tbuckley/cabot-search huggingface dataset:
Trends in cancer mortality under age 50 in 15 upper-Middle and high-income countries
Authors: Claudia Santucci, Silvia Mignozzi, Gianfranco Alicandro, Margherita Pizzato, Matteo Malvezzi, Eva Negri, Prabhat Jha, Carlo La Vecchia
Journal: JNCI Journal of the National Cancer Institute (2024)

Abstract Background Rising cancer incidence, particularly for colorectal cancer, has been reported in young adults. This study examined whether this is related to an increase in mortality. Methods We analysed World Health Organization (WHO) mortality data among young adults aged 25-49 in 15 most populous upper-middle and high-income countries from 1990 to 2021 with reliable data. Mid-year populations were retrieved from the United Nations for the American Countries and from the WHO for the other countries. We compared age-standardised mortality rates (ASMRs) in 2019-2021 to 2009-2011 and performed joinpoint regression analysis for all cancers and selected most common cancer sites: colorectum, pancreas, lung and breast. Results In 2019-2021, the highest ASMRs (per 100,000) were in Romanian males (38.6) and Argentinian females (45.9), while the lowest ones in Japan (males: 16.3; females: 22.7). ASMRs for colorectal cancers increased in 2019-2021 compared to 2009-2011 in nine countries among men and in seven countries among women. The highest increases were in the UK (males: +26.1%; females: +33.7%), Canada (males: +25.3%), and Mexico (males: +33.5%; females: +29.7%). Long-term analysis over the last three decades showed declining trends in total cancer mortality in the majority of countries, in lung cancer mortality across all countries, and in breast cancer in all countries except in Latin America. Conclusions While mortality from common cancers has generally decreased over the past three decades, mortality from colorectal cancer has increased in some countries. This highlights the need to control the obesity epidemic and implement targeted surveillance strategies in young populations.
ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics
Authors: Ziyi Li, Cory A Weller, Syed Shah, Nicholas L Johnson, Ying Hao, Paige B Jarreau, Jessica Roberts, Deyaan Guha, Colleen Bereda, Sydney Klaisner, Pedro Machado, Matteo Zanovello, Mercedes Prudencio, Björn Oskarsson, Nathan P Staff, Dennis W Dickson, Pietro Fratta, Leonard Petrucelli, Priyanka Narayan, Mark R Cookson, Michael E Ward, Andrew B Singleton, Mike A Nalls, Yue A Qi
Journal: Genomics Proteomics & Bioinformatics (2024)

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and Major histocompatibility complex (MHC) -peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical postprocessing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available online at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.
Liver Elastography-based Risk Score for Predicting Hepatocellular Carcinoma Risk
Authors: Chan Tian, Chunyan Ye, Haiyan Guo, Kun Lu, Juan Yang, Xiao Wang, Xinyuan Ge, Chengxiao Yu, Jing Lu, Longfeng Jiang, Qun Zhang, Ci Song
Journal: JNCI Journal of the National Cancer Institute (2024)

Abstract Background &amp; Aims Liver stiffness measurement (LSM) via vibration-controlled transient elastography (VCTE) accurately assesses fibrosis. We aimed to develop a universal risk score for predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis. Methods We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the HBV training cohort (n = 2,251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1,191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1,189, median follow-up of 3.3 years). A HCC risk score was then constructed based on a nomogram. An online risk evaluation tool (LEBER) was developed using ChatGPT4.0. Results Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P &amp;lt; .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index = 0.866) and the HBV validation cohort (C-index = 0.852), with good performance in the extrapolation CLD cohort (C-index = 0.769). The model demonstrated significantly superior discrimination compared to six previous models across the three cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http://ccra.njmu.edu.cn/LEBER669.html) was developed to facilitate the use of HCC-LSM. Conclusion The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.
Gut microbiota-derived tryptophan metabolites regulated by Wuji Wan to attenuate colitis through AhR signaling activation
Authors: Wanghui Jing, Sijing Dong, Yinyue Xu, Jingjing Liu, Jiawei Ren, Xue Liu, Min Zhu, Menggai Zhang, Hehe Shi, Na Li, Peng Xia, Haitao Lu, Sicen Wang
Journal: Acta Pharmaceutica Sinica B (2024)

