The human cell count and size distribution.

Cell size and cell count are adaptively regulated and intimately linked to growth and function. Yet, despite their widespread relevance, the relation between cell size and count has never been formally examined over the whole human body. Here, we compile a comprehensive dataset of cell size and count over all major cell types, with data drawn from >1,500 published sources. We consider the body of a representative male (70 kg), which allows further estimates of a female (60 kg) and 10-y-old child (32 kg). We build a hierarchical interface for the cellular organization of the body, giving easy access to data, methods, and sources (https://humancelltreemap.mis.mpg.de/). In total, we estimate total body counts of ≈36 trillion cells in the male, ≈28 trillion in the female, and ≈17 trillion in the child. These data reveal a surprising inverse relation between cell size and count, implying a trade-off between these variables, such that all cells within a given logarithmic size class contribute an equal fraction to the body's total cellular biomass. We also find that the coefficient of variation is approximately independent of mean cell size, implying the existence of cell-size regulation across cell types. Our data serve to establish a holistic quantitative framework for the cells of the human body, and highlight large-scale patterns in cell biology.

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data.

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e. the higher-rank matrices. However, the existing analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. SCOIT incorporates various distributions, including Gaussian, Poisson, and negative binomial distributions, to deal with sparse, noisy, and heterogeneous single-cell data. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to eight single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to nine state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming current imputation methods with the Pearson correlation coefficient increased by 3.38-39.26%; moreover, SCOIT accommodates the scenario that subsets of the cells are with merely one omic profile available.

Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci.

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.

Solid validity evidence for two tools assessing competences in musculoskeletal ultrasound: a validity study.

OBJECTIVES: Musculoskeletal ultrasound (MSUS) is increasingly used by rheumatologists in daily clinical practice. However, MSUS is only valuable in trained hands, and assessment of trainee competences is therefore essential before independent practice. Thus, this study aimed to establish validity evidence for the EULAR and the Objective Structured Assessment of Ultrasound Skills (OSAUS) tools used for assessing MSUS competences. METHODS: Thirty physicians with different levels of MSUS experience (novices, intermediates, and experienced) performed four MSUS examinations of different joint areas on the same rheumatoid arthritis patient. All examinations were video recorded (n = 120), anonymized, and subsequently assessed in random order by two blinded raters using first the OSAUS assessment tool followed by the EULAR tool 1 month after. RESULTS: The inter-rater reliability between the two raters was high for both the OSAUS and EULAR tools, with a Pearson correlation coefficient (PCC) of 0.807 and 0.848, respectively. Both tools demonstrated excellent inter-case reliability, with a Cronbach's alpha of 0.970 for OSAUS and 0.964 for EULAR. Furthermore, there was a strong linear correlation between the OSAUS and the EULAR performance scores and the participants' experience levels (R2 = 0.897 and R2 = 0.868, respectively) and a significant discrimination between different MSUS experience levels (P < 0.001 for both). CONCLUSIONS: MSUS operator competences can be assessed reliably and valid using either the OSAUS or the EULAR assessment tool, thereby allowing a uniform competency-based MSUS education in the future. Although both tools demonstrated high inter-rater reliability, the EULAR tool was superior to OSAUS. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov, NCT05256355.

Preoperative proteinuria correlates with renal function after partial nephrectomy for renal cell carcinoma.

PURPOSE: Preoperative proteinuria is a prognostic factor of chronic kidney disease (CKD). We assessed the association between preoperative proteinuria and postoperative renal function after partial nephrectomy (PN). METHODS: We retrospectively reviewed our records of patients with a single malignant renal mass who underwent PN between 2000 and 2021. Patients with data on preoperative proteinuria were included. Baseline characteristics and eGFR differences over time between patients with and without proteinuria were evaluated. Univariate and multivariable logistic regression models (LRM) tested for presence of CKDIII or higher at 12-month and at last follow-up. RESULTS: Two hundred ninety-five patients were included. Twenty-two of them had preoperative proteinuria. No differences of age, smoking status, hypertension or diabetes, tumor size and use of ischemia were observed. Patients with proteinuria had a higher rate of CKD-III at baseline. At a median follow-up of 46.5 months (IQR 19-82), 117 patients developed de novo CKD-III, without differences in the two groups. No differences in decline in eGFR were observed. At univariate LRM, predictors of CKD-III at 12 months after PN were preoperative proteinuria (OR 3.2, 95%CI 1.4-7.8, p = 0.005), age and baseline eGFR, while predictors of CKD-III at last follow-up were age and baseline eGFR. At multivariable LRM, only baseline eGFR predicted CKD-III at 12-month and at last-follow-up. CONCLUSIONS: Preoperative eGFR is the only independent predictor of long-term renal function after PN. Preoperative proteinuria correlates with renal function at 12 months. Proteinuria should be assessed before PN to identify patients at higher risk of renal functional deterioration in the 12 months following PN.

