Validation Study Research Articles

Validity and Reliability of the Arabic Cough Severity Index

ABSTRACTObjectivesThis study aimed to translate and validate the Cough Severity Index (CSI) into Arabic (A‐CSI) and to evaluate its validity and reliability among patients with chronic cough.MethodsThis cross‐sectional descriptive questionnaire‐based validation study was conducted at two tertiary hospitals in Riyadh, Saudi Arabia, between December 2023 and August 2024. The CSI was translated from English into Arabic using the forward‐backward method. Its reliability was assessed using Cronbach's alpha and test–retest reliability. Its construct validity was assessed using exploratory factor analysis (EFA), and its internal consistency was assessed using Cronbach's alpha. Its discriminant validity was determined using the Mann–Whitney U test, and its reproducibility was evaluated using the intraclass correlation coefficient (ICC).ResultsData were collected from 100 participants, 50 with chronic cough and 50 healthy controls. The mean age was 41.56 ± 14.28 years in the chronic cough group and 35.48 ± 10.02 years in the healthy control group. The A‐CSI exhibited high reproducibility (ICC = 0.896) and excellent internal consistency (Cronbach's alpha = 0.966). EFA identified three factors explaining 66.41% of the variance, with all items having communalities > 0.3. The A‐CSI exhibited significant discriminant validity between the chronic cough and healthy control groups (p < 0.001).ConclusionThe A‐CSI is a reliable and valid tool for assessing chronic cough in Arabic‐speaking patients, making it suitable for both clinical practice and research.Level of EvidenceLevel 3.

Bridging Experimentation and Computation: OMSP for Advanced Acrylate Characterization and Digital Photoresin Design in Vat Photopolymerization

Vat photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic and composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses attenuated total reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall, and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R2) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology’s reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R2 of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP; the streamlining of photoresin formulation design; and transforming the way acrylates are characterized, selected, and used.

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS. To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs). In this multicenter diagnostic validation study conducted between July 2023 and December 2023, participants aged 21 years or older with shortness of breath recruited from 4 clinical sites underwent 2 ultrasound examinations: 1 examination by a THCP operator using Lung Guidance AI and the other by a trained LUS expert without AI. The THCPs (including medical assistants, respiratory therapists, and nurses) underwent standardized AI training for LUS acquisition before participation. Lung Guidance AI software uses deep learning algorithms guiding LUS image acquisition and B-line annotation. Using an 8-zone LUS protocol, the AI software automatically captures images of diagnostic quality. The primary end point was the proportion of THCP-acquired examinations of diagnostic quality according to a panel of 5 masked expert LUS readers, who provided remote review and ground truth validation. The intention-to-treat analysis included 176 participants (81 female participants [46.0%]; mean [SD] age, 63 [14] years; mean [SD] body mass index, 31 [8]). Overall, 98.3% (95% CI, 95.1%-99.4%) of THCP-acquired studies were of diagnostic quality, with no statistically significant difference in quality compared to LUS expert-acquired studies (difference, 1.7%; 95% CI, -1.6% to 5.0%). In this multicenter validation study, THCPs with AI assistance achieved LUS images meeting diagnostic standards compared with LUS experts without AI. This technology could extend access to LUS to underserved areas lacking expert personnel. ClinicalTrials.gov Identifier: NCT05992324.

Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms

BackgroundDespite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.MethodsLDD was defined as a diarrhea episode lasting ≥ 7 days. We used 7 ML algorithms to build prognostic models for the prediction of LDD among children < 5 years using de-identified data from Vaccine Impact on Diarrhea in Africa study (N = 1,482) in model development and data from Enterics for Global Health Shigella study (N = 682) in temporal validation of the champion model. Features included demographic, medical history and clinical examination data collected at enrolment in both studies. We conducted split-sampling and employed K-fold cross-validation with over-sampling technique in the model development. Moreover, critical predictors of LDD and their impact on prediction were obtained using an explainable model agnostic approach. The champion model was determined based on the area under the curve (AUC) metric. Model calibrations were assessed using Brier, Spiegelhalter’s z-test and its accompanying p-value.ResultsThere was a significant difference in prevalence of LDD between the development and temporal validation cohorts (478 [32.3%] vs 69 [10.1%]; p < 0.001). The following variables were associated with LDD in decreasing order: pre-enrolment diarrhea days (55.1%), modified Vesikari score(18.2%), age group (10.7%), vomit days (8.8%), respiratory rate (6.5%), vomiting (6.4%), vomit frequency (6.2%), rotavirus vaccination (6.1%), skin pinch (2.4%) and stool frequency (2.4%). While all models showed good prediction capability, the random forest model achieved the best performance (AUC [95% Confidence Interval]: 83.0 [78.6–87.5] and 71.0 [62.5–79.4]) on the development and temporal validation datasets, respectively. While the random forest model showed slight deviations from perfect calibration, these deviations were not statistically significant (Brier score = 0.17, Spiegelhalter p-value = 0.219).ConclusionsOur study suggests ML derived algorithms could be used to rapidly identify children at increased risk of LDD. Integrating ML derived models into clinical decision-making may allow clinicians to target these children with closer observation and enhanced management.

The Validity and Reliability of the Turkish Version of the Rowland Universal Dementia Assessment Scale (RUDAS) for Alzheimer’s Disease and Mild Cognitive Impairment

Background: With demographic aging, the frequency of cognitive and behavioral disorders gradually increases. Screening tests, which are simple to perform, help detect dementia for the clinician. The Rowland Universal Dementia Assessment Scale (RUDAS) is a screening test developed in Australia, where people from many cultures and languages ​​live together, and it is stated that it is not affected by language and education level. Our aim in this study is to create a wider field of use by evaluating the patients with mild cognitive impairment of this test, whose Turkish validity and reliability study has been done previously for Alzheimer disease patients. Method: The study group was composed of a group with Alzheimer's disease (n = 87), with a diagnosis of mild cognitive impairment (n = 95), and a non-dementia control group (n = 80) with similar age, education level, and gender distribution Mini-mental status exam (MMSE) and RUDAS were applied to the patients and the control group. Tests were re-administered seven days later for test-retest evaluation for reliability tests. The internal consistency coefficient was calculated. For validation, patient groups and control group were compared. For parallel test validity, MMSE and RUDAS scores were compared. For time validity, first day and seventh day scores were compared. Results: The Cronbach alpha coefficient of the RUDAS scale is 0.8529. In our analysis, the scale was found to be highly reliable. In our study, the difference between the RUDAS scale mean scores of the groups was found to be statistically significant (p&lt;0.001). In estimating the Alzheimer's Disease patient group, the AUC value under the curve of the RUDAS score was obtained as 0.998, and the mild cognitive impairment AUC value was obtained as 0.971, which was found to be significant. (p&lt;0.001). The degree of the relationship between the RUDAS scale and the MMSE scale was found to be very strong and statistically significant in the positive direction. (r=0.938 p&lt;0.001) Conclusion: This study showed that the Turkish version of the RUDAS is a valid and reliable scale for the evaluation and follow-up of Alzheimer's disease and mild cognitive impairment in the Turkish population.

Predicting Semen Analysis Parameters from Testicular Ultrasonography Images Using Deep Learning Algorithms: An Innovative Approach to Male Infertility Diagnosis

Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. Materials and Methods: This study included male patients aged 18–54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023. All patients underwent comprehensive assessments, including blood hormone profiling, semen analysis, and scrotal ultrasonography, with each procedure being performed by the same operator. Longitudinal-axis images of both testes were obtained and subsequently segmented. Based on the semen analysis results, the patients were categorized into groups according to sperm concentration, progressive motility, and morphology. Following the initial classification, each semen parameter was further subdivided into “low” and “normal” categories. The testicular images from both the right and left sides of all patients were organized into corresponding folders based on their associated laboratory parameters. Three distinct datasets were created from the segmented images, which were then augmented. The datasets were randomly partitioned into an 80% training set and a 20% test set. Finally, the images were classified using the VGG-16 deep learning architecture. Results: The area under the curve (AUC) values for the classification of sperm concentration (oligospermia versus normal), progressive motility (asthenozoospermia versus normal), and morphology (teratozoospermia versus normal) were 0.76, 0.89, and 0.86, respectively. Conclusions: In our study, we successfully predicted semen analysis parameters using data derived from testicular ultrasonography images through deep learning algorithms, representing an innovative application of artificial intelligence. Given the limited published research in this area, our study makes a significant contribution to the field and provides a foundation for future validation studies.

The External Validation of a Multivariable Prediction Model for Recurrent Pelvic Organ Prolapse After Native Tissue Repair: A Prospective Cohort Study

Background/Objectives: A prediction model for anatomical cystocele recurrence after native tissue repair was developed and internally validated in 2016. This model estimates a patients’ individual risk of recurrence and can be used for counseling. Before implementation in urogynecological clinical practice, external validation is needed. The aim of this study was to assess the external validity of this previously developed prediction model. The secondary aim was to test the performance of this model with a composite and subjective outcome of pelvic organ prolapse (POP) recurrence. Furthermore, the aim was to investigate whether risk factors for POP recurrence were in line with the population in which the original model was developed. Methods: In this prospective multicenter cohort study, 246 patients who underwent anterior colporrhaphy were included. Inclusion criteria were patients scheduled to undergo a primary anterior colporrhaphy (with a POP Quantification (POPQ) stage ≥ 2 cystocele). A combination of a primary anterior colporrhaphy with other POP or incontinence surgery (without the use of vaginal or abdominal mesh material) was permitted. Patients with prolapse or incontinence surgery prior to index surgery could not participate. All patients filled in questionnaires, pelvic floor ultrasound was performed preoperatively, and data from the medical file concerning POPQ stage and obstetric and general history were obtained. Results: Thirty women (12.2%) were lost at follow up. Anatomical cystocele recurrence was present in 107/216 (49.5%), subjective recurrence in 19/208 (9.1%), and 39/219 (17.8%) patients met the criteria for composite outcome. The area under the receiver operating characteristic curves for anatomical, composite, and subjective recurrence were 65.5% (95% CI: 58.7–72.4), 55.8% (95% CI 47.3–64.3%, NS), and 55.1% (95% CI 45.1–65.2%), respectively. In the multivariable analysis, preoperative cystocele stage 3 or 4 and a complete levator defect on ultrasound were independent risk factors for anatomical recurrence. For composite recurrence, younger age and an active employment status were only risk factors in univariable analysis. No significant risk factors for subjective recurrence could be identified. Conclusions: This external validation study showed a moderate performance for a prediction model for anatomical recurrence. The model cannot be used for a composite or subjective outcome prediction because of poor performance. For composite and subjective recurrence, new prediction models need to be developed.

Dynamic switching between brain networks predicts creative ability

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain’s Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)—constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Validation of Diagnosis Codes for Low Birth Weight and Small-for-Gestational Age in the Medicaid Analytic Extract Database

Abstract Background: The accuracy of low birth weight (LBW) and small for gestational age (SGA) in administrative healthcare records is crucial for perinatal studies but has few validity studies. Methods: Using 1999–2010 MAX linked to birth certificates (BC), we identified mother-infant dyads (≥30 days enrollment after delivery, with valid gestational age (GA) and birth weight (BW)). LBW and SGA were identified based on ICD-9-CM codes. Infants with BW &amp;lt;10% of the U.S. reference were flagged as SGA. For LBW group diagnoses, we imputed birthweight using median, mean BW from BCs, and ICD code boundaries of infants in the same LBW group. We calculated the sensitivity, specificity, and positive/negative predictive values to assess performance. Results: We identified 1,536,272 live births. All LBW groups had low SEs, high SPs, and NPVs, whereas PPVs varied. Among infants with SGA diagnoses based on GA/BW from the BC, SE of the SGA codes was 13.36%; SP 99.01%; PPV 67.37%. Combining imputation with LBW codes increased SE up to 22.09% (lower boundary) but decreased PPV to 41.53% (lower boundary). Conclusions: ICD-9-CM codes from administrative healthcare records had low SE but high SP. Imputation based on GA and BW did not add much value to SGA identification.

Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men.

Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population. Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC. Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%). This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.

Predicting acute kidney injury in trauma using extreme gradient boosting model: a temporal validation study

Abstract Background Acute kidney injury (AKI) is a significant complication in patients with trauma. The early identification of AKI in these patients poses challenges. This study aimed to predict AKI in trauma patients 24 or 48 hours in advance using an extreme gradient boosting (XGBoost) model. Methods We analyzed 17 859 trauma patients admitted to a regional trauma center between January 2015 and July 2023. Demographic, clinical, and laboratory parameters were collected. The model was developed using data until July 2021 and validated using data from August 2021. We developed models to predict AKI stages 1 to 3 and AKI stages 2 and 3 occurring 48 and 24 h later and measured predictive performance in the validation group. The models' performance was evaluated using the area under the receiver operating characteristic curve (AUROC), and feature importance was assessed through SHapley Additive exPlanations values. Results The study population exhibited an incidence of AKI of 6.6% in the development group and 5.4% in the validation group. The models demonstrated predictive performances with AUROCs of 0.864 and 0.886 for 48-hour predictions of AKI stages 1 to 3 and stages 2 and 3, and 0.904 and 0.903 for 24-hour predictions of AKI stages 1 to 3 and stages 2 and 3, respectively. Key features influencing model predictions included baseline and in-hospital serum creatinine values, injury severity score, age, lactate dehydrogenase, D-dimer, platelets, albumin, and C-reactive protein levels. Conclusions The XGBoost models effectively predicted AKI in trauma patients up to 48 h in advance using clinical data.

Development and validation of inverted head form orientation for helmet testing using a pneumatic linear impact system

Despite growing public health awareness of concussions and their cognitive sequelae, research has predominantly focused on generic bulk-head form impact metrics rather than the study of how brain tissue responds to head impacts. In this paper, the authors develop and validate a novel testing fixture that can configure standard head forms in an inverted orientation, with the future goal of testing inverted cadaveric specimens with gravity-driven perfusion of the head. The inverted testing fixture was integrated with an existing pneumatic linear impactor system and suspended a Hybrid III instrumented head and neck form on an x–y linear sliding carriage. Validation studies compared impacts in upright and inverted orientations, with and without a football helmet, at 5.5 and 7.4 m/s. Angular velocity and linear acceleration measures were found to be similar. The results support the use of the novel fixture for studying inverted cadaveric specimens during simulated concussive impacts.

A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study

ObjectiveTo develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.MethodsWe retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model’s diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios.ResultsIn total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model’s diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p &amp;lt; 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists’ sensitivity in diagnosing plaques. Additionally, the model’s diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p &amp;lt; 0.001).ConclusionThis AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.

Development and Beta Validation of an mHealth-Based Hearing Screener (SRESHT) for Young Children in Resource-Limited Countries: Pilot Validation Study