Disruption of the intestinal mucosal barrier caused by gut dysbiosis and metabolic imbalance is the underlying pathology of inflammatory bowel disease (IBD). Traditional Chinese medicine Wuji Wan (WJW) is commonly used to treat digestive system disorders and showed therapeutic potential for IBD. In this interdisciplinary study, we aim to investigate the pharmacological effects of WJW against experimental colitis by combining functional metabolomics and gut-microbiota sequencing techniques. Treatment with WJW altered the profile of the intestinal microbiota and notably increased the abundance of Lactobacillus, thereby facilitating the conversion of tryptophan into indole-3-acetic acid (IAA) and indoleacrylic acid (IA). These indole derivatives activated the aryl hydrocarbon receptor (AhR) pathway, which reduced colonic inflammation and restored the expression of intestinal barrier proteins. Interestingly, the beneficial effects of WJW on gut barrier function improvement and tryptophan metabolism were disappeared in the absence of gut microbiota. Finally, pre-treatment with the AhR antagonist CH-223191 confirmed the essential role of IAA-mediated AhR activation in the therapeutic effects of WJW. Overall, WJW enhanced intestinal barrier function and reduced colonic inflammation in a murine colitis model by modulating Lactobacillus-IAA-AhR signaling pathway. This study provides novel insights into colitis pathogenesis and presents an effective therapeutic and preventive approach against IBD.
RASopathies in Cardiac Disease
Authors: Saravanakkumar Chennappan, Maria Irene Kontaridis
Journal: Annual Review of Medicine (2024)

RASopathies are a group of clinically overlapping autosomal dominant disorders caused primarily by mutations in genes that reside along the canonical Ras–mitogen-activated protein kinase signaling cascade. Though individually rare, collectively, these disorders constitute one of the largest families of congenital disorders worldwide, particularly for infantile hypertrophic cardiomyopathy. Significantly, despite almost five decades of RASopathy research, therapeutic options remain limited and focused primarily on treating symptoms rather than disease etiology. Targeting the genes causal to these disorders, and the nodal pathways critical for their regulation, however, has been challenging. In this review, we highlight these challenges, particularly with respect to congenital heart defects and cardiac diseases and discuss limitations and future directions for approaches to new therapeutic strategies.
Differentiating separate primary lung adenocarcinomas from intrapulmonary metastases with emphasis on pathological and molecular considerations: Recommendations from the IASLC Pathology Committee
Authors: Teh-Ying Chou, Sanja Dacic, Ignacio Wistuba, Mary Beth Beasley, Sabina Berezowska, Yeun-Chung Chang, Jin-Haeng Chung, Casey Connolly, Yuchen Han, Fred R Hirsch, David M Hwang, Andrew Janowczyk, Philippe Joubert, Keith M Kerr, Dongmei Lin, Yuko Minami, Mari Mino-Kenudson, Andrew G Nicholson, Mauro Papotti, Natasha Rekhtman, Anja C Roden, Erik Thunnissen, J H von der Thüsen, William Travis, Ming-Sound Tsao, Yasushi Yatabe, Yi-Chen Yeh, Lukas Bubendorf, Wei-Chin Chang, Valeria Denninghoff, Fabio Rocha Fernandes Tavora, Takuo Hayashi, Paul Hofman, Deepali Jain, Tae-Jung Kim, Sylvie Lantuejoul, John Le Quesne, Fernando Lopez-Rios, Daisuke Matsubara, Masayuki Noguchi, Teodora Radonic, Anjali Saqi, Kurt Schalper, Hyo Sup Shim, Lynette Sholl, Annikka Weissferdt, Wendy A Cooper
Journal: Journal of Thoracic Oncology (2024)
Continuous Glucose Monitoring Access—Addressing Racial and Ethnic Disparities in Diabetes
Authors: Rocio I. Pereira
Journal: JAMA Network Open (2024)

Amisha Wallia, MD, MSCI; Shivani Agarwal, MD, MPH; Andrew L. Owen, PhD, MSc; Emily L. Lam, BA; Ka'Derricka Davis, BS; Stacy C. Bailey, PhD, MPH; Sean E. DeLacey, MD; Allison P. Pack, PhD, MPH; Juan Espinoza, MD; Dana Bright, MSW; Alice Eggleston, PA-C, MPH; Eve Walter, PhD; Matthew J. O'Brien, MD, MSc
Artificial Intelligence Impact on Burnout in Radiologists—Alleviation or Exacerbation?
Authors: Farid Ghareh Mohammadi, Ronnie Sebro
Journal: JAMA Network Open (2024)

