The Utility of a Novel Neuropsychological Measurement to Analyze Event-Related Attentional Behaviors among Young Children with Attention Deficit Hyperactivity Disorder-a Pilot Study.

OBJECTIVE: The identification and diagnosis of children with attention deficit hyperactivity disorder (ADHD) traits is challenging during the preschool stage. Neuropsychological measures may be useful in early assessments. Furthermore, analysis of event-related behavior appears to be an unmet need for clinical treatment planning. Conners' Kiddie Continuous Performance Test (K-CPT) is the most popular well-established neuropsychological measurement but lacks event markers to clarify the heterogeneous behaviors among children. This study utilized a novel commercially available neuropsychological measure, the ΣCOG, which was more game-like and provided definite event markers of individual trial in the test. METHODS: Thirty-three older preschool children (14 were diagnosed with ADHD, mean age: 66.21 ± 5.48 months; 19 demonstrated typical development, mean age: 61.16 ± 8.11 months) were enrolled and underwent comprehensive medical and developmental evaluations. All participants underwent 2 versions of neuropsychological measures, including the K-CPT, Second Edition (K-CPT 2) and the ΣCOG, within a short interval. RESULTS: The study indicated the omissions and response time scores measured in this novel system correlated with clinical measurement of the behavioral scales in all participants and in the group with ADHD; additionally, associations with the traditional K-CPT 2 were observed in commissions and response time scores. Furthermore, this system provided a within-task behavioral analysis that identified the group differences in the specific trial regarding omission and commission errors. CONCLUSIONS: This innovative system is clinically feasible and can be further used as an alternative to the K-CPT 2 especially in research by revealing within-task event-related information analysis.

Reliability of dynamic causal modelling of resting-state magnetoencephalography.

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center.

The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.

Association of Perinatal Cardiovascular Features with Angiotensin System Expressions in Maternal Preeclampsia.

We hypothesized and investigated whether prenatal exposure to preeclampsia (PE) would simultaneously affect perinatal cardiovascular features and angiotensin system expressions. This prospective study was composed of mother-neonate dyads with (n = 49) and without maternal preeclampsia (n = 48) in a single tertiary medical center. The neonates exposed to PE had significantly larger relative sizes for the left and right coronary arteries and a higher cord plasma level of aminopeptidase-N, which positively correlated with the maternal diastolic blood pressures and determined the relative sizes of the left and right coronary arteries, whereas the encoding aminopeptidase-N (ANPEP) mRNA level in the PE cord blood leukocytes was significantly decreased, positively correlated with the neonatal systolic blood pressures (SBPs), and negatively correlated with the cord plasma-induced endothelial vascular cell adhesion molecule-1 mRNA levels. The PE cord plasma significantly induced higher endothelial mRNA levels of angiotensin II type 1 receptor (AT1R) and AT4R, whereas in the umbilical arteries, the protein expressions of AT2R and AT4R were significantly decreased in the PE group. The endothelial AT1R mRNA level positively determined the maternal SBPs, and the AT4R mRNA level positively determined the neonatal chamber size and cardiac output. In conclusion, PE may influence perinatal angiotensin system and cardiovascular manifestations of neonates across placentae. Intriguing correlations between these two warrant further mechanistic investigation.

Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration.

Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.

New look at the power of zero coronary artery calcium (CAC) in Asian population: a systemic review and meta-analysis.

Background: Numerous studies have validated a 5-year warranty period for heart health in Western populations with a coronary artery calcium (CAC) score of zero. While the calcium score is a crucial cardiovascular risk indicator, its interpretation in Asian populations remains unclear. This meta-analysis aimed to clarify the uncertainty surrounding the prevalence, warranty period, and prognostic implications of zero CAC scores in Asian populations. It also examined the impact of sex on subclinical CAC progression. While the calcium score is a crucial cardiovascular risk indicator, its interpretation in Asian populations remains unclear. The study aimed to shed light on these issues by exploring the specificities of subclinical CAC progression in the Asian context. Methods: Our systematic literature search, from the study's inception to October 2023, targeted studies on subclinical CAC progression in the Asian population with a zero CAC score. We searched the Cochrane Library, and PubMed. The search terms included "zero score", "coronary calcification", "zero CAC score", and "CAC scan". Results: We evaluated seven published studies through a meta-analysis and assessed the risk of bias using the Newcastle-Ottawa Scale (NOS). In this meta-analysis of three observational studies addressing zero CAC prevalence (n=7,661), the pooled prevalence of zero CAC scores in the Asian population was 18.2% [95% confidence interval (CI): 12.5-25.9%]. A significant difference in follow-up warranty period was observed between the CAC zero group and subclinical CAC progression group (mean difference, 1.26 years; 95% CI: 0.94-1.58; P<0.001). Furthermore, the conversion rate of subclinical CAC progression differed significantly between males and females (risk ratio, 2.37; 95% CI: 1.98-2.84; P<0.001). Analysis of four studies revealed a notable discrepancy in the major adverse cardiovascular event (MACE) rate between the CAC (-) and CAC (+) groups (risk ratio, 4.78; 95% CI: 2.21-10.36; P<0.001). Conclusions: The meta-analysis of zero CAC scores in Asian populations suggested an 18.2% prevalence. A 5-year warranty period was noted, with heightened subclinical CAC progression likelihood after this duration. Additionally, sex-based differences were observed in subclinical CAC progression rates. These findings will provide clinical cardiovascular risk stratification for guiding gender-specific clinical decision-making in asymptomatic in Asian individuals.

