Comparison of Extraction Methods for the Detection of Avian Influenza Virus RNA in Cattle Milk.

Since early 2024, a multistate outbreak of highly pathogenic avian influenza H5N1 has been affecting dairy cattle in the USA. The influenza viral RNA concentrations in milk make it an ideal matrix for surveillance purposes. However, viral RNA detection in multi-component fluids such as milk can be complex, and optimization of influenza detection methods is thus required. Raw bulk tank milk and mastitis milk samples were artificially contaminated with an avian influenza strain and subjected to five extraction methods. HCoV-229E and synthetic RNA were included as exogenous internal process controls. Given the high viral load usually observed in individual raw milk samples, four out of five tested methods would enable influenza detection in milk with normal texture, over a time window of at least 2 weeks post-onset of clinical signs. Nevertheless, sample dilution 1:3 in molecular transport medium prior to RNA extraction provided the best results for dilution of inhibitory substances and a good recovery rate of influenza RNA, that reached 12.5 ± 1.2% and 10.4 ± 3.8% in two independent experiments in bulk milk and 11.2 ± 3.6% and 10.0 ± 2.9% on two cohorts of mastitis milk samples. We have also shown compatibility of an influenza RT-qPCR system with synthetic RNA detection for simultaneous validation of the RNA extraction and RT-qPCR processes.

Tracking anthropometric dimensions and grip strength among children, adolescents and adults in an indigenous community of southern Mexico: 1968-1978-2000.

To track body size and proportions, arm dimensions and grip strength in children, adolescents, and adults resident in an indigenous community in Oaxaca who were measured on two or three occasions across surveys in 1968, 1978, and 2000. The three cross-sectional surveys included measures of height, weight, sitting height, arm circumference, triceps skinfold, and grip strength in surveys of schoolchildren in 1968 and of schoolchildren, adolescents and adults in 1978 and 2000. Cross-checks of surnames, forenames and ages/dates of birth of participants in the three surveys identified three samples of individuals measured on two occasions (1968-1978, two age groups in 1978-2000) and a subsample of individuals measured in the three surveys. Partial correlations controlling for age at each observation were calculated for each variable in the three sex-specific samples measured on two occasions, and for the subsamples of males and females measured on three occasions. Allowing for variation in age among subsamples, inter-age correlations were moderate to high for stature, moderate for sitting height and estimated leg length, and low to moderate for weight, BMI, arm and estimated arm muscle circumference, triceps skinfold, and grip strength. Allowing for the relatively broad chronological age intervals, the inter-age correlations for height, weight and BMI were at the low end, while those for grip strength and for strength per unit body weight for males (though not females) were generally in the range of correlations noted in studies of European samples. Likely associated with improved health, nutritional, and sanitation conditions, obesity and overweight were emerging among adults by 2000. Obesity and overweight in adults paralleled the introduction of mechanized agriculture that reduced routine physical work. Among children, the association of obesity and overweight is likely with increased nutritional availability, but poor choices in diet.

Understanding Telehealth Adoption among the Elderly: An Empirical Investigation

The adoption of telemedicine among the elderly is vital due to their unique healthcare needs and growing engagement with technology. This study explores the factors influencing their adoption behaviors, identifying both facilitating and inhibiting elements. While previous research has examined these factors, few have empirically assessed the simultaneous influence of barriers and enablers using a sample of elderly individuals. Using behavioral reasoning theory (BRT), this research investigates telehealth adoption behaviors of the elderly in India. A conceptual model incorporates both “reasons for” and “reasons against” adopting telehealth, capturing the nuanced dynamics of adoption behaviors. Data from 375 elderly individuals were collected to validate the model through structural equation modeling. The findings reveal that openness to change significantly enhances attitudes towards telehealth and “reasons for” adoption, influencing behaviors. This research contributes to the healthcare ecosystem by improving the understanding of telehealth adoption among the elderly. It validates the impact of openness to change alongside reasons for and against adoption, refining the understanding of behavior. By addressing impediments and leveraging facilitators, this study suggests strategies to maximize telehealth usage among the elderly, particularly those who are isolated, improving their access to medical services.