Hui Liu, PhD; Ning Ding, MD; Xinying Li, MD; Yunli Chen, MSc; Hao Sun, MD, MPH; Yuanyuan Huang, PhD; Chen Liu, MD; Pengpeng Ye, PhD; Zhengyu Jin, MD; Heling Bao, PhD; Huadan Xue, MD
Targeted Nipple-Areola Complex Reinnervation for Sensory Restoration in Gender-Affirming Mastectomy
Authors: Blair R. Peters
Journal: JAMA Network Open (2024)

Katya Remy, MD; Chase Alston, MHS; Elyse Gonzales, BS; Merel H. J. Hazewinkel, MD; Katherine H. Carruthers, MD; Leslie E. Cohen, MD; Eleanor Tomczyk, MD; Jonathan M. Winograd, MD; William G. Austen Jr, MD; Ian L. Valerio, MD; Lisa Gfrerer, MD, PhD
COVID-19 Pandemic Coping, Social Support, and Emotional Health in American Indian and Alaska Native Peoples
Authors: Cole Haskins, Carolyn Noonan, Ann Collier, Richard MacLehose, Dedra Buchwald, Spero M. Manson
Journal: JAMA Network Open (2024)

Importance The COVID-19 pandemic has placed a burden on the health of many people, including significant disparities in American Indian and Alaska Native communities. Objective This study examines the associations between coping behaviors, social support, and emotional health among American Indian and Alaska Native peoples during the COVID-19 pandemic. Design, Setting, and Participants This cross-sectional study included survey data collected from November 2021 to May 2022 from American Indian and Alaska Native adults aged 18 years or older without dementia or other serious cognitive impairments who were seen at 6 urban health organizations primarily in urban settings (in New Mexico, Alaska, Colorado, Minnesota, Utah, and Kansas) in the year prior to the survey. Exposures Exposures of interest included avoidant and direct problem-solving coping behaviors and functional and emotional social support. Main Outcome and Measures The study outcome was self-reported change in emotional health since COVID-19 pandemic onset. Poisson regression was used to model adjusted multivariate associations. Data were weighted to account for age, nonresponse, and disproportionate representation by clinic population. Results A total of 1164 participants were included in the analysis, with a mean (SD) age of 42.5 (13.4) years; 830 (61%, weighted sample percentage) were female. Since COVID-19 pandemic onset, 465 patients (39% weighted) reported worsened emotional health. Problem-solving coping mean (SD) utilization score was 2.5 (0.5), avoidant coping mean (SD) utilization score was 2.3 (0.5), mean (SD) functional social support score was 11.4 (2.9), and 219 participants (18% weighted) reported that emotional support was always available. Using problem-solving coping skills was associated with better emotional health (adjusted prevalence ratio [APR], 0.66 [95% CI, 0.54-0.81] for highest vs lowest tertile), as was always (vs never or rarely) getting emotional support (APR, 0.40; 95% CI, 0.30-0.55) and having more functional support (APR, 0.90 [95% CI, 0.87-0.92] per 1-unit increase in functional social support). In examination of psychological resilience potentially modifying primary exposure associations, no interactions were statistically significant. Conclusions and Relevance In this cross-sectional study of urban American Indian and Alaska Native peoples, problem-solving coping skills and more social support were associated with better emotional health during the COVID-19 pandemic. These findings can be used to identify strengths-based approaches to support community emotional health during social upheavals.
Medication Exposure and Mortality in Patients With Schizophrenia
Authors: Sébastien Brodeur, Yohann M. Chiu, Josiane Courteau, Marc Dorais, Dominic Oliver, Emmanuel Stip, Marie-Josée Fleury, Marc-André Roy, Alain Vanasse, Alain Lesage, Jacinthe Leclerc
Journal: JAMA Network Open (2024)