Lead exposure estimation through a physiologically based toxicokinetic model using human biomonitoring data and comparison with scenario-based exposure assessment: A case study in Korean adults.

Pb toxicity is linked to cardiovascular and nephrotoxicity issues. Exposure to this heavy metal can occur through food and drinking water. Therefore, this study aimed to evaluate Pb exposure and assess health risks in Korean adults using a physiologically based toxicokinetic (PBTK) model. Human blood Pb concentrations were monitored using the Korean National Environmental Health Survey (KoNEHS) Cycle 4. The average Pb exposure in Korean adults was 0.520 µg/kg bw/day. The PBTK results were compared with scenario-based results from the 2021 risk assessment report of five heavy metals, including Pb, conducted by the MFDS. Exposure determined through reverse dosimetry was approximately two times higher than scenario-based exposure (0.264 µg/kg bw/day). The higher exposure levels obtained during PBTK analysis may be attributed to sustained exposure within historically more contaminated living environments and the long half-life of Pb. These findings suggest that the PBTK-based method can quantify aggregated exposure levels in the body over time, potentially serving as a complementary tool to address the constraints of scenario-based assessment methods for integrated risk assessment. Moreover, this model is convenient and cost-effective compared with scenario-based exposure estimation. These findings can facilitate the application of model for tracking continuous national changes in hazardous substance levels.

Treatment outcomes of elective neck dissection in intrathoracic esophageal carcinoma.

In the present study, the outcomes of elective neck dissection in patients with intrathoracic esophageal squamous cell carcinoma were investigated. From January 2016 to December 2022, 21 patients who underwent esophagectomy and elective neck dissection (both neck level IV) for intrathoracic esophageal squamous cell carcinoma were enrolled. Of these 21 patients, 19 patients were male and 2 were female. A total of 11 patients received concurrent chemoradiotherapy (CCRT) as preoperative treatment. As a result of elective neck dissection at both neck level IV, occult neck metastasis of esophageal squamous cell carcinoma was diagnosed in 3 cases, all of which involved left neck lymph nodes. The incidence of occult neck metastasis was statistically significant in patients with preoperative CCRT, high T stage and high N stage (P<0.05). In addition, 16 out of 21 patients had been under follow-up without disease recurrence after the completion of treatment. However, 3 out of 21 patients succumbed to esophageal squamous cell carcinoma and 2 out of 21 patients were alive with stable disease of esophageal carcinoma. The follow-up period was 19.2±18.4 months. In conclusion, three-field lymph node dissection for intrathoracic esophageal squamous cell carcinoma may be necessary in patients with certain phenotypes, such that collaboration between thoracic surgeons and otolaryngologists may help reduce surgical complications.

Benchmarking of a multi-biomarker low-volume panel for Alzheimer's Disease and related dementia research.

Alzheimer's Disease (AD) biomarker measurement is key to aid in the diagnosis and prognosis of the disease. In the research setting, participant recruitment and retention and optimization of sample use, is one of the main challenges that observational studies face. Thus, obtaining accurate established biomarker measurements for stratification and maximizing use of the precious samples is key. Accurate technologies are currently available for established biomarkers, mainly immunoassays and immunoprecipitation liquid chromatography-mass spectrometry (IP-MS), and some of them are already being used in clinical settings. Although some immunoassays- and IP-MS based platforms provide multiplexing for several different coding proteins there is not a current platform that can measure all the stablished and emerging biomarkers in one run. The NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) is a mid-throughput platform with antibody-based measurements with a sequencing output that requires 15µL of sample volume to measure more than 100 analytes, including those typically assayed for AD. Here we benchmarked and compared the AD-relevant biomarkers including in the NULISA against validated assays, in both CSF and plasma. Overall, we have found that CSF measures of Aß42/40, NfL, GFAP, and p-tau217 are highly correlated and have similar predictive performance when measured by immunoassay, mass-spectrometry or NULISA. In plasma, p-tau217 shows a performance similar to that reported with other technologies when predicting amyloidosis. Other established and exploratory biomarkers (total tau, p-tau181, NRGN, YKL40, sTREM2, VILIP1 among other) show a wide range of correlation values depending on the fluid and the platform. Our results indicate that the multiplexed immunoassay platform produces reliable results for established biomarkers in CSF that are useful in research settings, with the advantage of measuring additional novel biomarkers using minimal sample volume.