Self-Reported Illicitly Manufactured Fentanyl Use and Associated Health and Substance Use Risks in the United States, 2022

Objective Illicitly manufactured fentanyl (IMF) has emerged as a catalyst of the recent drug epidemic in the United States. To devise more targeted and effective prevention and treatment strategies, it is crucial to understand the demographics of the population who consumes IMF and their health and associated substance use risks. Therefore, this study explores the sociodemographic characteristics, health diagnoses, and drug injection practices of individuals reporting IMF use. Methods Data were derived from the 2022 National Survey on Drug Use Health, based on a nationally representative sample of non-institutionalized individuals aged 12 and older in the United States. Focusing on 306 adults who reported ever using IMF, we examined their sociodemographic characteristics, health diagnoses, and substance-related behaviors in comparison to adults with a drug use disorder who did not report IMF use, using logistic regression analyses. Results The majority of U.S. adults reporting IMF use were aged 35–64, male, non-Hispanic White, with a high school education or lower, never married, and had an annual household income below $40,000. Compared to adults with a drug use disorder who did not report IMF use, they were more likely to report heart conditions (AOR = 2.67, 95% CI = 1.29–5.54) and Hepatitis B or C (AOR = 8.35, 95% CI = 4.05–17.02). Nearly half of this group had an opioid use disorder (OUD) in the past year, and 65.7% (95% CI = 56.7–74.8) reported a history of injecting drugs. Conclusions To effectively curb the current drug epidemic, incorporating effective treatment for OUD and harm reduction strategies is crucial.

Development and validation of a 66K SNP array for the hard clam (Mercenaria mercenaria)

BackgroundThe hard clam (Mercenaria mercenaria), a marine bivalve distributed along the U.S. eastern seaboard, supports a significant shellfish industry. Overharvest in the 1970s and 1980s led to a reduction in landings. While the transition of industry from wild harvest to aquaculture since that time has enhanced production, it has also exacerbated challenges such as disease outbreaks. In this study, we developed and validated a 66K SNP array designed to advance genetic studies and improve breeding programs in the hard clam, focusing particularly on the development of markers that could be useful in understanding disease resistance and environmental adaptability.ResultsWhole-genome resequencing of 84 individual clam samples and 277 pooled clam libraries yielded over 305 million SNPs, which were filtered down to a set of 370,456 SNPs that were used as input for the design of a 66K SNP array. This medium-density array features 66,543 probes targeting coding and non-coding regions, including 70 mitochondrial SNPs, to capture the extensive genetic diversity within the species. The SNPs were distributed evenly throughout the clam genome, with an average interval of 25,641 bp between SNPs. The array incorporates markers for detecting the clam pathogen Mucochytrium quahogii (formerly QPX), enhancing its utility in disease management. Performance evaluation on 1,904 samples demonstrated a 72.7% pass rate with stringent quality control. Concordance testing affirmed the array's repeatability, with an average agreement of allele calls of 99.64% across multiple tissue types, highlighting its reliability. The tissue-specific analysis demonstrated that some tissue types yield better genotyping results than others. Importantly, the array, including its embedded mitochondrial markers, effectively elucidated complex genetic relationships across different clam groups, both wild populations and aquacultured stocks, showcasing its utility for detailed population genetics studies.ConclusionsThe 66K SNP array is a powerful and robust genotyping tool that offers unprecedented insights into the species’ genomic architecture and population dynamics and that can greatly facilitate hard clam selective breeding. It represents an important resource that has the potential to transform clam aquaculture, thereby promoting industry sustainability and ecological and economic resilience.

A UAV-based method for efficiently measuring the spatial pattern of multiple air pollutants in street canyons

Street canyons are fundamental units of urban spaces where individuals engage in social activities. The air quality within street canyons directly impacts the health and behavior of residents. Therefore, it is crucial to comprehensively assess and depict the actual distribution of multiple air pollutants (SO2, CO, NO2, O3, PM2.5, and PM10) in street canyons, for both investigation and validation purposes. However, an efficient and accurate method for doing so is rarely available. To address this issue, indoor and field measurements were conducted to determine the response times of these pollutants in various polluted environments, identify the optimal stay duration at individual sampling points, and optimize the sampling layout. A method based on Unmanned Aerial Vehicle (UAV) was developed to efficiently measure the spatial pattern of pollutants in street canyons and verified through field application. The results showed that the actual response times of pollutants in heavily and slightly polluted environments were less than 30 s. The recommended stay duration at an individual sampling point in an outdoor environment was determined to be 2 min, with uncertainty of 2 %. For the sampling layout, it is recommended to take 12 samples to obtain a spatial pattern of a street canyon. These samplings should be arranged at the air inlet, middle, and air outlet along the middle of the street canyon, as well as at their bottom, 20 %–30 %, 50 %–60 %, and top in the vertical direction, respectively. The UAV-based method can efficiently complete the sampling process in just 24 min.

