tbuckley/cabot-search huggingface dataset:
Why digital innovation may not reduce healthcare’s environmental footprint
Authors: Gabrielle Samuel, Geoffrey M Anderson, Federica Lucivero, Anneke Lucassen
Journal: BMJ (2024)

Digital innovations come with their own environmental cost and should not be seen as a simple fix for healthcare emissions, argue Gabrielle Samuel and colleagues Healthcare is becoming increasingly digitalised through innovations in information and communication technologies as well as advances in machine learning and artificial intelligence (AI).1 Advocates enthuse that this digitalisation—including monitoring devices, streaming, and data storage—will improve key aspects of healthcare delivery such as safety, accessibility, quality of care, effectiveness, and efficiency.2 Others debate whether these promises can be met because of complex social, cultural, economic, and political implementation challenges.3 More recently, digital innovation has been promoted as a means to reduce the environmental harms associated with healthcare delivery.4 Healthcare systems contribute to roughly 5% of a country's total greenhouse gas emissions, with this figure often being higher in high income countries.5 Although digitalisation can reduce environmental harms, technologies could also be implemented in ways that do not lead to reductions. Indeed, given the paradoxical increase in energy use associated with the introduction of energy saving technologies—the so called rebound effect—digital innovation may increase resource use with little change to health outcomes. Digital innovations have the potential to decrease the environmental harm from health systems in several ways (box 1). First, digital innovations are expected to help reduce the greenhouse gas emissions associated with existing healthcare facilities by improving their efficiency. In the UK the NHS has predicted carbon savings through the use of realtime monitoring, including artificial intelligence, to better control buildings (eg, lights, heating, and cooling) and to forecast resource allocation more effectively.6 Use of digital technologies to predict electricity and water consumption across various healthcare facilities has allowed hospital managers to identify variation in usage and deal with the causes.7 Box 1 ### How digital technologies might reduce the environmental harms of healthcare #### Improving the operational efficiency of existing healthcare infrastructureRETURN TO TEXT
Time to Think About Myocardial Infarction in Acute Ischemic Stroke
Authors: Alison Seitz, Alexander E. Merkler
Journal: JAMA Neurology (2024)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Neurology HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2024 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Neurology journal
COVID-19 in Nursing Homes—Learning the Hard Way
Authors: James S. Goodwin, Huiwen Xu
Journal: JAMA Internal Medicine (2024)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2024 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
Can I have a side hustle as a doctor?
Authors: Abi Rimmer
Journal: BMJ (2024)

Can I have a side hustle as a doctor?There are ways that doctors can use their skills outside of medicine without compromising their professional ethics, Abi Rimmer hears Abi Rimmer Act with honesty and professionalismEllie Mein, Medical Defence Union medicolegal adviser, says, "MDU hears from many doctors who are considering additional ways to generate income to counter the cost-of-living crisis.Examples include working as a social media influencer, carrying out cosmetic procedures, and producing educational material.
Private Equity in Healthcare—Safeguarding Patient Care
Authors: Jason E. Liebowitz
Journal: JAMA Internal Medicine (2024)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2024 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
Type 1 Myocardial Infarction in Patients With Acute Ischemic Stroke
Authors: Christian H. Nolte, Regina von Rennenberg, Simon Litmeier, David M. Leistner, Kristina Szabo, Stefan Baumann, Annerose Mengel, Dominik Michalski, Timo Siepmann, Stephan Blankenberg, Gabor C. Petzold, Martin Dichgans, Hugo Katus, Burkert Pieske, Vera Regitz-Zagrosek, Tim Bastian Braemswig, Ida Rangus, Amra Pepic, Eik Vettorazzi, Andreas M. Zeiher, Jan F. Scheitz, Karl Wegscheider, Ulf Landmesser, Matthias Endres
Journal: JAMA Neurology (2024)

Elevated values of high-sensitivity cardiac troponin (hs-cTn) are common in patients with acute ischemic stroke and are associated with poor prognosis. However, diagnostic and therapeutic implications in patients with ischemic stroke remain unclear.
Mitochondrial metabolism blockade nanoadjuvant reversed immune-resistance microenvironment to sensitize albumin-bound paclitaxel-based chemo-immunotherapy
Authors: Zaigang Zhou, Wenjuan Luo, Chunjuan Zheng, Haoxiang Wang, Rui Hu, Hui Deng, Jianliang Shen
Journal: Acta Pharmaceutica Sinica B (2024)