Background The prevalence of hearing loss in infants in India varies between 4 and 5 per 1000. Objective-based otoacoustic emissions and auditory brainstem response have been used in high-income countries for establishing early hearing screening and intervention programs. Nevertheless, the use of objective screening tests in low- and middle-income countries (LMICs) such as India is not feasible. Mobile health (mHealth) solutions have been demonstrated to be a viable option for hearing screening in LMICs. Objective This study aims to develop and beta-validate an affordable hearing screener for children younger than 6 years of age to identify moderately severe or higher degrees of hearing loss. Methods In phase 1, a mHealth-based hearing screener (SRESHT) was developed using a single board computer with wireless commercial headphones and speakers as transducers, which were calibrated according to the standard procedure. Three subjective hearing screening modules were conceptualized and developed for different age groups: (1) behavioral observation audiometry–screening for infants aged from 0 to 1 year; (2) speech spectrum awareness task–screening for children 1 to 3 years old; and (3) speech recognition task–screening for children 3 to 6 years old. Different auditory stimuli for the screening modules were generated and suitability was assessed: (1) noisemakers, animal sounds, and environmental sounds for infants (birth to 1 year old); (2) animal sounds and nonsense syllables for children (1 to 3 years old); and (3) eighteen picturable spondee words for children (3 to 6 years old). In phase 2, the SRESHT screener was beta-validated in children aged below 6 years to establish the agreement between SRESHT modules and the gold-standard procedure in identifying moderately severe and higher degrees of hearing loss. Results Off-the-shelf commercial speakers and headphones were selected and calibrated. On comparison of stimuli for behavioral observation audiometry on 15 children, Noisemaker stimuli were found suitable based on the average minimum response levels. On comparison of different stimuli for speech spectrum awareness task on 15 children, animal sounds were found to be suitable. On familiarity check of 18 spondee words for speech recognition task among 20 children, 12 spondee words had the eligibility cutoff (85%) and a presentation level of 5 dB SL (re-pure tone threshold) was sufficient to achieve 80% psychometric function. In phase 2, a total of 55 children aged 0 to 6 years (31 normal hearing and 24 hearing impairment) underwent SRESHT screening for beta validation. Cohen κ indicated that the overall SRESHT screener had a very good agreement (κ=0.82) with gold-standard audiometric screening for identifying moderately severe and higher degrees of hearing loss. Conclusions The development and beta validation of the SRESHT screener using the selected auditory stimuli showed that the stimuli were suitable for screening children.

GWAS Identifies SNP Markers and Candidate Genes for Off-Flavours and Protein Content in Faba Bean (Vicia faba L.)

Faba bean (Vicia faba L.) is a valuable ingredient in plant-based foods such as meat and dairy analogues. However, its typical taste and aroma are considered off-flavours in these food applications, representing a bottleneck during processing. Breeding is needed to develop varieties with minimal off-flavours and high protein content. The genetic regulation of these traits is underexplored. To dissect their genetic architecture, we performed a genome-wide association study (GWAS). A total of 245 faba bean accessions (the CGN population) were genotyped using the 90K-SPET targeted assay. These accessions were phenotyped in 2021 and 2022 in the Netherlands for protein, oil, fatty acids, lipid-derived products, phenolic acids, flavonoids, and tannins. The CGN population showed large phenotypic variation and moderate-to-high narrow-sense heritability for most traits. The growing environment significantly affected all traits, with trait-specific genotype-by-year (GxY) interactions. Condensed tannins and fatty acids were the most stable across the two years and had the highest heritability estimates (h2 &gt; 0.6). GWAS identified a total of 148 single nucleotide polymorphisms (SNPs) loci in 2021 and 167 in 2022. Key candidate regulators included genes involved in lipid biosynthesis (ATS2, KAS, LPP), amino acid transport (CAT4) for protein storage, zero tannins locus-1 (zt-1), and regulators of the phenylpropanoid pathway, such as a shikimate kinase gene and transcription factors bHLH137-like and MYB. These results pave the way for validation studies and biotechnological applications to improve the quality of faba bean-based foods.

Prognostic scores in patients with severe tricuspid regurgitation: An external validation study.

