Organotin mixtures reveal interactions that modulate adipogenic differentiation in 3T3-L1 preadipocytes.

Organotin chemicals (butyltins and phenyltins) are the most widely used organometallic chemicals worldwide and are used in industrial applications, such as biocides and anti-fouling paints. Tributyltin (TBT) and more recently, dibutyltin (DBT) and triphenyltin (TPT) have been reported to stimulate adipogenic differentiation. Although these chemicals co-exist in the environment, their effect in combination remains unknown. We first investigated the adipogenic effect of eight organotin chemicals (monobutyltin (MBT), DBT, TBT, tetrabutyltin (TeBT), monophenyltin (MPT), diphenyltin (DPT), TPT, and tin chloride (SnCl4)) in the 3T3-L1 preadipocyte cell line in single exposures at two doses (10 and 50 ng/ml). Only three out of the eight organotins induced adipogenic differentiation with TBT eliciting the strongest adipogenic differentiation (in a dose-dependent manner) followed by TPT and DBT, as demonstrated by lipid accumulation and gene expression. We then hypothesized that, in combination (TBT, DBT, and TPT), adipogenic effects will be exacerbated compared to single exposures. However, at the higher dose (50 ng/ml), TBT-induced differentiation was reduced by TPT and DBT when in dual or triple combination. We tested whether TPT or DBT would interfere with adipogenic differentiation stimulated by a peroxisome proliferator-activated receptor (PPARγ) agonist (rosiglitazone) or a glucocorticoid receptor agonist (dexamethasone). Both DBT50 and TPT50 reduced rosiglitazone-, but not dexamethasone-stimulated adipogenic differentiation. In conclusion, DBT and TPT interfere with TBT's adipogenic differentiation possibly via PPARγ signaling. These findings highlight the antagonistic effects among organotins and the need to understand the effects and mechanism of action of complex organotin mixtures on adipogenic outcomes.

Development of Imaging Criteria for Geriatric Blunt Trauma Patients.

INTRODUCTION: Current decision tools to guide trauma computed tomography (CT) imaging were not validated for use in older patients. We hypothesized that specific clinical variables would be predictive of injury and could be used to guide imaging in this population to minimize risk of missed injury. METHODS: Blunt trauma patients aged 65 y and more admitted to a Level 1 trauma center intensive care unit from January 2018 to November 2020 were reviewed for histories, physical examination findings, and demographic information known at the time of presentation. Injuries were defined using the patient's final abbreviated injury score codes, obtained from the trauma registry. Abbreviated injury score codes were categorized by corresponding CT body region: Head, Face, Chest, C-Spine, Abdomen/Pelvis, or T/L-Spine. Variable groupings strongly predictive of injury were tested to identify models with high sensitivity and a negative predictive value. RESULTS: We included 608 patients. Median age was 77 y (interquartile range, 70-84.5) and 55% were male. Ground-level fall was the most common injury mechanism. The most commonly injured CT body regions were Head (52%) and Chest (42%). Variable groupings predictive of injury were identified in all body regions. We identified models with 97.8% sensitivity for Head and 98.8% for Face injuries. Sensitivities more than 90% were reached for all except C-Spine and Abdomen/Pelvis. CONCLUSIONS: Decision aids to guide imaging for older trauma patients are needed to improve consistency and quality of care. We have identified groupings of clinical variables that are predictive of injury to guide CT imaging after geriatric blunt trauma. Further study is needed to refine and validate these models.

What If We Do Not Operate? Outcomes of Nonoperatively Managed Emergency General Surgery Patients.

INTRODUCTION: Although two-thirds of patients with emergency general surgery (EGS) conditions are managed nonoperatively, their long-term outcomes are not well described. We describe outcomes of nonoperative management in a cohort of older EGS patients and estimate the projected risk of operative management using the NSQIP Surgical Risk Calculator (SRC). MATERIALS AND METHODS: We studied single-center inpatients aged 65 y and more with an EGS consult who did not undergo an operation (January 2019-December 2020). For each patient, we recorded the surgeon's recommendation as either an operation was "Not Needed" (medical management preferred) or "Not Recommended" (risk outweighed benefits). Our main outcome of interest was mortality at 30 d and 1 y. Our secondary outcome of interest was SRC-projected 30-day postoperative mortality risk (median % [interquartile range]), calculated using hypothetical low-risk and high-risk operations. RESULTS: We included 204 patients (60% female, median age 75 y), for whom an operation was "Not Needed" in 81% and "Not Recommended" in 19%. In this cohort, 11% died at 30 d and 23% died at 1 y. Mortality was higher for the "Not Recommended" cohort (37% versus 5% at 30 d and 53% versus 16% at 1 y, P < 0.05). The SRC-projected 30-day postoperative mortality risk was 3.7% (1.3-8.7) for low-risk and 5.8% (2-11.8) for high-risk operations. CONCLUSIONS: Nonoperative management in older EGS patients is associated with very high risk of short-term and long-term mortality, particularly if a surgeon advised that risks of surgery outweighed benefits. The SRC may underestimate risk in the highest-risk patients.