Detection of bornavirus-reactive antibodies and BoDV-1 RNA only in encephalitis patients from virus endemic areas: a comparative serological and molecular sensitivity, specificity, predictive value, and disease duration correlation study.

PURPOSE: Human Borna disease virus (BoDV-1) encephalitis is an emerging disease in Germany. This study investigates the spectrum of human BoDV-1 infection, characterizes anti-BoDV-1-antibodies and kinetics, and compares laboratory test performances. METHODS: Three hundred four encephalitis cases, 308 nation-wide neuropsychiatric conditions, 127 well-defined psychiatric cases from Borna disease-endemic areas, and 20 persons with contact to BoDV-1 encephalitis patients or animals were tested for BoDV-1 infections by serology and PCR. RESULTS: BoDV-1 infections were only found in encephalitis patients with residence in, or recent travel to, virus-endemic areas. Antibodies were detected as early as 12 days after symptom onset. Serum antibody levels correlated with disease duration. Serology was ordered after 50% of the disease duration had elapsed, reflecting low awareness. BoDV-1-antibodies were of IgG1 subclass, and the epitope on BoDV-1 antigens was determined. Specificity of the indirect immunofluorescence antibody test (IFAT) and lineblot (LB) from serum and cerebrospinal fluid (CSF), as well as PCR testing from CSF, was 100%. Sensitivity, depending on first or all samples, reached 75-86% in serum and 92-94% in CSF for the IFAT, and 33-57% in serum and 18-24% in CSF for the LB. Sensitivity for PCR in CSF was 25-67%. Positive predictive values were 100% each, while negative predictive values were 99% (IFAT), 91-97% (LB), and 90% (PCR). CONCLUSIONS: There is no hint that BoDV-1 causes other diseases than encephalitis in humans. Awareness has to be increased in virus-endemic areas. Tests are robust but lack sensitivity. Detection of IgG1 against specific peptides may facilitate diagnosis. Screening of healthy individuals is likely not beneficial.

Clinical variables influencing the perception of fatigue in people with multiple sclerosis: a cross-sectional study using FSIQ-RMS.

BACKGROUND: Physical fatigue is one of the most disabling symptoms in people with Multiple Sclerosis (PwMS). Several factors might influence the development of fatigue, such as gender, education, body mass index (BMI), Expanded Disability Status Scale (EDSS), disease duration, working status (Ws), physiotherapy (Ph), and disease-modifying therapies (DMTs). Fatigue Symptoms and Impacts Questionnaire-Relapsing Multiple Sclerosis (FSIQ-RMS) is a patient-reported outcome (PRO) that allows one to define the impact of fatigue in PwMS clearly. This study aimed to assess fatigue impact on PwMS by using FSIQ-RMS. METHODS: The participants were enrolled from May to July 2021 in MS Centers of Sant'Andrea Hospital and Policlinico Umberto I Hospital in Rome. Fatigue was evaluated using the FSIQ-RMS, validated, and culturally adapted in Italian. Clinical and demographic data were collected at the same time. RESULTS: We enrolled 178 PwMS [Female 74.16%; RMS 82.58%, SPMS 17.52%]. FSIQ-RMS scores were significantly correlated with EDSS (p-value < 0.01). Analysis of variance between means showed a statistically significant difference between the BMI groups at the 24hours_FSIQ-RMS score and the 7days_FSIQ-RMS score (p < 0.01), with the lower BMI group having the highest scores. Furthermore, perceived fatigue significantly improved both in subjects performing Ph (p < 0.05) and in those who actively work (p < 0.01). CONCLUSIONS: The use of FSIQ-RMS in a real-world setting confirmed that underweight and high levels of disability are closely related to fatigue. In addition, Ph and active Ws are strongly correlated with fatigue in PwMS.

Correlation study on microbial communities and volatile flavor compounds in cigar tobacco leaves of diverse origins.

