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BackgroundThe use of interactive patient scenarios has long been a valuable component of medical school curricula, as this type of learning facilitates empathy, comprehensive understanding, and cultural sensitivity.[1]The COVID-19 pandemic, however, has precipitated a shift to more virtual strategies to keep students, faculty, and patients safe.[2]ObjectivesTo evaluate second year medical students’ (MS2s) perceptions on the use of live patient encounters during the teaching of the skin and rheumatology course (BMS 6635) using different teaching formats due to changes from the COVID-19 pandemic.MethodsFour to five patients with dermatologic, autoimmune, and musculoskeletal diseases volunteered to participate in an interactive teaching session with MS2s at the University of Central Florida College of Medicine. MS2s enrolled in BMS 6635 were asked to voluntarily complete a survey about their learning experiences using these patient cases. Students who did not respond to the survey were excluded. Data analysis using Chi Square testing was performed on survey responses obtained pre-pandemic as compared to those collected in academic years 2020-2021 and 2021-2022 during the COVID-19 pandemic.Results700 surveys were obtained after patient cases given in different formats. When the interactive patient cases were given in person before COVID-19, 93% of students enjoyed the cases and 95% of students believed that the cases were an appropriate learning experience in their education. When these cases were delivered virtually beginning in the academic year 2020-2021, however, students’ enjoyment of these cases decreased to 86%, with 92% of students believing that the cases were an appropriate learning experience. This is a 7% and 9% decrease, respectively, from pre-pandemic years. During the academic year 2021-2022, use of a hybrid model, with students and faculty in-person and patients participating virtually, resulted in 81% of students enjoying the interactive patient cases and 83% of students believing that the cases were an appropriate learning experience. This was a 12% decrease from before the COVID-19 pandemic (p<.001) and a 5% and 9% decrease, respectively, from the previous year (p<.001) (Figure 1). 37% of students who had their cases in a completely virtual format preferred the interactive patient sessions to stay completely virtual, while 51% of students who participated in hybrid sessions during COVID-19 preferred the sessions to be completely virtual (p<.029) (Table 1).Table 1.Medical student survey responses comparing live patient encounters given in person, completely virtually, and a hybrid formatIn person pre-Covid(2016-2020)Completely virtual-Covid(2020-2021)Hybrid Format-Covid(2021-2022)Totalp-valueI enjoyed the Live Patient cases43993%9186%9881%628<.001*The Live Patient cases were an appropriate learning experience at this stage in my education44895%9792%10183%646<.001*The Live Patient cases helped me remember the diseases well for the exam9583%8075%8671%261.111Would you prefer the Live Patient sessions to be on Zoom?3937%6251%49.029** = Statistical significance defined as p<0.05Figure 1.Medical students’ feedback on live patient cases given in different platforms before COVID-19 and during the COVID-19 pandemic.ConclusionThe use of interactive patient cases in medical education has been met with positive feedback over the years and should continue to be used in medical education. This study showed that MS2s enjoyed the patient encounters more when delivered in-person vs a virtual or hybrid format. Careful consideration should be given to delivery format to optimize student learning and enjoyment.References[1]Spencer J, Blackmore D, Heard S, et al. Patient-oriented learning: a review of the role of the patient in the education of medical students.Med Educ.2000;34(10):851-857. doi: 10.1046/j.1365-2923.2000.00779.x.[2]Rose S. Medical Student Education in the Time of COVID-19.JAMA.2020;323(21):2131-2132. doi: 10.1001/jama.2020.5227.