Explore the vast range of titles available.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self‐reported outcomes. In this mini‐review, we propose a device‐agnostic multi‐domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health‐related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high‐resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi‐institutional pre‐competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient‐centric approach to trial design. Sensor‐based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single‐arm, open‐label interventional trial of long‐acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described. Participants will complete twice‐daily mSpirometry at home and clinic spirometry during weekly visits, alongside continuous use of a wrist‐worn wearable and regular completion of several diaries capturing asthma symptoms as well as participant‐ and site‐reported satisfaction and ease of use of mSpirometry. The co‐primary objectives of this study are (A) to quantify the treatment effect of LABA therapy among participants with moderate asthma, using both clinical spirometry (FEV1 c ) and mSpirometry (FEV1 m ); and (B) to investigate whether FEV1 m is as accurate as FEV1 c in detecting the treatment effect using a mixed‐effect model for repeated measures. Study results will help inform whether the deployment of mSpirometry and a wrist‐worn wearable for remote data collection are feasible in a multicenter setting among participants with moderate asthma, which may then be generalizable to other populations with respiratory disease.

Background: The Extension for Community Health Outcomes (ECHO) Autism Program trains clinicians to screen, diagnose, and care for children with autism spectrum disorder (ASD) in primary care settings. This study will assess the feasibility and impact of integrating an artificial intelligence (AI)–based ASD diagnosis aid (the device) into the existing ECHO Autism Screening Tool for Autism in Toddlers and Young Children (STAT) diagnosis model. The prescription-only Software as a Medical Device, designed for use in children aged 18 to 72 months at risk for developmental delay, produces ASD diagnostic recommendations after analyzing behavioral features from 3 distinct inputs: a caregiver questionnaire, 2 short home videos analyzed by trained video analysts, and a health care provider questionnaire. The device is not a stand-alone diagnostic and should be used in conjunction with clinical judgment. Objective: This study aims to assess the feasibility and impact of integrating an AI-based ASD diagnosis aid into the ECHO Autism STAT diagnosis model. The time from initial ECHO Autism clinician concern to ASD diagnosis is the primary end point. Secondary end points include the time from initial caregiver concern to ASD diagnosis, time from diagnosis to treatment initiation, and clinician and caregiver experience of device use as part of the ASD diagnostic journey. Methods: Research participants for this prospective observational study will be patients suspected of having ASD (aged 18-72 months) and their caregivers and up to 15 trained ECHO Autism clinicians recruited by the ECHO Autism Communities research team from across rural and suburban areas of the United States. Clinicians will provide routine clinical care and conduct best practice ECHO Autism diagnostic evaluations in addition to prescribing the device. Outcome data will be collected via a combination of electronic questionnaires, reviews of standard clinical care records, and analysis of device outputs. The expected study duration is no more than 12 months. The study was approved by the institutional review board of the University of Missouri-Columbia (institutional review board–assigned project number 2075722). Results: Participant recruitment began in April 2022. As of June 2022, a total of 41 participants have been enrolled. Conclusions: This prospective observational study will be the first to evaluate the use of a novel AI-based ASD diagnosis aid as part of a real-world primary care diagnostic pathway. If device integration into primary care proves feasible and efficacious, prolonged delays between the first ASD concern and eventual diagnosis may be reduced. Streamlining primary care ASD diagnosis could potentially reduce the strain on specialty services and allow a greater proportion of children to commence early intervention during a critical neurodevelopmental window. Trial Registration: ClinicalTrials.gov NCT05223374; https://clinicaltrials.gov/ct2/show/NCT05223374 International Registered Report Identifier (IRRID): PRR1-10.2196/37576