To elucidate the significant influence of microorganisms on geographically dependent flavor formation by analyzing microbial communities and volatile flavor compounds (VFCs) in cigar tobacco leaves (CTLs) obtained from China, Dominica, and Indonesia. Microbiome analysis revealed that the predominant bacteria in CTLs were Staphylococcus, Aerococcus, Pseudomonas, and Lactobacillus, while the predominant fungi were Aspergillus, Wallemia, and Sampaiozyma. The microbial communities of CTLs from different origins differed to some extent, and the diversity and abundance of bacteria were greater than fungi. Metabolomic analysis revealed that 64 VFCs were identified, mainly ketones, of which 23 VFCs could be utilized to identify the geographical origins of CTLs. Sixteen VFCs with OAV greater than 1, including cedrol, phenylacetaldehyde, damascone, beta-damascone, and beta-ionone, play important roles in shaping the flavor profile of CTLs from different origins. Combined with the correlation analysis, bacterial microorganisms were more closely related to key VFCs and favored a positive correlation. Bacillus, Vibrio, and Sphingomonas were the main flavor-related bacteria. The study demonstrated that the predominant microorganisms were essential for the formation of key flavor qualities in CTLs, which provided a theoretical reference for flavor control of CTLs by microbial technology. KEY POINTS: • It is the high OAV VFCs that determine the flavor profile of CTLs. • The methylerythritol phosphate (MEP) pathway and the carotenoid synthesis pathway are key metabolic pathways for the formation of VFCs in CTLs. • Microbial interactions influence tobacco flavor, with bacterial microorganisms contributing more to the flavor formation of CTLs.

A postpartum functional assessment tool for women based on the international classification of functioning, disability and health.

BACKGROUND: Postpartum dysfunctions and complications can occur in women. However, functional assessment should be conducted to make treatment plans before any intervention is implemented. In this context, the International Classification of Functioning, Disability and Health (ICF) may be a useful tool for women postpartum to document functional data and set rehabilitation goals. The purpose of this study was to determine the corresponding domains that should be considered in the evaluation of women's postpartum functioning based on the International Classification of Functioning, Disability and Health (ICF) model using the Delphi method. METHODS: Fifteen domestic experts were invited to conduct two rounds of expert consensus survey on the ICF-based postpartum functional assessment category pool obtained through literature retrieval, clinical investigation, and reference to relevant literature. The sample was medical staff with professional knowledge of women's health. The opinions of experts were summarized, and the positive coefficient, authority coefficient and coordination degree of experts were calculated. RESULTS: A total of 15 domestic experts participated in this expert consensus. Through two rounds of a questionnaire survey, 69 items were finally selected to form the ICF-based postpartum functional assessment tool for women. The items included 32 items of body function, 12 items of body structure, 17 items of activity and participation, and 8 items of environmental factors. In addition, we identified 8 items of personal factors. The expert positive coefficients of the two rounds of expert consensus were both 100%, the authority coefficient was 0.789, and the coefficient of variation was between 0.09 to 0.31. CONCLUSION: A postpartum functional assessment tool for women based on the ICF model was constructed based on the Delphi method, which can provide more comprehensive health management and life intervention for postpartum women. TRIAL REGISTRATION: The Registration number of the Chinese Clinical Trial Registry is ChiCTR2200066163, 25/11/2022.

Correlation between the incidence of inguinal hernia and risk factors after radical prostatic cancer surgery: a case control study.

