Abstract Objectives To assess the feasibility of drawing, processing, safety-testing, and banking term umbilical cord blood to meet the packed red blood cell transfusion (RBC Tx) needs of extremely-low-gestational-age neonates (ELGANs). Design (1) Retrospectively analyze all ELGANs RBC Tx over the past three years, (2) Estimate local cord blood availability, (3) Assess interest in this project, and implementation barriers, through stakeholder surveys. Results In three years we cared for 266 ELGANs; 165 (62%) received ≥1 RBC Tx. Annual RBC Tx averaged 197 (95% CI, 152–243). If 10% of our 10,353 annual term births had cord blood drawn and processed, and half of those tested were acceptable for Tx, collections would exceed the 95th % upper estimate for need by >four-fold. Interest exceeded 97%. Identified barriers included FDA approval, training to collect cord blood, and cost. Conclusion RBC Tx needs of ELGANS could be met by local cord blood collection. This is a preview of subscription content, access via your institution Access options Access through your institution Change institution Buy or subscribe /* style specs start */ style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login>li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login>li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login>li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube,.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-buybox-to{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center}.BuyBoxSection-683559780 .price-info-text{font-size:16px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .price-value{font-size:30px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .price-per-period{font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .tax-buybox{display:block;width:100%;color:#222;padding:20px 16px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:NaNpx}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container>*{flex:1px}.BuyBoxSection-683559780 .button-container>a:hover,.Button-1078489254:hover,.Button-2496381730:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3296148077,.ButtonLabel-1651148777{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-1078489254,.Button-2496381730{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-1078489254 .readcube-label,.Button-2496381730 .readcube-label{color:#069} /* style specs end */ Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Learn more Rent or buy this article Prices vary by article type from$1.95 to$39.95 Learn more Prices may be subject to local taxes which are calculated during checkout Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support Data availability Deidentified data will be shared upon written request to the corresponding author. References Joseph RM, O’Shea TM, Allred EN, Heeren T, Hirtz D, et al. Neurocognitive and academic outcomes at age 10 years of extremely preterm newborns. Pediatrics. 2016;137:e20154343. Article PubMed PubMed Central Google Scholar Bell EF, Hintz SR, Hansen NI, Bann CM, Wycoff MH, DeMauro SB, et al. Mortality, in-hospital morbidity, care practices, and 2-year outcomes for extremely Preterm infants in the US, 2013-2018. JAMA. 2022;327:248–63. Article PubMed Google Scholar Younge N, Goldstein RF, Bann CM, Hintz SR, Patel RM, Smith PB, et al. Survival and neurodevelopmental outcomes among periviable infants. N. Engl J Med. 2017;376:617–28. Article PubMed PubMed Central Google Scholar Del Vecchio A, Henry E, D’Amato G, Cannuscio A, Corriero L, Motta M, et al. Instituting a program to reduce the erythrocyte transfusion rate was accompanied by reductions in the incidence of bronchopulmonary dysplasia, retinopathy of prematurity and necrotizing enterocolitis. J Matern Fetal Neonatal Med. 2013;26:77–9. Article PubMed Google Scholar Christensen RD, Baer VL, Lambert DK, Ilstrup SJ, Eggert LD, Henry E. Association, among very-low-birthweight neonates, between red blood cell transfusions in the week after birth and severe intraventricular hemorrhage. Transfusion. 2014;54:104–8. Article PubMed Google Scholar Keir AK, New H, Robitaille N, Crighton GL, Wood EM, Stanworth SJ. Approaches to understanding and interpreting the risks of red blood cell transfusion in neonates. Transfus Med. 2019;29:231–8. Article CAS PubMed Google Scholar Song J, Dong H, Xu F, Wang Y, Li W, Jue Z, et al. The association of severe anemia, red blood cell transfusion and necrotizing enterocolitis in neonates. PLoS One. 2021;16:e0254810. Article CAS PubMed PubMed Central Google Scholar Khashu M, Dame C, Lavoie PM, De Plaen IG, Garg PM, Sampath V, et al. Current understanding of transfusion-associated necrotizing enterocolitis: review of clinical and experimental studies and a call for more definitive evidence. Newborn (Clarksville). 2022;1:201–8. Article PubMed PubMed Central Google Scholar Odom TL, Eubanks J, Redpath N, Davenport E, Tumin D, Akpan US. Development of necrotizing enterocolitis after blood transfusion in very premature neonates. World J Pediatr. 2023;19:68–75. Article PubMed Google Scholar Fontana C, Raffaeli G, Pesenti N, Boggini T, Cortesi V, Manzoni F, et al. Red blood cell transfusions in preterm newborns and neurodevelopmentaloutcomes at 2 and 5 years of age. Blood Transfus. 2022;20:40–49. PubMed PubMed Central Google Scholar Vu PT, Ohls RK, Mayock DE, German KR, Comstock BA, Heagerty PJ, et al. Transfusions and neurodevelopmental outcomes in extremely low gestation neonates enrolled in the PENUT Trial: a randomized clinical trial. Pediatr Res. 2021;90:109–16. Article CAS PubMed PubMed Central Google Scholar D’Amato G, Faienza MF, Palladino V, Bianchi FP, Natale MP, Christensen RD, et al. Red blood cell transfusions and potentially related morbidities in neonates under 32 weeks’ gestation. Blood Transfus. 2021;19:113–9. PubMed PubMed Central Google Scholar Bahr TM, Snow GL, Christensen TR, Davenport P, Henry E, Sarah M et al. Can transfusions have a pathogenic role in bronchopulmonary dysplasia? J Pediatr. 2023 (in press). Stark CM, Juul SE. New frontiers in neonatal red blood cell transfusion research. J Perinatol. 2023;43:1349–56. Puia-Dumitrescu M, Tanaka DT, Spears TG, Daniel CJ, Kumar KR, Athavale K, et al. Patterns of phlebotomy blood loss and transfusions in extremely low birth weight infants. J Perinatol. 2019;39:1670–5. Article PubMed PubMed Central Google Scholar Ratanasopa K, Strader MB, Alayash AI, Bulow L. Dissection of the radical reactions linked to fetal hemoglobin reveals enhanced pseudoperoxidase activity. Front Physiol. 2015;6:39. Article PubMed PubMed Central Google Scholar Perez M, Robbins ME, Revhaug C, Saugstad OD. Oxygen radical disease in the newborn, revisited: oxidative stress and disease in the newborn period. Free Radic Biol Med. 2019;142:61–72. Article CAS PubMed PubMed Central Google Scholar Arbell D, Bin-Nun A, Zugayar D, Eventov-Friedman S, Chepel N, Srebnik N, et al. Deformability of cord blood vs. newborns’ red blood cells: implication for blood transfusion. J Matern Fetal Neonatal Med. 2022;35:3270–5. Article PubMed Google Scholar Wendelbo O, Hervig T, Haugen O, Seghatchian J, Reikvam H. Microcirculation, and red cell transfusion in patients with sepsis. Transfus Apher Sci. 2017;56:900–5. Article PubMed