Circulating tumor cells are cells that have shed from the primary tumor into the vasculature. They can be heterogeneous from the primary tumor as well as within the CTC population and are of prognostic value in metastatic disease. The ability to expand these cells in a closed and automated system has the potential be useful for establishing effective treatment strategies. Using an established breast cancer cell line, CAMA, a protocol has been developed, to expand these cells using a hollow-fiber bioreactor system. CAMA cells can successfully expanded inside the Quantum system with an average of a 50 fold increase in 4.7 weeks in culture. There was a difference in viability between tissue culture plastic and the cells grown inside the Quantum (average of 83.9% SD 1.2% vs. 91.2% SD 2.3% respectively), which was significant (t =4.7, p=.01). There was no statistical significant difference for surface marker expression. Expansion of rare cell populations such as CTCs has the potential to be a valuable tool for testing the effectiveness of chemotherapy treatments. This aspect is critical when looking ahead to possible clinical or diagnostic use. Adjuvant therapies targeting circulating tumor cells are critical when looking at true remission, particularly for breast cancer patients. An established circulating tumor breast cancer cell line was used to test a combination of UV light and riboflavin at various intensities to see if cells could be rendered replication incompetent, while still retaining cell structure and surface marker expression. No cell proliferation was detected after 48 hours after illumination. The results also suggest that not all intensities are equal with respect to sustained viability readings (F = 54.7, p-value= 0.0001) and sustained surface marker expression readings (F =115.1, p-value= 0.0001). All intensities show a downward trend over time for viability and surface marker expression. The ability to retain surface marker expression while halting proliferation, holds promise in autologous therapy.

Abstract Background: Insufficient sleep is widespread among adolescents and has consequences that extend far beyond hampering day-to-day functioning. It may influence eating and physical activity patterns and be an important determinant of adolescent overweight/obesity status. Methods: We assessed how self-reported sleep duration on school nights was associated with weight-related behaviors (eating, diet, and physical activity) and overweight/obesity at the baseline wave (ninth grade year) of the START study (n = 2134). Results: Fifteen percent of our sample reported optimal sleep duration (8.5–10.0 hours); nonwhites, participants of lower socioeconomic status, and girls were at greater risk for insufficient sleep. Suboptimal sleep was associated with various poor weight-related behaviors such as increased sugar-sweetened beverage consumption, decreased vegetable consumption, and decreased breakfast eating (p < 0.001). Fewer hours of sleep were also associated with less physical activity and an increased likelihood of obesity (p = 0.02 for both associations). Conclusions: The influence of adolescent sleep insufficiency on diet and activity could impact childhood obesity and following chronic disease risk especially if lack of sleep sets the stage for enduring, lifelong, poor, weight-related behavior patterns.

In the United States, more than 50% of cervical cancers are diagnosed in underscreened women. Cervical cancer screening guidelines now include primary human papillomavirus (HPV) testing as a recommended strategy. Home-based HPV self-sampling is a viable option for increasing screening compliance and effectiveness; however, US data are needed to inform health care system implementation. To evaluate effectiveness of mailed HPV self-sampling kits vs usual care reminders for in-clinic screening to increase detection and treatment of cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and uptake of cervical cancer screening. Randomized clinical trial conducted in Kaiser Permanente Washington, a US integrated health care delivery system. Women aged 30 to 64 years with health plan enrollment for 3 years and 5 months or more, a primary care clinician, no Papanicolaou test within 3 years and 5 months, and no hysterectomy were identified through electronic medical records and enrolled from February 25, 2014, to August 29, 2016, with follow-up through February 26, 2018. The control group received usual care (annual patient reminders and ad hoc outreach from primary care clinics). The intervention group received usual care plus a mailed HPV self-sampling kit. Two primary outcomes were (1) CIN2+ detection within 6 months of screening and (2) treatment within 6 months of CIN2+ detection. Screening uptake within 6 months of randomization was a secondary outcome. A total of 19 851 women (mean [SD] age, 50.1 [9.5] years) were included, with 9960 randomized to the intervention group and 9891 randomized to the control group. All women randomized were included in analysis. In the intervention group, 12 participants with CIN2+ were detected compared with 8 in the control group (relative risk, 1.49; 95% CI, 0.61-3.64) and 12 cases were treated vs 7 in the control group (relative risk, 1.70; 95% CI, 0.67-4.32). Screening uptake was higher in the intervention group (2618 participants [26.3%] vs 1719 participants [17.4%]; relative risk, 1.51; 95% CI, 1.43-1.60). Mailing HPV kits to underscreened women increased screening uptake compared with usual care alone, with no significant differences in precancer detection or treatment. Results support the feasibility of mailing HPV kits to women who are overdue for screening as an outreach strategy to increase screening uptake in US health care systems. Efforts to increase kit uptake and follow-up of positive results are warranted to maximize detection and treatment of CIN2+. ClinicalTrials.gov identifier: NCT02005510.