Currently, the efficacy of albumin-bound paclitaxel (PTX@Alb) is still limited due to the impaired PTX@Alb accumulation in tumors partly mediated by the dense collagen distribution. Meanwhile, acquired immune resistance always occurs due to the enhanced programmed cell death-ligand 1 (PD-L1) expression after PTX@Alb treatment, which then leads to immune tolerance. To fill these gaps, we newly revealed that tamoxifen (TAM), a clinically widely used adjuvant therapy for breast cancer with mitochondrial metabolism blockade capacity, could also be used as a novel effective PD-L1 and TGF-
The effectiveness of digital twins in promoting precision health across the entire population: a systematic review
Authors: Mei-di Shen, Si-bing Chen, Xiang-dong Ding
Journal: npj Digital Medicine (2024)

Abstract Digital twins represent a promising technology within the domain of precision healthcare, offering significant prospects for individualized medical interventions. Existing systematic reviews, however, mainly focus on the technological dimensions of digital twins, with a limited exploration of their impact on health-related outcomes. Therefore, this systematic review aims to explore the efficacy of digital twins in improving precision healthcare at the population level. The literature search for this study encompassed PubMed, Embase, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, and Wanfang Database to retrieve potentially relevant records. Patient health-related outcomes were synthesized employing quantitative content analysis, whereas the Joanna Briggs Institute (JBI) scales were used to evaluate the quality and potential bias inherent in each selected study. Following established inclusion and exclusion criteria, 12 studies were screened from an initial 1321 records for further analysis. These studies included patients with various conditions, including cancers, type 2 diabetes, multiple sclerosis, heart failure, qi deficiency, post-hepatectomy liver failure, and dental issues. The review coded three types of interventions: personalized health management, precision individual therapy effects, and predicting individual risk, leading to a total of 45 outcomes being measured. The collective effectiveness of these outcomes at the population level was calculated at 80% (36 out of 45). No studies exhibited unacceptable differences in quality. Overall, employing digital twins in precision health demonstrates practical advantages, warranting its expanded use to facilitate the transition from the development phase to broad application. PROSPERO registry: CRD42024507256.
Discovery and evaluation of a novel 18F-labeled vasopressin 1a receptor PET ligand with peripheral binding specificity
Authors: Junqi Hu, Yinlong Li, Chenchen Dong, Huiyi Wei, Kai Liao, Junjie Wei, Chunyu Zhao, Ahmad Chaudhary, Jiahui Chen, Hao Xu, Ke Zhong, Steven H. Liang, Lu Wang, Weijian Ye
Journal: Acta Pharmaceutica Sinica B (2024)

The arginine-vasopressin (AVP) hormone plays a pivotal role in regulating various physiological processes, such as hormone secretion, cardiovascular modulation, and social behavior. Recent studies have highlighted the V1a receptor as a promising therapeutic target. In-depth insights into V1a receptor-related pathologies, attained through
The Balance Between Shear Flow and Extracellular Matrix in Ovarian Cancer‐on‐chip
Authors: Changchong Chen, Alphonse Boché, Zixu Wang, Elliot Lopez, Juan Peng, Franck Carreiras, Marie‐Claire Schanne‐Klein, Yong Chen, Ambroise Lambert, Carole Aimé
Journal: Advanced Healthcare Materials (2024)

Ovarian cancer is the most lethal gynecologic cancer in developed countries. In the tumor microenvironment, the extracellular matrix (ECM) and flow shear stress are key players in directing ovarian cancer cells invasion. Artificial ECM models based only on ECM proteins are used to build an ovarian tumor-on-chip to decipher the crosstalk between ECM and shear stress on the migratory behavior and cellular heterogeneity of ovarian tumor cells. This work shows that in the shear stress regime of the peritoneal cavity, the ECM plays a major role in driving individual or collective ovarian tumor cells migration. In the presence of basement membrane proteins, migration is more collective than on type I collagen regardless of shear stress. With increasing shear stress, individual cell migration is enhanced; while, no significant impact on collective migration is measured. This highlights the central position that ECM and flow shear stress should hold in in vitro ovarian cancer models to deepen understanding of cellular responses and improve development of ovarian cancer therapeutic platforms. In this frame, adding flow provides significant improvement in biological relevance over the authors' previous work. Further steps for enhanced clinical relevance require not only multiple cell lines but also patient-derived cells and sera.
Ten-Year Outcomes Following Roux-en-Y Gastric Bypass vs Duodenal Switch for High Body Mass Index
Authors: Odd Bjørn Kjeldaas Salte, Torsten Olbers, Hilde Risstad, Morten Wang Fagerland, Torgeir Thorson Søvik, Ingvild Kristine Blom-Høgestøl, Jon A. Kristinsson, My Engström, Tom Mala
Journal: JAMA Network Open (2024)