Abstract Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD. Introduction Ischemic heart disease (IHD) is the leading cause of global mortality and among the top causes of morbidity. In 2019, it was responsible for over 9 million deaths worldwide and the loss of more than 180 million disability-adjusted life years (http://ghdx.healthdata.org/gbd-results-tool). Preventive treatments including lifestyle modifications and pharmacologic interventions (e.g., cholesterol-lowering medications) can be guided by risk assessment. The Framingham coronary heart disease risk score (FRS) and the Pooled Cohort Equations (PCE) are commonly utilized risk estimation methods for IHD and atherosclerotic cardiovascular disease, respectively1,2. The FRS uses demographic risk factors and cholesterol values to predict 10-year IHD risk in individuals aged 30–74 years old without known IHD at baseline examination. The PCE were developed to model the 10-year risk of major atherosclerotic cardiovascular disease events, including fatal and nonfatal IHD as well as fatal and nonfatal stroke. These risk scores have been used as a standard for IHD risk assessment in current clinical practice guidelines and policy recommendations, including the most recent American College of Cardiology/American Heart Association guideline on primary prevention of cardiovascular disease3. Validation of both risk scores has shown varying performance depending on the subpopulation analyzed. Performance is typically reported as a c-statistic value, which corresponds to the proportion of case–control pairs in which a higher risk is assigned to the case (a measure of discrimination). Previously reported c-statistic values for the FRS and PCE are modest with typical ranges of 0.66–0.76 and 0.68–0.76, respectively4, leaving potential room for improvement. Thus, the discovery of additional biomarkers that improve or independently inform the predictive power of these existing models has been the objective of multiple recent research endeavors5,6. Imaging biomarkers derived from computed tomography (CT) have shown promise in the assessment of cardiovascular risk. For example, the coronary artery calcium (CAC) score measures the extent of plaque in the coronary arteries from coronary CTs, and is an important tool for IHD risk stratification7,8. Although CAC scoring is a strong independent predictor of cardiovascular events9, the integration of both clinical factors (e.g., FRS) and imaging factors (e.g., CAC score) has been shown to significantly improve prediction of major cardiac events and all-cause mortality (compared with clinical or imaging metrics alone)10,11. Other studies have combined metrics from coronary CT angiography with blood biomarkers such as high-sensitivity cardiac troponin to successfully improve upon current measures of cardiovascular risk12,13. These specialized methods apply to a subset of patients already being assessed for cardiovascular risk. Alternatively, abdominopelvic CTs contain body composition (BC) imaging biomarkers for atherosclerotic cardiovascular disease, such as hepatic steatosis14, low muscle mass15, an increased ratio of visceral to subcutaneous adipose tissue (VAT/SAT)16, and abdominal aortic calcification17. Notably, 20 million abdominopelvic CTs are acquired annually almost twice as often as CT scans that image the heart or coronary vessels, such as non-contrasted chest CT and coronary CT18,19. According to the National Hospital Ambulatory Care Survey (https://bit.ly/2SL6957), in 2016 over 10 million abdominopelvic CTs were acquired in the US during emergency department visits alone, often in relation to abdominal pain—the most common principal reason for visiting an emergency department20. By comparison, roughly 3 million chest CTs were performed during emergency department visits in 2016. Within abdominopelvic CTs, these biomarkers could be measured during such routine imaging procedures without resulting in additional costs or radiation exposure, referred to as opportunistic imaging21. However, the current clinical workflow and volume of imaging is not well-suited to allow practical utilization of the additional resources required to manually extract measurements of imaging biomarkers22. Consequently, despite the potential value, cardiovascular risk is not routinely assessed upon abdominopelvic CT acquisition, thereby missing opportunities for early disease detection and prevention. In this work, we developed IHD risk assessment models that use automatically measured imaging features from abdominopelvic CT examinations in combination with the patient’s EMR. We evaluate the benefit of extracting BC imaging biomarkers from an axial slice at the level of the third lumbar vertebra (L3) in addition to traditional PCE metrics. We also develop an IHD risk assessment tool using the raw L3 slice image in an end-to-end manner using deep learning. We further develop a method to quantitatively assess the contribution of imaging features to the model prediction, aggregated at the tissue level. We introduce this method, Tissue Saliency, in this work. Finally, we combine features derived from the EMR in addition to the L3 slice, yielding the greatest risk prediction performance, and interpret the individual contribution of clinical features. To spur further research, we publicly release the Opportunistic L3 for IHD prediction (OL3I) dataset. Overall, we depict how opportunistic utilization of already-acquired CT imaging and EMR data can facilitate primary prevention of IHD without requiring additional testing, radiation, cost, or radiological assessment. Methods Study population Following Stanford University Institutional Review Board approval and in accordance with relevant guidelines and regulations, we identified an initial cohort of 36,354
Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239–42. Article CAS PubMed Google Scholar Kesheh MM, Hosseini P, Soltani S, Zandi M. An overview on the seven pathogenic human coronaviruses. Rev Med Virol. 2022;32(2): e2282. Article CAS PubMed Google Scholar Zandi M. ORF9c and ORF10 as accessory proteins of SARS-CoV-2 in immune evasion. Nat Rev Immunol. 2022;22(5):331–331. Article CAS PubMed PubMed Central Google Scholar Hernandez-Teran A, Mejia-Nepomuceno F, Herrera MT, Barreto O, Garcia E, Castillejos M, Boukadida C, Matias-Florentino M, Rincon-Rubio A, Avila-Rios S, et al. Dysbiosis and structural disruption of the respiratory microbiota in COVID-19 patients with severe and fatal outcomes. Sci Rep. 2021;11(1):21297. Article CAS PubMed PubMed Central Google Scholar Mizutani T, Ishizaka A, Koga M, Ikeuchi K, Saito M, Adachi E, Yamayoshi S, Iwatsuki-Horimoto K, Yasuhara A, Kiyono H et al. Correlation Analysis between Gut Microbiota Alterations and the Cytokine Response in Patients with Coronavirus Disease during Hospitalization. Microbiol Spectr. 2022;10(2):e0168921. Buttenschon J, Vogt S, Mattner J. Compartmentalized immune responses and the local microbiota determine mucosal and systemic immunity against SARS-CoV-2. Cell Mol Immunol. 2022;19(2):130–2. Article PubMed PubMed Central Google Scholar Xu R, Liu P, Zhang T, Wu Q, Zeng M, Ma Y, Jin X, Xu J, Zhang Z, Zhang C. Progressive deterioration of the upper respiratory tract and the gut microbiomes in children during the early infection stages of COVID-19. J Genet Genomics. 2021;48(9):803–14. Article CAS PubMed PubMed Central Google Scholar Mazzarelli A, Giancola ML, Farina A, Marchioni L, Rueca M, Gruber CEM, Bartolini B, Ascoli Bartoli T, Maffongelli G, Capobianchi MR, et al. 16S rRNA gene sequencing of rectal swab in patients affected by COVID-19. PLoS ONE. 2021;16(2): e0247041. Article CAS PubMed PubMed Central Google Scholar Gu S, Chen Y, Wu Z, Chen Y, Gao H, Lv L, Guo F, Zhang X, Luo R, Huang C, et al. Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza. Clin Infect Dis. 2020;71(10):2669–78. Article CAS PubMed Google Scholar Tao W, Zhang G, Wang X, Guo M, Zeng W, Xu Z, Cao D, Pan A, Wang Y, Zhang K, et al. Analysis of the intestinal microbiota in COVID-19 patients and its correlation with the inflammatory factor IL-18. Med Microecol. 2020;5: 100023. Article PubMed PubMed Central Google Scholar Lv LX, Gu SL, Jiang HY, Yan R, Chen YF, Chen YB, Luo R, Huang CJ, Lu HF, Zheng BW et al. Gut mycobiota alterations in patients with COVID-19 and H1N1 infections and their associations with clinical features. Commun Biol. 2021;4(1):480. Chen Y, Gu S, Chen Y, Lu H, Shi D, Guo J, Wu WR, Yang Y, Li Y, Xu KJ, et al. Six-month follow-up of gut microbiota richness in patients with COVID-19. Gut. 2022;71(1):222–5. Article PubMed Google Scholar Zhou Y, Zhang J, Zhang D, Ma WL, Wang X. Linking the gut microbiota to persistent symptoms in survivors of COVID-19 after discharge. J Microbiol. 2021;59(10):941–8. Article CAS PubMed PubMed Central Google Scholar Newsome RC, Gauthier J, Hernandez MC, Abraham GE, Robinson TO, Williams HB, Sloan M, Owings A, Laird H, Christian T, et al. The gut microbiome of COVID-19 recovered patients returns to uninfected status in a minority-dominated United States cohort. Gut Microbes. 2021;13(1):1–15. Article CAS PubMed Google Scholar Hurst JH, McCumber AW, Aquino JN, Rodriguez J, Heston SM, Lugo DJ, Rotta AT, Turner NA, Pfeiffer TS, Gurley TC, et al. Age-Related Changes in the Nasopharyngeal Microbiome Are Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Symptoms Among Children, Adolescents, and Young Adults. Clin Infect Dis. 2022;75(1):e928–37. Rueca M, Fontana A, Bartolini B, Piselli P, Mazzarelli A, Copetti M, Binda E, Perri F, Gruber CEM, Nicastri E et al. Investigation of Nasal/Oropharyngeal Microbial Community of COVID-19 Patients by 16S rDNA Sequencing. Int J Environ Res Public Health. 2021;18(4):2174. Ventero MP, Cuadrat RRC, Vidal I, Andrade BGN, Molina-Pardines C, Haro-Moreno JM, Coutinho FH, Merino E, Regitano LCA, Silveira CB, et al. Nasopharyngeal Microbial Communities of Patients Infected With SARS-CoV-2 That Developed COVID-19. Front Microbiol. 2021;12: 637430. Article PubMed PubMed Central Google Scholar Nagata N, Takeuchi T, Masuoka H, Aoki R, Ishikane M, Iwamoto N, Sugiyama M, Suda W, Nakanishi Y, Terada-Hirashima J, et al. Human Gut Microbiota and Its Metabolites Impact Immune Responses in COVID-19 and Its Complications. Gastroenterology. 2023;164(2):272–88. Article CAS PubMed Google Scholar Sarkar A, Harty S, Moeller AH, Klein SL, Erdman SE, Friston KJ, Carmody RN. The gut microbiome as a biomarker of differential susceptibility to SARS-CoV-2. Trends Mol Med. 2021;27(12):1115–34. Article CAS PubMed PubMed Central Google Scholar Khan M, Mathew BJ, Gupta P, Garg G, Khadanga S, Vyas AK, Singh AK. Gut Dysbiosis and IL-21 Response in Patients with Severe COVID-19. Microorganisms. 2021;9(6):1292. Moreira-Rosario A, Marques C, Pinheiro H, Araujo JR, Ribeiro P, Rocha R, Mota I, Pestana D, Ribeiro R, Pereira A, et al. Gut Microbiota Diversity and C-Reactive Protein Are Predictors of Disease Severity in COVID-19 Patients. Front Microbiol. 2021;12: 705020. Article PubMed PubMed Central Google Scholar Estaki M, Jiang L, Bokulich NA, McDonald D, Gonzalez A, Kosciolek T, Martino C, Zhu Q, Birmingham A, Vazquez-Baeza Y, et al. QIIME 2 enables comprehensive End-to-End analysis of diverse microbiome data and comparative studies with publicly available data. Curr Protoc Bioinformatics. 2020;70(1): e100. Article PubMed PubMed Central Google Scholar Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. Article CAS PubMed PubMed Central Google Scholar Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059–66. Article CAS PubMed PubMed Central Google Scholar Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5(3): e9490. Article PubMed PubMed Central Google Scholar McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz
Abstract To investigate the clinical and molecular characteristics and evolution of the Zika virus (ZIKV) in Thailand from March 2020 to March 2023. In all, 751 serum samples from hospitalized patients in Bangkok and the surrounding areas were screened for ZIKV using real-time RT-PCR. Demographic data and clinical variables were evaluated. Phylogenetic and molecular clock analysis determined the genetic relationships among the ZIKV strains, emergence timing, and their molecular characteristics. Among the 90 confirmed ZIKV cases, there were no significant differences in infection prevalence when comparing age groups and sexes. Rash was strongly associated with ZIKV infection. Our ZIKV Thai isolates were categorized into two distinct clades: one was related to strains from Myanmar, Vietnam, Oceania, and various countries in the Americas, and the other was closely related to previously circulating strains in Thailand, one of which shared a close relation to a neurovirulent ZIKV strain from Cambodia. Moreover, ZIKV Thai strains could be further