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Background:Behçet’s disease (BD) is a vasculitic multisystem inflammatory disorder. It may also involve the skin, mucosa, eyes, blood vessels, joints, gastrointestinal system, and central nervous system.Objectives:In this study, we aimed to present venous involvement data in patients followed up with a diagnosis of BD.Methods:The clinical, demographic, laboratory and medication data of 394 patients who were followed up with a diagnosis of BD in our rheumatology outpatient clinic between 2000 and 2020 were retrospectively evaluated.Results:Venous involvement was detected in 17.6% (n:76) patients who were followed up with a diagnosis of BD. 75% of the patients were male, and their mean diagnosis age was 27.1±9.9, disease duration was 14.2±6.3 years. While the thrombosis of lower extremity veins, cervical veins, and vena cava were observed in 85.5% of BD patients, superficial thrombophlebitis was found in 31.6%. Cerebral venous involvement (CVI) was evaluated in cranial involvement. Oral aphthous ulcer was found in 97.4 % of the patients, genital ulcer in 65.8%, pathergy positivity in 44.7%, papulopustular lesion in 34.2%, erythema nodosum in 40.8%. 27.6% of the patients were evaluated for hereditary thrombophilia and 5.3% (n:7) had hereditary thrombophilia. Deep venous thrombosis was found in 85.7% (6/7) of these patients, renal artery occlusion in 14.3%, pulmonary artery thrombosis in 14.3%, and cerebral venous thrombosis in 28.6%. Patients with and without venous involvement were compared in terms of clinical findings. A significant difference was observed in terms of retinal vasculitis, artery occlusion, arterial aneurysm, family history for BD and gender. In logistic regression analysis, a significant relationship was observed between venous involvement and gender, family history, retinal vasculitis, artery occlusion, arterial aneurysm (Table1).Table 1.Comparison of data of patients with and without venous involvementVenous Involvement (+)n:76Venous Involvement (-) n:318POR* (%95CI)Gender0.0002.78(1.58-4.88) Female19(25)153(48.1) Male57(75)165(51.9)Family History4(5.3)44(13.8)0.0490.035(0.12-0.99)Oral Aphthous Ulcer74(97.4)305(95.9)0,745Genital Ulcer50(65.8)206(64.8)0,894Papulopustular lesion26(34.2)103(32.4)0,786Erythema Nodosum31(40.8)119(37.4)0,601Pathergy34(44.7)151(47.5)0,772Uveitis20(26.3)118(37.1)0,083Retinal Vasculitis5(6.6)6(1.9)0.0363.66(1.08-12.33)Arthritis13(17.1)85(26.7)0,104Artery Occlusion6(7.9)3(0.9)0.0028.97(2.19-36.74)Arterial Aneurysm7(9.2)8(2.5)0.0133.93(1.37-11.20)Gastrointestinal involvement6(7.9)11(3.5)0,111Cardiac involvement3(3.9)3(0.9)0,089Cranial involvement7(9.2)17(5.3)0,282* Significant data in logistic regression analysis were presentedConclusion:Superficial venous thrombosis and deep vein thrombosis are the most frequent vascular involvements in BD patients. Significant correlations exist between CVI and pulmonary artery involvement (PAI), intracardiac thrombosis and PAI. It should be recalled that lower extremity venous thrombosis is often present in these associations, and even precede them (1).References:[1]Seyahi E. Behçet’s disease: How to diagnose and treat vascular involvement. Best Practice & Research Clinical Rheumatology. 30 (2016) 279-295Disclosure of Interests:None declared

BackgroundPsoriatic arthritis (PsA) is a chronic inflammatory systemic disease associated with several comorbidities. Few data are available concerning the association between psoriasis (PsO) and lung disease (LD), such as chronic obstructive pulmonary disease (COPD)[1]and interstitial lung disease (ILD)[2]. This association has also been hypothesized in PsA. Therefore, evaluating distinct lung manifestations in PsA could be an important driver to improve patients’ management.ObjectivesTo assess the prevalence and clinical features of PsA, to examine the co-occurrence of different LDs patterns and to evaluate their impact on clinical outcomes.MethodsWe performed a cross sectional observational study including patients with diagnosis of moderate to severe PsA and at least one chest imaging (as CT scan) performed at the beginning of bDMARD therapy, referred to our Rheumatologic Unit. Demographic, clinical data and concomitant comorbidities were collected. We used Chi-square test for categorical variables and Student’s T test for continuous variables.Results288 PsA patients were evaluated. Patients’ characteristics are shown inTable 1.We observed 28 patients affected by COPD (9.7%) and 7 by ILD (2.4%). About 20% of patients had radiological abnormalities without a distinct clinical diagnosis. In particular, 27 patients presented aspecific nodules (9.4%), 19 signs of bronchitis or emphysema (6.6%) and 17 interstitial thickening (5.9%). Notably, smoke (HR 2.2, p 0.04), higher age at PsA diagnosis (53.8 ± 8.5, p<0.001), higher BMI (28.6 ± 5.6, p 0.05) and recurrent infections (HR 