Long-term patient-reported outcome measures (PROMs) after primary ventral or small midline incisional hernia repair.

BACKGROUND: Ventral hernia repair is a common elective surgical procedure lacking strong evidence for specific operative approaches. This study aimed to evaluate the outcomes of primary suture repair or polypropylene sandwich mesh repair for ventral hernias. The main outcome measures were the rate of hernia recurrence, and evaluation of long-term complications and patient-reported outcomes. METHODS: This retrospective cohort study evaluated patient perceived recurrence and pain in patients who had undergone a primary ventral hernia (epigastric, supraumbilical, or umbilical) repair or small (≤20 mm) midline incisional hernia repair 10 years after the procedure. Short-term follow-up occurred up to 6 weeks after the initial operation, while long-term follow-up included patients who were reviewed clinically or interviewed via telephone at or beyond 3 years after the procedure. RESULTS: Most (75/100, 75.0%) patients had an extra-peritoneal sandwich mesh repair. Short-term follow-up showed minimal pain and normal activities for all patients (97/97, 100%). Long-term follow-up (median 12 years [IQR 11-13]) was achieved in 95.9% (93/97) of patients with only a small number reporting a slight bulge (5/93, 5.4%) and intermittent mild discomfort (8/93, 8.6%). Nine patients (9/97, 9.3%) experienced hernia recurrence, diagnosed at a median of 26 months [interquartile range, IQR, 7-58] post-operatively. CONCLUSIONS: These findings suggest that an open sandwich mesh technique is a safe and effective method for repairing primary ventral hernias and small midline incisional hernias and is associated with favourable long-term patient-reported outcomes.

Characterization of early non-responders within behavioral weight loss treatment.

Objective: Given that low early (4 weeks) weight loss (WL) predicts longer-term WL, the purpose of this study was to identify factors associated with poor early WL. Methods: 438 adults with overweight/obesity participating in an Internet-delivered behavioral WL program provided weights at baseline and 4 weeks. Participants were stratified by percent WL at 4 weeks: LOW: <2% WL, MEDIUM: 2 to <4% WL, HIGH: ≥4% WL and groups were compared on baseline variables (demographics, physical activity, and psychosocial measures) and 4-week intervention adherence. Results: 37.4%, 40.9%, and 21.7% of participants had LOW, MEDIUM, and HIGH early WL respectively. LOW was more likely to be female compared to HIGH and less likely to be non-Hispanic White compared to MEDIUM and HIGH (p's<0.05). After controlling for demographic differences, LOW had lower baseline physical activity compared to HIGH and watched fewer video lessons, self-monitored calorie intake and weight on fewer days, and were less likely to achieve the exercise goal compared to MEDIUM and HIGH (p's<0.05). Conclusion: Findings can inform future adaptive interventions which tailor treatment based upon early WL to improve WL outcomes for more individuals.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Ocean warming and acidification adjust inter- and intra-specific variability in the functional trait expression of polar invertebrates.

Climate change is known to affect the distribution and composition of species, but concomitant alterations to functionally important aspects of behaviour and species-environment relations are poorly constrained. Here, we examine the ecosystem ramifications of changes in sediment-dwelling invertebrate bioturbation behaviour-a key process mediating nutrient cycling-associated with near-future environmental conditions (+ 1.5 °C, 550 ppm [pCO2]) for species from polar regions experiencing rapid rates of climate change. We find that responses to warming and acidification vary between species and lead to a reduction in intra-specific variability in behavioural trait expression that adjusts the magnitude and direction of nutrient concentrations. Our analyses also indicate that species behaviour is not predetermined, but can be dependent on local variations in environmental history that set population capacities for phenotypic plasticity. We provide evidence that certain, but subtle, aspects of inter- and intra-specific variation in behavioural trait expression, rather than the presence or proportional representation of species per se, is an important and under-appreciated determinant of benthic biogeochemical responses to climate change. Such changes in species behaviour may act as an early warning for impending ecological transitions associated with progressive climate forcing.

Assessing Laterality Errors in Radiology: Comparing Generative AI and Natural Language Processing.