Quantifying the interrelationships between physical, social, and cognitive-emotional components of mental fitness using digital technology.

Mental fitness is a construct that goes beyond a simple focus on subjective emotional wellbeing to encompass more broadly our ability to think, feel, and act to achieve what we want in our daily lives. The measurement and monitoring of multiple (often interacting) domains is crucial to gain a holistic and complete insight into an individual's mental fitness. We aimed to demonstrate the capability of a new mobile app to characterise the mental fitness of a general population of Australians and to quantify the interrelationships among different domains of mental fitness. Cross-sectional data were collected from 4901 adults from the general population of Australians engaged in work or education who used a mobile app (Innowell) between September 2021 and November 2022. Individuals completed a baseline questionnaire comprised of 26 questions across seven domains of mental fitness (i.e., physical activity, sleep and circadian rhythms, nutrition, substance use, daily activities, social connection, psychological distress). Network analysis was applied at both a domain-level (e.g., 7 nodes representing each cluster of items) and an individual item-level (i.e., 26 nodes representing all questionnaire items). Only 612 people (12%) were functioning well across all domains. One quarter (n = 1204, 25%) had only one problem domain and most (n = 3085, 63%) had multiple problem domains. The two most problematic domains were physical activity (n = 2631, 54%) and social connection (n = 2151, 44%), followed closely by daily activity (n = 1914, 39%). At the domain-level, the strongest association emerged between psychological distress and daily activity (r = 0.301). Psychological distress was the most central node in the network (as measured by strength and expected influence), followed closely by daily activity, sleep and circadian rhythms and then social connection. The item-level network revealed that the nodes with the highest centrality in the network were: hopelessness, depression, functional impairment, effortfulness, subjective energy, worthlessness, and social connectedness. Social connection, sleep and circadian rhythms, and daily activities may be critical targets for intervention due to their widespread associations in the overall network. While psychological distress was not among the most common problems, its centrality may indicate its importance for indicated prevention and early intervention. We showcase the capability of a new mobile app to monitor mental fitness and identify the interrelationships among multiple domains, which may help people develop more personalised insights and approaches.

Regulating epithelial-mesenchymal plasticity from 3D genome organization.

Epithelial-mesenchymal transition (EMT) is a dynamic process enabling polarized epithelial cells to acquire mesenchymal features implicated in development and carcinoma progression. As our understanding evolves, it is clear the reversible execution of EMT arises from complex epigenomic regulation involving histone modifications and 3-dimensional (3D) genome structural changes, leading to a cascade of transcriptional events. This review summarizes current knowledge on chromatin organization in EMT, with a focus on hierarchical structures of the 3D genome and chromatin accessibility changes.

Label-Free Three-Dimensional Morphological Characterization of Cell Death Using Holographic Tomography.

This study presents a novel label-free approach for characterizing cell death states, eliminating the need for complex molecular labeling that may yield artificial or ambiguous results due to technical limitations in microscope resolution. The proposed holographic tomography technique offers a label-free avenue for capturing precise three-dimensional (3D) refractive index morphologies of cells and directly analyzing cellular parameters like area, height, volume, and nucleus/cytoplasm ratio within the 3D cellular model. We showcase holographic tomography results illustrating various cell death types and elucidate distinctive refractive index correlations with specific cell morphologies complemented by biochemical assays to verify cell death states. These findings hold promise for advancing in situ single cell state identification and diagnosis applications.

Exploring the Regulatory Landscape of Dementia: Insights from Non-Coding RNAs.

Dementia, a multifaceted neurological syndrome characterized by cognitive decline, poses significant challenges to daily functioning. The main causes of dementia, including Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and vascular dementia (VD), have different symptoms and etiologies. Genetic regulators, specifically non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are known to play important roles in dementia pathogenesis. MiRNAs, small non-coding RNAs, regulate gene expression by binding to the 3' untranslated regions of target messenger RNAs (mRNAs), while lncRNAs and circRNAs act as molecular sponges for miRNAs, thereby regulating gene expression. The emerging concept of competing endogenous RNA (ceRNA) interactions, involving lncRNAs and circRNAs as competitors for miRNA binding, has gained attention as potential biomarkers and therapeutic targets in dementia-related disorders. This review explores the regulatory roles of ncRNAs, particularly miRNAs, and the intricate dynamics of ceRNA interactions, providing insights into dementia pathogenesis and potential therapeutic avenues.