Results from long-term follow-up after biliopancreatic diversion with duodenal switch (DS) are scarce.
Enhancing cancer immunotherapy: Nanotechnology-mediated immunotherapy overcoming immunosuppression
Authors: Yunna Chen, Qianqian Zhou, Zongfang Jia, Nuo Cheng, Sheng Zhang, Weidong Chen, Lei Wang
Journal: Acta Pharmaceutica Sinica B (2024)

Immunotherapy is an important cancer treatment method that offers hope for curing cancer patients. While immunotherapy has achieved initial success, a major obstacle to its widespread adoption is the inability to benefit the majority of patients. The success or failure of immunotherapy is closely linked to the tumor's immune microenvironment. Recently, there has been significant attention on strategies to regulate the tumor immune microenvironment in order to stimulate anti-tumor immune responses in cancer immunotherapy. The distinctive physical properties and design flexibility of nanomedicines have been extensively utilized to target immune cells (including tumor-associated macrophages (TAMs), T cells, myeloid-derived suppressor cells (MDSCs), and tumor-associated fibroblasts (TAFs)), offering promising advancements in cancer immunotherapy. In this article, we have reviewed treatment strategies aimed at targeting various immune cells to regulate the tumor immune microenvironment. The focus is on cancer immunotherapy models that are based on nanomedicines, with the goal of inducing or enhancing anti-tumor immune responses to improve immunotherapy. It is worth noting that combining cancer immunotherapy with other treatments, such as chemotherapy, radiotherapy, and photodynamic therapy, can maximize the therapeutic effects. Finally, we have identified the challenges that nanotechnology-mediated immunotherapy needs to overcome in order to design more effective nanosystems.
Raman-based machine learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma
Authors: Adrian Lita, Joel Sjöberg, David Păcioianu, Nicoleta Siminea, Orieta Celiku, Tyrone Dowdy, Andrei Păun, Mark R Gilbert, Houtan Noushmehr, Ion Petre, Mioara Larion
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. METHODS Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. RESULTS Here we develop APOLLO (rAman-based PathOLogy of maLignant glioma) – a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wildtype (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. CONCLUSIONS Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.
Why Should the FDA Focus on Pragmatic Clinical Research?
Authors: Ali B. Abbasi, Lesley H. Curtis, Robert M. Califf
Journal: JAMA (2024)