PURPOSE: We compared the performance of generative AI (G-AI, ATARI) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images. METHODS: We used an NLP-based (mPower) tool to identify radiology reports flagged for laterality errors in its QA Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error - true positive) or absent (NLP error - false positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true positive (118 reports) and false positive (119 reports) laterality errors. We estimated accuracy of NLP and G-AI tools to identify overall and modality-wise laterality errors. RESULTS: Among the 898 NLP-flagged laterality errors, 64% (574/898) had NLP errors and 36% (324/898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false positives) with a 97.4% accuracy (115/118 reports; 95% CI = 96.5% - 98.3%). Combined Vision and text query resulted in 98.3% accuracy (116/118 reports/images; 95% CI = 97.6% - 99.0%) query alone had a 98.3% accuracy (116/118 images; 95% CI = 97.6% - 99.0%). CONCLUSION: The generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.

Durable complete response in a patient with leptomeningeal melanoma after treatment with dabrafenib, trametinib, and nivolumab.

Leptomeningeal disease (LMD) is a devastating complication of melanoma with a dismal prognosis. We present the case of a young man with stage IV BRAF V600E mutant melanoma with lung, lymph node, and brain metastases initially treated with ipilimumab and nivolumab, who subsequently developed LMD. Upon change to BRAF/MEK targeted therapy with nivolumab, a durable complete response was achieved and remains ongoing, off treatment, 7 years from diagnosis. Management of symptomatic LMD remains a critical unmet clinical challenge, with limited clinical trial data. This exceptional case is instructive, as the first published case of the use of the triplet, and the first durable response with therapy discontinuation, in melanoma LMD. The triple-drug regimen may be considered a viable option in fit patients. This case highlights the potential for long-term disease control and the critical and urgent need to develop clinical trials inclusive of patients with LMD to define the best treatment strategies.

Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.

BACKGROUND: Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS: Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS: 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION: Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

Field longevity of methyl eugenol and cue-lure plugs and associated insecticidal strips: captures of Bactrocera dorsalis and Zeugodacus cucurbitae (Diptera: Tephritidae) in Hawaii.

Certain species of true fruit flies (Diptera: Tephritidae) cause tremendous damage to commercially important fruits and vegetables, and many countries operate continuous trapping programs which rely on male-specific lures such as trimedlure (TML), methyl eugenol (ME), and cue-lure (CL). Traditionally, these attractants have been applied as liquids to cotton wicks inside traps, although this results in high evaporative loss of the lure. Slow-release, polymeric plugs have been widely adopted for TML, but such devices are not widely used for ME or CL. Recent data, however, suggest that ME and CL plugs may be attractive for as long as 12 wk in the field. The present study investigates whether ME and CL plugs weathered for 18 or 24 wk are effective in capturing males of Bactrocera dorsalis (Hendel) and Zeugodacus cucurbitae (Coquillett), respectively. For B. dorsalis, 6 g ME plugs were as effective as the control treatment (fresh liquid on a wick) after 12 wk of weathering but not after 18 or 24 wk. For Z. cucurbitae, 3 g CL plugs were as effective as the control treatment (fresh CL plugs) after 12 and 18 wk of weathering but not after 24 wk. The residual content and release rate of the 2 lures were also measured over time, but, with the exception of the residual content of ME, we did not find a direct correlation between these parameters and numbers of flies captured.

North American Cancer Center Clinical Research Capacity and Benchmarking in the Postpandemic Era.

PURPOSE: Cancer center clinical trial offices (CCTOs) support trial development, activation, conduct, regulatory adherence, data integrity, and compliance. In 2018, the Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) Steering Committee conducted and published survey results to benchmark North American CCTOs, including trial volume, accrual, full time equivalents (FTEs), and budget. The survey was readministered in 2023 to assess contemporary CCTO performance and capacity with results presented here. METHODS: The 28 question 2023 survey was sent to directors of AACI's clinical member cancer centers. Survey participation was voluntary, no compensation was provided, and data requested covered operations during 2022. Definitions were consistent with National Cancer Institute (NCI) CCTO reporting requirements and AACI staff anonymously compiled results for descriptive statistical reporting. RESULTS: The survey response rate was 61% (60/99). The median annual CCTO budget was $11.5 million (M) US dollars (USD) versus $8.2M USD in 2018. These budgets support a median of 150 FTEs versus 104 previously, and a median total of 384 versus 280 interventional treatment trials and a median of 479 versus 531 interventional treatment accruals. Sources of support for CCTO annual budgets were primarily from industry revenue (45.3%) or institutional support (31.7%). Nearly 60% of centers reported activating NCI-sponsored studies within 90 days but only 9% reported meeting a 90-day activation timeline for industry sponsored studies. CONCLUSION: Contemporary benchmarks for CCTO operations through this survey demonstrate larger staff sizes, larger budgets, more trials supported, but fewer patients enrolled to interventional treatment trials in comparison with 2018. These data shine a critical light on the increasing complexity of cancer clinical trials, the importance of external funding sources, and necessary operational efficiency upgrades to provide cutting-edge cancer research and care.

