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It is evidenced biostatistically that the eruption of the permanent teeth is retarded in cleft patients. This retardation concerns both the maxillary and mandibular teeth. Both sexes are involved. In female cleft patients the change to the permanent dentition occurs earlier than in male cleft patients. There is no sidedness.
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Explore the source record for details and available documents.
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The paper reviews progress made in the past 30 years in the development of statistical and epidemiological methods in the mental health field. Applications have included determinations of need for psychiatric care and supporting personnel; interpretation of morbidity indices, and cross-national comparisons of diagnoses of mental disorders. Much remains to be done. Progress would include better measurement of incidence, duration, and prevalence of mental disorders; more precise estimates of service needs; more effective programs to prevent or reduce disability. Particularly needed are field-research units under long-term funding with the task of assessing effectiveness of mental health programs at the catchment-area level.
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In conformity with the practice in other biological sciences, it is suggested to adopt in dentition chronology the double of the range around the mean as the range of normal. The great variance especially in the second dentition agrees with the variance of other developmental characteristics and must be considered to be typical of human development. The integration of dentition with the complex of the characteristics of physical and psychical development is advocated.
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The cardinal challenges to every practicing physician are to interpret clinical data correctly and to place them in proper perspective. Clinical investigations frequently lack the rigidly controlled conditions and the careful experimental designs usually found in preclinical animal studies, and this deficiency is partially attributable to the inherent complexities of clinical medicine. Consequently, a great deal of controversy results from conflicting interpretations, extrapolations and overextension of limited data that are often equivocal. More careful appraisal of data and increased awareness of the well-known pitfalls found in retrospective and prospective studies, in which biostatistical design and clinical relevance are often incompatible, are emphasized, and personal biases and the flagrant sensationalism expounded by the media are condemned. The clinician is cautioned to sift through the data, consider the benefit/risk ratio for each patient and then to subordinate the role of critical scientist and assume the role of physician, exercising good judgment in light of the existing evidence and the immediate problems at hand.
MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. RESULTS: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. AVAILABILITY AND IMPLEMENTATION: APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.
An unidentified female mummy found in a cache of great kings and queens in 1898 in the Valley of the Kings was examined from the viewpoint of Egyptology, x-ray cephalometry, biostatistics, and biochemistry. The result was the identification of Queen Tiye, of the Eighteenth Dynasty, wife of Amenhotep III and mother of Akhenaton.
Recent history and developments related to the increase in statistical computing activities in the United States and by U.S. participants in international efforts are reviewed, with emphasis on important events, organizations, references, and products which contribute to informed selection and use of statistical programs. Three features matrices for major statistical packages are included as potential aids to Japanese statisticians in assessing the utility of these packages in biostatistical applications.
Problems associated with the classification of cavum oris and labial carcinomata were discussed with particular reference to patients admitted to and treated in seven different clinical hospitals where identical methods of diagnosis and case history evaluation had been used. Using electronic data processing and biostatistical methods it was possible to study the effects of two clinically detectable factors (growth of primary tumor and degree of regional metastasizing) on both the prognosis and classification according to UICC rules. It was possible to show that a determination of the size of primary tumor (T) alone was not sufficient for three homogeneous, prognostically different collectives of tumors to be satisfactorily classified by the new UICC rules. It has been shown that a classification of three collectives of tumors (new classification according to UICC rules), especially as regards the proportion of N3 metastases within the collectives of tumors, was made possible. Therefore, it will be necessary to study the prognostic influences of additional clinically determinable factors with a view to arriving at a useful and practicable classification of oral cavity carcinomata.