Advanced Biostatistical Methods for Curved and Censored Biomedical Data

This research was dedicated to analyze two types of biomedical data: curved data lying on a manifold and censored survival data from clinical trials. The main part of the research aims at developing a general regression framework for the analysis of a manifold-valued response in a Riemannian symmetric space (RSS) and its association with Euclidean covariates of interest, such as age. Such data arises frequently in medical imaging, computational biology, and computer vision, among many others. We developed an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution on RSS. We proposed various link functions from the Euclidean space of covariates to the RSS of responses. We constructed parameter estimates and test statistics, and determined their asymptotic distributions and geometric invariant properties. Simulation studies were used to evaluate the finite sample properties of our method. We applied our model to investigate the association between covariates, including gender, age, and diagnosis, and the shape of the Corpus Callosum contours from the Alzheimer's Disease Neuroimaging Initiative dataset, in both cross-sectional and longitudinal cases. In oncology clinical trials, progression-free survival (PFS) has been a key endpoint to support licensing approval, and it is recommended to have the investigator's tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. Agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. We proposed a new method to assess temporal agreement between two time-to-event endpoints, assuming they have a positive probability of being identical. Overall scores of agreement over a period of time are also proposed. We used maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small-sample via extensive simulation studies and is illustrated through a head and neck cancer trial.Doctor of Philosoph

Regression models on Riemannian symmetric spaces

The aim of this paper is to develop a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods

Assessing temporal agreement between central and local progression-free survival times: D. ZENGET AL.

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression (PD) or death, has been a key endpoint to support licensing approval (e.g. [1]). When PFS is the primary or co-primary endpoint, it is recommended to have tumor assessments verified by an independent review committee (IRC) blinded to study treatments, especially in open-label studies (see [1]). It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators’ and IRCs’ evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small-sample via extensive simulation studies and is illustrated through a head and neck cancer trial

„Brain death” donor. Sampling of organs and tissues with their subsequent transplantation

Introduction. Organ and tissue transplantation can save lives or significantly improve the quality of life. In order to organize the activity of taking and transplanting organs and tissues from the donor to the recipient on the territory of the Republic of Moldova, the Law no. 42 of 03.06.2008 "Regarding the transplantation of organs, tissues and human cells" was adopted (published on 25.04.2008 in the Official Gazette, no. 81, art. no. 273, date of entry into force 25.10.2008) and revised Law No. 42 on the transplantation of human organs, tissues and cells of 06.03.2020. Objectives. Steps in sampling of organs and tissues from a "brain death" donor based on the clinical case. Materials and methods. Description of a clinical case about a patient hospitalized in the Municipal Clinical Hospital “Saint Trinity”, Intensive Care department, 2022. Results. A man, 67 years old, was hospitalized in extremely serious condition, from the anamnesis (collected from relatives) - the patient had several comorbidities, including hypertension. The clinical diagnosis: Ischemic stroke. Arterial hypertension grade III very high additional risk. Mixed cardiomyopathy (hypertensive, ischemic, dysmetabolic). Heart failure III NYHA. Despite the complex treatment administered, "brain death" was found, which was confirmed according to the Standardized Clinical Protocol. Following the discussion with the relatives, consent was received for the removal of organs and tissues. The kidney transplant recipient was selected based on HLA typing, Cross-match, pre-existing antibody titer, general condition and lack of contraindications for surgery. 2 kidneys, liver, 2 corneas, 4 vessels were taken from the donor, but one kidney was not transplanted due to the presence of suspicious lesions, after histopathological examination it was established: Atherosclerotic nephropathy. Multiple renal infarcts. Atherosclerosis of the renal artery st. IV, degree II. Conclusions. 1. In the given case, organ removal was possible after the consent of the donor's relatives; 2. Organs, tissues and cells can be taken from the deceased person only if the death has been confirmed according to the criteria established by the Standardized Clinical Protocol (which were confirmed in this case); 3. Following investigations and complex compatibility tests, a patient with liver failure and another with end-stage renal failure were transplanted, other tissues taken (cornea-no.2, vessels-no.4) were processed and stored at the Bank of tissues

Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study

The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n = 27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy

Solutions Near Singular Points To The Eikonal And Related First Order Non-Linear Partial Differential Equations In Two Independent Variables

. A detailed study of solutions to the rst order partial dierential equation H(x; y; z x ; z y ) = 0, with special emphasis on the eikonal equation z 2 x + z 2 y = h(x; y), is made near points where the equation becomes singular in the sense that dH = 0, in which case the method of characteristics does not apply. The main results are that there is a strong lack of uniqueness of solutions near such points and that solutions can be less regular than both the function H and the initial data of the problem, but that this loss of regularity only occurs along a pair of curves through the singular point. The main tools are symplectic geometry and the Sternberg normal form for Hamiltonian vector elds. 1. Introduction The eikonal equation in two independent variables for z = z(x; y) is z 2 x + z 2 y = h(x; y) (1.1) where h is a non-negative smooth function on the plane R 2 , and subscripts denote partial derivatives. Near points (x 0 ; y 0 ) where h 6= 0 all local solutions to th..

Sex differences in resting state functional connectivity across the first two years of life

Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease

Structural and functional connectome relationships in early childhood

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual’s functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models

Cartea Unirii : 1918-1928

Bucureşti : Luceafărul, 1929. - 175 p. : portr., il., facs