Dilute concentrations of maritime fuel can modify sediment reworking activity of high-latitude marine invertebrates.

Multiple expressions of climate change, in particular warming-induced reductions in the type, extent and thickness of sea ice, are opening access and providing new viable development opportunities in high-latitude regions. Coastal margins are facing these challenges, but the vulnerability of species and ecosystems to the effects of fuel contamination associated with increased maritime traffic is largely unknown. Here, we show that low concentrations of the water-accommodated fraction of marine fuel oil, representative of a dilute fuel oil spill, can alter functionally important aspects of the behaviour of sediment-dwelling invertebrates. We find that the response to contamination is species specific, but that the range in response among individuals is modified by increasing fuel concentrations. Our study provides evidence that species responses to novel and/or unprecedented levels of anthropogenic activity associated with the opening up of high-latitude regions can have substantive ecological effects, even when human impacts are at, or below, commonly accepted safe thresholds. These secondary responses are often overlooked in broad-scale environmental assessments and marine planning yet, critically, they may act as an early warning signal for impending and more pronounced ecological transitions.

Efficient High-Order Space-Angle-Energy Polytopic Discontinuous Galerkin Finite Element Methods for Linear Boltzmann Transport.

We introduce an hp-version discontinuous Galerkin finite element method (DGFEM) for the linear Boltzmann transport problem. A key feature of this new method is that, while offering arbitrary order convergence rates, it may be implemented in an almost identical form to standard multigroup discrete ordinates methods, meaning that solutions can be computed efficiently with high accuracy and in parallel within existing software. This method provides a unified discretisation of the space, angle, and energy domains of the underlying integro-differential equation and naturally incorporates both local mesh and local polynomial degree variation within each of these computational domains. Moreover, general polytopic elements can be handled by the method, enabling efficient discretisations of problems posed on complicated spatial geometries. We study the stability and hp-version a priori error analysis of the proposed method, by deriving suitable hp-approximation estimates together with a novel inf-sup bound. Numerical experiments highlighting the performance of the method for both polyenergetic and monoenergetic problems are presented.

Physiological characteristics predictive of passing military physical employment standard tasks for ground close combat occupations in men and women.

Challenges for some women meeting the physical employment standards (PES) for ground close combat (GCC) roles stem from physical fitness and anthropometric characteristics. The purpose of this study was to identify the modifiable and nonmodifiable characteristics predictive of passing GCC-based PES tasks and determine the modifiable characteristics suitable to overcome nonmodifiable limitations. 107 adults (46 women) underwent multiday testing assessing regional and total lean mass (LM), percent body fat (BF%), aerobic capacity (V̇O2peak), strength, power, and PES performance. Predictors with p-value <0.200 were included in stepwise logistic regression analysis or binary logistic regression when outcomes among sexes were insufficient. Relative and absolute arm LM (OR: 4.617-8.522, p < 0.05), leg LM (OR: 2.463, p < 0.05), and upper body power (OR: 2.061, p < 0.05) predicted medicine ball chest throw success. Relative and absolute arm LM (OR: 3.734-11.694, p < 0.05), absolute trunk LM (OR: 2.576, p < 0.05), and leg LM (OR: 2.088, p < 0.05) predicted casualty drag success. Upper body power (OR: 3.910, p < 0.05), absolute trunk LM (OR: 2.387, p < 0.05), leg LM (OR: 2.290, p < 0.05), and total LM (OR: 1.830, p < 0.05) predicted maximum single lift success. Relative and absolute arm LM (OR: 3.488-7.377, p < 0.05), leg LM (OR: 1.965, p < 0.05), and upper body power (OR: 1.957, p < 0.05) predicted water can carry success. %BF (OR: 0.814, p = 0.007), V̇O2peak (OR: 1.160, p = 0.031), and lower body strength (OR: 1.059, p < 0.001) predicted repeated lift and carry success. V̇O2peak (OR: 1.540, p < 0.001) predicted 2-km ruck march success. Modifiable characteristics were the strongest predictors for GCC-based PES task success to warrant their improvement for enhancing PES performance for women.