classified into multiple sub-clades, each exhibiting specific mutations suggesting the genetic diversity among the circulating strains of ZIKV in Thailand. Understanding ZIKV epidemiology and genetic diversity is crucial for tracking the virus’s evolution and adapting prevention and control strategies. Introduction Zika virus (ZIKV) is a single-stranded RNA flavivirus primarily transmitted by the Aedes mosquitoes. It was first discovered in Uganda in 1947 and identified in Asia in 19661,2. Prior to 2007, only sporadic ZIKV infection cases with self-limiting or mild symptoms were documented in Africa and Asia3. In 2007, the first ZIKV outbreak occurred in Yap Islands, Micronesia, affecting 73% residents4. Subsequent outbreaks occurred in French Polynesia in 2013–2014, during which the association between ZIKV infection and Guillain–Barré syndrome was noted5,6. ZIKV was first identified in Brazil in 20157 and rapidly spread throughout the Americas8. Brazil experienced a dramatic rise in ZIKV-linked neonatal microcephaly cases, resulting in the declaration of a public health emergency of international concern by the WHO in early 2016 to establish a causal connection between ZIKV and congenital disabilities9. Since then, many countries have increased their focus on monitoring ZIKV infections. Before 2016, multiple lines of evidence indicated that ZIKV circulated at low levels, and sporadic cases were reported in Southeast Asian countries including Thailand for decades10. From 2016 to 2017, the number of ZIKV infection cases in Thailand dramatically increased by over 1500 cases; however, it remains unclear whether this rise was because of higher infection rates or increased awareness11. According to the Bureau of Epidemiology, Ministry of Public Health, Thailand, the morbidity rate in 2016 was 1.69 per 100,000 population. From 2019 to 2022, the morbidity rates of ZIKV in Thailand were < 0.5 yearly; the rates were 0.41, 0.36, 0.10, and 0.29 per 100,000 population, respectively12. From 2016 to 2022, the Bureau reported 234 confirmed cases of ZIKV in pregnant women. Among them, 11 patients experienced miscarriages, of which four were related to ZIKV infection. Furthermore, clinical surveillance of 2217 neonates with microcephaly revealed 15 cases of congenital Zika syndrome. While the ZIKV epidemic and its genetic characterization in the Americas are well documented, its presence and molecular epidemiology in Southeast Asia, particularly in Thailand, are areas of concern and ongoing investigation. Few studies have explored the molecular epidemiology of the Thai ZIKV strains. Hence, more research is required on the current genetic characterization and diversity of ZIKV strains in Thailand since the COVID-19 pandemic. This research aimed to comprehensively evaluate the ZIKV prevalence, clinical presentation, and genetic characteristics in Thailand from 2020 to 2023. Investigating the genetic diversity of the current ZIKV circulating in Thailand can help assess the risk of outbreaks and guide public health strategies and preparedness efforts. Results Demographic characteristics and clinical features Out of the 751 samples (Table 1), 12.0% (90/751; 56.7% female and 43.3% male) tested positive for ZIKV infection based on Zika viral RNA presence. There was no significant sex-related difference in ZIKV prevalence (p = 0.507). The median age of patients with confirmed ZIKV was 37 (IQR: 29–46) years (range: 1–71 years). Most patients were in the 36–45 years age group (32.2%), followed by 26–35 years (22.2%) and 46–55 years (13.3%). Prevalence was lower among participants aged ≤ 15 years (10%) and 16–25 years (10%). Age was not significantly associated with increased ZIKV infection (p = 0.187). The median duration from illness onset to Zika RNA diagnosis was 3.5 days (IQR: 3–5 days). Table 1 Demographic characteristics and clinical presentation of individuals according to ZIKV infection, Thailand (2020–2023) (N = 751).Full size table The common clinical symptoms among ZIKV patients included rash (83.1%), fever (71.2%), arthralgia (54.2%), myalgia (39%), and conjunctivitis (22%). Skin rash was strongly associated with ZIKV infection (odds ratio [OR] 19.89, p < 0.001), as were arthralgia (OR 2.63, p < 0.001), and conjunctivitis (OR 11.73, p < 0.001). There was no evidence of ZIKV-associated neurological complications. Next, we examined the correlation between age groups and clinical characteristics and found that only arthralgia or joint pain (p = 0.022) showed a significant association with age groups (Table 2). In addition, the percentage of ZIKV-positive samples in each study year was analyzed and showed that there were 4.67% (12/257) tested positive for ZIKV infection in March 2020-December 2020, 7.54% (8/109) in 2021, 17.5% (47/269) in 2022. Interestingly, 19.8% (23/116) tested positive for ZIKV in the first three months of 2023. Table 2 Clinical characteristics in different age groups of ZIKV-infected participants (N = 59).Full size table Genome sequence and phylogenetic analysis of ZIKV detected in Thailand during 2020–2023 We constructed a maximum likelihood phylogenetic tree and examined the nucleotide identity using complete coding sequences of ZIKV Thai strains of 2020–2023 from this study (n = 17) and additional sequences representing various strains sourced from the GenBank database. Our ZIKV Thai isolates belonged to the Asian lineage and could be classified into two clades: Southeast Asian (SEA) and Asian-American (AA). Out of the 17 ZIKV Thai isolates from 2020 to 2023 (Figs. 1 and 2), 11 were in the SEA clade, which includes strains from Thailand in 2016–2017 (98.5–99.4% sequence identity), Singapore in 2016 (99.0–99.4% sequence identity), and Cambodia in 2019 (98.8–99.5% sequence identity). Most of our SEA ZIKV strains
Specialty
C1QA and COMP: plasma-based biomarkers for early diagnosis of pancreatic neuroendocrine tumors