Study of parasitosis in laboratory mice and rats in different types of vivariums

The purpose of the research is to study the parasite fauna in laboratory rats and mice in SPF-status and open-type vivariums and to compare the infection rate depending on the maintenance type within each type of samples.Materials and methods. The studies were conducted using life-time diagnosis (flotation method and Scotch-tape test), and the material were fecal samples from laboratory rodents and litter samples. Ten percent of the vivarium population and all newly arrived mice and rats in quarantine were examined. Fifty-four individual samples from pure line mice, and 24 litter samples were examined. A total of 234 samples were collected from the laboratory rats: 93 individual samples, 55 combined samples, and 17 sawdust samples; 69 samples were examined by Scotch-tape test.Results and discussion. Twenty six percent of the laboratory SPF vivarium mice were found to have protozoa: Giardia muris (11.1%) and Trichomonas sp. (20.4%). Nematodes Aspiculuris tetraptera (51.0%) and Syphacia obvelata (20.6%), and cestode Rodentolepis nana (12.0%) were recorded in the open-type vivarium mice. The laboratory open-type vivarium rats were found to have nematodes S. muris (up to 60.9%), A. tetraptera (5.4%), Trichosomoides crassicauda (1.8%), cestode R. nana (27.3%), protozoa Eimeria sp. (7.2%) and Giardia sp. (9.0%). The compared infection of individual with combined mouse fecal samples showed statistically significant differences for all parasites in general and for individual species. Pairwise comparisons of the infection showed that the combined sample detected R. nana statistically significantly more often versus the individual sample (27.3 vs. 5.4%, P &lt; 0.001). The comparison of the infection depending on the type of rat maintenance within each sample type did not show statistically significant differences.

Qualiy of life in displaced earthquake survivors

IntroductionWe investigated Quality of Life (QOL) in a sample of individuals (n = 341) who experienced different consequences in terms of displacement from their house after earthquake. MethodsThree groups were studied: those who had been displaced but are no longer so, those still displaced, and those who were never displaced. QOL for four time points was assessed: pre-earthquake, during displacement, at the time of the survey, and in ten years. ResultsDifferent trajectories of QOL were observed in the three groups: not displaced individuals showed no significant variation, those who were displaced had a significant decline in QOL after the earthquake but a significant recovery after the displacement experience, and those who were still displaced at the time of the survey reported lowest QOL both after the earthquake and in the future, with no recovery. Predictors of perceived QOL decline were quality and type of temporary accommodation, place attachment, and perceived health impairment. Subsequent QOL perceived improvement was predicted by quality and type of temporary accommodation, risk awareness, and emotional well-being. DiscussionOur findings highlight the importance of minimizing the duration of temporary displacement and providing high-quality temporary accommodations, considering individual needs in the local contexts and communities.

Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate

ObjectivesElectrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.Materials and methodsThe effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.ResultsThe efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019:1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.ConclusionThis study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.

Real-world data analysis of next-generation sequencing and corresponding clinical characteristics in thyroid tumor.