This Viewpoint from the FDA discusses how pragmatic clinical research—assessment that uses real-world data, often in combination with research data, after initial marketing approval—can help in evaluation of new technologies, benefit research sites in underresourced settings, and better inform regulatory decisions and clinical practice.
Serum Markers of Neurodegeneration Are Strongly Linked to Heart Failure Severity and Outcome
Authors: Raphael Wurm, Suriya Prausmüller, Markus Ponleitner, Georg Spinka, Annika Weidenhammer, Henrike Arfsten, Gregor Heitzinger, Noel Gilian Panagiotides, Guido Strunk, Philipp Bartko, Georg Goliasch, Elisabeth Stögmann, Christian Hengstenberg, Martin Hülsmann, Noemi Pavo
Journal: JACC Heart Failure (2024)
Global and Regional Cardiovascular Mortality Attributable to Nonoptimal Temperatures Over Time
Authors: Samuel Hundessa, Wenzhong Huang, Qi Zhao, Yao Wu, Bo Wen, Barrak Alahmad, Ben Armstrong, Antonio Gasparrini, Francesco Sera, Shilu Tong, Joana Madureira, Jan Kyselý, Joel Schwartz, Ana Maria Vicedo-Cabrera, Simon Hales, Amanda Johnson, Shanshan Li, Yuming Guo, Jouni J.K. Jaakkola, Niilo Ryti, Aleš Urban, Aurelio Tobias, Dominic Royé, Eric Lavigne, Martina S. Ragettli, Christofer Åström, Raanan Raz, Mathilde Pascal, Haidong Kan, Patrick Goodman, Ariana Zeka, Masahiro Hashizume, Magali Hurtado Diaz, Xerxes Seposo, Baltazar Nunes, Ho Kim, Whanhee Lee, Carmen Íñiguez, Yue Leon Guo, Shih-Chun Pan, Antonella Zanobetti, Tran Ngoc Dang, Do Van Dung, Alexandra Schneider, Alireza Entezari, Antonis Analitis, Bertil Forsberg, Caroline Ameling, Danny Houthuijs, Ene Indermitte, Fatemeh Mayvaneh, Fiorella Acquaotta, Francesca de'Donato, Gabriel Carrasco-Escobar, Hans Orru, Klea Katsouyanni, Micheline de Sousa Zanotti Stagliorio Coelho, Nicolás Valdés Ortega, Noah Scovronick, Paola Michelozzi, Patricia Matus Correa, Paulo Hilario Nascimento Saldiva, Rosana Abrutzky, Samuel Osorio, Valentina Colistro, Veronika Huber, Yasushi Honda, Yoonhee Kim, Michelle Bell, Rongbin Xu, Zhengyu Yang, Hematollah Roradeh, Eunice Elizabeth Félix Arellano, Shilpa Rao, Paul Lester Carlos Chua, Susana das Neves Pereira da Silva, Susana das Neves Pereira da Silva, César De la Cruz Valencia
Journal: Journal of the American College of Cardiology (2024)
AI/ML-derived mechanistically interpretable whole-genome biomarkers of patient survival in pre-treatment primary neuroblastoma tumors and whole blood.
Authors: Orly Alter, Elizabeth Newman, Sri Priya Ponnapalli, Jessica W. Tsai
Journal: Journal of Clinical Oncology (2024)

10043 Background: Prediction, understanding, and management, of neuroblastoma (NBL) outcomes, from spontaneous regression to relapse and death, remain limited, and rely mostly on the International Neuroblastoma Staging System (INSS) stage, age, and the one-gene test for MYCN amplification. The entire multi-ome affects every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) cannot discover effective biomarkers from real-life, small-cohort, multi-omic, and noisy data. Methods: We use our AI/ML to identify two whole-genome biomarkers from open-source pre-treatment NBL patient-matched primary tumor and whole blood DNA copy numbers, and primary tumor mRNA expression. Our data-agnostic, unsupervised algorithms extend the mathematics that underlies quantum mechanics to overcome the limitations of typical AI/ML [1,2]. Our platform- and reference genome-agnostic biomarkers in astrocytoma, including glioblastoma, and lung, ovarian, and uterine adenocarcinomas, were mathematically discovered, and computationally and experimentally validated, repeatedly, in federated, imbalanced datasets from as few as 50–100 patients [3]. Results: We discover the biomarkers in “skinny” datasets of minimally preprocessed ≈3M-bin whole-genome sequences from tumors and blood of a set of 101 patients. Blindly, i.e., label-free, we separate both biomarkers from the normal X chromosome-number variation, demonstrating sex-agnostic learning. Combined, the biomarkers are statistically independent of all standard-of-care indicators, with the univariate Cox hazard ratio of 4.0 (log-rank P-value=2.3×10 –5 ) within the 95% confidence intervals of the bivariate ratios. The risk that the tumor’s whole genome confers upon outcome, as is reflected by the bivariate ratios, is greater than that conferred by all standard-of-care indicators, including DNA ploidy, the Children’s Oncology Group (COG) risk, and the mitosis-karyorrhexis index (MKI), except for histopathology. We validate the biomarkers in ≈10K-bin target-capture sequences of a mutually-exclusive set of 419 patients, demonstrating generalizability as well as site-, platform-, and protocol-agnostic transfer learning. At 73–80% concordance, in both the tumor and blood profiles of both the discovery and validation sets, the biomarkers combined are more accurate than MYCN. We show that the biomarkers identify known and previously unrecognized disease mechanisms and druggable gene alterations, including co-amplification of MYCN with genes encoding for extra-embryonic transcripts, as well as the hijacking of embryonic development toward aneuploidy, which can spontaneously regress via embryonic self-correction. Conclusions: Our AI/ML is uniquely suited for personalized medicine. 1. doi: 10.1063/1.5099268 2. doi: 10.1073/pnas.0530258100 3. doi: 10.1063/1.5142559.