Abstract Pancreatic Neuroendocrine tumors (PanNET) are challenging to diagnose and often detected at advanced stages due to a lack of specific and sensitive biomarkers. This study utilized proteomics as a valuable approach for cancer biomarker discovery; therefore, mass spectrometry-based proteomic profiling was conducted on plasma samples from 12 subjects (3 controls; 5 Grade I, 4 Grade II PanNET patients) to identify potential proteins capable of effectively distinguishing PanNET from healthy controls. Data are available via ProteomeXchange with the identifier PXD045045. 13.2% of proteins were uniquely identified in PanNET, while 60% were commonly expressed in PanNET and controls. 17 proteins exhibiting significant differential expression between PanNET and controls were identified with downstream analysis. Further, 5 proteins (C1QA, COMP, HSP90B1, ITGA2B, and FN1) were selected by pathway analysis and were validated using Western blot analysis. Significant downregulation of C1QA (p = 0.001: within groups, 0.03: control vs. grade I, 0.0013: grade I vs. grade II) and COMP (p = 0.011: within groups, 0.019: control vs grade I) were observed in PanNET Grade I & II than in controls. Subsequently, ELISA on 38 samples revealed significant downregulation of C1QA and COMP with increasing disease severity. This study shows the potential of C1QA and COMP in the early detection of PanNET, highlighting their role in the search for early-stage (Grade-I and Grade-II) diagnostic markers and therapeutic targets for PanNET. Introduction Neuroendocrine tumors (NETs) encompass a collection of tumors that arise from neuroendocrine cells and can be detected across various organs, with notable prevalence in the lung, digestive tract, and pancreas1. NETs rarely occur in 2 cases per 100,000 individuals, representing approximately 0.5% of all tumors2,3. The clinical features of neuroendocrine tumors (NETs) in the Indian population exhibit significant variations compared to Western nations, particularly regarding the distribution of neuroendocrine tumors by anatomical site and tumor type. Recent studies conducted in India have revealed that the pancreas (approx. 35%) stands as the primary and prevailing site of origin for neuroendocrine tumors (NETs)3,4. The term “pancreatic neuroendocrine tumors” (PanNET) refers to a broad category of neoplasms that develop from neuroendocrine cells in the pancreas. These tumors stand out from other pancreatic cancers due to their distinctive clinical, histomorphologic, and prognostic characteristics5,6,7. PanNET can differ significantly in their clinical characteristics. They might be benign, slowly expanding tumors with no symptoms, or they can be more aggressive varieties that result in hormonal imbalances and different clinical disorders8. Various biochemical tests, such as complete blood count (CBC), serum calcium, renal and liver function tests (RFT/LFT), chromogranin A, neuron-specific enolase, pancreatic polypeptide, pancreastatin, CA 19-9, serotonin derivatives (5-hydroxyindoleacetic acid), insulin, glucagon, gastrin-1, and vasoactive intestinal peptide, play a crucial role in screening, diagnosis and prognosis of PanNETs patients 9,10. The available laboratory tests lack sensitivity and specificity in diagnosing pancreatic neuroendocrine tumors. Additionally, the imaging techniques employed for diagnosis, such as endoscopic ultrasound, CT scans, X-rays, Octreotide scintigraphy, [68Ga] Ga-DOTATATE PET (Dota-Octreotate Positron Emission Tomography), and [18F]-FDG (Fluorodeoxyglucose)-PET scans, are invasive and expensive. Moreover, these advanced imaging techniques may not be accessible in all medical centers, making them particularly inaccessible for individuals in developing countries11,12,13,14. Therefore, there is an urgent need for more specific, sensitive, and cheap biomarkers for early screening and diagnosis of this disease. Proteomics carries significant potential for advancements in molecular medicine, as evidenced by studies exploring its novel perspectives in cancer research. A notable aspect of proteomics is its promise in discovering biomarkers and tumor markers, which can be helpful in the early detection and diagnosis of various diseases, with a particular focus on cancer15,16,17. Additionally, discovering specific protein markers can aid in creating personalized medicines that maximize therapeutic effectiveness while minimizing adverse effects for each patient16,18. So, proteomics continues to be the preferred method for conducting biochemical investigations on several cancers, yielding crucial insights such as protein profiles, protein levels, modification sites, and protein interactions 18,19. Among the applications of proteomic techniques, Mass spectrometry offers significant advancements in proteomic studies, particularly in enhancing signal specificity by effectively eliminating false-positive results during database searching. It enables the quantification and identification of proteins within complex protein mixtures, analysis of protein–protein interactions, investigation of post-translational modifications, Structural proteomics, and the identification of differential protein modifications20. Proteomics has been invaluable in discovering numerous cancer biomarkers such as breast, esophageal, Gastric, lung, colorectal, liver, etc21. Proteomics analysis has been reported in cases with neuroendocrine tumors, greatly aiding the understanding of neuroendocrine tumors (NETs) pathogenesis. The current investigation examined the proteomic profiles of plasma samples obtained from individuals with PanNET (stage I and stage II) and healthy individuals serving as controls. This is the first study focuses on the plasma sample of PanNET and healthy individuals. Our objective was to identify particular plasma proteins that could prove beneficial in detecting PanNET. Results Demographical and clinical characteristics of pancreatic neuroendocrine tumor (PanNET) patients and healthy control subjects The mean age of PanNET patients is 41.89 ± 2.75 years (age range 20–70 years). In this study, out of 28 PanNET patients, 15 were male, and 13 were female, whereas in 10 healthy controls, 6 subjects were male and 4 were female. WHO grading of PanNET patients was done according to the 2017 WHO classification. Out of 28 patients, approx. 50% are with grade-I tumors, and 50% are with grade-II tumors; however, > 50% are found to be diagnosed at the metastatic stage (Fig. 1). 50% of patients have Ki67 index > 3%. The detailed history of the patients has been mentioned in Table 1. Figure 1 A flowchart illustrating the process of selecting PanNET patients. Full size image Table 1 Demographic details of PanNET patients.Full size table Proteomic profiles of PanNET plasma Partial least squares-discriminant analysis (PLS-DA) modeling used proteomics data to distinguish the distinct separation between Patients (Grade I and Grade II PanNET) and controls. This analysis simultaneously identified proteins whose expression contributed to the discrimination among the three groups. Figure 2A and B show PLSDA and the associated VIP classification model. Significant separation of Grade II PanNET from Grade I and Control in Component 1 was observed via the PLSDA