Four scores have been published in 2022 for assessing mortality risk of patients with tricuspid regurgitation (TR): the TRI-SCORE, those reported by Hochstadt and Wang and the TRIO score. Our objective was to perform an external validation of available scores for predicting mortality and the combined endpoint of mortality and heart failure (HF) admission, in an independent cohort of patients with severe TR and to compare their discriminative ability. Discriminative ability of the scores for predicting events was assessed by means of receiver operating characteristics (ROC) curves. The validation cohort retrospectively included 614 consecutive patients (69 ± 13 years, 72% women) with severe TR studied with echocardiography in a tertiary care hospital and followed for up to 14 years (median 5 years, p25-75 2-7 years), with 358 deaths and 620 HF admissions on follow-up. Discriminative abilities for predicting death (C-statistic .72 [95% CI .68-.76] for the TRI-SCORE; .75 [.71-.78] for the Hochstadt score; .72 [.68-.76] for the Wang score; and .74 [.70-.78] for the TRIO score, p < .0005 for all) or the combined endpoint (C-statistic .74 [.70-.78]; .74 [.70-.78], .73 [.69-.77] and .76 [.72-.80], respectively, p < .0005 for all) on follow-up were statistically significant for all of them. Paired comparisons among them for predicting both endpoints were all non-significant. All tested scores showed significant and similar discriminative ability for predicting the combined endpoint of mortality or HF admission in this independent validation study of patients with severe TR.

Evolving trends in novel sleep tracking and sleep testing technology publications between 2020-2022.

To update sleep medicine providers regarding (1) published research on the uses and performance of novel sleep tracking and testing technologies (2) the use of artificial intelligence to acquire and process sleep data and (3) research trends and gaps regarding the development and/or evaluation of these technologies. Medline and Embase electronic databases were searched for studies utilizing screening and diagnostic sleep technologies, published between 2020 and 2022 in journals focusing on human sleep. Studies' quality was determined based on the Study Design criteria of The Oxford Centre for Evidence-Based Medicine Levels of Evidence. 96 of 3849 articles were included. Most studies were adult performance evaluation (validation) studies, often comparing a novel technology to polysomnography. Sleep tracker publications tended to be USA-based, non-industry funded, performance studies on healthy adults using non-FDA (Food and Drug Administration) cleared technologies. Sleep apnea testing technologies were more frequently industry-funded and FDA-cleared. All studied technologies utilized software with an algorithm and/or artificial intelligence. Few studies used randomized control designs, or accounted for recruitment/attrition biases associated with participants' age, race/ethnicity, or comorbid health conditions. Evidence-based publications have not kept pace with the proliferation and landscape of' consumer and clinical sleep technologies. Due to the variance in technologies used within sleep research, careful review of the software used within studies is recommended. Future publications may fill identified gaps by including underrepresented populations, maintaining independence from industry, and through rigorous study design.

Estimating hip impact velocity and acceleration from video-captured falls using a pose estimation algorithm

Analyzing video footage of falls in older adults has emerged as an alternative to traditional lab studies. However, this approach is limited by the labor-intensive process of manually labeling body parts. To address this limitation, we aimed to validate the use of the AI-based pose estimation algorithm (OpenPose) in assessing the hip impact velocity and acceleration of video-captured falls. We analyzed 110 videos of 13 older adults (64.0 ± 5.9 years old) falling sideways in an experimental setting. By applying OpenPose to each video, we generated a time series of hip positions in the video, which were then analyzed using custom MATLAB code to calculate hip impact velocity and acceleration. These calculations were compared against ground truth measurements obtained from motion capture systems (VICON for hip impact velocity) and inertial measurement units (MC10 for hip impact acceleration). We examined the agreement between the ground truth and OpenPose measurements in terms of mean of absolute error (MAE), mean of absolute percentage error (MAPE), and bias (mean of error). Results showed that OpenPose had a good accuracy in estimating hip impact velocity with minimal bias (MAE: 0.17 ± 0.13 m/s, MAPE: 7.28 ± 5.21%; percent bias: − 1.27%). However, its estimation of hip impact acceleration (i.e., peak vertical hip acceleration at impact) showed poor accuracy (MAPE: 26.3 ± 19.4%), showing substantial underestimation in instances of high acceleration impacts (> 3.0 g). Further ANOVA analysis revealed OpenPose’s ability to discern significant differences in hip impact velocity and acceleration based on the movement response utilized during the fall (e.g., stick-like fall, tuck-and-roll, knee block). This is the first study to validate the use of a pose estimation algorithm for identifying the hip impact kinematics in video-captured falls among older adults. Future validation studies involving diverse camera settings, fall contexts, and biomechanical parameters are warranted to extend this support for using pose estimation algorithms in this field.