Diagnostic accuracy of a 'sarcopenia index' based on serum biomarkers creatinine and cystatin C in 458,702 UK Biobank participants.

BACKGROUND & AIMS: There is an emerging and urgent need to identify biomarkers of sarcopenia. A novel sarcopenia index (SI), based on serum creatinine and cystatin C, has emerged as a potential biomarker for use. The SI can predict clinical outcomes and discriminate between the presence of sarcopenia in a range of chronic and acute conditions. However, the SI has not yet been tested in a large real-world general population dataset. This study aimed to investigate the accuracy of the SI in the identification of sarcopenia in a large prospective general population cohort. METHODS: Data were taken from UK Biobank, a large prospective epidemiological study in the United Kingdom (UK). Serum creatinine and cystatin C values were used to calculate the SI [creatinine (mg/dl)/cystatin C (mg/dl) × 100]. Probable sarcopenia was defined by maximum handgrip strength (HGS). Muscle mass was assessed using bioelectrical impedance analysis. Low muscle mass was defined as an appendicular lean mass (ALM) index below prespecified thresholds. Confirmed sarcopenia was defined as both low HGS and low muscle mass. Pearson correlation coefficients and logistic regression were used to explore the association between various sarcopenia traits (probable sarcopenia, low ALM index, and confirmed sarcopenia) and the SI. The diagnostic value of the SI was investigated using the area under the receiver operating characteristic curve (area under the curve, AUC). RESULTS: 458,702 participants were included in the analysis (46.4% males, mean age, males: 68.7 (±8.2) years; females: 68.2 (±8.0) years)). Probable sarcopenia was observed in 4.5% of males and 6.1% of females; low ALM index in 2.8% of males and 0.7% of females; confirmed sarcopenia in 0.3% of males and 0.1% of females. SI was significantly lower in individuals with confirmed sarcopenia (males: 86.3 ± 16.6 vs. 99.5 ± 15.3, p < .01; females: 73.6 ± 13.7 vs. 84.6 ± 14.0, p < .01). For every 1-unit increase in the SI, the odds of confirmed sarcopenia were reduced by 5% in males (odds ratio (OR): 0.95, p < 0.001) and 7% in females (OR: 0.923, p < 0.001). The AUC showed acceptable discriminative ability of confirmed sarcopenia (males: AUC = 0.731; females: AUC = 0.711). CONCLUSIONS: Using a large real-world dataset of almost half a million people, our study indicated the SI has acceptable diagnostic accuracy when identifying those with sarcopenia and may be a useful biomarker to aid the stratification of those at risk and in need of intervention.

Role of atmospheric rivers in shaping long term Arctic moisture variability.

Atmospheric rivers (ARs) reaching high-latitudes in summer contribute to the majority of climatological poleward water vapor transport into the Arctic. This transport has exhibited long term changes over the past decades, which cannot be entirely explained by anthropogenic forcing according to ensemble model responses. Here, through observational analyses and model experiments in which winds are adjusted to match observations, we demonstrate that low-frequency, large-scale circulation changes in the Arctic play a decisive role in regulating AR activity and thus inducing the recent upsurge of this activity in the region. It is estimated that the trend in summertime AR activity may contribute to 36% of the increasing trend of atmospheric summer moisture over the entire Arctic since 1979 and account for over half of the humidity trends in certain areas experiencing significant recent warming, such as western Greenland, northern Europe, and eastern Siberia. This indicates that AR activity, mostly driven by strong synoptic weather systems often regarded as stochastic, may serve as a vital mechanism in regulating long term moisture variability in the Arctic.

Primary nasal influenza infection rewires tissue-scale memory response dynamics.

The nasal mucosa is often the initial site of respiratory viral infection, replication, and transmission. Understanding how infection shapes tissue-scale primary and memory responses is critical for designing mucosal therapeutics and vaccines. We generated a single-cell RNA-sequencing atlas of the murine nasal mucosa, sampling three regions during primary influenza infection and rechallenge. Compositional analysis revealed restricted infection to the respiratory mucosa with stepwise changes in immune and epithelial cell subsets and states. We identified and characterized a rare subset of Krt13+ nasal immune-interacting floor epithelial (KNIIFE) cells, which concurrently increased with tissue-resident memory T (TRM)-like cells. Proportionality analysis, cell-cell communication inference, and microscopy underscored the CXCL16-CXCR6 axis between KNIIFE and TRM cells. Secondary influenza challenge induced accelerated and coordinated myeloid and lymphoid responses without epithelial proliferation. Together, this atlas serves as a reference for viral infection in the upper respiratory tract and highlights the efficacy of local coordinated memory responses.