Fang C, Liu H, Wang S. The coupling curve between urbanization and the eco-environment: China’s urban agglomeration as a case study. Ecol Indic. 2021;130: 108107. Article Google Scholar Tian H, Hu S, Cazelles B, Chowell G, Gao L, Laine M, et al. Urbanization prolongs hantavirus epidemics in cities. Proc Natl Acad Sci USA. 2018;115:4707–12. Article CAS PubMed PubMed Central Google Scholar National Bureau of Statistics of China. China statistics yearbook-2021. Beijing: China Statistics Press; 2021. Google Scholar Alirol E, Getaz L, Stoll B, Chappuis F, Loutan L. Urbanisation and infectious diseases in a globalised world. Lancet Infect Dis. 2011;11:131–41. Article PubMed PubMed Central Google Scholar Worrell CM, Wiegand RE, Davis SM, Odero KO, Blackstock A, Cuellar VM, et al. A cross-sectional study of water, sanitation, and hygiene-related risk factors for soil-transmitted helminth infection in urban school- and preschool-aged children in Kibera, Nairobi. PLoS ONE. 2016;11: e0150744. Article PubMed PubMed Central Google Scholar Turley R, Saith R, Bhan N, Rehfuess E, Carter B. Slum upgrading strategies involving physical environment and infrastructure interventions and their effects on health and socio-economic outcomes. Evid Based Med. 2013;6:57–57. Article Google Scholar Combs MA, Kache PA, VanAcker MC, Gregory N, Plimpton LD, Tufts DM, et al. Socio-ecological drivers of multiple zoonotic hazards in highly urbanized cities. Glob Chang Biol. 2022;28:1705–24. Article CAS PubMed Google Scholar Wilke ABB, Beier JC, Benelli G. Complexity of the relationship between global warming and urbanization – an obscure future for predicting increases in vector-borne infectious diseases. Curr Opin Insect Sci. 2019;35:1–9. Article PubMed Google Scholar Ortiz DI, Piche-Ovares M, Romero-Vega LM, Wagman J, Troyo A. The Impact of deforestation, urbanization, and changing land use patterns on the ecology of mosquito and tick-borne diseases in Central America. Insects. 2021;13:20. Article PubMed PubMed Central Google Scholar Wang W, Bergquist R, King CH, Yang K. Elimination of schistosomiasis in China: current status and future prospects. PLoS Negl Trop Dis. 2021;15: e0009578. Article PubMed PubMed Central Google Scholar Chen C, Guo Q, Fu Z, Liu J, Lin J, Xiao K, et al. Reviews and advances in diagnostic research on Schistosoma japonicum. Acta Trop. 2021;213: 105743. Article CAS PubMed Google Scholar Xu J, Lv S, Cao CL, Li SZ, Zhou XN. Progress and challenges of schistosomiasis elimination in China. Chin J Schisto Control. 2018;30:605–9 (In Chinese). Google Scholar Utzinger J, Zhou XN, Chen MG, Bergquist R. Conquering schistosomiasis in China: the long march. Acta Trop. 2005;96:69–96. Article PubMed Google Scholar Zhang LJ, Xu ZM, Yang F, He JY, Dang H, Li YL, et al. Progress of schistosomiasis control in People’s Republic of China in 2021. Chin J Schisto Control. 2022;34:329–36 (In Chinese). CAS Google Scholar Adekiya TA, Aruleba RT, Oyinloye BE, Okosun KO, Kappo AP. The effect of climate change and the snail-schistosome cycle in transmission and bio-control of Schistosomiasis in Sub-Saharan Africa. Int J Environ Res Public Health. 2020;17:181. Article Google Scholar Stensgaard AS, Vounatsou P, Sengupta ME, Utzinger J. Schistosomes, snails and climate change: current trends and future expectations. Acta Trop. 2019;190:257–68. Article PubMed Google Scholar Liu MM, Feng Y, Yang K. Impact of micro-environmental factors on survival, reproduction and distribution of Oncomelania hupensis snails. Infect Dis Poverty. 2021;10:47. Article PubMed PubMed Central Google Scholar Hong QB, Jiang YJ, Yang K, Xi WP, Huang YX, Sun LP, et al. Analysis of endemic situation of surveillance sites of schistosomiasis in Jiangsu Province, 2006 I. Status of residents and livestock infection. Chin J Schisto Control. 2007;4:263–7 (In Chinese). Google Scholar Zheng J, Wang XF, Wang YA, Zhu HQ, Wu X, Qiu ZL. The relationship between socioeconomic factors and schistosomiasis in moutainous region in Yunnan. Chin J Parasitol Parasit Dis. 1996;2:24–8 (In Chinese). Google Scholar El Katsha S, Watts S. Schistosomiasis in two Nile delta villages: an anthropological perspective. Trop Med Int Health. 1997;2:846–54. Article PubMed Google Scholar Grimes JE, Croll D, Harrison WE, Utzinger J, Freeman MC, Templeton MR. The roles of water, sanitation and hygiene in reducing schistosomiasis: a review. Parasit Vectors. 2015;8:156. Article PubMed PubMed Central Google Scholar Zhang LJ, He JY, Yang F, Dang H, Li YL, Guo SY, et al. Progress of schistosomiasis control in People’s Republic of China in 2022. Chin J Schisto Control. 2023;35(3):217–24 (In Chinese). CAS Google Scholar Xu XJ, Wang TP, He JC, Wang H, Gao FH, Dai B, et al. Evaluation of schistosomiasis integrated control effect in Anhui province from 2016 to 2020. J Trop Med. 2022;22(11):1588–95 (In Chinese). Google Scholar Hu Y, Li R, Bergquist R, Lynn H, Gao FH, Wang QZ, et al. Spatio-temporal transmission and environmental determinants of schistosomiasis japonica in Anhui Province, China. PLoS Negl Trop Dis. 2015;9:15. Article Google Scholar Hu Y, Li R, Chen Y, Gao FH, Wang QZ, Zhang SQ, et al. Shifts in the spatiotemporal dynamics of schistosomiasis: a case study in Anhui Province. China PLoS Negl Trop Dis. 2015;9: e0003715. Article PubMed Google Scholar Gao F, Ward MP, Wang Y, Zhang Z, Hu Y. Implications from assessing environmental effects on spatio-temporal pattern of schistosomiasis in the Yangtze Basin. China Geospat Health. 2018;13(2):359–64. Google Scholar Yu JM, de Vlas SJ, Jiang QW, Gryseels B. Comparison of the Kato-Katz technique, hatching test and indirect hemagglutination assay (IHA) for the diagnosis of Schistosoma japonicum infection in China. Parasitol Int. 2007;56:45–9. Article CAS PubMed Google Scholar Xia CC, Bergquist R, Lynn H, Hu F, Lin D, Hao Y, et al. Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region. China Parasit Vectors. 2017;10:136. Article PubMed Google Scholar Lange CN. Environmental factors influencing land snail diversity patterns in Arabuko Sokoke forest. Kenya Afr J Ecol. 2003;41:352–5. Article Google Scholar Xu YM, Zhang SQ. Effects of environmental factors on the growth, development and distribution of Oncomelania hupensis. Int J Med Parasit Dis. 2011;28:218–22. Google Scholar Xu FS, Qian XH, Wen S, Liang S, Gu XG, Luo P. Relationship between plant and soil characteristics and snail distribution in Anning River Basin. Sichuan J Zool. 1999;2:16–7 (In Chinese). Google Scholar M’Bra RK, Kone B, Yapi YG, Silue KD, Sy I, Vienneau
Global Anorectal Malformation Treatment Market is estimated to undergo a 7.4% CAGR by 2033, as per F
