Diagnostic Discrimination Research Articles

Peripheral protein inflammatory biomarkers in bipolar disorder and major depressive disorder: A systematic review and meta-analysis.

Bipolar disorder (BD) and major depressive disorder (MDD) are mood disorders. The most frequent clinical presentation of BD and MDD is depression, which contributes to high rates of misdiagnosis between disorders. To support diagnostic discrimination and therapeutic stratification, we aim to perform a systematic review and meta-analysis evaluating peripheral protein inflammatory biomarkers between BD and MDD, with a focus on the depressive state. We conducted a literature search on PubMed, PsycInfo and Embase with no year/language restrictions. Original studies including human participants with a BD or MDD diagnosis which directly compared levels of peripheral protein inflammatory biomarkers between groups were included. A random effects meta-analysis was performed. 35 studies were included in the systematic review. 9 studies were included in the meta-analysis. The meta-analysis showed IL-7 (p < 0.01) levels were significantly decreased in BD, and IL-9 (p < 0.01), CCL3 (p = 0.03), CCL4 (p = 0.01), CCL5 (p = 0.02) and CCL11 (p = 0.04) levels were significantly increased in BD. High heterogeneity and limited dataset size restricted our meta-analysis to a small subset of biomarkers and limited our exploration of the effects of moderator variables. This study found differences in IL-7, IL-9, CCL3, CCL4, CCL5 and CCL11 between BD and MDD in a depressive state. These findings support the notion that inflammation is associated with mood disorder pathophysiology, particularly with respect to T-cell network dysregulation. Further studies can assist in better understanding differences between disorders and work towards clinical applications.

Soluble Membrane Attack Complex (sMAC) as a Potential Diagnostic Biomarker Differentiating Acute Viral Encephalitis from Guillain-Barré Syndrome, a Post-infectious Autoimmune State.

Acute encephalitis syndrome (AES) presents with the onset of fever, altered sensorium and/or seizures, known to be caused by various infectious and non-infectious aetiological agents, among which viruses are the commonest. The severity of AES prompts rapid diagnosis, which is not met by time-consuming conventional diagnostic techniques. In this study, archived cerebrospinal fluid samples of laboratory-confirmed viral AES, an acute infectious condition and Guillain-Barré Syndrome (GBS), a post-infectious, autoimmune condition was assessed for soluble membrane attack complex (sMAC) using ELISA. Statistical analysis was performed to understand the diagnostic potential of sMAC in AES versus GBS patients. sMAC levels were significantly increased in viral encephalitis compared with GBS samples (43.69ng/mL vs. 29.33ng/mL, P < 0.05). The diagnostic potential of sMAC was assessed using the receiver operating characteristic(ROC) curve, which demonstrated excellent diagnostic discrimination between viral AES and GBS (area under curve = 0.8125, 95% CI, P < 0.0001). Using Youden's index, the optimal sMAC cut-off was calculated as 33.6ng/mL for distinguishing AES from GBS. The findings of our study revealed significant increase in sMAC levels in AES patients in comparison to those with GBS. This underscores the utility of sMAC as a valuable tool in distinguishing between AES and GBS, thereby facilitating more tailored patient management strategies, which varies for acute infectious and post-infectious conditions especially those mediated by autoimmunity.

Diagnostic discrimination of social network indicators in alcohol use disorder: Initial examination using high-resolution and brief assessments.

Social network analysis (SNA) characterizes the structure and composition of a person's social relationships. Network features have been associated with alcohol consumption in observational studies, primarily of university undergraduates. No studies have investigated whether indicators from a person's social network can accurately identify the presence of alcohol use disorder (AUD), offering an indirect strategy for identifying AUD. Two cross-sectional case-control designs examined the clinical utility of social network indicators for identifying individuals with AUD (cases) versus demographically matched drinkers without AUD (controls). Study 1 (N = 174) used high-resolution egocentric SNA assessment, whereas Study 2 (N = 189) used a brief assessment. In Study 1, significant differences between AUD+ participants and controls were present for network alcohol severity (i.e., heavy drinking days; d = 1.23) and frequency (d = 0.35), but not network structural features. Network alcohol severity exhibited very good classification of AUD+ individuals versus controls (area under the curve [AUC] = 0.80), whereas network frequency did not (AUC = 0.61). In Study 2, significant differences were present for network alcohol severity (d = 1.02), quantity (d = 0.74), and frequency (d = 0.43), and severity exhibited good differentiation (AUC = 0.76). Social network indicators of alcohol involvement robustly differentiated AUD+ individuals from matched controls, and the brief assessment performed almost as well as the high-resolution assessment. These findings provide proof-of-concept for severity-related SNA indicators as promising novel clinical assessments for AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

SERS Platform for Integrated Enrichment, Isolation, and Identification of Multiple Respiratory Viruses in a Single Assay Using 3D Stereoscopic SERS Tags and Flocked Swabs.