OBJECTIVE: The incidence of recurrent hernia after radical resection of prostate cancer is high, so this article discusses the incidence and risk factors of inguinal hernia after radical resection of prostate cancer. METHODS: This case control study was conducted in The First People's Hospital of Huzhou clinical data of 251 cases underwent radical resection of prostate cancer in this hospital from March 2019 to May 2021 were retrospectively analyzed. According to the occurrence of inguinal hernia, the subjects were divided into study group and control group, and the clinical data of each group were statistically analyzed, Multivariate Logistic analysis was performed to find independent influencing factors for predicting the occurrence of inguinal hernia. The Kaplan-Meier survival curve was drawn according to the occurrence and time of inguinal hernia. RESULTS: The overall incidence of inguinal hernia after prostate cancer surgery was 14.7% (37/251), and the mean time was 8.58 ± 4.12 months. The average time of inguinal hernia in patients who received lymph node dissection was 7.61 ± 4.05 (month), and that in patients who did not receive lymph node dissection was 9.16 ± 4.15 (month), and there was no significant difference between them (P > 0.05). There were no statistically significant differences in the incidence of inguinal hernia with age, BMI, hypertension, diabetes, PSA, previous abdominal operations and operative approach (P > 0.05), but there were statistically significant differences with surgical method and pelvic lymph node dissection (P < 0.05). The incidence of pelvic lymph node dissection in the inguinal hernia group was 24.3% (14/57), which was significantly higher than that in the control group 11.8% (23/194). Logistic regression analysis showed that pelvic lymph node dissection was a risk factor for inguinal hernia after prostate cancer surgery (OR = 0.413, 95%Cl: 0.196-0.869, P = 0.02). Kaplan-Meier survival curve showed that the rate of inguinal hernia in the group receiving pelvic lymph node dissection was significantly higher than that in the control group (P < 0.05). CONCLUSION: Pelvic lymph node dissection is a risk factor for inguinal hernia after radical resection of prostate cancer.

Anterior segment optical coherence tomography meibography compared with keratograph meibography.

PURPOSE: The aim of this study was to determine the feasibility of using readily accessible technology, anterior segment optical coherence tomography (AS-OCT), to detect and grade meibomian gland dropout and examine its interchangeability with the Oculus Keratograph 5M (K5M). METHODS: A total of 30 participants (30 eyes) with a median age of 21 (range = 19-28 years) were recruited. Meibography was performed using two commercially available imaging devices to look at the structure of the meibomian glands and grade them subjectively in real time, and image analysis was used to quantify meibomian gland loss objectively. Gland loss as imaged by the two techniques was graded using the meiboscore grading schema. Test-retest reliability was determined with intraclass correlation coefficients (ICCs). Weighted kappa was used to evaluate agreement between the two imaging devices and four methods of image analysis. Spearman and Pearson correlation coefficients were used to determine the association of structural measurements between each of the techniques. The agreement between the two imaging techniques was determined with the Bland-Altman analysis. RESULTS: Reliability of subjective grading was strong for AS-OCT (ICC: 0.92, 95% CI: 0.83-0.96, p < 0.001) and K5M (ICC: 0.96, 95% CI: 0.96-0.91, p = 0.001). Image analysis with ImageJ reliability was strong between the imaging devices (ICC: 0.84, 95% CI: 0.55-0.94, p < 0.001). Agreement between each subjective technique was fair, κ = 0.45 (95% CI: 0.17-0.73, p < 0.001) and a positive Spearman correlation was also observed (r = 0.52, p < 0.001). There was no significant difference between the mean meibomian gland loss measured with ImageJ between AS-OCT and K5M (0.92 ± 6.28, p = 0.26). The 95% limits of agreement were -12.45% to +14.04%. CONCLUSION: These findings suggest subjective real-time grading of meibomian gland loss could be performed using readily available AS-OCT technology and that this method was interchangeable with the K5M.

In subjects with chronic low back pain, does neuropathia exclusively correlated to neuronal compression? A correlation study of PainDETECT questionnaire and corresponding MRI and X-ray findings.

INTRODUCTION: Understanding the complex nature of low back pain (LBP) is crucial for effective management. The PainDETECT questionnaire is a tool that distinguishes between neuropathic (NeP), nociceptive (NoP), and ambiguous pain. This study aimed to investigate the relationship between pain classification and lumbar intervertebral degenerative parameters obtained from imaging. METHODS: A cohort study was conducted involving 279 patients, aged 18 years and above, who completed PainDETECT questionnaires and underwent lumbar MRI and/or X-ray scans. RESULTS: The study included 102 patients with NoP, 78 with ambiguous pain, and 99 with NeP. The NeP group had lower mean age (58.21 vs. 53.63, p < 0.05) and higher mean numerical rating scale score (7.9 vs. 5.9, p < 0.001) compared to the NoP group. A negative correlation was found between PainDETECT scores and pelvic incidence (τ = - 0.177, p = 0.043). The NeP group exhibited significantly higher severity of foraminal stenosis (U = 18.962, p = 0.002), spinal stenosis (U = 14.481, p = 0.005), and Pfirrmann grade (U = 14.221, p = 0.028) compared to the NoP group. A higher proportion of NeP patients had intervertebral disk bulge (96% vs. 78% vs. 78%, p = 0.002) and high-intensity zones (51% vs. 41% vs. 19%, p < 0.001) compared to those with NoP and ambiguous pain. CONCLUSION: NeP, as determined by the PainDETECT questionnaire, is associated with more severe neural compression, increased presence of discogenic disease and inflammatory disk severity, and decreased pelvic incidence. This pioneering study establishes a connection between pathological findings and pain categorization, providing clinicians with valuable guidance for formulating tailored management plans and reducing the need for unnecessary pharmacotherapy, imaging, and non-targeted surgical interventions.

