Enhanced recovery after surgery pathways for deep inferior epigastric perforator flap breast reconstruction: A systematic review and meta-analysis.

BACKGROUND: Deep inferior epigastric perforator (DIEP) surgery is one of the most difficult breast reconstruction techniques available, both in terms of operating complexity and patient recovery. Enhanced recovery after surgery (ERAS) pathways were recently introduced in numerous subspecialties to reduce recovery time, patient pain, and cost by providing multimodal perioperative care. Plastic surgery has yet to widely integrate ERAS with DIEP reconstruction, mostly due to insufficient data on patient outcomes with this combined approach. METHODS: Five major medical databases were queried using predetermined search criteria according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Statistical analysis was performed using Cochrane's RevMan (v5.4). RESULTS: A total of 466 articles were identified. A total of 14 studies were included in the review with a combined sample of 2102 patients. Eight studies were included in the meta-analysis with a combined sample of 1679 patients. On average, the included studies utilized 11.69 of 18 suggested protocols for ERAS with breast reconstruction. Our primary outcome, length of stay, was reduced by a mean of 1.12 (95% confidence interval [CI] [-1.30, -0.94], n = 1627, p < 0.001) days in the ERAS group. Postoperative oral morphine equivalents (OME) were also reduced in the ERAS group by 104.02 (95% CI [-181.43, -26.61], n = 545, p = 0.008) OME. The ERAS group saw a significant 3.54 (95% CI [-4.43, -2.65], n = 527, p < 0.001) standardized mean difference cost reduction relative to the control groups. The surgery time was reduced by 60.46 (95% CI [-125, 4.29], n = 624, p < 0.07) min, although this was not statistically significant. CONCLUSIONS: The ERAS pathway in DIEP breast reconstruction is consistently associated with reduced hospital stay, opioid use, and patient cost. Moreover, there appears to be no evidence of serious adverse outcomes associated with the application of the ERAS protocol.

A cost analysis of medical students applying to neurological surgery residency: An analysis of the Texas STAR database.

INTRODUCTION: Medical Students applying to neurosurgery residency programs incur substantial costs associated with interviews, away rotations, and application fees. However, few studies have compared expenses prior to and during the COVID-19 pandemic. This study evaluates the financial impact of COVID-19 on the neurosurgery residency application and identifies strategies that may alleviate the financial burden of prospective neurosurgery residents. METHODS: The TEXAS STAR database was surveyed for applicants of neurosurgical residency programs during the COVID-19 pandemic (2021) and post-pandemic (2022). 66 applicants for the 2021 application cycle and 50 applicants for the 2022 application cycle completed the survey. We compared application fees, away rotations cost, interview cost, and total expenses as reported by the neurosurgery applicants of the 2021 and 2022 application cycle. A Shapiro-Wilk test was used to test for data normality, and a Mann-Whitney U-Test was used to compare costs during the 2021 and 2022 neurosurgery application cycle. RESULTS: There was a statistically significant reduction in total expenses in 2021 vs 2022 ($3,934 vs $9,860). Interview and away rotation expenses decreased in 2021 vs 2022 (interview expenses $786 vs $4511, away rotation $1,083 vs $3,000, p < 0.001). Application fee expenses were not different between 2021 and 2022. The greatest reduction in application cost ($11,908) was seen in the South for 2021. CONCLUSIONS: The COVID-19 pandemic significantly reduced total fees associated with the neurosurgical residency application. Virtual platforms in place of in-person interviews could lessen the financial burden on applicants and alleviate socioeconomic barriers in the neurosurgical application process after COVID-19.

Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review.

Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

Tumor Suppressor p53 Down-Regulates Programmed Cell Death Protein 4 (PDCD4) Expression.

The programmed cell death protein 4 (PDCD4), a well-known tumor suppressor, inhibits translation initiation and cap-dependent translation by inhibiting the helicase activity of EIF4A. The EIF4A tends to target mRNAs with a structured 5'-UTR. In addition, PDCD4 can also prevent tumorigenesis by inhibiting tumor promoter-induced neoplastic transformation, and studies indicate that PDCD4 binding to certain mRNAs inhibits those mRNAs' translation. A previous study demonstrated that PDCD4 inhibits the translation of p53 mRNA and that treatment with DNA-damaging agents down-regulates PDCD4 expression but activates p53 expression. The study further demonstrated that treatment with DNA-damaging agents resulted in the downregulation of PDCD4 expression and an increase in p53 expression, suggesting a potential mechanism by which p53 regulates the expression of PDCD4. However, whether p53 directly regulates PDCD4 remains unknown. Herein, we demonstrate for the first time that p53 regulates PDCD4 expression. Firstly, we found that overexpression of p53 in p53-null cells (H1299 and Saos2 cells) decreased the PDCD4 protein level. Secondly, p53 decreased PDCD4 promoter activity in gene reporter assays. Moreover, we demonstrated that mutations in p53 (R273H: contact hotspot mutation, and R175H: conformational hotspot mutation) abolished p53-mediated PDCD4 repression. Furthermore, mutations in the DNA-binding domain, but not in the C-terminal regulatory domain, of p53 disrupted p53-mediated PDCD4 repression. Finally, the C-terminal regulatory domain truncation study showed that the region between aa374 and aa370 is critical for p53-mediated PDCD4 repression. Taken together, our results suggest that p53 functions as a novel regulator of PDCD4, and the relationship between p53 and PDCD4 may be involved in tumor development and progression.