12.3, p<0.001) were associated to COPD. Among comorbidities, cardiovascular diseases (HR 3.8, p 0.005), dyslipidaemia (HR 3.6, p 0.007) and diabetes (HR 4.1, p 0.001) were associated to COPD. Both COPD patients (HR 3.3, p 0.01) and patients with other LDs (HR 2.9; p 0.002) had a history of bDMARDs failure of at least>3 bDMARDs underwent, significantly more than patients without LD. Even in ILD patients, we observed an older age at PsA diagnosis (58 ± 12.2, p 0.01) and a higher prevalence of diabetes (HR 4.1, p <0.001) compared to patients without LD. Furthermore, nail psoriasis (HR 4.7, p 0.05) and malignancy (HR 7.1, p 0.01) were related to ILD.ConclusionOur results confirm the prevalence of COPD in PsA[3]and add evidence about the co-occurrence of other LDs in PsA. Risk factors associated with the presence of LD emerged from our analysis, highlightening an occult unmet need in PsA patients. Moreover, we observed as LDs could influence bDMARDs failure. Otherwise, focusing on pulmonary comorbidities could improve therapeutic response and clinical outcomes.References[1]Ungprasert et al.J Dermatolog Treat(2016)[2]Kharibam et al.Cureus(2022)[3]Pina Vegas et al.Rheumatology (Oxford) (2021)Table 1.CohortLD(N=85)No LD(N=206)P valueCOPDILDp1p2No. of patients, N (%)28828 (9.7)7 (2.4)206 (71.5)--Female, N (%)185 (64.2)19 (67.8)2 (28.6)131 (63.6)0.60.06BMI, mean ± SD27 ± 5.728.6 ± 5.627.7 ± 3.726.5 ± 5.20.050.6Smoke, N (%)151 (52.5)16 (57.1)5 (71.4)102 (49.5)0.040.06Age at diagnosis, mean ± SD44.7 ± 13.153.8 ± 8.558 ± 12.242.1 ± 12.7<0.0010.01Psoriasis, N (%)Nail psoriasis, N (%)Dactylitis, N (%)Enthesitis, N (%)240 (83.3)106 (36.8)75 (26)99 (34.4)21 (75)8 (28.6)8 (28.6)11 (39.3)6 (85.7)5 (71.4)3 (42.8)1 (14.3)174 (84.5)72 (34.9)23 (11.2)71 (34.5)0.10.50.70.60.90.050.30.2CV, N (%)Dyslipidaemia, N (%)Diabetes, N (%)Hyperuricemia, N (%)Malignancy, N (%)114 (39.6)104 (36.1)50 (17.4)52 (18)19 (6.6)15 (53.6)13 (46.4)9 (32.1)7 (25)3 (10.7)5 (71.4)3 (42.8)4 (57.1)1 (14.3)2 (28.6)74 (35.9)67 (32.5)29 (14.1)36 (17.5)11 (5.3)0.005 0.007 0.0010.30.20.060.5<0.0010.80.01bDMARDs ≥3, N (%)37 (12.8)7 (25)019 (9.2)0.01-p1:p valuebetween COPD and No LDp2:p valuebetween ILD and No LDLegend:BMI Body Mass Index; bDMARDs biologic Disease Modifying Anti-Rheumatic Drugs; COPD Chronic Obstructive Pulmonary Disease; CV Cardiovascular; ILD Interstitial Lung Disease; LD Lung Disease.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Background Recommendations on erythropoiesis-stimulating agents for the management of chemotherapy-induced anemia (CIA) are well established (Schrijvers D et al. Ann Oncol 2010;21[suppl 5]:v244-7). Iron supplementation can further improve treatment response of CIA, particularly in the case of iron deficiency (Pedrazzoli P et al. J Clin Oncol 2008;26:1619-25; Auerbach M et al. J Clin Oncol 2004;22:1301-7), but is under-used. Objective To evaluate the effect of epoetin alfa biosimilar, with or without iron, on CIA in current oncology and hematology practice. Methods SYNERGY was an observational, longitudinal, prospective, multicenter study conducted in France, from a representative, random sample of oncologists and hematologists. Patients of these clinicians were aged ≥18 years with solid tumors, lymphoma and/or myeloma and CIA, eligible for treatment with epoetin alfa biosimilar and followed for 12-16 weeks. A subanalysis of patients with lymphoma or multiple myeloma is presented here. Results Overall, 2167 patients were enrolled by 195 French oncologists and hematologists during June 2012-December 2014. Of these patients, 12.7% (n=264) had lymphoma and 6.6% (n=136) had multiple myeloma and were included in the analysis population, where the disease categories were non-exclusive; 84.5% (n=223) of patients with lymphoma were diagnosed with non-Hodgkin's lymphoma. Half of the patients with lymphoma and multiple myeloma were male. The majority of patients were ≥70 years old (60.6% of patients with lymphoma and 69.1% with multiple myeloma) and had a performance status of 0 or 1 (72.9% [n=180] of patients with lymphoma and 71.8% [n=94] with multiple myeloma). Baseline mean ± standard deviation (SD) hemoglobin (Hb) levels of patients with lymphoma and multiple myeloma were 9.5±0.8 g/dL and 9.5±0.9 g/dL; 40.2% (n=106) and 39.0% (n=53) of these patients had moderate anemia (Hb 8.0-9.5 g/dL), while 2.3% (n=6) and 4.4% (n=6) had severe or very severe anemia (Hb ≤8.0 g/dL), respectively. Iron status assessment was available for 