Finite element analysis of the cervical spine: dynamic characteristics and material property sensitivity study.

The study aimed to investigate the dynamic characteristics of the cervical spine and determine the effect of the material properties of the cervical spinal components on it. A finite element model of the head-cervical spine was developed based on CT scan data, and the first six orders of modes (e.g. flexion-extension, lateral bending, and vertical, etc.) were verified by experimental and simulation studies. The material sensitivity study was conducted by varying elasticity modulus of cervical hard tissues (cortical bone, cancellous bone, endplates, and posterior elements) and soft tissues (intervertebral disc and ligaments). The results showed that increasing the elastic modulus of ligaments by 4 times increased the natural frequency by 77%, while increasing that of cancellous bone by 4 times only increased the natural frequency by 6%. In the axial mode, the cervical spine had not only axial deformation but also anterior-posterior deformation, with the largest deformation located at the intervertebral disc C6-C7. Decreasing the elastic modulus of a component in soft tissues by 80% increased modal displacement by up to 62%. The material properties of the intervertebral discs and ligaments had opposite effects on the modal displacement and deformation of the cervical spine. Low cervical discs were more susceptible to injury in a vertical vibration environment. Cervical spine dynamics were more sensitive to soft tissue material properties than to hard tissue material properties. Disc degeneration could reduce the range of vibratory motion of the cervical spine, thereby reducing the ability of the cervical spine to cushion head impacts.

Impact of Sacroiliac Interosseous Ligament Tension and Laxity on the Biomechanics of the Lumbar Spine: A Finite Element Study.

OBJECTIVE: To investigate the influence of sacroiliac interosseous ligament tension and laxity on the biomechanics of the lumbar spine. METHODS: A static analysis of a three-dimensional finite element model of the Lumbar-Pelvic is conducted to verify the model's effectiveness. Adjusting the sacroiliac ligament's elasticity modulus under a 10Nm lumbar flexion/extension moment, it simulates ligament tension/laxity to calculate vertebrae displacements, intervertebral disc stress and deformation, nucleus pulposus pressure, facet joint force, and ligament stress. RESULTS: With the elastic modulus of the sacroiliac ligament changing by +50%, -50%, and -90%, the angular displacement of vertebra 3 in forward flexion changes by +1.64%, -4.84%, and -42.3%, and the line displacements change by +5.7%, -16.4%, and -144.9%, respectively; and the angular displacements in backward extension change by +0.2%, -0.6%, -5.9% and the line displacements change by +5.5%, -14.3%, and -125.8%. However, the angular displacement and center distance between adjacent vertebrae do not change, leading to no change in the maximum stress of the intervertebral disc and the maximum pressure in the nucleus pulposus. Flexion and extension directly affect the deformation and stress magnitude and distribution in the lumbar spine. CONCLUSIONS: While sacroiliac interosseous ligament laxity and tension have little effect on disc deformation and stress, and nucleus pulposus pressure, they reduce the stability of the lumbar-sacral vertebrae. In a forward flexion state, the lumbar ligaments bear a large load and are prone to laxity, thereby increasing the risk of lumbar injury.

Myeloid sarcoma as the only manifestation in a rare mixed lineage leukemia-fusion-driven acute myeloid leukemia: A case report.

BACKGROUND: The mixed lineage leukemia (MLL)-eleven-nineteen lysine-rich leukemia (ELL) fusion gene is a rare occurrence among the various MLL fusion genes. We present the first case in which myeloid sarcoma (MS) was the only manifestation of adult MLL-ELL-positive acute myeloid leukemia (AML). CASE SUMMARY: We report a case of a 33-year-old male patient who was admitted in June 2022 with a right occipital area mass measuring approximately 7 cm × 8 cm. Blood work was normal. The patient underwent right occipital giant subscalp mass excision and incisional flap grafting. Immunohistochemistry was positive for myeloperoxidase, CD43 and CD45 and negative for CD3, CD20, CD34, and CD56. The bone marrow aspirate showed hypercellularity with 20% myeloblasts. Flow cytometry showed that myeloblasts accounted for 27.21% of the nucleated cells, which expressed CD33, CD38, and CD117. The karyotype was 46, XY, t (11, 19) (q23; p13.1), -12, + mar/46, XY. Next-generation sequencing showed a fusion of MLL exon 7 to exon 2 of ELL. A diagnosis of MLL-ELL-positive AML (M2 subtype) with subcutaneous MS was made. CONCLUSION: MLL-ELL-positive AML with MS is a rare clinical entity. Additional research is needed to elucidate the molecular mechanisms of the pathogenesis of MS.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