Influenza (flu) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibit similar clinical symptoms, complicating the diagnosis and clinical management of these critical respiratory infections. Thus, there is an urgent need for rapid on-site detection technologies that can simultaneously detect SARS-CoV-2 and influenza A viruses. Here, we have developed the first platform that combines in situ sampling with immune swabs and multichannel surface-enhanced Raman spectroscopy (SERS) for simultaneous screening of these two respiratory viruses in a single assay. A seed-mediated growth method was used to assemble a number of silver spheres on the surface of Fe3O4@SiO2 spheres, which not only creates extensive Raman hotspots but also provides numerous sites for Raman signaling molecules, enhancing the sensing sensitivity. Integrating two specific Raman signaling molecules into the nanospheres allows for the parallel detection of both viruses, improving the efficiency of SERS signal read-out. Rapid quantitative screening of both SARS-CoV-2 and H1N1 is achievable within 15 min, with detection limits of 7.76, and 8.13 pg·mL-1 for their respective target proteins. The platform demonstrated excellent performance in testing and analyzing 98 clinical samples (SARS-CoV-2:50; influenza A:48), achieving sensitivities of 88.00, and 95.83% for SARS-CoV-2 and influenza A, respectively. Pearson's correlation analysis revealed a significant correlation with the clinical CT values (P < 0.0001), underscoring the great potential of this platform for the early, rapid, and simultaneous diagnostic discrimination of multiple pathogens.

Preliminary evaluation of a novel method for computed tomography quantification of lumbosacral articular process displacement in dogs with and without degenerative lumbosacral stenosis.

This study aimed to describe the diagnostic discrimination and reliability of a novel technique for quantifying lumbosacral articular process displacement (LSAPD) on dorsal plane computed tomography (DPCT) imaging in dogs with and without degenerative lumbosacral stenosis (DLSS). DPCT surveys of the lumbosacral vertebral column were performed with dogs positioned in extension and flexion. LSAPD is defined as the distance between the cranial aspects of the L7 and S1 articular processes. The LSAPD ratio is identified by dividing the LSAPD by the length of the L7 articular process. Intraclass correlation coefficients (ICCs) for intra- and inter-observer reliability were calculated, and logistic regressions were used to test for the association of LSAPD and LSAPD ratio with odds of DLSS. Significance was set at 0.05. Receiver operator characteristic (ROC) curves were calculated to determine diagnostic discrimination and optimal cutoff for LSAPD and LSAPD ratio in the diagnosis of DLSS. Intra- and inter-observer reliabilities were excellent for most measurements. In the current cohort, excluding covariates, the area under the curve (AUC) (95%CI) for LSAPD and LSAPD ratio measured in a flexed position were both 0.89 (0.82-0.96), suggesting potentially excellent discrimination for using this measurement as a marker for diagnosing DLSS, pending further studies. The cutoffs for flexed LSAPD and LSAPD ratio that maximizes Youden's index were ≥ 1.2 mm and ≥ 9%, respectively. When age and weight were subsequently included as covariates in a multivariable analysis, a significant relationship between LSAPD or LSAPD ratio and odds of diagnosis of DLSS was not demonstrated, suggesting the need for a larger sample size. The results of this study suggest that measurements of LSAPD and LSAPD ratio on DPCT are feasible and reliable, although their diagnostic discrimination in DLSS should be evaluated further in future prospective studies.