60.2% (n=159) of patients with lymphoma and for 59.6% (n=81) with multiple myeloma. Concomitant iron supplementation was not prescribed with epoetin alfa biosimilar for the majority of patients. A total of 11.4% (n=30) of patients with lymphoma and 6.6% (n=9) of patients with multiple myeloma received iron, of whom 63.3% (n=19) and 77.8% (n=7) were prescribed oral iron formulations, respectively. Patients who reached their target Hb level (increase of ≥1 g/dL since enrollment or an increase of ≥2 g/dL, with no transfusions in the three previous weeks) was 79.1% (n=201) of patients with lymphoma and 84.6% (n=104) of patients with multiple myeloma, higher than the overall analyzed population (74.2%, n=1390). The response rate to epoetin alfa biosimilar (epoetin alfa biosimilar discontinued as Hb > target level, with no transfusions in the three previous weeks) was lower in patients with lymphoma given iron supplementation versus those not given iron; 66.7% (n=20) of the iron supplementation group were responders, compared with 75.4% (n=169) of patients without iron. Response to epoetin alfa biosimilar was similar in patients with multiple myeloma regardless of the iron supplementation status (85.7% [n=6] of patients with iron supplementation and 81.0% [n=94] of patients without). The response to epoetin alfa biosimilar translated into an improvement in patient perception of fatigue (66.7% [n=20] of patients with iron supplementation and 72.5% [n=161] of patients without who had lymphoma; 71.4% [n=5] of patients with iron supplementation and 81.3% [n=91] of patients without who had multiple myeloma). Conclusions These results indicate that epoetin alfa biosimilar was effective in treating patients with CIA and lymphoma/myeloma in France and agrees with previous studies (Michallet M et al. BMC Cancer 2014;14:503). Iron supplementation did not appear to increase the response to epoetin alfa treatment in this population; however, confirmatory studies in larger patient cohorts are required. Disclosures Scotté: Hospira SAS: Research Funding. Laribi:Hospira SAS: Research Funding. Gisselbrecht:Hospira SAS: Research Funding; Roche: Consultancy, Research Funding; Baxter: Research Funding; Chugai Pharmaceutical: Research Funding; Bertram Glass: Research Funding. Spaeth:Hospira SAS: Research Funding. Kasdaghli:Hospira: Employment. Albrand:Hospira: Employment. Leutenegger:GECEM: Employment; Hospira SAS: Research Funding. Ray-Coquard:Hospira SAS: Research Funding; Amgen: Consultancy, Other: Paid instructor; PharmaMar: Consultancy, Other: Paid instructor; Roche: Consultancy, Other: Paid instructor.

Abstract Background: BC remains the most commonly diagnosed cancer for women. TNBC is an aggressive form with a poorer prognosis compared with other subtypes. Neoadjuvant therapy (NAT) is the standard-of-care approach to shrink tumors in the breast and axilla and to improve patient outcomes. Few RW studies exist of US patients with early BC (eBC); this study aimed to describe clinical parameters by receipt of systemic therapy and to assess overall survival (OS) and progression-free survival (PFS) after NAT and adjuvant therapy (AT) in women with early HR+/HER2− or TNBC using RW evidence in the US. Methods: This retrospective observational study used the Flatiron Health nationwide electronic health record-derived de-identified database, including women ([pts], age ≥18 years) diagnosed with early HR+/HER2− BC or TNBC between 01/01/2011 and 05/31/2018. The primary outcome was to describe pt demographics, clinical characteristics, and treatment patterns. Secondary outcomes included OS and PFS. Results: Of the pts identified for inclusion (N = 5,299), 13.3% (n = 707) were diagnosed with early TNBC and 86.7% (n = 4,592) with HR+/HER2− eBC, of whom 34.7% (n = 245) and 10.9% (n = 502), respectively, did not receive systemic therapy (Table). Systemically treated pts with TNBC vs HR+/HER2− tended to be younger (59.0 years vs 64.0 years); were represented by a higher proportion of Black women (18.0% vs 7.2%); had a greater proportion presenting with invasive ductal carcinoma (IDC) (91.6% vs 78.2%); had a higher proportion with progression to metastasis (19.0% vs 5.7%) and presented with a more aggressive disease (Grade 3) at diagnosis (79.0% vs 18.4%). Most pts (98.4%) received surgery, predominantly breast-conserving surgery (BCS; unilateral lumpectomy: 62.8%); however, 17.8% received bilateral mastectomies. Overall, 9.1% of pts received NAT. More