Post-Trauma Discharge Instructions: Are We Dropping the Ball?

INTRODUCTION: Complex follow-up plans for polytrauma patients are compiled at the end of hospitalization into discharge instructions. We sought to identify how often patient discharge instructions incorrectly communicated specialist recommendations. We hypothesized that patients with more complex hospitalizations would have more discharge instruction errors (DI-errors). METHODS: We reviewed adult trauma inpatients (March 2017-March 2018), excluding those who left against medical advice or were expected to follow up outside our system. Complex hospitalizations were represented using injury severity (ISS), hospital length of stay (LOS), intensive care unit length of stay (iLOS), and number of consultants (NC). We recorded the type of consultant (surgical or nonsurgical), and consultant recommendations for follow-up. DI-errors were defined as either follow-up necessary but omitted or follow-up not necessary yet present on the instructions. Patients with DI-errors were compared to patients without DI-errors. Groups were compared using Wilcoxon rank sum or chi-square (alpha <.05). RESULTS: We included 392 patients (median age 45 [IQR 26-58], ISS 14 [10-21], LOS 6 [3-11]). 55 patients (14%) had DI-errors. Factors associated with DI-errors included the total number of consultants and use of nonsurgical consultants. ISS, LOS, iLOS, were not associated with DI-errors. CONCLUSION: Common measures of admission complexity were not associated with DI-errors, although the number and type of consultants were associated with DI-errors. Non-surgical specialty consultant recommendations were more likely to be omitted. It is crucial for patients to receive accurate discharge instructions, and systematic processes are needed to improve communication with the patients at discharge.

Race and trauma mortality: The effect of hospital-level Black-White patient race distribution.

BACKGROUND: Race-related health disparities have been well documented in the United States. In some settings, Black patients have better outcomes in hospitals that serve high proportions of Black patients. We hypothesized that Black trauma patients would have lower mortality in high Black-serving (H-BS) hospitals. METHODS: We identified all adult patients with Black or White race and with an Injury Severity Score of ≥4 from the 2017 National Inpatient Sample. We collected hospital identifier, mechanism, age, sex, comorbidities, urban-rural location, insurance, zip code income quartile, and injury severity calculated from International Classification of Diseases, Tenth Revision, codes. We used a previously published method to group hospitals by proportion of Black patients served: HB-S (top 5%), medium Black serving (5-25%), and low Black serving (L-BS; bottom 75%). Adjusted logistic regression using an interaction variable between race and hospital service rank (reference: White patients in H-BS) was used to identify factors associated with mortality. RESULTS: We analyzed 184,080 trauma patients (median age, 72 years [interquartile range, 55-84 years]; Injury Severity Score, 9 [4-10]), of whom 11.7% were Black. Overall mortality was 4%. Of 2,376 hospitals, 126 (5.3%) were H-BS and 469 (19.7%) were medium Black serving. Furthermore, 29.8% of Black and 3.6% of White patients were treated at H-BS hospitals, while 71.7% of White and 23.6% of Black patients were treated at L-BS hospitals (p < 0.001). Black patients had the lowest mortality at H-BS hospitals (odds ratio [OR], 0.76 [0.64-0.92]) and the highest mortality (OR, 1.43 [1.13-1.80]) at L-BS hospitals. White patients had the lowest mortality at L-BS hospitals (OR, 0.76 [0.64-0.92]). CONCLUSION: After adjusting for patient and hospital factors, disparities exist such that Black and White patients have the best outcomes in hospitals that treat those patients most frequently, suggesting potential for racial bias at the institutional level. Further efforts must be made to promote equitable treatment at all hospitals and reduce these disparities. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level IV.

Automated lipid droplet quantification system for phenotypic analysis of adipocytes using CellProfiler.

Adipogenic differentiation is the process by which preadipocytes become mature adipocytes, cells that store energy and regulate metabolic homeostasis. During differentiation, neutral lipids that accumulate in adipocytes can be detected using stains and used as an index of cell differentiation. However, imaging tools for evaluating intracellular lipid droplets remain at their infancy. Nutrition, stress, or chemical exposure can dysregulate adipogenic differentiation and lipid metabolism. Therefore, the aims of this study were to develop an accurate, standardized approach to quantify lipid droplet size of mature adipocytes and a clustering approach to analyze the total lipid content per adipocyte. For the lipid droplet analysis, we used two approaches, the free online computer software of reference, ImageJ, and another free online computer software, CellProfiler. For ImageJ, we used an already developed macro designed to identify particles and quantify their area, and for CellProfiler, we developed a new analysis pipeline. Our results show that CellProfiler is able to accurately identify a greater number of lipid droplets compared to ImageJ. A clustering analysis is also possible using CellProfiler which allows for the quantification of total lipid content per individual adipocyte to provide insight into single-cell responsiveness to adipogenic stimuli. CellProfiler streamlines the lipid droplet phenotypic analysis of adipocytes compared to more traditional analysis methods. In conclusion, this novel image analysis tool can provide a more precise evaluation of lipid droplet and adipogenesis dysregulation, a critical need in the understanding of metabolic disorders.