Background: For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC. Methods: Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model's generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied. Results: Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701). Conclusions: ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

[Associated factors for the infection of Beijing genotype Mycobacterium tuberculosis].

OBJECTIVE: to analyze the risk factors for the infection of Beijing genotype Mycobacterium tuberculosis (MTB) and the relationship to drug resistance and clinical symptoms. METHODS: sputum samples were collected from patients with pulmonary tuberculosis who were admitted to the hospital during July, 2007 to March, 2008. The sputum was cultured with L-J method, and then the bacterial DNA was isolated and genotyped with VNTR-7 (variable-number tandem repeats, VNTR) and RD105 deletion method respectively. The association between different genotypes and risk factors was analyzed. RESULTS: a hundred and sixteen clinical sputum isolates were obtained from 172 positive sputum cases. There were 112 isolates of MTB, and isolates of non-tuberculosis mycobacterium (NTM). Among the 97 isolates from Shanghai, Zhejiang and Jiangsu areas, Beijing genotype accounted for 86.6% (84/97), and non-Beijing genotype for 13.4% (13/97). The rates of MDR (multi-drug resistance), PDR (poly-drug resistance) and single drug resistance in Beijing genotype were not significantly higher than those in non-Beijing genotype. Among the risk factors, female gender, and CD(4)/CD(8)< 1 in patients with newly-treated tuberculosis, were associated with higher rate of Beijing genotype, chi(2) = 4.436, 4.494 and all P < 0.05, respectively. The Beijing genotype isolates were subdivided into 31 VNTR-7 types, and the distribution of quantity and resistance among different VNTR-7 genotypes was not even. A large number of MTB isolates (47.6%, 40/84) and drug resistant isolates (43.6%, 17/39) were among four VNTR-7 genotypes. CONCLUSION: Beijing genotype is the most prevalent MTB in Shanghai, Zhejian and Jiangsu areas. Female gender and low CD(4)/CD(8) ratio in patients with newly-treated TB are risk factors for infecting Beijing genotype MTB, which may have no relationship with drug resistance and clinical symptoms.

Growth hormone reverses dyslipidemia in adult offspring after maternal undernutrition.

The abnormal intrauterine milieu of fetal growth retardation could lead to dyslipidemia in adulthood. Studies have shown that growth hormone (GH) therapy in small for gestational age (SGA) children would be beneficial for metabolic parameters. Here we investigated whether GH treatment introduced at adolescent period in SGA could reverse dyslipidemia during later life. SGA rat model was established by using semi-starvation treatment during the whole pregnancy. SGA or appropriate for gestational age (AGA) offspring were assigned to receive GH or normal saline (NS). Once-daily subcutaneous injections of GH were administered between 21-35 days of age. In adulthood, as compared to AGA, SGA showed: (1) decreased body weight and length; (2) increased serum triglycerides; (3) down-regulated hepatic AMPK-α1 but up-regulated SREBP-1c and ACC-1; (4) a significant reduction in histone H3 acetylation at the promoter of AMPK-α1. Exogenous GH administration led to a restoration of body weight and length and normalized serum triglycerides by reversing expression of AMPK-α1 and its targeted genes SREBP-1c and ACC-1, through increasing H3 acetylation at the promoter of AMPK-α1 in SGA in adult period. These results demonstrated positive effects on lipid metabolism by a short treatment course of GH in SGA adult period.