pts with TNBC vs HR+/HER2− received NAT (34.0% vs 7.9%) and achieved a pathologic complete response (pCR; 36.3% vs 6.2%). Consistent with treatment guidelines, pts with TNBC were treated with chemotherapy (CT)-doublet or single-agent regimens and pts with HR+/HER2− received hormone and CT-based regimens. Duration of NAT was similar for both subtypes (3.3 months) but was shorter for AT in pts with TNBC vs HR+/HER2− (3.4 vs 38.2 months). From initial diagnosis, the 36-month survival probability [standard error] was lower for systemically treated pts with TNBC vs HR+/HER2− (85.7% [1.8%] vs 95.6% [0.3%]) and from start of therapy by line setting (NAT: 80.6% [3.5%] vs 91.9% [1.7%]; AT: 84.7% [2.2%] vs 95.8% [0.4%]). Similarly, the 36-month PFS probability was lower for pts with TNBC vs HR+/HER2− from diagnosis (77.9% [2.1%] vs 93.3% [0.4%]) and from start of therapy by line setting (NAT: 68.7% [4.1%] vs 85.2% [2.1%]; AT: 79.5% [2.5%] vs 93.5% [0.4%]). Conclusion: This analysis of US RWE further confirms early TNBC to be a particularly aggressive form of BC, with poorer survival compared with pts with HR+/HER2− eBC. While these RW data indicate BCS is becoming more routine, almost one-fifth of pts still receive bilateral mastectomies. Overall, these data confirm there remains a high unmet need to reduce the need for aggressive treatments while further improving outcomes in pts with early TNBC and HR+/HER2− BC. Table: Patient demographics, clinical characteristics, OS and PFSPatient selection criteriaNumber of patients, n (%)SubgroupsEarly HR+/HER2− BC4,592 (86.7)Patients who received systemic therapy4,090 (89.1)Early TNBC707 (13.3)Patients who received systemic therapy462 (65.3)All patients [1] (N = 5,299), n (%)Systemically treated patients with early HR+/HER2− BC (n = 4,090), n (%)Systemically treated patients with early TNBC (n = 462), n (%)Patient demographicsMedian age (years)64.064.059.0RaceBlack or African American449 (8.5)294 (7.2)83 (18.0)White3,602 (68.0)2,835 (69.3)283 (61.3)Asian139 (2.6)111 (2.7)9 (1.9)Hispanic or Latino15 (0.3)12 (0.3)1 (0.2)Clinical characteristicsHistology at initial diagnosisIDC4,222 (79.7)3,197 (78.2)423 (91.6)ILC684 (12.9)612 (15.0)7 (1.5)Infiltrating ductal mixed and infiltrating lobular mixed132 (2.5)108 (2.6)3 (0.6)Mucinous adenocarcinoma97 (1.8)88 (2.2)0 (0.0)Other [2]122 (2.3)63 (1.5)26 (5.6)Unknown/ND42 (0.8)22 (0.5)3 (0.6)Tumor grade at initial diagnosisGrade 11,350 (25.5)1,161 (28.4)5 (1.1)Grade 22,462 (46.5)2,098 (51.3)88 (19.0)Grade 31,374 (25.9)752 (18.4)365 (79.0)Unknown/ND113 (2.1)79 (1.9)4 (0.9)Surgery at initial diagnosisYes5,215 (98.4)4,032 (98.6)450 (97.4)Surgery type [3]Unilateral lumpectomy3,277 (62.8)2,568 (63.7)241 (53.6)Unilateral mastectomy1,168 (22.4)883 (21.9)128 (28.4)Bilateral lumpectomy75 (1.4)61 (1.5)5 (1.1)Bilateral mastectomy927 (17.8)713 (17.7)89 (19.8)Treatment line settingNumber of patients who received NAT481 (9.1)324 (7.9)157 (34.0)Number of patients who received AT4,263 (80.4)3,949 (96.6)314 (68.0)pCR to NATAchieved pCR77 (16.0)20 (6.2)57 (36.3)OSFrom initial diagnosis: survival probability at Month 36, % (SE)94.0 (0.4)95.6 (0.3)85.7 (1.8)From NAT: survival probability at Month 36, % (SE)88.3 (1.6)91.9 (1.7)80.6 (3.5)From AT: survival probability at Month 36, % (SE)94.9 (0.4)95.8 (0.4)84.7 (2.2)PFSFrom initial diagnosis: progression-free probability at Month 36, % (SE)91.3 (0.4)93.3 (0.4)77.9 (2.1)From NAT: progression-free probability at Month 36, % (SE)79.9 (2.0)85.2 (2.1)68.7 (4.1)From AT: progression-free probability at Month 36, % (SE)92.5 (0.4)93.5 (0.4)79.5 (2.5)Duration of treatment, Months (n)Median duration of NAT3.3 (481)3.3 (324)3.3 (157)Median duration of AT35.3 (4263)38.2 (3,949)3.4 (314)[1] All patients includes both patients systemically treated and systemically untreated. [2] Other includes Inflammatory, Papillary, Metaplastic, Medullary and Tubular histologies. [3] Patient may have received more than one surgery. AT, adjuvant therapy; IDC, invasive ductal carcinoma; NAT, neoadjuvant therapy; ND, not documented; SE, standard error. Citation Format: Anagha Gogate, Amanda Crosbie, Trong Kim Le, Ying Zhang, Rolee Das, Catherine Davis. Clinical characteristics, treatment patterns, and survival outcomes in women with early triple-negative (TN) or hormone receptor-positive/human epidermal growth factor receptor-2 negative (HR+/HER2−) breast cancer (BC) in the real-world (RW) setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-12-15.