A novel framework for assessing the sustainability of wild species use is presented to address weaknesses in current formulations and support global and national policies relating to sustainable use. The novelty of the framework is its addition of animal health and welfare and human health to the conventional ecological, social, and economic dimensions of sustainability. The five-dimensional sustainability assessment framework (5DSAF) consists of 42 principles, which have been derived from an analysis and synthesis of existing international, national, sectoral and species-specific guidelines and standards. It can be applied by use of an Excel-based tool that allows scores to be allocated to each principle with results graphically displayed in the form of a radar chart. The 5DSAF has been successfully piloted as a self-assessment tool at industry and enterprise level and has the potential to evolve into a universal standard that government, private sector, and civil society actors could use to assess the sustainability, legality, and safety of all value chains for wild species and products.

Rationale: Intrinsic brain tumors, such as gliomas are largely resistant to immunotherapies including immune checkpoint blockade. Adoptive cell therapies (ACT) including chimeric antigen receptor (CAR) or T cell receptor (TCR)-transgenic T cell therapy targeting glioma-associated antigens are an emerging field in glioma immunotherapy. However, imaging techniques for non-invasive monitoring of adoptively transferred T cells homing to the glioma microenvironment are currently lacking. Methods: Ultrasmall iron oxide nanoparticles (NP) can be visualized non-invasively by magnetic resonance imaging (MRI) and dedicated MRI sequences such as T2* mapping. Here, we develop a protocol for efficient ex vivo labeling of murine and human TCR-transgenic and CAR T cells with iron oxide NPs. We assess labeling efficiency and T cell functionality by flow cytometry and transmission electron microscopy (TEM). NP labeled T cells are visualized by MRI at 9.4 T in vivo after adoptive T cell transfer and correlated with 3D models of cleared brains obtained by light sheet microscopy (LSM). Results: NP are incorporated into T cells in subcellular cytoplasmic vesicles with high labeling efficiency without interfering with T cell viability, proliferation and effector function as assessed by cytokine secretion and antigen-specific killing assays in vitro. We further demonstrate that adoptively transferred T cells can be longitudinally monitored intratumorally by high field MRI at 9.4 Tesla in a murine glioma model with high sensitivity. We find that T cell influx and homogenous spatial distribution of T cells within the TME as assessed by T2* imaging predicts tumor response to ACT whereas incomplete T cell coverage results in treatment resistance. Conclusion: This study showcases a rational for monitoring adoptive T cell therapies non-invasively by iron oxide NP in gliomas to track intratumoral T cell influx and ultimately predict treatment outcome.Rationale: Intrinsic brain tumors, such as gliomas are largely resistant to immunotherapies including immune checkpoint blockade. Adoptive cell therapies (ACT) including chimeric antigen receptor (CAR) or T cell receptor (TCR)-transgenic T cell therapy targeting glioma-associated antigens are an emerging field in glioma immunotherapy. However, imaging techniques for non-invasive monitoring of adoptively transferred T cells homing to the glioma microenvironment are currently lacking. Methods: Ultrasmall iron oxide nanoparticles (NP) can be visualized non-invasively by magnetic resonance imaging (MRI) and dedicated MRI sequences such as T2* mapping. Here, we develop a protocol for efficient ex vivo labeling of murine and human TCR-transgenic and CAR T cells with iron oxide NPs. We assess labeling efficiency and T cell functionality by flow cytometry and transmission electron microscopy (TEM). NP labeled T cells are visualized by MRI at 9.4 T in vivo after adoptive T cell transfer and correlated with 3D models of cleared brains obtained by light sheet microscopy (LSM). Results: NP are incorporated into T cells in subcellular cytoplasmic vesicles with high labeling efficiency without interfering with T cell viability, proliferation and effector function as assessed by cytokine secretion and antigen-specific killing assays in vitro. We further demonstrate that adoptively transferred T cells can be longitudinally monitored intratumorally by high field MRI at 9.4 Tesla in a murine glioma model with high sensitivity. We find that T cell influx and homogenous spatial distribution of T cells within the TME as assessed by T2* imaging predicts tumor response to ACT whereas incomplete T cell coverage results in treatment resistance. Conclusion: This study showcases a rational for monitoring adoptive T cell therapies non-invasively by iron oxide NP in gliomas to track intratumoral T cell influx and ultimately predict treatment outcome.