Investigating Fryette's mechanics in computed tomography scans: an analysis of vertebrae spinal physiology using open-sourced datasets and three-dimensional vertebral orientation.

CONTEXT: Fryette's mechanics is taught as a simplistic model of coupled vertebral movement, fundamental in osteopathic practice. This study seeks to better understand the likelihood of Fryette's model by calculating vertebral orientation in computed tomography (CT) scans. Given previous findings of low angular coupled movements during overall spinal motion, static calculations provide a unique perspective on the likelihood of Fryette's mechanics. OBJECTIVES: This analysis aims to evaluate the efficacy of Fryette's principles in predicting vertebral positioning in CT scans by comparing their 3-dimensional (3D) orientation to movements described by Fryette. METHODS: 3D models of 953 thoracic and lumbar vertebrae were obtained from 82 CT scans within the VerSe`20 open-source dataset. A stepwise algorithm generated three unique symmetry planes for each vertebra, offering 3D angular orientation with respect to the vertebral level below. A total of 422 vertebrae were omitted from the analysis due to the presence of pathologies significant enough to affect their motion, inaccurate symmetry plane calculations, or absence of vertebral level below. The remaining 531 vertebra were analyzed to compare quantitative coupled positioning against expected coupled spinal movements in line with Fryette's mechanics. One-sample proportional z-scoring was implemented for all vertebral levels with an ∝=0.05 and a null hypothesis of Fryette's primed positioning occurring by chance of 50 %. Further analysis was performed with individual z-scoring for each individual level to see which levels were statistically significant. RESULTS: Data from the VerSe`20 dataset revealed that 56.9 % of successfully analyzed vertebrae demonstrated positions compatible with Fryette's mechanics (p=0.0014, power=89 %). The 302 vertebral levels that did display coupled positioning consisted of Type I (166 vertebrae) and Type II (136 vertebrae) compatible with Fryette's mechanics. Levels that demonstrated statistical significance consisted of T5 (p=0, power=99 %), T6 (p=0.0023, power=77 %), T7 (p=0.041, power=46 %), and T10 (p=0.017, power=60 %). CONCLUSIONS: Our analysis suggests that the static positions of vertebrae in CT scans may align with Fryette's descriptions, although not very often. Notably, vertebral levels T5-T7 and T10 exhibit strong evidence of their static positions aligning with expected movements, warranting further investigation into the Fryette phenomenon at these levels. Future studies should explore the dynamic implications of these findings to enhance our understanding of spinal biomechanics.

Association of urinary arsenic, polycyclic aromatic hydrocarbons, and metals with cancers among the female population in the US.

BACKGROUND: Cancers that primarily affect women in the US include breast, uterine, and cervical cancers. There may be associations between these different types of cancer in women and environmental pollutant exposure. PURPOSE: This study aimed to assess seven species of arsenic, six polycyclic aromatic hydrocarbon (PAH) compounds, and fourteen different metal concentrations in urine and their correlation with cancer among women. METHODS: We conducted a cross-sectional analysis using 2011--2012 to 2015-2016 National Health and Nutrition Examination Survey data (n = 4,956) and logistic regression modeling of the complex weighted survey design. RESULTS: Breast cancer was inversely correlated with arsenocholine (3rd quantile), monomethylarsonic acid (4th quantile), manganese (4th quantile), and antimony (3rd, 4th quantiles). Cervical cancer was inversely correlated with 3-hydroxyfluorene (3rd quantile), molybdenum (2nd, 4th quantiles), antimony (3rd quantile), tin (4th quantile), and thallium (4th quantile) exposure and positively associated with arsenic acid (3rd quantile), arsenobetaine (2nd, 4th quantiles). Uterine cancer was correlated with 1-hydroxynaphthalene (3rd, 4th quantiles), 2-hydroxynaphthalene (4th quantile), 1-hydroxyphenathrene (2nd, 4th quantiles), 1-hydroxypyrene (3rd quantile), cobalt (2nd, 3rd quantiles) and inversely with mercury (4th quantile). CONCLUSION: This study determined breast cancer and arsenic and some metal species exposure, indicating an inverse association. Arsenic acid and arsenobetaine exposure showed a positive correlation with cervical cancer. For uterine cancer, the correlations for the PAH compounds and cobalt showed a positive correlation, and the arsenic species and mercury were inversely associated. Further research is required to establish or refute the findings.

Exposure to arsenic, polycyclic aromatic hydrocarbons, metals, and association with skin cancers in the US adults.

Worldwide, skin cancer affects millions of people yearly and is broadly classified into melanoma and nonmelanoma types of skin cancer. The toxicity of metals to human health is a public and clinical health problem due to their widespread use in tools, machinery, and appliances as well as their widespread distribution in the air, water, and soil. Arsenic is a carcinogenic metalloid and available in the Earth's crust. Polycyclic aromatic hydrocarbons (PAHs) are toxic to humans, and incomplete combustion of fossil fuels is the main source of PAHs. Human populations exposed to metals from various sources can lead to various diseases including cancer. Limited studies are conducted to simultaneously assess the correlation of multiple arsenic, PAHs, metals with the occurrence of skin cancer. This study aimed to analyze the association between six PAHs compounds, seven types of arsenic, and fourteen metals from urine specimen with skin cancer in US adults. We performed a cross-sectional analysis using data from a total of 14,716 adults from the National Health Examination and Nutrition Survey (NHANES) database for three cycles ranging from 2011-2012 to 2015-2016. Specialized weighted complex survey logit regressions were conducted. Linear logit regression models using only main effects were performed first to identify the correlation between the selected demographic and lifestyle variables and melanoma, nonmelanoma, and unknown types of skin cancer. A second set of linear, main-effects logit regression models were constructed to examine the correlation between melanoma, nonmelanoma, and other types of skin cancers and seven types of arsenic (arsenous acid, arsenic acid, arsenobetaine, arsenocholine, dimethylarsinic acid, monomethylacrsonic acid, and total arsenic), six PAHs (1-hydroxynaphthalene, 2-hydroxynaphthalene, 3-hydroxyfluorene, 2-hydroxyfluorene, 1-hydroxyphenathrene, and 1-hydroxypyrene), and fourteen metals (barium, cadmium, cobalt, cesium, molybdenum, manganese, lead, antimony, tin, strontium, thallium, tungsten, uranium, and mercury) when adjusted for the selected covariates. The statistical analysis was conducted using R software, version 4.0.4. A marginal positive significant correlation between total arsenic and nonmelanoma was observed. This study identified a significant positive association between barium, cadmium, cesium, mercury, tin, and melanoma development. Cesium showed a significant positive statistical association for nonmelanoma, and thallium showed a borderline significant statistical association for nonmelanoma. A statistically significant positive association was found between cadmium and an unknown type of skin cancer. The findings of this study indicated a statistically significant positive association between skin cancer and barium, cadmium, cesium, tin, mercury, and thallium. Further studies are recommended in humans to refute or confirm these findings.