Classification of gait variation under mental workload in big five personalities.

BACKGROUND: Human behavior patterns involve mutual interactions among psychology, physiology, and stress, which are all associated with gait at different grades. RESEARCH QUESTIONS: The study aims to reveal the interrelationship among personality, mental workload, and gait patterns by capturing gait variations using inertial sensors. It also assesses individual personality traits and simulates stress to construct a gait classification model. METHODS: Sixty participants were instructed to perform regular, low, and high mental workload walking on the corridor to simulate a natural setting walking. Meanwhile inertial measurement units (IMUs) were placed on eight body parts. Mental workload was induced using the auditory n-back task, and their Big Five personality traits were evaluated. Gait data from IMUs were categorized into nine classifications of average, low, and high Big Five Inventory scores with three levels of mental workload walking. Subsequently, the segmentation gait data were used as input features for classifications in deep learning models, employing a sliding window long short-term memory network for nine classifications for different personality dimensions. RESULTS: The results indicated average accuracies of nine classifications were 83.6 % for Openness, 84.4 % for Conscientiousness, 82.0 % for Extraversion, 85.2 % for Agreeableness, and 84.5 % for Neuroticism across all IMU placements. Remarkably, gait data from the lower back IMU achieved the highest model performance, with an average accuracy of 92.7 %, in classifying the different levels of personality and mental workload walking. In contrast, the left wrist and chest showed several misclassifications among regular, low, and high mental workload walking across personality traits. SIGNIFICANCE: Successful classification can help monitor an individual's mental state in real time and analyze personality dimensions, providing feedback and suggestions. The present study demonstrated that gait characteristics can contribute to more profound and personalized health information.

Clinicopathological features and cancer transcriptomic profiling of poorly cohesive gastric carcinoma subtypes.

Gastric poorly cohesive carcinoma (PCC) manifests with a diffuse pattern and diverse tumor cell morphologies, often indicating a more unfavorable prognosis. Recent consensus has reclassified PCC based on the proportion of signet-ring cells (SRCs) in tumors for research purposes. The two most distinct subtypes, poorly cohesive carcinoma not otherwise specified (PCC-NOS) and signet-ring cell carcinoma (SRCC), are characterized by less than 10% and more than 90% SRCs, respectively. However, research comparing the clinicopathological and transcriptomic differences between these subtypes remains limited. In this study, we conducted a comparative analysis of clinicopathological features in 55 advanced-stage PCCs, consisting of 43 PCC-NOS and 12 SRCC cases. Subsequently, 12 PCC-NOS and 5 SRCC cases were randomly selected for initial cancer-related gene expression profiling and pathway enrichment analysis using the GeoMx digital spatial profiler, followed by validation in a separate validation group comprising 16 PCC-NOS and 6 SRCC cases. These transcriptomic findings were then correlated with tumor morphology and clinicopathological data. PCC-NOS cases exhibited larger tumor size, a higher prevalence of pathological N3 disease, and a worse 1-year progression-free survival rate compared to SRCC cases. Clustering of PCC-NOS and SRCC was successfully achieved using the GeoMx Cancer Transcriptome Atlas. Among all studied genes, only MMP7 showed differential expression, with its overexpression significantly associated with the PCC-NOS subtype, increased perineural invasion, and earlier disease progression. Pathway analysis revealed significantly enriched pathways in PCC-NOS related to vesicle-mediated transport, adaptive immune systems, oncogenic signaling, and extracellular matrix organization, while SRCC displayed significant enrichment in pathways associated with respiratory electron transport and the cell cycle. In conclusion, this study compares and correlates clinicopathological features and transcriptomic data between PCC-NOS and SRCC at advanced stages, employing the latest consensus classification and a novel platform for analysis.

Modification of Risk for All-cause and Cardiovascular Disease-related Mortality with Changes in the Body Mass Index in Older Individuals: A Population-based Cohort Study.