Next-generation sequencing (NGS) is of great benefit to clinical practice in terms of identifying genetic alterations. This study aims to clarify the gene background and its influence on thyroid tumors in the Chinese population. NGS data and corresponding clinicopathological features (sex, age, tumor size, extrathyroidal invasion, metastasis, multifocality, and TNM stage) were collected and analyzed retrospectively from 2844 individual thyroid tumor samples from July 2021 to August 2022. Among the cohort, 2337 (82%) cases possess genetic alterations, including BRAF (71%), RAS (4%), RET/PTC (4%), TERT (3%), RET (2.2%), and TP53 (1.4%). Diagnostic sensitivity before surgery can be significantly increased from 0.76 to 0.91 when cytology is supplemented by NGS. Our results show that BRAF-positive papillary thyroid cancer (PTC) patients tend to have older age, smaller tumor size, less vascular invasion, more frequent tumor multifocality, and a significantly higher cervical lymph node metastatic rate. Mutation at RET gene codons 918 and 634 is strongly correlated with medullary thyroid cancer. However, it did not display more invasive clinical characteristics. TERT-positive patients are more likely to have older age, and have larger tumor size, more tumor invasiveness, and more advanced TNM stage, indicating a poor prognosis. Patients with TERT, RET/PTC1, and CHEK2 mutations are more susceptible to lateral lymph node metastasis. In conclusion, NGS can be a useful tool that provides practical gene evidence in the process of diagnosis and treatment in thyroid tumors.

Cognitive vulnerabilities and depressed mood in acquired brain injury

ABSTRACT Acquired Brain Injury (ABI), an important cause of long-term disability, is associated with increased rates of depression in addition to common cognitive and physical consequences. Past research has linked post-ABI depression to injury severity (e.g., extent of physical or cognitive impairment) and premorbid mood problems. In the general (non-ABI) population, depression is associated with cognitive vulnerabilities that have informed the development of psychological interventions. In this observational study in a heterogeneous sample of individuals with chronic stage ABI, we examine two cognitive vulnerabilities – dysfunctional attitudes (DAs) and autobiographical memory specificity – and explore whether these are linked to depression symptoms and ongoing cognitive difficulties as in the general population. Compared to control participants, individuals with an ABI demonstrated increased endorsement of DAs and reduced specificity of autobiographical memory recall. Within the ABI group, cognitive vulnerability–depression symptom correlations were detected for an explicit measure of DAs, but not for a more implicit DA measure or for autobiographical memory specificity. While individual differences in injury severity and other factors likely obscured subtle relationships between mood and cognitive vulnerabilities, evidence of these vulnerabilities may be relevant to changes in identity and psychological interventions that target low mood in ABI.

Development of a simple HPLC method for the determination of urinary O-aminohippuric acid (OAH) and an establishment of OAH reference interval

Background: O-aminohippuric acid (OAH) is considered a low-abundance urinary fluorescent metabolite with the potential to be an innovative lung cancer biomarker. There is a lack of simple methods for measuring urinary OAH metabolite, and the measurement needs to be normalized by urinary creatinine, which is produced at a constant rate. Thus, the newly developed method must be able to determine urinary creatinine and OAH simultaneously in the same run. Objective: This work aimed to develop and validate a simple high-performance liquid chromatography (HPLC) method for measuring urinary OAH and to establish the reference intervals of OAH in healthy individuals. Materials and methods: We synthesized OAH standard in a simple route and optimized simple HPLC method for simultaneous measurement of creatinine and OAH in a single run. Analysis was performed on a RP-C18 column with a gradient elution system of acetonitrile - ammonium acetate buffer (pH 6.5, 100 mM). After implementing optimal conditions, the procedure was compiled according to the International Council for Harmonisation (ICH) validation parameters. The developed method was used for the establishment of reference intervals of a total of 120 random urine samples of healthy individuals. Results: The linear range of the calibration curve for creatinine and OAH were 1-1000 µg/mL and 0.1-100 µg/mL, respectively. The recoveries ranged for both metabolites were between 91.35 % and 109.12%. The relative standard deviations (RSDs) for the intra-day and inter-day results ranged from 0.11-0.66 % to 0.16-1.73 %, respectively. The limits of detection (LOD) and quantification (LOQ) were 0.258 µg/mL and 0.783 µg/mL for creatinine, while OAH was 0.045 µg/mL and 0.137 µg/mL, respectively. The method was successfully applied to establish reference intervals of OAH in healthy individuals and was defined as 0.420-2.287 mmol/mol creatinine. Conclusion: According to various validated parameters, the proposed method was proven to quantify urinary OAH and creatinine in a single run. It can also be analyzed noninvasively without additional sample processing. Reported herein is the first establishment of OAH reference intervals in healthy individuals, which may benefit the utilization of OAH as a noninvasive biomarker for lung cancer detection in the future.