Abstract Background: Allo-SCT is the only curative option for patients with high risk and relapsed/refractory T-cell malignancies. Even among allo-SCT recipients, survival is less than 50% and relapse rates are 55-60%. We developed a clinical trial to decrease relapse after allo-SCT for these patients using romidepsin (rom), a histone deacetylase inhibitor approved for the treatment of relapsed T-cell lymphomas. Based on pre-clinical data demonstrating enhanced and synergistic cell killing with the addition of rom to busulfan (Bu) and fludarabine (Flu) in malignant T-cells, we created a novel transplant regimen (BuFluRom). We hypothesized this regimen, coupled with maintenance rom (m-rom), would enhance malignant T-cell killing, eradicate MRD at transplant, decrease relapse, and stimulate the GVL effect by stimulating NK-cells. Here we present results of this clinical trial, with correlative data evaluating NK-cytotoxicity. This is the first trial designed specifically to treat T-cell malignancies with allo-SCT. (NCT02512497) Methods: This is a phase I/II clinical trial. Eligible patients had: a diagnosis of T-cell leukemia (including T-acute lymphoblastic leukemia) or T-cell lymphoma (cutaneous or peripheral) in at least a partial remission requiring an allo-SCT, &lt;70 years of age, with a matched sibling/unrelated donor. The primary objective was to determine the recommended phase 2 dose (RP2D) of rom from 3 dose levels (1, 2, 3 mg/m2) when combined with BuFlu (AUC 20000 or 16000, Figure). Patients received standard tacrolimus/methotrexate GVHD prophylaxis with ATG for MUDs. Once RP2D was determined, an expansion cohort of up to 30 patients (total) was included. M-rom was initiated between day +28 and +100 for 1 year (2 years max). The effect of rom on NK-cell cytotoxicity was assessed on samples taken pre-transplant, and 1, 3, 6, 12 months post allo-SCT. NK cytotoxicity was assessed by isolating mononuclear cells from patient samples and targeting them against K562 and T-cell lymphoma targets using the calcein-AM assay. Fine-Gray models were used to estimate PFS, OS, and cumulative incidence, and compare survival curves across groups. Results: 21 patients have been enrolled (Table). One DLT was observed (VOD), at dose level 2, and the RP2D of rom in conditioning was determined to be 2 mg/m2. With a median follow-up time of 10.1 months, the median OS has not been reached (3.3-NR months), with a 1 and 3-year OS probability of 62.8% & 55.8%. The median PFS is 28.2 months (3.8-28.1), with 1 and 3 year PFS of 57% & 30.4%. Cumulative incidence (CI) of NRM at day 100 and 1 year were 14.8% and 20%. CI of grade II-IV aGHVD and extensive cGVHD were 47.6% and 18.5%. The CI of relapse (CIR) was 22.8% at 1 year (95% CI 6.6-44.9%). There was no difference between PFS among patients with MRD versus those without MRD prior to transplant (p=0.96), and no difference in 1-year CIR (p=0.9). PFS and CIR at 1 year was substantially better in the lymphoma than leukemia patients (85.7% vs 44%, p=0.049), and (0% vs 32.1%, p=0.05). No patients with PTCL relapsed, and 3/5 patients with T-PLL are alive, disease free. 13/21 (62%) of patients received m-rom with a median number of 10 cycles (range 1-41). (Table) 7 patients experienced grade 3/4 adverse events (AE), though no patients discontinued m-rom due to toxicity. NK-cytotoxicity was higher at each time point in patients who received m-rom compared to those who did not, though there were insufficient patients to reach statistical significance. When NK-cytotoxicity was assessed between the two groups after starting maintenance, NK-cytotoxicity in the m-rom group was significantly higher than in those without m-rom (p=0.05) (Figure). Conclusions: BuFluRom with m-rom is effective at decreasing relapse in patients with T-cell malignancies, with 1-year CI relapse below expected relapse rates for this set of diseases. Toxicities were similar to standard BuFlu alone and the RP2D of rom in conditioning was established at 2 m g/m2. Intriguingly, BuFluRom mitigated the poor outcomes of patients with MRD prior to transplant. Further, early data suggests m-rom enhances NK-cell cytotoxicity post allo-SCT, potentially augmenting the GVL effect and accounting for decreased relapse rates. Long-term follow-up is needed to evaluate these results, but these results suggest the BuFluRom regimen with m-rom could become a new option for patients receiving allo-SCT for T-cell malignancies to mitigate relapse. Figure 1 Figure 1. Disclosures Hosing: Nkarta Therapeutics: Membership on an entity's Board of Directors or advisory committees. Popat: Bayer: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Incyte: Research Funding. Vasu: Boehringer Ingelheim: Other: Travel support; Seattle Genetics: Other: travel support; Kiadis, Inc.: Research Funding; Omeros, Inc.: Membership on an entity's Board of Directors or advisory committees. de Lima: Miltenyi Biotec: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. William: Dova Pharmaceuticals: Research Funding; Incyte: Research Funding; Kyowa Kirin: Consultancy; Merck: Research Funding; Guidepoint Global: Consultancy. Lee: Kiadis Pharma: Divested equity in a private or publicly-traded company in the past 24 months, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Courier Therapeutics: Current holder of individual stocks in a privately-held company. Brammer: Kymera Therapeutics: Consultancy; Celgene: Research Funding; Seattle Genetics: Speakers Bureau.