Indomethacin and Cefazolin for Physical Exam-Indicated Cerclage to Improve Latency: A Protocol Validation Study.

A single-center randomized trial showed improved latency with use of indomethacin and cefazolin (I/C) during and following exam-indicated cerclage (EIC). The same center recently published a pre/post comparison demonstrating similar results. This research aimed to validate the protocol in a different setting. Retrospective cohort study of singleton pregnancies undergoing EIC at a single center between 2013 and 2022. EIC was performed for painless cervical dilation between 16 and 23 weeks' gestation with dilation ≥1 cm. Exposure was defined as receipt of I/C during and following EIC. The primary outcome was latency ≥ 28 days after cerclage placement. Secondary outcomes included latency; gestational age at delivery; delivery ≤ 28 weeks; preterm premature rupture of membranes; intra-amniotic infection (IAI); and median birth weight. Bivariate statistics were used to analyze data; multivariable regression analyses were used to control for confounders (progesterone use, cervical dilation at time of cerclage placement, history of preterm birth, and prolapsing membranes). EIC was placed in 81 pregnancies and 48 (59%) received I/C. Baseline characteristics did not differ between groups, except that prolapsing membranes were significantly less likely in patients receiving I/C (6.2 vs. 21.2%; p = 0.04). Latency ≥ 28 days occurred in 90% of I/C recipients and 82% of the controls (p = 0.32); this difference remained nonsignificant after controlling for confounders (adjusted relative risk: 1.02 [95% confidence interval: 0.85, 1.21]). I/C recipients had lower rates of IAI (4.2 vs. 24.2%, p = 0.007), even after adjustment (adjusted relative risk: 0.18 [95% confidence interval: 0.04, 0.74]). Other secondary outcomes did not differ. Use of I/C at the time of EIC at this center was not associated with increased latency to delivery (albeit in a small cohort) but was associated with lower rates of intra-amniotic infection. Larger-scale validation studies would be helpful to confirm the value of this intervention. · Indomethacin/cefazolin (I/C) increased latency for exam-indicated cerclage in a trial.. · We performed an observational comparison to validate these findings.. · I/C use for exam-indicated cerclages was associated with decreased intra-amniotic infection.. · I/C was not associated with change in latency.. · Larger-scale validation studies needed to confirm the value of intervention..

A Nomogram for Predicting Perirenal Hematoma After Percutaneous Nephrolithotomy.

Perirenal hematoma (PRH) is a notable complication following percutaneous nephrolithotomy (PCNL) with significant implications for patient outcomes. This study aimed to develop a nomogram predictive model for PRH after PCNL. Retrospective data from patients who underwent PCNL were analyzed. Patient demographics, stone characteristics, and operative details were assessed for their association with PRH using univariate and multivariate analyses. A nomogram was constructed based on identified predictors. Among 1047 patients, 6.2% developed PRH. Factors significantly associated with PRH included age, urine culture, stone surface area, operative time and estimate blood loss. These factors were incorporated into the nomogram, providing a user-friendly tool for preoperative risk assessment of PRH following PCNL. We developed a nomogram predictive model for PRH after PCNL, facilitating individualized risk assessment and preventive strategies. Implementation of this nomogram may enhance patient safety and optimize surgical outcomes in PCNL procedures. Further validation studies are warranted to assess its generalizability and accuracy.