Importance The use of antipsychotics, antidepressants, and benzodiazepines may influence the risk of mortality in people with schizophrenia. However, many observational studies have not accounted for immortal time bias (ITB), which occurs when there is a period during which patients in the exposed group are necessarily alive and misclassified as exposed (the period between start of follow-up and initiation of drug). Ignoring ITB may lead to misinterpretation of the association between these drugs and mortality. Objectives To examine whether the cumulative dose of antipsychotics, antidepressants, and benzodiazepines is associated with mortality risk in patients with schizophrenia and discuss the potential impacts of ignoring ITB. Design, Setting, and Participants This cohort study used administrative data from Québec, Canada, including patients aged 17 to 64 years diagnosed with schizophrenia between January 1, 2002, and December 31, 2012. Data analysis was performed from June 22, 2022, to September 30, 2024. Main Outcomes and Measures The primary outcome was all-cause mortality, with follow-up from January 1, 2013, to December 31, 2017, or until death. Mortality risk was assessed for low, moderate, and high exposure to antipsychotics, antidepressants, and benzodiazepines. Cox proportional hazards regression models with time-fixed exposure (not controlling for ITB) and time-dependent exposure (controlling for ITB) were performed. Results The cohort included 32 240 patients (mean [SD] age, 46.1 [11.6] years; 19 776 [61.3%] men), of whom 1941 (6.0%) died during follow-up. No dose-response association was found for antipsychotics with mortality using the time-fixed method. However, high-dose antipsychotic use was associated with increased mortality after correcting for ITB (adjusted hazard ratio [AHR], 1.28; 95% CI, 1.07-1.55; P = .008). Antidepressants showed a reduced mortality risk using the time-fixed method, but only at high doses when correcting for ITB (AHR, 0.86; 95% CI, 0.74-1.00; P = .047). Benzodiazepines were associated with increased mortality risk regardless of the method. Conclusions and Relevance The findings of this study do not dispute the known efficacy of antipsychotics in schizophrenia, but they call into question the magnitude of long-term mortality benefits.
Neonatal Mortality Rates With and Without a Minimum Threshold
Authors: Fu-Wen Liang, Yu-Shan Chang, Chia-Sui Chou, Mei-Jy Jeng, Ichiro Kawachi, Tsung-Hsueh Lu
Journal: JAMA Network Open (2024)

This cross-sectional study examines international variations in neonatal mortality rates with and without minimum thresholds for gestational age (22 weeks) and birth weight (500 g) in 2010 and 2020.
Disparities by Race and Urbanicity in Online Health Care Facility Reviews
Authors: Neil K. R. Sehgal, Anish K. Agarwal, Lauren Southwick, Arthur P. Pelullo, Lyle Ungar, Raina M. Merchant, Sharath Chandra Guntuku
Journal: JAMA Network Open (2024)

Online review platforms offer valuable insights into patient satisfaction and the quality of health care services, capturing content and trends that traditional metrics might miss. The COVID-19 pandemic has disrupted health care services, influencing patient experiences.
Disparities in Continuous Glucose Monitoring Among Patients Receiving Care in Federally Qualified Health Centers
Authors: Amisha Wallia, Shivani Agarwal, Andrew L. Owen, Emily L. Lam, Ka’Derricka Davis, Stacy C. Bailey, Sean E. DeLacey, Allison P. Pack, Juan Espinoza, Dana Bright, Alice Eggleston, Eve Walter, Matthew J. O’Brien
Journal: JAMA Network Open (2024)

Importance While continuous glucose monitoring (CGM) has been found to improve diabetes care processes and outcomes, adoption remains low. Objective To examine the association between CGM prescriptions and individual characteristics among patients with type 1 or 2 diabetes (T1D and T2D, respectively). Design, Setting, and Participants Retrospective cross-sectional study using electronic health record data for patients with T1D or T1D from 275 clinic sites nationwide between January 2014 and February 2021. All participating clinics were federally qualified health centers (FQHCs), the largest US system of primary care for vulnerable populations. Data were analyzed from September 2022 to August 2024. Main Outcomes and Measures Sociodemographic factors, clinical characteristics, and CGM prescription orders. Results A total of 1168 patients with T1D (mean [SD] age, 41.8 [16.0] years; 600 [51.4%] male; 372 [31.9%] Black; 262 [22.4%] Hispanic, and 750 [64.2%] White patients) and 35 216 patients with T2D (mean [SD] age, 58.4 [13.1] years; 19 772 [56.1%] female; 12 030 [34.2%] Black; 12 979 [36.9%] Hispanic, and 20 413 [58.0] White patients) were included. Overall, CGM prescriptions were infrequent (129 [11.0%] for patients with T1D and 362 [1.0%] for those with T2D) but increased throughout the study period. Among patients with T1D, those who reported Hispanic ethnicity (odds ratio [OR], 0.30; 95% CI, 0.16-0.57), Black race (OR, 0.61; 95% CI, 0.38-0.99), or were uninsured (OR, 0.42; 95% CI, 0.23-0.74) had lower multivariable odds of receiving a CGM prescription than White or insured adults, respectively. Similar findings were observed among patients with T2D reporting Hispanic ethnicity (OR, 0.43; 95% CI, 0.32-0.57), Black race (OR, 0.76; 95% CI, 0.59-0.98), or being uninsured (OR, 0.42; 95% CI, 0.31-0.58), relative to their counterparts. Among patients with T2D, hemoglobin A 1c values higher than 9.0% (OR, 3.17; 95% CI, 2.37-4.21) and a greater burden of diabetes complications were associated with higher odds of CGM prescription. Conclusions and Relevance In this cross-sectional study of electronic health record data, rates of CGM prescription orders were low among FQHC patients with T1D and T2D. Disparities in CGM orders were observed among patients reporting Hispanic ethnicity, Black race, and those who lacked health insurance. Future research is needed to understand the causes of infrequent CGM orders in FQHCs and drivers of observed disparities in this vulnerable patient population.
Toward Disaggregated Racial and Ethnic Data in Early-Onset Colorectal Cancer
Authors: Clare E. Jacobson, Pasithorn A. Suwanabol
Journal: JAMA Network Open (2024)