ImportanceAir pollution is increasingly recognized as an important environmental risk factor for mental health. However, epidemiologic evidence on long-term exposure to low levels of air pollutants with incident depression and anxiety is still very limited.ObjectivesTo investigate the association of long-term joint exposure to multiple air pollutants with incident depression and anxiety.Design, Setting, and ParticipantsThis prospective, population-based cohort study used data from the UK Biobank. The participants were recruited between March 13, 2006, and October 1, 2010, and included individuals who had never been diagnosed with depression or anxiety at baseline and had full information on exposure and covariates. Data were analyzed from May 1 to October 10, 2022.ExposuresAnnual mean air pollution concentrations of particulate matter (PM) with aerodynamic diameter of 2.5 μm or less (PM2.5) and PM with aerodynamic diameter between 2.5 μm and 10 μm (PM2.5-10). Nitrogen dioxide (NO2) and nitric oxide (NO) were estimated for each participant’s residential address using the land use regression model, and joint exposure to air pollution reflected by air pollution score was calculated by principal components analysis.Main Outcomes and MeasuresIncidence of diagnosed depression (F32-F33) and anxiety (F40-F48) were ascertained with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes.ResultsDuring a median (IQR) follow-up of 10.9 (10.1-11.6) years, among 389 185 participants (mean [SD] age, 56.7 [8.1] years, 205 855 female individuals [52.9%]), a total of 13 131 and 15 835 patients were diagnosed with depression and anxiety, respectively. The median (IQR) concentration of pollutants was as follows: PM2.5, 9.9 (9.3-10.6) μg/m3; PM2.5-10, 6.1 (5.8-6.6) μg/m3; NO2, 26.0 (21.3-31.1) μg/m3; and NO, 15.9 (11.6-20.6) μg/m3. Long-term estimated exposure to multiple air pollutants was associated with increased risk of depression and anxiety, and the exposure-response curves were nonlinear, with steeper slopes at lower concentrations and plateauing trends at higher exposure. The hazard ratios (HRs) and 95% CIs for depression and anxiety were 1.16 (95% CI, 1.09-1.23; P &amp;lt; .001) and 1.11 (95% CI, 1.05-1.17; P &amp;lt; .001) in the highest quartile compared with the lowest quartile of air pollution score, respectively. Similar trends were shown for PM2.5, NO2, and NO. Subgroup analysis showed the association between PM2.5 and anxiety tended to be higher in male individuals than in female individuals (quartile 4: male individuals, 1.18; 95% CI, 1.08-1.29; female individuals, 1.07; 95% CI, 1.00-1.14; P = .009).Conclusions and RelevanceStudy results suggest that estimates of long-term exposure to multiple air pollutants was associated with increased risk of depression and anxiety. The nonlinear associations may have important implications for policy making in air pollution control. Reductions in joint exposure to multiple air pollutants may alleviate the disease burden of depression and anxiety.

BackgroundEmerging research suggests changes in dietary patterns among adults and youth during the global nutrition transition, but there is no dietary score to measure the extent of nutrition transition that may be occurring.ObjectiveTo develop and validate an index‐based Nutrition Transition Diet Score for adolescents in India.MethodsAn evidence‐driven index‐based apriori Nutrition Transition Diet Score for adolescents was developed and validated against an exploratory factor analysis (EFA) derived Diet Score. The EFA with varimax rotation was used to derive dietary patterns from answers to a validated food frequency questionnaire provided by 198 adolescents in Vijayapura, India. The evidence‐driven index‐based Diet Score among adolescents who were in the top quartile (≥75 percentile) of the main dietary pattern were compared using diet score means to the rest of the group.ResultsThe apriori index‐based Nutrition Transition Diet Score (range 0 – 10) included six food groups: fried foods, sugar‐sweetened beverages, dairy, fruits and vegetables, and bread, and four nutrients, namely total dietary fat, saturated fat, cholesterol, and simple sugars (table 1). The Spearman correlations between the index‐based Diet Score and EFA‐driven Diet Score was high (r=0.68, p <0.0001). Among the adolescents, the mean evidence‐driven index‐based Nutrition Transition Diet Score was 4.7 ± 1.1. The