The developmental genetic architecture of vocabulary skills during the first three years of life: Capturing emerging associations with later-life reading and cognition.

Individual differences in early-life vocabulary measures are heritable and associated with subsequent reading and cognitive abilities, although the underlying mechanisms are little understood. Here, we (i) investigate the developmental genetic architecture of expressive and receptive vocabulary in early-life and (ii) assess timing of emerging genetic associations with mid-childhood verbal and non-verbal skills. We studied longitudinally assessed early-life vocabulary measures (15-38 months) and later-life verbal and non-verbal skills (7-8 years) in up to 6,524 unrelated children from the population-based Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We dissected the phenotypic variance of rank-transformed scores into genetic and residual components by fitting multivariate structural equation models to genome-wide genetic-relationship matrices. Our findings show that the genetic architecture of early-life vocabulary involves multiple distinct genetic factors. Two of these genetic factors are developmentally stable and also contribute to genetic variation in mid-childhood skills: One genetic factor emerging with expressive vocabulary at 24 months (path coefficient: 0.32(SE = 0.06)) was also related to later-life reading (path coefficient: 0.25(SE = 0.12)) and verbal intelligence (path coefficient: 0.42(SE = 0.13)), explaining up to 17.9% of the phenotypic variation. A second, independent genetic factor emerging with receptive vocabulary at 38 months (path coefficient: 0.15(SE = 0.07)), was more generally linked to verbal and non-verbal cognitive abilities in mid-childhood (reading path coefficient: 0.57(SE = 0.07); verbal intelligence path coefficient: 0.60(0.10); performance intelligence path coefficient: 0.50(SE = 0.08)), accounting for up to 36.1% of the phenotypic variation and the majority of genetic variance in these later-life traits (≥66.4%). Thus, the genetic foundations of mid-childhood reading and cognitive abilities are diverse. They involve at least two independent genetic factors that emerge at different developmental stages during early language development and may implicate differences in cognitive processes that are already detectable during toddlerhood.

Impact of lateral cervical lymph node metastasis on lung metastasis in pediatric thyroid cancer: A correlation study.

BACKGROUND: Thyroid cancer (TC) frequently manifests with lung metastases in the pediatric population, occurring at a significant rate of 30 %. This study aims to evaluate the impact of regional patterns of cervical lymph node metastases on lung metastases in pediatric TC. METHODS: Retrospective analysis was conducted on data from pediatric TC patients spanning the years 2000 to 2018. We compared the rates of lymph node metastasis (LNR), the number of lymph node metastases, and the number of dissected lymph nodes in the central and lateral cervical regions between patients with and without lung metastases. Statistical methods were employed to adjust for confounders during hypothesis testing. RESULTS: A total of 227 pediatric patients, with a median age of 15.12 ± 2.84 years, were included in the study. Of these, 202 (89 %) exhibited LN metastasis, with 40(17.62 %) patients presenting with lung metastasis. Patients with lung metastases were found to be younger (13.40 ± 3.11 vs. 15.50 ± 2.64, p < 0.001), had larger primary tumor diameters (3.49 ± 1.98 vs. 2.31 ± 1.45, p < 0.001), and exhibited a higher number of lymph node metastases (23.40 ± 10.75 vs. 14.65 ± 13.16, p < 0.001). Notably, in patients with LN metastases, the presence of >12 lateral cervical lymph node metastases emerged as a significant risk factor for lung metastases. Among children with metachronous lung metastases, the median time to detection of lung metastases was 43 (12-132) months, and they appeared to receive a greater proportion of radioactive iodine (RAI) treatment compared to those with synchronous lung metastases. CONCLUSION: Lateral cervical lymph node metastasis independently predicts the likelihood of lung metastases in pediatric TC. Furthermore, our findings emphasize the importance of thorough examination of the lungs during follow-up, particularly when the number of metastatic lateral cervical lymph nodes exceeds 12.