Romberg's test revisited: Changes in classical and advanced sway metrics in patients with pure sensory neuropathy

ObjectivesThe Romberg test, undoubtedly a classical and well-established method in physical neurological assessment of patients with sensory ataxia, has long been suspected to be prone to several limitations. Here, we quantified upright stance before and after visual deprivation in a selected cohort of patients with pure sensory neuropathy. MethodsStatic balance was assessed in sensory neuropathy patients during quiet stance on a force platform under different visual and proprioceptive feedback conditions. Sural nerve neurography was employed to evaluate the severity of peripheral neuropathy. Conventional and advanced postural sway metrics were investigated to draw a quantitative analogy to the clinical Romberg test. ResultsPosturographic analyses showed that patients displayed Romberg and vestibular Romberg quotient values around 2, indicating an approximately twofold increase in body sway in the absence of vision. However, the diagnostic discrimination ability between patients and controls was only modest. Even less impactful were the diagnostic contributions of frequency domain and non-linear sway analyses. This was primarily attributed to the heightened body sway exhibited by patients with sensory neuropathy under 'eyes open' conditions, diminishing the contrast with the 'eyes closed' condition as assessed in the classical Romberg test. ConclusionWe conclude that the Romberg test, even in its quantitative form with the aid of an apparatus, had an unsatisfactory classification value in terms of distinguishing patients from healthy controls. Instead, it should be interpreted within the comprehensive context of the broader neurological examination and the electrodiagnosis of peripheral nerve function.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n=5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Development and validation of a machine learning-based interpretable model for predicting sepsis by complete blood cell parameters

BackgroundSepsis, a severe infectious disease, carries a high mortality rate. Early detection and prompt treatment are crucial for reducing mortality and improving prognosis. The aim of this research is to develop a clinical prediction model using machine learning algorithms, leveraging complete blood cell (CBC) parameters, to detect sepsis at an early stage. MethodsThe study involved 572 patients admitted to West China Hospital of Sichuan University between July 2020 and September 2021. Among them, 215 were diagnosed with sepsis, while 357 had local infections. Demographic information was collected, and 57 CBC parameters were analyzed to identify potential predictors using techniques such as the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The prediction model was built using Logistic Regression and evaluated for diagnostic specificity, discrimination, and clinical applicability including metrics such as the area under the curve (AUC), calibration curve, clinical impact curve, and clinical decision curve. Additionally, the model's diagnostic performance was assessed on a separate validation cohort. Shapley's additive explanations (SHAP), and breakdown (BD) profiles were used to explain the contribution of each variable in predicting the outcome. ResultsAmong all the machine learning methods' prediction models, the LASSO-based model (λ = min) demonstrated the highest diagnostic performance in both the discovery cohort (AUC = 0.9446, P < 0.001) and the validation cohort (AUC = 0.9001, P < 0.001). Furthermore, upon local analysis and interpretation of the model, we demonstrated that LY-Z, MO-Z, and PLT-I had the most significant impact on the outcome. ConclusionsThe predictive model based on CBC parameters can be utilized as an effective approach for the early detection of sepsis.

Supplemental Material for Diagnostic Discrimination of Social Network Indicators in Alcohol Use Disorder: Initial Examination Using High-Resolution and Brief Assessments

Supplemental Material for Diagnostic Discrimination of Social Network Indicators in Alcohol Use Disorder: Initial Examination Using High-Resolution and Brief Assessments

Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Prostate MRI followed by prostate biopsy. The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

Abstract A050: MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium

Abstract Epithelial ovarian cancer (EOC) has poor survival given no effective early detection strategies and non-specific early disease symptoms. We have formed a consortium with European and American partners to provide the necessary validation of a microRNA (miRNA) panel for earlier detection of EOC using serum samples and data from prospective cohorts, paired with clinical data and biospecimens. Our objectives are to validate a serologic miRNA panel that, together with CA125, would have sufficient diagnostic discrimination to be used as a tool that, complementary to imaging, would allow early identification and direction of patients with suspected malignancy to personalized gyn-oncological care. We will validate serum miRNA profiles as biomarkers for early detection in samples collected from cases ⇐3 years prior to EOC diagnosis and matched controls and characterize sources of variation in miRNA profiles and their impact on discrimination. Evaluate miRNA profiles in the clinical setting and the discrimination between EOC cases and women with (i) benign ovarian tumors in blood samples collected prior to diagnostic surgery and (ii) underlying comorbidities (including other cancer types). Further, we will evaluate serum miRNA profiles as predictors of recurrence and will compare tumor tissue and serum miRNA profiles. Develop and optimize protocols for data integration and methods for algorithm development. Direct in silico validation, including miRNA and CA125 levels, and clinical and epidemiologic data. Address Ethical, Legal and Social Aspects (ELSA), including activities in key stakeholder groups. Challenges in health data privacy and sharing, including for development of an artificial intelligence model, will be addressed, as will the definition of a “healthy” population with respect to miRNA profiles for EOC. The consortium was launched in 2023, with the first findings expected in 2024. Beyond the initial direct aims of the consortium, expanded aims on further promising markers are envisaged. Citation Format: Renée Turzanski Fortner, Hilde Langseth. MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A050.

Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET

PurposeAccording to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively.MethodsEighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied.ResultsTBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97).ConclusionThe findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Long noncoding RNAs MEG3, TUG1, and hsa-miR-21-3p are potential diagnostic biomarkers for coronary artery disease

Peripheral blood biomarkers are of particular importance to diagnose certain diseases including coronary artery disease (CAD) due to their non-invasiveness. Investigating the expression of noncoding RNAs (ncRNAs) paves the way to early disease diagnosis, prognosis, and treatment. Consequently, in this research, we aimed to investigate a panel of ncRNAs as potential biomarkers in patients with coronary artery disease. Two different groups have been designed (control and CAD). All participants were subjected to interviews and clinical examinations. Peripheral blood samples were collected, and plasma was extracted. At the same time, target ncRNAs have been selected based on literature review and bioinformatic analysis, and later they underwent investigation using quantitative real-time PCR. The selected panel encompassed the long non-coding RNAs (lncRNAs) MEG3, TUG1, and SRA1, and one related microRNA (miRNA): hsa-miR-21-3p. We observed statistically significant upregulation in MEG3, TUG1, and hsa-miR21-3p in CAD patients compared to control participants (p-value 0.01). Nevertheless, SRA1 exhibited downregulation with no statistical significance (p-value 0.05). All ncRNAs under study displayed a significantly strong correlation with disease incidence, age, and smoking. Network construction revealed a strong relationship between MEG3 and TUG1. ROC analysis indicated high potentiality for hsa-miR-21-3p to be a promising biomarker for CAD. Moreover, MEG3 and TUG1 displayed distinguished diagnostic discrimination but less than hsa-miR-21-3p, all of them exhibited strong statistical significance differences between CAD and control groups. Conclusively, this research pinpointed that MEG3, TUG1, and hsa-miR-21-3p are potential biomarkers of CAD incidence and diagnosis.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma

Background & aimsThe present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model.MethodsWe recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison.ResultsSeven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973–0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835– 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).ConclusionCT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Replication of a neuroimaging biomarker for striatal dysfunction in psychosis.

To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.

Pediatric Sport-Related Concussion: Recommendations From the Amsterdam Consensus Statement 2023.

The 6th International Consensus Conference on Concussion in Sport, Amsterdam 2022, addressed sport-related concussion (SRC) in adults, adolescents, and children. We highlight the updated evidence-base and recommendations regarding SRC in children (5–12 years) and adolescents (13–18 years). Prevention strategies demonstrate lower SRC rates with mouthguard use, policy disallowing bodychecking in ice hockey, and neuromuscular training in adolescent rugby. The Sport Concussion Assessment Tools (SCAT) demonstrate robustness with the parent and child symptom scales, with the best diagnostic discrimination within the first 72 hours postinjury. Subacute evaluation (&amp;gt;72 hours) requires a multimodal tool incorporating symptom scales, balance measures, cognitive, oculomotor and vestibular, mental health, and sleep assessment, to which end the Sport Concussion Office Assessment Tools (SCOAT6 [13+] and Child SCOAT6 [8–12]) were developed. Rather than strict rest, early return to light physical activity and reduced screen time facilitate recovery. Cervicovestibular rehabilitation is recommended for adolescents with dizziness, neck pain, and/or headaches for greater than 10 days. Active rehabilitation and collaborative care for adolescents with persisting symptoms for more than 30 days may decrease symptoms. No tests and measures other than standardized and validated symptom rating scales are valid for diagnosing persisting symptoms after concussion. Fluid and imaging biomarkers currently have limited clinical utility in diagnosing or assessing recovery from SRC. Improved paradigms for return to school were developed. The variable nature of disability and differences in evaluating para athletes and those of diverse ethnicity, sex, and gender are discussed, as are ethical considerations and future directions in pediatric SRC research.