The global anorectal malformation treatment market is expected to surpass an impressive valuation of US$ 794.91 million in 2023 and is projected to exhibit a CAGR of 7.4% from 2023 to 2033, reaching US$ 1,475.19 million. The market is driven by a number of factors, including the rising prevalence of ARM, increasing awareness of the condition, and growing demand for advanced treatment options. Anorectal malformation (ARM) is a congenital birth defect that affects the rectum and/or anus. It is a spectrum of disorders with a wide range of anatomical presentations. ARM occurs in approximately 1 in 5,000 newborns and is more common in boys than in girls. To Get The Sample Copy Of Report Visit! https://www.futuremarketinsights.com/reports/sample/rep-gb-16432 Anorectal abnormalities are birth defects that affect a baby’s anus or rectum and cause irregular bowel movements. Once the anus is blocked, the condition is referred to as a portion of the current anus. In kids with anorectal malformations, the anus may not exist, be blocked by a thin or thick layer of tissue, or be narrower than typical. Increased incidence of gastrointestinal disorders, irritable bowel syndrome, and a family history of illnesses and diseases like cancer can all lead to anorectal malformations. A sizable market for efficient, dependable, and cutting-edge medical therapy for anorectal malformation will emerge during the coming decades. Anorectal Malformation Treatment Market by 2023 to 2033: Key Takeaways: The global anorectal malformation treatment market is expected to reach US$ 794.91 million in 2023 and exhibit a CAGR of 7.4% from 2023 to 2033, reaching US$ 1,475.19 million by 2033. The increasing prevalence of gastrointestinal disorders, irritable bowel syndrome, and a family medical history of certain ailments and diseases, such as cancer, is driving the demand for anorectal malformation treatment. The growing sophistication of healthcare facilities is also propelling the market growth. The North American region is expected to remain the largest market for anorectal malformation treatment, followed by the European region. The Asia Pacific region is expected to witness the fastest growth during the forecast period, owing to the increasing awareness of anorectal malformation and the rising disposable incomes in the region. Reach Out To Our Analyst And Get All Your Queries Answered! https://www.futuremarketinsights.com/ask-question/rep-gb-16432 Key Drivers of the Anorectal Malformation Treatment Market: Rising prevalence of ARM: The prevalence of ARM is on the rise globally, due to factors such as increasing consanguineous marriages, exposure to environmental toxins, and maternal infections. Increasing awareness of ARM: There is a growing awareness of ARM among parents and healthcare professionals, which is leading to earlier diagnosis and treatment of the condition. Growing demand for advanced treatment options: Parents of children with ARM are increasingly seeking advanced treatment options that can improve their child’s quality of life. Key Challenges in the Anorectal Malformation Treatment Market: High cost of treatment: ARM treatment can be expensive, especially for complex cases. Lack of skilled surgeons: There is a shortage of skilled surgeons who can perform ARM surgery. Risk of complications: ARM surgery is complex and carries a risk of complications, such as infection, bleeding, and nerve damage. Regional Analysis of the Anorectal Malformation Treatment Market: North America is expected to remain the dominant market for anorectal malformation treatment throughout the forecast period. This is due to the high prevalence of ARM in the region, the presence of advanced healthcare infrastructure, and the availability of skilled surgeons. However, the Asia Pacific market is expected to witness the fastest growth during the forecast period, owing to the rising prevalence of ARM in the region and the growing demand for advanced treatment options. Art of Personalization: Dive into the World of Customization with Our Report! https://www.futuremarketinsights.com/customization-available/rep-gb-16432 Key Companies Profiled: Sanofi S.A. Bausch Health Companies Inc. Cleveland Clinic Mayo Clinic Children’s Health for Orange County (CHOC) St. Louis Children’s Hospital Nationwide Children’s Hospital Children’s Hospital of Pittsburgh Intermountain Healthcare Key Segments Profiled in the Anorectal Malformation Treatment Industry Survey: By Treatment Type: Colostomy Anorectal Repair Colostomy Closure By End User: Hospitals Specialty Clinics By Region: North America Europe Asia Pacific Latin America Middle East & Africa (MEA) Act Now to Explore In-Depth Market Analysis: Get Exclusive Purchase Now to Access! https://www.futuremarketinsights.com/checkout/16432 About Future Market Insights (FMI) Future Market Insights, Inc. (ESOMAR certified, recipient of the Stevie Award, and a member of the Greater New York Chamber of Commerce) offers profound insights into the driving factors that are boosting demand in the market. FMI stands as the leading global provider of market intelligence, advisory services, consulting, and events for the Packaging, Food and Beverage, Consumer Technology, Healthcare, Industrial, and Chemicals markets. With a vast team of over 5000 analysts worldwide, FMI provides global, regional, and local expertise on diverse domains and industry trends across more than 110 countries. Contact Us: Nandini Singh Sawlani Future Market Insights Inc.Christiana Corporate, 200 Continental Drive,Suite 401, Newark, Delaware – 19713, USAT: +1-845-579-5705For Sales Enquiries: [email protected]: https://www.futuremarketinsights.comLinkedIn| Twitter| Blogs | YouTube
People who get the flu vaccine may have improved heart health, according to a recent study published in Scientific Reports. Researchers from the Shahid Beheshti University of Medical Sciences in Tehran, Iran, concluded that patients who received flu vaccinations had a 26% reduced risk of having a heart attack and were 33% less likely to die from cardiovascular disease. This finding is based on a review of five randomized controlled trials that focused on myocardial disease and influenza vaccines. CHILDHOOD PNEUMONIA SURGE REPORTED IN NETHERLANDS AMID OUTBREAK IN CHINA The patients who participated in the studies had all been diagnosed with cardiovascular disease previously and were 61 years old, on average. Out of the total of 9,059 patients, 4,529 of them received the flu vaccine, while 4,530 received a placebo shot. People who get the flu vaccine may have improved heart health, according to a recent study published in Scientific Reports. (iStock) After a nine-month period, 621 of the people who received the placebo shot experienced “major cardiovascular events,” compared to 517 of the patients who received the flu vaccine. Those outcomes included myocardial infarction, cardiovascular death and stroke. WHEN MEASURING HEART ATTACK RISK, ONE