Variations and thicknesses of anterior belly of the digastric muscle: an ultrasonographic study.

PURPOSE: This study aims to evaluate the anatomy and anatomical variations of the anterior belly of the digastric muscle. METHODS: Hundred and fifty one ultrasonographic images of the digastric muscle pairs were evaluated in Near East University Faculty of Dentistry Department of Dentomaxillofacial Radiology. Morphological variations were recorded using the classification of the digastric muscle into 12 types by Kim et al. For the analysis, the Mann-Whitney U test and Chi-square test were used, and for the correlational analysis, Spearman's rho test was applied. P < 0.05 was considered statistically significant in all tests. RESULTS: Seventy female and 81 male patients aged 19-60 years were evaluated. Type 1 digastric muscle was observed in 145 of 151 patients, Type 2 in 3 patients, and Type 7 in 3 patients. The thicknesses of the right and left digastric muscles were measured, and a statistically significant difference was observed between the groups when the genders were compared. It was observed that both right and left digastric muscle thicknesses were higher in males than females. There was a statistically significant positive high correlation between right and left digastric muscle thicknesses (p = 0.000; r = 0.736). No statistically significant difference was found between genders (p = 0.596) in terms of anatomical variations. CONCLUSION: Considering that the variations of the digastric muscle may have a clinical significance role, the normal anatomy and variations of this muscle should be well known by maxillofacial surgeons and radiologists.

Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals.

Non-invasive detection of hemoglobin (Hb) concentration is of great clinical value for health screening and intraoperative blood transfusion. However, the accuracy and stability of non-invasive detection still need to be improved to meet clinical requirement. This paper proposes a non-invasive Hb detection method using ensemble extreme learning machine (EELM) regression based on eight-wavelength PhotoPlethysmoGraphic (PPG) signals. Firstly, a mathematical model for non-invasive Hb detection based on the Beer-Lambert law is established. Secondly, the captured eight-channel PPG signals are denoised and fifty-six feature values are extracted according to the derived mathematical model. Thirdly, a recursive feature elimination (RFE) algorithm is used to select the features that contribute most to the Hb prediction. Finally, a regression model is built by integrating several independent ELM models to improve prediction stability and accuracy. Experiments conducted on 249 clinical data points (199 cases as the training dataset and 50 cases as the test dataset) evaluate the proposed method, achieving a root mean square error (RMSE) of 1.72 g/dL and a Pearson correlation coefficient (PCC) of 0.76 (p < 0.01) between predicted and reference values. The results demonstrate that the proposed non-invasive Hb detection method exhibits a strong correlation with traditional invasive methods, suggesting its potential for non-invasive detection of Hb concentration.

Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

Levator Scapulae Stiffness Measurement Reliability in Individuals with and without Chronic Neck Pain by Experienced and Novel Examiners.

The levator scapulae muscle is a key structure in the etiopathology of neck and shoulder musculoskeletal pain. Although previous studies used shear-wave elastography (SWE) for characterizing this muscle elasticity, limited evidence assessed the inter-examiner reliability of this procedure. This study aimed to analyze the inter-examiner reliability for calculating Young's modulus and shear wave speed in a cohort of participants with and without chronic neck pain. A diagnostic accuracy study was conducted, acquiring a set of SWE images at the C5 level in participants with and without neck pain (n = 34 and 33, respectively) by two examiners (one experienced and one novel). After blinding the participants' identity, examiner involved, and side, the stiffness indicators were calculated by an independent rater in a randomized order. Intra-class correlation coefficients (ICC), standard error of measurement, minimal detectable changes, and coefficient of variation were calculated. Both cohorts had comparable sociodemographic characteristics (p > 0.05). No significant levator scapulae elasticity differences were found between genders, sides, or cohorts (all, p > 0.05). Inter-examiner reliability for calculating Young's modulus and shear wave speed was moderate-to-good for assessing asymptomatic individuals (ICC = 0.714 and 0.779, respectively), while poor-to-moderate in patients with neck pain (ICC = 0.461 and 0.546, respectively). The results obtained in this study support the use of this procedure for assessing asymptomatic individuals. However, reliability estimates were unacceptable to support its use for assessing elasticity in patients with chronic neck pain. Future studies might consider that the shear wave speed is more sensitive to detect real changes in comparison with Young's modulus.

Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects, held during 2014-2017 and 2020-2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.