Diagnostics from Polarization of Scattered Optical Light from Galactic Infrared Cirrus

We propose polarization of scattered optical light from intermediate Galactic latitude infrared cirrus as a new diagnostic to constrain models of interstellar dust and the anisotropic interstellar radiation field (aISRF). For single scattering by a sphere, with Mie scattering phase functions for intensity and polarized intensity for a dust model at a given wavelength (Sloan r and g bands), and with models of anisotropic illumination from the entire sky (represented in HEALPix), we develop the formalism for calculating useful summary parameters for an integrated flux nebula (IFN): the average of the phase function weighted by the illumination, polarization angle (ψ), and polarization fraction (p). To demonstrate the diagnostic discrimination of polarization from scattered light, we report on the effects of different anisotropic illumination models and different dust models on the summary parameters for the Spider IFN. The summary parameters are also sensitive to the IFN location, as we illustrate using FRaNKIE illumination models. For assessing the viability of dust and aISRF models, we find that observations of ψ and p of scattered light are indeed powerful new diagnostics to complement joint modeling of the intensity of scattered light (related to the average phase function) and the intensity of thermal dust emission. However, optically thin IFNs that can be modeled using single scattering are faint and p is not large, as it could be with Rayleigh scattering, and so these observations need to be carried out with care and precision. Results for the Draco nebula compared to the Spider illustrate the challenge.

Telomerase RNA component lncRNA as potential diagnostic biomarker promotes CRC cellular migration and apoptosis evasion via modulation of β-catenin protein level.

Long non-coding RNA (LncRNA) telomerase RNA component (TERC) has telomerase-dependent and independent activity in numerous cancer types. The present study purposes to demonstrate the role of lncRNA TERC as a diagnostic serum biomarker in colorectal cancer (CRC) patients and the molecular mechanism of lncRNA TERC in inducing tumor in CRC cell lines. PCR array was performed to examine lncRNAs dysregulated in CRC. LncRNA TERC expression level was evaluated in 70 CRC patients and 35 control subjects using RT-qPCR. Then transfection was performed to build down-expression models of lncRNA TERC. ROC curve analysis was applied to assess the diagnostic value of serum LncRNA CRC. In addition, RT-qPCR was used to detect expression level of lncRNA TERC and β-catenin mRNA. Moreover, ELISA and Western blot were used to detect the level of β-catenin protein in sera of CRC patients and cell lines. The biological functions such as cell growth and migration of CRC cells were assessed using a wound healing assay. Cell cycle analysis and apoptosis analysis were performed using flow cytometry. The lncRNA TERC is overexpressed in the sera of CRC patients with high diagnostic and stage discrimination accuracy. Furthermore, lncRNA TERC expression was upregulated in CRC cell lines and lncRNA TERC silencing induced cell arrest and apoptosis and inhibited cell migration. Furthermore, inhibition of lncRNA TERC reduces β-catenin protein levels. The lncRNA TERC could be considered as an early stages CRC diagnostic biomarker with a good ability to discriminate between CRC stages. lncRNA TERC induces CRC by promoting cell migration and evading apoptosis by elevating the level of β-catenin protein.

Artificial intelligence for dementia-Applied models and digital health.

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Multiscale Diffusion Entropy Analysis for the Detection of Crucial Events in Cardiac Pathology.

The significance of crucial events in explaining the dynamics of a physiological system has only been recently emerging. Crucial events are yet to be fully understood and implemented in clinical applications of physiological signal processing. This paper proposes the application of modified diffusion entropy (MDEA) and novel multiscale diffusion entropy analyses (MSDEA) on measuring the temporal complexity of the ECG time series to improve crucial events detection performance. Thirty samples of each of three groups of ECG datasets from PhysioNet with recordings of cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythm (NSR) were analyzed using MDEA with stripes followed by MSDEA. Healthy NSR ECGs showed an approximate 15% greater inverse power law (IPL) and scaling δ indices than pathologic CHF and ARR signals. Additionally, the scaling indices for the pathologic groups showed higher standard deviations, indicating that crucial events determined by MDEA reveal latent differences in ECG complexity that could better be investigated across multiple time scales of temporally decomposed signals using MSDEA which combines multiscale entropy (MSE) and MDEA. Hence, MSDEA showed an improved, clearer discrimination between the healthy and pathological cardiac signals (p<0.0005) characterized by a range of NSR complexity indices twice the range of the pathological values associated with ARR and CHF across twenty temporal scales as well as more reliable trend lines (R2>=0.95).Clinical Relevance- This research proposes a novel and enhanced diagnostic discrimination across healthy and pathologic cardiac conditions based on biomedical signal processing of ECG recordings utilizing the principle of crucial events detection.