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This study quantifies the differences in infant outcomes by mother's self-identified race among Arab Americans and by self-identified race and ethnicity for Arabs and non-Arabs.

This study used data from the Standard Certificate of Live Birth on 8,204 infants born to Arab and 325,354 infants born to non-Arab mothers between 2012 and 2016 in Massachusetts; data were analyzed between 2019 and 2020. Mothers' race was categorized as White, Black, or Other. Mothers' ethnicity was categorized as Arab or non-Arab. Outcomes included birth weight, preterm birth, low-birth weight, small for gestational age, and large for gestational age. Linear and logistic regression models assessed the association between race and infant health outcomes.

Black Arab mothers had higher odds of preterm birth (AOR=1.37, 95% CI=1.07, 1.76) and low-birth weight (AOR=1.35, 95% CI=0.99, 1.84) than White Arab mothers. Arab mothers who self-identified as Other had babies that were 51.4 grams lighter than babies born to White Arab mothers. White Arab mothers had higher odds of low birth weight (AOR=1.19, 95% CI=1.06, 1.34) and small-for-gestational-age babies (AOR=1.22, 95% CI=1.11, 1.36) but lower odds of large-for-gestational-age babies (AOR=0.77, 95% CI=0.70, 0.86) than White non-Arab mothers.

Both ethnicity and race are important determinants of the health of Arab American infants. Arab ethnicity may play a negative role in the infant health of Arab Americans who identify as White. A better understanding of the lived experiences of Arab American mothers, with regard to their racial and ethnic identity, may help better inform clinical practice.

Both ethnicity and race are important determinants of the health of Arab American infants. Arab ethnicity may play a negative role in the infant health of Arab Americans who identify as White. A better understanding of the lived experiences of Arab American mothers, with regard to their racial and ethnic identity, may help better inform clinical practice.Advances in immunotherapy have changed the landscape of oncology over the past decade. Still, most patients with solid organ tumors do not derive a durable benefit from immunotherapies. How these tumors evade treatment has not been fully elucidated, but several studies are seeking ways to stimulate treatment response in these immunologically quiescent tumors. Of these, the combination of locoregional therapy with immune checkpoint inhibition is of interest to the interventional radiologist. MK571 manufacturer This brief report provides an overview of current trials testing the effectiveness of locoregional therapy in combination with immune checkpoint inhibitors and identifies future research goals.

Elevated inflammatory markers are predictive of COVID-19 infection severity and mortality. It is unclear if these markers are associated with severe infection in patients with cancer due to underlying tumor related inflammation. We sought to further understand the inflammatory response related to COVID-19 infection in patients with gynecologic cancer.

Patients with a history of gynecologic cancer hospitalized for COVID-19 infection with available laboratory data were identified. Admission laboratory values and clinical outcomes were abstracted from electronic medical records. Severe infection was defined as infection requiring ICU admission, mechanical ventilation, or resulting in death.

86 patients with gynecologic cancer were hospitalized with COVID-19 infection with a median age of 68.5years (interquartile range (IQR), 59.0-74.8). Of the 86 patients, 29 (33.7%) patients required ICU admission and 25 (29.1%) patients died of COVID-19 complications. Fifty (58.1%) patients had active cancer and 36 (41.9%) were in remission. Patients with severe infection had significantly higher ferritin (median 1163.0 vs 624.0ng/mL, p<0.01), procalcitonin (median 0.8 vs 0.2ng/mL, p<0.01), and C-reactive protein (median 142.0 vs 62.3mg/L, p=0.02) levels compared to those with moderate infection. White blood cell count, lactate, and creatinine were also associated with severe infection. D-dimer levels were not significantly associated with severe infection (p= 0.20).

The inflammatory markers ferritin, procalcitonin, and CRP were associated with COVID-19 severity in gynecologic cancer patients and may be used as prognostic markers at the time of admission.

The inflammatory markers ferritin, procalcitonin, and CRP were associated with COVID-19 severity in gynecologic cancer patients and may be used as prognostic markers at the time of admission.

We used a novel machine learning algorithm to develop a precision prognostication system for endometrial cancer.

The Ensemble Algorithm for Clustering Cancer Data (EACCD) unsupervised machine learning algorithm was applied to women with endometrioid endometrial cancer in the Surveillance, Epidemiology, and End Results database from 2004 to 2015. The prognostic system was created based on TNM stage, grade, and age. The concordance (C-index) was used to cut dendrograms and create prognostic groups. Kaplan-Meier cancer-specific survival was employed to visualize the survival function of EACCD-based prognostic groups and AJCC groups.

A total of 46,773 women were identified. Using the machine learning algorithm with TNM stage, grade, and three age groups, eleven prognostic groups were generated with a C-index of 0.8380. The five-year survival rates for the eleven groups ranged from 37.9-99.8%. To simplify the classification system further, using visual inspection of the data we created a modified EACCD grouping, and combined the top six survival groups into three new prognostic groups. The new five-year survival rates for these eight modified prognostic groups included 99.1% for group 1, 96.5% for group 2, 92.2% for group 3, 84.8% for group 4, 72.7% for group 5, 61.1% for group 6, 52.6% for group 7, and 37.9% for group 8. The C-index for the modified eight prognostic groups was 0.8313.

This novel machine learning algorithm demonstrates improved prognostic prediction for patients with endometrial cancer. Using machine learning for endometrial cancer allows for the integration of multiple factors to develop a precision prognostication system.

This novel machine learning algorithm demonstrates improved prognostic prediction for patients with endometrial cancer. Using machine learning for endometrial cancer allows for the integration of multiple factors to develop a precision prognostication system.