Massachusetts Academy of Math and Science

In humans, a neomorphic isocitrate dehydrogenase mutation (idh-1neo) causes increased levels of cellular D-2-hydroxyglutarate (D-2HG), a proposed oncometabolite. However, the physiological effects of increased D-2HG and whether additional metabolic changes occur in the presence of an idh-1neo mutation are not well understood. We created a Caenorhabditis elegans model to study the effects of the idh-1neo mutation in a whole animal. Comparing the phenotypes exhibited by the idh-1neo to ∆dhgd-1 (D-2HG dehydrogenase) mutant animals, which also accumulate D-2HG, we identified a specific vitamin B12 diet-dependent vulnerability in idh-1neo mutant animals that leads to increased embryonic lethality. Through a genetic screen, we found that impairment of the glycine cleavage system, which generates one-carbon donor units, exacerbates this phenotype. In addition, supplementation with alternate sources of one-carbon donors suppresses the lethal phenotype. Our results indicate that the idh-1neo mutation imposes a heightened dependency on the one-carbon pool and provides a further understanding of how this oncogenic mutation rewires cellular metabolism.

Gastrointestinal Stromal Tumors (GISTs) are the most common mesenchymal tumors of the GI tract. SEER is an extensive cancer database which proves useful in analyzing population trends. This analysis investigated GIST outcomes between geriatric & non-geriatric patients. SEER*STAT 8.4.0.1 was used to extract relevant GIST data from 2000 to 2019. Geriatric age was defined as ≥70years. Variables included age, sex, surgery, cancer-specific death, and overall survival. Statistical tests included univariate analysis using KM survival estimate (95% confidence interval) to calculate 5-year survival (5YS). Log-Rank tests determined statistical significance. Multivariable Cox's PH regression estimated the geriatric hazard death ratio adjusted for sex, stage, and surgery. The number of patients included was 13,579, yielding overall 5YS of 68.6% (95% CI 67.7-69.5). Cancer-specific death was 39.11% in 2000 & 3.33% in 2019. Non-geriatric & geriatric patient data yielded 5YS of 77.4% (76.4%-78.3%) and 53.3% (51.7%-54.8%) respectively (p<0.0001). For no surgery/surgery, younger patient data yielded 5YS of 48.7% (45.8%-51.4%) and 83.7% (82.7%-84.7%) respectively (p<0.0001); geriatric data yielded 5YS of 29.3% (26.5%-32.1%) and 62.8% (60.8%-64.6%) respectively (p<0.0001). Multivariable analysis yielded a geriatric hazard death of 2.56 (2.42-2.70) (p<0.0001). Cancer-specific death decreased since 2000, indicating an improvement in survival & treatment methods. Observed lower survival rates overall in the geriatric group. Surgery appeared to enhance survival rates in both groups, suggesting that surgery is an important factor in GIST survival regardless of age. Large prospective studies will help define clinical management for geriatric patients.

Introduction: Conventional coronary angiography (CCA) is the gold standard for the diagnosis of coronary artery disease (CAD). It is an invasive test and carries risks. Increased mortality is linked with reduced coronary flow, thus aggressively measuring coronary flow is important. Cardiac MRI (cMRI) is an emerging non-invasive test that can also help in assessing ventricular function, myocardial perfusion, and detecting anomalies. Aim: We aimed to analyze the diagnostic accuracy of cMRI compared to CCA through an umbrella meta-analysis. Methods: We searched PubMed articles comparing the diagnostic accuracy of cMRI with CCA in patients having CAD published from 2000 to 2022 according to PRISMA guidelines. We used the generic inverse variance method to pool sensitivity (SN), specificity (SP), negative likelihood ratio, positive likelihood ratio, and diagnostic odds ratio (DOR) with 95% confidence interval keeping alpha criteria of 0.05 as significant. RevMan 5.4 was used to calculate random effects models. Results: In this umbrella meta-analysis, 9 studies with 14615 patients met inclusion criteria. Overall pooled SN was 0.88 (95% CI: 0.87-0.90), SP was 0.85 (0.81-0.89), PLR was 6.07 (5.02-7.33), NLR was -1.99 (-2.10 to -1.87), and DOR 38.94 (31.96-47.46). (p&lt;0.00001) Conclusion: The diagnostic accuracy of a non-invasive, cMRI is comparable, if not superior to an invasive test like CCA.