Joshua Demb, PhD; Scarlett L. Gomez, PhD; Alison J. Canchola, MS; Alexander Qian, MD; James D. Murphy, MD; Robert A. Winn, MD; Matthew P. Banegas, PhD; Samir Gupta, MD, MSCS; Maria Elena Martinez, PhD
Data Equity and Multiracial and Multiethnic Communities
Authors: Stella S. Yi
Journal: JAMA Network Open (2024)

Camillia K. Lui, PhD; Yu Ye, MA; Joyce Gee, PhD; Won Kim Cook, PhD; Christina C. Tam, PhD; Sicong Sun, PhD, MSW; Regina Miranda, PhD; Andrew Subica, PhD; Nina Mulia, DrPH
Artificial Intelligence and Radiologist Burnout
Authors: Hui Liu, Ning Ding, Xinying Li, Yunli Chen, Hao Sun, Yuanyuan Huang, Chen Liu, Pengpeng Ye, Zhengyu Jin, Heling Bao, Huadan Xue
Journal: JAMA Network Open (2024)

IMPORTANCE Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI. OBJECTIVE To estimate the association between AI use in radiology and radiologist burnout. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study conducted a questionnaire survey between May and October 2023, using the national quality control system of radiology in China. Participants included radiologists from 1143 hospitals. Radiologists reporting regular or consistent AI use were categorized as the AI group. Statistical analysis was performed from October 2023 to May 2024. EXPOSURE AI use in radiology practice. MAIN OUTCOMES AND MEASURES Burnout was defined by emotional exhaustion (EE) or depersonalization according to the Maslach Burnout Inventory. Workload was assessed based on working hours, number of image interpretations, hospital level, device type, and role in the workflow. AI acceptance was determined via latent class analysis considering AI-related knowledge, attitude, confidence, and intention. Propensity score–based mixed-effect generalized linear logistic regression was used to estimate the associations between AI use and burnout and its components. Interactions of AI use, workload, and AI acceptance were assessed on additive and multiplicative scales. RESULTS Among 6726 radiologists included in this study, 2376 (35.3%) were female and 4350 (64.7%) were male; the median (IQR) age was 41 (34-48) years; 3017 were in the AI group (1134 [37.6%] female; median [IQR] age, 40 [33-47] years) and 3709 in the non-AI group (1242 [33.5%] female; median [IQR] age, 42 [34-49] years). The weighted prevalence of burnout was significantly higher in the AI group compared with the non-AI group (40.9% vs 38.6%; P &amp;amp;lt; .001). After adjusting for covariates, AI use was significantly associated with increased odds of burnout (odds ratio [OR], 1.20; 95% CI, 1.10-1.30), primarily driven by its association with EE (OR, 1.21; 95% CI, 1.10-1.34). A dose-response association was observed between the frequency of AI use and burnout ( P for trend &amp;amp;lt; .001). The associations were more pronounced among radiologists with high workload and lower AI acceptance. A significant negative interaction was noted between high AI acceptance and AI use. CONCLUSIONS AND RELEVANCE In this cross-sectional study of radiologist burnout, frequent AI use was associated with an increased risk of radiologist burnout, particularly among those with high workload or lower AI acceptance. Further longitudinal studies are needed to provide more evidence.