The primary porosity heterogeneity characteristics of braided river sandbody and implications for predicting the current physical properties heterogeneities

Understanding the heterogeneity of reservoirs is crucial for enhancing the efficiency of hydrocarbon exploration and development. The primary porosity of samples from modern braided river sands and outcrops of braided river sandstone was calculated using a model previously proposed by the authors. The characteristic parameters (Vx) for calculating primary porosity are closely related to the architectural–elemental configurations (AEC), and the AEC of braided river sand bodies (BRSD) has apparent effects on the distribution of the primary porosity heterogeneities. Analysis of our results has established a simple primary porosity heterogeneity model of BRSD. The center of braided river channel and mid-channel bars have excellent strong primary petrophysical properties with high primary porosity exceeding 38%. The contact areas between the braided river channel and channel bars exhibit relatively low primary porosities of less than 33%. The area between the center and edge of the braided bars and channels displays medium primary porosities. The nonlinear correlation in the Q–Q plot of the primary porosity and present porosity of samples from BRSD in the Ahe Formation is mainly caused by chemical diagenesis. The present porosity heterogeneity of BRSD in the Ahe Formation is less influenced by compaction and cementation, it predominantly arises from the differential of dissolution. Q–Q plots attempt to correlate the geological information from an individual sample with the heterogeneity of present porosity in BRSD. In addition, by utilizing Q–Q plots of the primary and current petrophysical properties of the sand body, the relative extent of heterogeneity modification caused by different diagenetic processes can be assessed. This assessment is crucial for modeling macroscopic models of physical properties during geological history periods.

Eyeing DNA barcoding for species identification of fish larvae.

Identification of fish larvae based on morphology is typically limited to higher taxonomic ranks (e.g., family or order), as larvae possess few morphological diagnostic characters for precise discrimination to species. When many samples are presented at any one time, the use of morphology to identify such specimens can be laborious and time-consuming. Using a reverse workflow for specimen sorting and identification leveraging high-throughput DNA sequencing, thousands of fish larvae can be DNA barcoded and sorted into molecular operational taxonomic units (mOTUs) in a single sequencing run with the nanopore sequencing technology (e.g., MinION). This process reduces the time and financial costs of morphology-based sorting and instead deploys experienced taxonomists for species taxonomic work where they are needed most. In this study, a total of 3022 fish larval specimens from plankton tows across four sites in Singapore were collected and sorted based on this workflow. Eye tissue from individual samples was used for DNA extraction and sequencing of cytochrome c oxidase subunit I. We generated a total of 2746 barcodes after quality filtering (90.9% barcoding success), identified 2067 DNA barcodes (75.3% identification success), and delimited 256 mOTUs (146 genera, 52 families). Our analyses identified specific challenges to species assignment, such as the potential misidentification of publicly available sequences used as reference barcodes. We highlighted how the conservative application and comparison of a local sequence database can help resolve identification conflicts. Overall, this proposed approach enables and expedites taxonomic identification of fish larvae, contributing to the enhancement of reference barcode databases and potentially better understanding of fish connectivity.

استخدام تقنيات الذكاء الاصطناعي لتعزيز إدارة المخاطر في القطاع المصرفي بالمملكة العربية السعودية "بالتطبيق على مصرف الراجحي"

The research aimed to shed light on the impact of using artificial intelligence techniques to enhance risk management in the banking sector in the Kingdom of Saudi Arabia, with an application to Al Rajhi Bank, by clarifying the dimensions of artificial intelligence techniques, in addition to revealing the impact of artificial intelligence techniques in their dimensions on risk management. The researcher relied on the descriptive analytical approach in order to achieve the objectives of the study, as he used the questionnaire as a tool for collecting information, as this research was applied to a study community represented by employees and managers of Al Rajhi Bank, as the research sample consisted of a random sample of (196) individuals, and to analyze this information, the statistical analysis program (SPSS) was used. This research has reached a set of important results, the most prominent of which is that artificial intelligence technologies have an impact on risk management, and there is an influential role for each of the devices and software, effectiveness, knowledge and reasoning individually on risk management, and there is an influential role for artificial intelligence technologies on credit risks, market risks, and operational risks individually. The researcher recommended providing training programs for employees on the use of artificial intelligence tools in risk management, the necessity of investing in research and development of artificial intelligence applications in the banking field, and amending policies and procedures based on the results extracted from artificial intelligence applications.