With the release of ChatGPT, faculty started adopting AI content detectors to identify AI-generated content. However, faulty detector results led to wrongful accusations against some students. This study evaluates AI content detectors' performance in distinguishing human-written from AI-generated content. Three detectors, Writer, ZeroGPT, and OpenAI Text Classifier, were tested on three types of writing samples: human-written, human written and edited by ChatGPT, and fully AI-generated. Data was categorized into AI-generated, maybe AI, and human-written outputs for each detector. Statistical analysis, including chi-square tests, assessed detector outputs and text sample sources' relationship. Results revealed that only ZeroGPT's outputs depended on the sources but only correctly classified 47.2% of human-written texts.

As a dual GLP-1 receptor and GIP agonist, Tirzepatide (TZP) induces weight loss and improves glycemic control in patients with type 2 diabetes. Here, we performed insulin clamp and glucagon clamp under euglycemic conditions in male C57BL/6J mice fed a high-fat diet (HFD; 12 weeks) after once-daily treatment with TZP (3 nmol/kg) or GIP (25 nmol/kg) for 5 days (n=5). TZP and GIP caused HFD-fed mice to lose 10~15% of weight, but their fat mass remained 7-fold higher than chow-fed controls (C) (Fig. 1). Insulin clamp showed that GIP caused a 37% decrease in hepatic insulin action in chow-fed mice (Fig. 2). In HFD-fed mice, TZP induced significant increases in hepatic insulin action (4-fold) and glucose turnover (25%) compared to GIP, restoring insulin sensitivity to levels comparable to controls (Fig. 2-3). TZP also increased glucose metabolism in brown fat (1,528±155 vs. 798±166 nmol/g/min in GIP). Glucagon clamp showed that TZP significantly reduced hepatic glucose production (HGP) by ~50% compared to controls and GIP (Fig. 4). CONCLUSION: These data indicate that TZP improves insulin action and reduces hepatic glucagon action in obese mice while GIP causes hepatic insulin resistance in lean mice. Our findings suggest that TZP’s glycemic control may involve amelioration of insulin resistance and glucagon resistance, independent of weight loss and GIP action in the liver. Disclosure B.Kim: None. L.A.Tauer: None. X.Hu: None. J.K.Kim: None. Funding National Institutes of Health (5U2CDK093000)

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by spin-flipping intersystem crossing (ISC) into any dark high-spin states. The multiconfiguration nature of these radicals challenges their electronic structure calculations in the framework of single-reference density functional theory (DFT) and introduces room for method improvement. In the present study, we extend our earlier development of ML-ωPBE, a range-separated hybrid (RSH) exchange−correlation (XC) functional constructed using the stacked ensemble machine learning (SEML) algorithm, from closed-shell organic semiconducting molecules to doublet-spin organic semiconducting radicals. We assess its performance for a new test set of 64 radicals from five categories based on the original training set of 3,926 molecules. Interestingly, ML-ωPBE agrees with the first-principles OT-ωPBE functional regarding the molecule-dependent range-separation parameter (ω), with a small mean absolute error (MAE) of 0.0197 a0−1 but saves the computational cost by 2.46 orders of magnitude. This result demonstrates outstanding domain adaptation capacity of ML-ωPBE among various organic semiconducting species. To further assess the predictive power of ML-ωPBE, we also compare its performance on absorption and fluorescence energies (Eabs and Efl) evaluated using time-dependent DFT (TDDFT) with nine conventional functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.222 and 0.121 eV, only marginally different from OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to open-shell radicals and will open the venue for calculating optical properties using single-reference TDDFT.

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by spin-flipping intersystem crossing (ISC) into any dark high-spin states. The multiconguration nature of these radicals challenge their electronic structure calculations in the framework of single-reference density functional theory (DFT) and introduce room for method improvement. In the present study, we extend our earlier development of ML-ωPBE, a range-separated hybrid (RSH) exchange-correlation (XC) functional constructed using the stacked ensemble machine learning (SEML) algorithm, from closed-shell molecules to doublet-spin radicals. We assess its performance for an external test set of 64 radicals from five categories based on the original training set of 3,926 molecules. Interestingly, ML-ωPBE agrees with the first-principles OT-ωPBE functional regarding the molecule-dependent range-separation parameter (ω), with a small mean absolute error (MAE) of 0.0197 a0-1 but saves the computational cost by 2.46 orders of magnitude. This result demonstrates outstanding generalizability and transferability of ML-ωPBE among various organic semiconducting species. To further assess the predictive power of ML-ωPBE, we also compare its performance on absorption and fluorescence energies (Eabs and Efl) evaluated using time-dependent DFT (TDDFT), with nine conventional functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.222 and 0.121eV, only marginally different from OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to open-shell radicals and willopen the venue for calculating optical properties using single-reference TDDFT.