IMPORTANT RED FLAG IS OFTEN OVERLOOKED, DOCTORS SAY “Revealing a compelling insight into the potential benefits of influenza vaccination, our comprehensive meta-analysis, based on the latest randomized controlled trial data, demonstrates a significant interaction between influenza vaccination and the reduction of major cardiovascular events,” wrote the researchers of the study. “Notably, patients who received the influenza vaccine experienced a remarkable risk reduction of over 20% in cardiovascular death.” Patients who received flu vaccinations had a 26% reduced risk of having a heart attack and were 33% less likely to die from cardiovascular disease. (Joe Raedle/Getty Images) As for why influenza vaccines seem to reduce the risk, the researchers noted that the shot could prevent inflammation and secondary infections, while also stabilizing plaque amounts in the heart. The vaccine could also help stimulate the immune system, which the study authors noted is essential for cardiovascular health. SHOULD YOU GET THE COVID AND FLU VACCINES AT THE SAME TIME? Dr. Marc Siegel, clinical professor of medicine at NYU Langone Medical Center and a Fox News medical contributor, called the study “important” and said it “builds on what we already knew.” He was not involved in the research. Heart disease is the primary cause of death among U.S. adults, killing one person every 33 seconds, according to the CDC. (iStock) “It is not surprising that flu shots would decrease the risk of heart attacks,” Siegel told Fox News Digital. “The flu is one of the great enablers,” he went on. “It adds stress and inflammation to the body and decreases the overall immune response, all of which can lead to acute cardiac events.” The flu “adds stress and inflammation to the body and decreases the overall immune response.” The researchers called for further research to “elucidate the precise mechanisms driving this association and to explore the long-term impact of influenza vaccination on cardiovascular outcomes.” CLICK HERE TO SIGN UP FOR OUR HEALTH NEWSLETTER In the meantime, they recommended that “health care providers and policymakers should take heed of these findings and consider prioritizing influenza vaccination for patients with recent cardiovascular disease as a feasible and potentially life-saving preventive measure.” Fox News Digital reached out to the study authors for additional comment. CLICK HERE TO GET THE FOX NEWS APP Heart disease is the primary cause of death among U.S. adults, killing one person every 33 seconds, according to the Centers for Disease Control and Prevention (CDC). For more Health articles, visit www.foxnews.com/health. <!–> Melissa Rudy is health editor and a member of the lifestyle team at Fox News Digital. –>
by Beth Donovan | Special to the Courier No one wants to talk about cardiovascular disease around the holidays, but what better time to bring up the conversation than when you are with the people you love the most? Maybe we should talk more about how to determine your own risk and what we are doing to protect ourselves. Perhaps that will motivate someone you love to consider their own risk and whether they should be doing more. Cardiovascular disease is a broad term which can largely be equated to coronary heart disease leading to heart attacks, and ischemic stroke, and causes more disease and death than any other disease in the United States. That may seem unlikely, but not so much when you consider it is the complication resulting from numerous other very common conditions: diabetes, obesity, and a sedentary lifestyle. Genetics can be a factor as well. What we do every day contributes to or diminishes our risk of cardiovascular disease. There are many lifestyle factors that increase our risk, including smoking and eating foods high in cholesterol. There are also ways that we can reduce our risk including eating fresh fruits and vegetables, exercising, and keeping our blood sugar and blood pressure under control. You may think that if you are doing these things that your risk is low. But there is a better way to quantify your risk if you are between 40 and 75 years old. There is a free calculator which can predict your risk of having a cardiovascular disease event (heart attack or stroke) in the next 10 years at clincalc.com/cardiology. You just need to know your systolic blood pressure (the top number of your blood pressure calculation), total cholesterol, and HDL or “good” cholesterol. You simply plug those numbers into the calculator, answer a couple questions, hit calculate, and out comes the risk evaluation along with an interpretation. It goes on to recommend the statin dose that may be right for you, if needed. You can easily print the results to take to your next appointment to discuss with your healthcare provider. The U.S. Preventive Services Taskforce recommends clinicians prescribe a statin for the primary prevention of CVD for adults aged 40 to 75 who have one or more risk factors (i.e., dyslipidemia, diabetes, hypertension, or smoking) and an estimated 10-year risk of a cardiovascular event of 10% or greater. Furthermore, it recommends it may be appropriate for those with a 7.5% risk to take a statin medication even though the benefit is smaller in this group. A statin medication is not for everyone, but it could be lifesaving. Studies have shown that taking a statin medication can significantly lower the risk of death from a cardiovascular event such as a heart attack or stroke. Statins are not recommended to replace a healthy diet, regular exercise, maintaining a normal weight, and refraining from smoking. Because these medications are recommended by the USPSTF and are given a B rating, the medications should be covered for eligible adults by your health insurance company at no cost to you. Not every statin is required to be covered, but at least one must be available as a preventive medication for those adults who have an increased risk of cardiovascular disease. The U.S. Preventive Services Task Force is a panel of preventive medicine experts who volunteer to review literature and clinical guidelines; develop recommendations for preventive screenings, medications, and counseling; and assign grade recommendations. You can download an app or go the uspreventiveservicestaskforce.org/webview to learn which preventive screenings, medications or counseling services are recommended for you. Claremont resident Beth Donovan, PA-C, practiced as a physician assistant for 20 years and served as chair of legislative affairs for the California Academy of Physician Assistants from 2004 to 2012. She is on the advisory board for Keck Graduate Institute’s Physician Assistant Program.