following three dietary patterns were identified using EFA: the transition ‘westernized’ pattern (factor loadings >4 for breads, fried foods, processed foods, sugar‐sweetened beverages (fruits juices and carbonated beverages), and sweets and desserts), the animal‐source pattern (factor loadings >4 for egg, lean meat, and red meat), and the traditional ‘Indian’ pattern (factor loadings >4 for grains, fruits and vegetables, dairy, sugar, traditional fried foods, and ghee) (table 2). The loading of traditional fried foods on both traditional and transition patterns indicate that as diets may become more ‘westernized’, this food group may continue to remain as one of the main components of the diet. The mean index‐based Diet Score was significantly higher (p< 0.0001) among adolescents in the upper quartile of the transition pattern (score range: 4–8) when compared to the rest (score range: 1–6) (table 3). This shows that the evidence‐driven index‐based Nutrition Transition Diet Score is valid against an exploratory factor analysis (EFA) derived Diet Score for adolescents.ConclusionThe validated index‐based Nutrition Transition Diet Score can be used to measure nutrition transition among adolescents in India. To our knowledge, this is the first diet score to assess the extent of nutrition transition.Support or Funding InformationNida Shaikh was supported by funding from the Fogarty International Center of the National Institutes of Health (award number 1‐R25 TW009337‐01). Components of the Index‐based Nutrition Transition Diet Score Components of the evidence‐driven Nutrition Transition Diet Score Criteria for Score 1 (Indicates Nutrition Transition) Criteria for score 0 (Indicates no Nutrition Transition) 1. Processed foods >30 g/d < 30 g/d 2. Fried foods >30 g/d <30 g/d 3. Sugar‐sweetened beverages > 0 ml/d < 0 ml/d 4. Dairy > 500 g/d < 500 g/d 5. Fruits and vegetables < 400 g/d > 400 g/d 6. Breads > 25 g/d < 25 g/d 7. Simple sugars > 10% of total calories < 10% of total calories 8. Fat, total > 30% of total calories < 30% of total calories 9. Saturated fat > 10% of total calories < 10% of total calories 10. Cholesterol > 300 mg/d < 300 mg/d Dietary patterns using exploratory factor analysis and their factor loadings among adolescents in South India (n=198)a Food group Factor 1 ‘Transition Pattern’ Factor 2 ‘Animal foods pattern’ Factor 3 ‘Traditional Pattern’ Breads 0.42 — — Global Unhealthy Foods 0.61* — — Global Healthy Foods — — — Processed Foods 0.61* — — Snacks — — — Fried Snacks 0.76* — — Red Meat — 0.67* — Lean Meat — 0.80* — Eggs — 0.54* — Sweets and Desserts 0.46* — — Dairy — — 0.48* Tea and Coffee — — — Fruit Juices 0.45* — — Soda and Energy Drink* 0.54* — — Fruits — — 0.49* Vegetables — — 0.49* Pulses and Nuts — — — Traditional Grains — — 0.47* Fried Traditional Food 0.49* — 0.42* Sugar and Jaggery — — 0.58* Ghee — — 0.43* Eigen values 4.8 1.6 1.0 Variance explained (%) 3.0 2.2 20 Loading < 0.4 are designated with ‘—’. Factor loadings greater than 0.4. Intake of food groups (g/d) among adolescents with and without Nutrition Transition diets (n=198) Food group (g/d) Nutrition Transition diet group (n=49) No Nutrition Transition diet group (n=149) T‐test Mean SD Median Mean SD Median Breads 58.9 41.6 51.4 29.8 34.5 14.8 <.0001 Global Unhealthy Foods 67.9 53.6 50.4 15.4 22.1 8.5 <.0001 Global Healthy Foods 3.5 11.4 0.0 0.5 2.4 0.0 0.003 Processed Foods 59.3 35.5 53.4 25.3 20.2 19.6 <.0001 Snacks 59.1 40.1 52.6 31.2 32.7 22.2 <.0001 Fried Snacks 177.3 91.6 171.3 47.3 36.9 37.7 <.0001 Fried Traditional Food 37.6 23.9 31.6 19.1 15.7 14.2 <.0001 Sweets and Desserts 108.5 57.4 96.0 52.9 47.9 40.6 <.0001 Red Meat 5.7 11.3 0.0 4.1 9.8 0.0 0.33 Lean Meat 20.5 37.9 4.0 9.4 17.7 0.7 0.0058 Eggs 32.1 37.9 25.7 13.4 17.8 6.6 <.0001 Dairy 221.5 155.9 201.3 160.4 181.3 106.0 0.035 Tea and Coffee 202.8 139.3 200.0 144.5 100.5 142.9 <.0001 Soda and Energy Drinks 47.1 44.4 32.9 7.5 15.6 0.0 <.0001 Fruit Juices 109.9 74.2 85.7 46.4 56.6 28.8 <.0001 Fruits 147.4 75.9 136.7 97.2 82.4 78.3 0.0002 Vegetables 128.6 85.2 116.6 110.6 109.7 79.8 0.29 Pulse and Nuts 284.3 117.3 299.7 258.3 147.7 235.9 0.26 Traditional Grains 763.9 228.1 757.8 615.2 269.9 564.7 0.0006 Sugar and Jaggery 4.7 5.2 2.1 4.5 7.1 0.7 0.83 Ghee 1.3 1.9 0.3 1.3 2.9 0.3 0.92 Evidence‐drive Nutrition Transition Diet Score 5.7 0.9 6.0 4.4 1.0 4.0 <.0001 Evidence‐drive Nutrition Transition Diet Score Range 4–8 1–6