Geophysics最新文献

TRAIL, a member of the TNF family, is expressed in tumor and tumor surrounding tissue in many solid organ cancers. While the induction of tumor-specific apoptosis in correlation with cytokine stimulation may cause anti-tumoral effects, the pro-tumorigenic effects of its expression by tumor surrounding tissue members have been reported in the literature. In our study, it was aimed to evaluate the effect of the gene variant of TRAIL on soluble levels in patients with colorectal cancer (CRC) on the molecular pathological axis. TRAIL C1595 gene variant PCR-RFLP and sTRAIL levels were determined by ELISA in age and sex adjusted CRC and control groups. It was determined that CT carriage was high in patients without perineural invasion and sTRAIL levels were approximately 2.72 times lower than CC in patients with CT in this group (p < 0.05). Similarly, sTRAIL level was found to be high in patients with CC genotype in CRC without lymph node metastas. It was determined that CT carriage was high in patients without perineural invasion and sTRAIL levels were approximately 2.49 times lower than CC in patients with CT in this group.is (p < 0.05). We think that TRAIL C1595T in CRC can change sTRAIL levels in the clinicopathological axis in advanced stages such as metastasis and invasion, but both are not independent risk factors.

Background: We evaluated adverse short-term outcomes after open lower-extremity bypass surgery in patients with diabetes mellitus with a comparison performed based on patient height.

Methods: The American College of Surgeons National Surgical Quality Improvement Program database was analyzed to select patients with Current Procedural Terminology codes 35533, 35540, 35556, 35558, 35565, 35566, 35570, and 35571 and with the diagnosis of diabetes mellitus. This resulted in 83 patients 60 inches or less in height, 1,084 between 60 and 72 inches, and 211 patients 72 inches and taller.

Results: No differences were observed among groups with respect to the development of a superficial surgical site infection (9.6% versus 6.4% versus 5.7%; P = .458), deep incisional infection (1.2% versus 1.4% versus 2.8%; P = .289), sepsis (2.4% versus 2.0% versus 2.8%; P = .751), unplanned reoperation (19.3% versus 15.6% versus 21.8%; P = .071), or unplanned hospital readmission (19.3% versus 14.8% versus 17.1%; P = .573). A significant difference was observed among groups in the development of a wound disruption (4.8% versus 1.3% versus 4.7%; P = .001). A multivariate regression analysis was performed of the wound disruption outcome with the variables of age, sex, race, ethnicity, height, weight, current smoker, and open wound/wound infection. Race (P = .025) and weight (P = .003) were found to be independently associated with wound disruption, but height was not (P = .701).

Conclusions: The results of this investigation demonstrate no significant differences in short-term adverse outcomes after lower-extremity bypass surgery based on patient height.

Objective: To examine changes in quality of life (QoL) after an eight-week period of robotic exoskeleton training in a homogeneous group of patients with chronic complete spinal cord injury (SCI).

Design: Prospective single-group pre-post study.

Setting: Rehabilitation center.

Participants: Patients with a chronic (>6 months) motor complete SCI (T1-L1).

Intervention: Twenty-four training sessions with the ReWalk exoskeleton over an eight-week period.

Main outcome measure: QoL, assessed with the sum score of the Short Form-36 with Walk Wheel modification (SF-36ww). Secondary outcome measures were the eight SF-36ww subdomains, satisfaction with bladder and bowel management, lower extremity joint passive range of motion (pROM), and lower extremity spasticity.

Results: Twenty-one participants completed the training. QoL significantly improved after the training period (average SF-36 sum score 621 ± 90) compared to baseline (571 ± 133) (t(20)=-2.5, P=.02). Improvements were seen on the SF-36ww subdomains for pain (P=.003), social functioning (P=.03), mental health (P=.02), and general health perception (P=.01). Satisfaction with bladder management (range 1-5) improved from median 3 at baseline to 4 after exoskeleton training (P=0.01). No changes in satisfaction with bowel management (P=.11), pROM (hip-extension (P=.49), knee-extension (P=.36), ankle dorsiflexion (P=.69)), or spasticity (P=.94) were found.

Conclusion: Even in patients with chronic motor complete SCI and a relatively high level of QoL at baseline, a short-term exoskeleton training improved their QoL, pain and satisfaction with bladder management; findings that warrant further controlled studies in this specific SCI population.

Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.