INTRODUCTION: Existing evidence evaluating the impact of change in body mass index (BMI) on the risk of all-cause and cardiovascular disease (CVD)-related mortality in older people is limited and inconsistent. This population-based cohort study evaluated the association of changes in BMI over time with all-cause and CVD-related mortality in older adults. METHODS: We recruited 55,351 adults aged over 65 years between 2006-2011 from Taipei Elderly Health Examination Program who underwent repeated annual health examinations at 3.2 year-intervals and were followed-up for mortality over 5.5 years. Cox proportional hazard and Fine-Gray sub-distribution hazard models with death from non-CVD causes as the competing risk were used to determine the impact of changes in BMI status on the risk of all-cause or CVD-related mortality, respectively. RESULTS: Over 227,967 person-years of follow-up, 4,054 participants died, including 940 (23.2%) CVD-related deaths. After adjusting for other covariates, >10% decrease of BMI was significantly associated with a higher risk of all-cause (adjusted hazard ratio [AHR]= 1.93; 95% confidence interval [CI]: 1.74-2.13) and CVD-related mortality (AHR= 1.96; 95%CI: 1.60-2.40), compared with stable BMI. Sensitivity analysis showed that a >10% decrease in BMI was significantly associated with a high risk of all-cause and CVD-related mortality in participants with normal weight, underweight, overweight, or obesity at baseline. DISCUSSION/CONCLUSION: Older adults with >10% decrease in BMI are at high risk of all-cause and CVD-related mortality. Our findings suggest that older individuals experiencing a substantial reduction in BMI should undergo a thorough evaluation to minimize the risks associated with mortality.

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.

Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.

Background: Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift. Methods: This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter. Results: The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift. Conclusions: VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.

Trajectories of adaptive functioning from early childhood to adolescence in autism: Identifying turning points and key correlates of chronogeneity.

Background: Previous research has demonstrated heterogeneous adaptive outcomes across the autism spectrum; however, the current literature remains limited in elucidating turning points and associated factors for longitudinal variability (chronogeneity). To address these empirical gaps, we aimed to provide a finer-grained characterization of trajectories of adaptive functioning from early childhood to adolescence in autism. Methods: Our sample (N = 406) was drawn from an inception cohort of children diagnosed Autistic at ages 2-5. Adaptive functioning was assessed with Vineland Adaptive Behavior Scales (VABS, 2nd Edition) across 6 visits from the time of diagnosis by age 18. Parallel-process latent growth curve modeling were used to estimate domain-level VABS trajectories, followed by latent class growth analysis to identify trajectory subgroups. Child characteristics at diagnosis, family demographics, and participation outcomes at adolescence were compared across subgroups. Results: Piecewise latent growth models best described VABS trajectories with two turning points identified at around ages 5-6 and 9-10, respectively reflecting transitions into school age and early adolescence. We parsed four VABS trajectory subgroups that vary by level of functioning and change rate for certain domains and periods. Around 16% of the sample exhibited overall adequate functioning (standard score >85) with notable early growth and social adaptation during adolescence. About 21% showed low adaptive functioning (standard score ≤70), with decreasing slopes by age 6 followed by improvements in communication and daily-living skills by age 10. The other two subgroups (63% in total) were characterized by adaptive functioning between low and adequate levels, with relatively stable trajectories entering school age. These subgroups differed most in their cognitive ability at diagnosis, household income, and social participation in adolescence. Conclusions: We identified key individual and family characteristics and time windows associated with distinct adaptive functioning trajectories, which have important implications for providing timely and tailored supports to Autistic people across developmental stages.

Identification of early events in serrated pathway colorectal tumorigenesis by using digital spatial profiling.

INTRODUCTION: The colorectal serrated pathway involves precursor lesions known as sessile serrated lesions (SSL) and traditional serrated adenomas (TSA). Mutations in BRAF or KRAS are crucial early events in this pathway. Additional genetic and epigenetic changes contribute to the progression of these lesions into high-grade lesions and, eventually, invasive carcinoma. METHODS: We employed digital spatial profiling to investigate the transcriptional changes associated with SSL and TSA. The genes identified are confirmed by immunohistochemical (IHC) staining. Colorectal (CRC) cell lines with CEACAM6 overexpression and knockdown were established to study the roles of CEACAM6 on tumorigenesis of CRC. RESULTS: Ten genes were upregulated in SSL and TSA, and seven were upregulated in both types of lesions. IHC staining confirmed overexpression of CEACAM6, LCN2, KRT19, and lysozyme in SSL and TSA. CEACAM6 expression is an early event in the serrated pathway but a late event in the conventional pathway. Using cell line models, we confirmed that CEACAM6 promotes CRC cells' proliferation, migration, and invasion abilities. CONCLUSION: These results highlight that the transcriptional changes in the early stages of tumorigenesis exhibit relative uniformity. Identifying these early events may hold significant promise in elucidating the mechanisms behind tumor initiation.