Connectome-based Brain Marker Moderates the Relationship between Childhood Adversity and Transdiagnostic Psychopathology during Early Adolescence.

Importance: Identifying brain-based markers of resiliency that reliably predict who is and is not at elevated risk for developing psychopathology among children who experience adverse childhood experiences (ACEs) is important for improving our mechanistic understanding of these etiological links between child adversity and psychopathology and guiding precision medicine and prevention efforts for reducing psychiatric impact of ACEs. Objective: To examine associations between ACEs and transdiagnostic psychopathology during the transition from preadolescence to early adolescence and test whether these associations are moderated by a hypothesized resilience factor, a previously identified connectome variate (CV) that is associated with higher cognitive function and lower psychopathology. Design Setting and Participants: This study was conducted in a longitudinal design based on multicenter data from a community cohort of U.S. youth aged of 9-11 at baseline, who participated in the Adolescent Brain Cognitive Development (ABCD) study (N=7,382 at baseline and 6,813 at 2-year follow-up). Linear regression models and moderation analyses were used to characterize concurrent and prospective associations between lifetime ACEs and number of DSM-5 psychiatric disorders (indexing transdiagnostic psychopathology) and to determine if individual variations in these associations were moderated by the CV derived from resting-state fMRI at baseline. Main Outcomes and Measures: Cumulative number of current DSM-5 psychiatric disorders assessed using the computerized self-admin version Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5) and lifetime ACEs assessed from child and parent reports at baseline (9-10 years) and 2-year-follow-up (11-12 years). Results: ACE total scores correlated positively with the cumulative number of current DSM-5 psychiatric disorders at both baseline (r =.258, p < .001) and 2-year follow-up (r =.257, p < .001). The baseline CV score moderated the ACE-disorder associations at baseline (B = -0.021, p < .001) and at 2-year follow-up (B = -0.018, p = .008), as well as the association between the changes in ACE and in the number of disorders from baseline to year 2 (B = -0.012, p = .045). Post-hoc analyses further showed that the moderation effect of CV on ACE-psychopathology associations was specific to the threat-related ACEs and to female youth. Conclusions and Relevance: These findings provide preliminary evidence for a connectome-based resiliency marker and suggest that functional connectivity strength in a broad system including frontal-parietal cortices and subcortical nuclei relevant to cognitive control may protect preadolescents who have experienced lifetime ACEs--especially females and those experiencing threat-related ACEs--from developing transdiagnostic psychopathology.

Nucleolin acute degradation reveals novel functions in cell cycle progression and division in TNBC.

Nucleoli are large nuclear sub-compartments where vital processes, such as ribosome assembly, take place. Technical obstacles still limit our understanding of the biological functions of nucleolar proteins in cell homeostasis and cancer pathogenesis. Since most nucleolar proteins are essential, their abrogation cannot be achieved through conventional approaches. Additionally, the biological activities of many nucleolar proteins are connected to their physiological concentration. Thus, artificial overexpression might not fully recapitulate their endogenous functions. Proteolysis-based approaches, such as the Auxin Inducible Degron (AID) system paired with CRISPR/Cas9 knock-in gene-editing, have the potential to overcome these limitations, providing unprecedented characterization of the biological activities of endogenous nucleolar proteins. We applied this system to endogenous nucleolin (NCL), one of the most abundant nucleolar proteins, and characterized the impact of its acute depletion on Triple-Negative Breast Cancer (TNBC) cell behavior. Abrogation of endogenous NCL reduced proliferation and caused defective cytokinesis, resulting in bi-nucleated tetraploid cells. Bioinformatic analysis of patient data, and quantitative proteomics using our experimental NCL-depleted model, indicated that NCL levels are correlated with the abundance of proteins involved in chromosomal segregation. In conjunction with its effects on sister chromatid dynamics, NCL abrogation enhanced the anti-proliferative effects of chemical inhibitors of mitotic modulators such as the Anaphase Promoting Complex. In summary, using the AID system in combination with CRISPR/Cas9 for endogenous gene editing, our findings indicate a novel role for NCL in supporting the completion of the cell division in TNBC models, and that its abrogation could enhance the therapeutic activity of mitotic-progression inhibitors.