Alteration of cystic airway mesenchyme in congenital pulmonary airway malformation.

Congenital pulmonary airway malformation (CPAM) is the most common congenital lesion detected in the neonatal lung, which may lead to respiratory distress, infection, and pneumothorax. CPAM is thought to result from abnormal branching morphogenesis during fetal lung development, arising from different locations within the developing respiratory tract. However, the pathogenic mechanisms are unknown, and previous studies have focused on abnormalities in airway epithelial cells. We have analyzed 13 excised lung specimens from infants (age < 1 year) with a confirmed diagnosis of type 2 CPAM, which is supposed to be derived from abnormal growth of intrapulmonary distal airways. By examining the mesenchymal components including smooth muscle cells, laminin, and elastin in airway and cystic walls using immunofluorescence staining, we found that the thickness and area of the smooth muscle layer underlining the airway cysts in these CPAM tissue sections were significantly decreased compared with those in bronchiolar walls of normal controls. Extracellular elastin fibers were also visually reduced or absent in airway cystic walls. In particular, a layer of elastin fibers seen in normal lung between airway epithelia and underlying smooth muscle cells was missing in type 2 CPAM samples. Thus, our data demonstrate for the first time that airway cystic lesions in type 2 CPAM occur not only in airway epithelial cells, but also in adjacent mesenchymal tissues, including airway smooth muscle cells and their extracellular protein products. This provides a new direction to study the molecular and cellular mechanisms of CPAM pathogenesis in human

Oromotor variability in children with mild spastic cerebral palsy: a kinematic study of speech motor control

<p>Abstract</p> <p>Background</p> <p>Treating motor speech dysfunction in children with CP requires an understanding of the mechanism underlying speech motor control. However, there is a lack of literature in quantitative measures of motor control, which may potentially characterize the nature of the speech impairments in these children. This study investigated speech motor control in children with cerebral palsy (CP) using kinematic analysis.</p> <p>Methods</p> <p>We collected 10 children with mild spastic CP, aged 4.8 to 7.5 years, and 10 age-matched children with typical development (TD) from rehabilitation department at a tertiary hospital. All children underwent analysis of percentage of consonants correct (PCC) and kinematic analysis of speech tasks: poly-syllable (PS) and mono-syllable (MS) tasks using the Vicon Motion 370 system integrated with a digital camcorder. Kinematic parameters included spatiotemporal indexes (STIs), and average values and coefficients of variation (CVs) of utterance duration, peak oral opening displacement and velocity. An ANOVA was conducted to determine whether PCC and kinematic data significantly differed between groups.</p> <p>Results</p> <p>CP group had relatively lower PCCs (80.0-99.0%) than TD group (<it>p </it>= 0.039). CP group had higher STIs in PS speech tasks, but not in MS tasks, than TD group did (<it>p </it>= 0.001). The CVs of utterance duration for MS and PS tasks of children with CP were at least three times as large as those of TD children (<it>p </it>< 0.01). However, average values of utterance duration, peak oral opening displacement and velocity and CVs of other kinematic data for both tasks did not significantly differ between two groups.</p> <p>Conclusion</p> <p>High STI values and high variability on utterance durations in children with CP reflect deficits in relative spatial and/or especially temporal control for speech in the CP participants compared to the TD participants. Children with mild spastic CP may have more difficulty in processing increased articulatory demands and resulted in greater oromotor variability than normal children. The kinematic data such as STIs can be used as indices for detection of speech motor control impairments in children with mild CP and assessment of the effectiveness in the treatment.</p

Mobile digital education for health professions: Systematic review and meta-analysis by the Digital Health Education Collaboration

Background: There is a pressing need to implement efficient and cost-effective training to address the worldwide shortage of health professionals. Mobile digital education (mLearning) has been mooted as a potential solution to increase the delivery of health professions education as it offers the opportunity for wide access at low cost and flexibility with the portability of mobile devices. To better inform policy making, we need to determine the effectiveness of mLearning. Objective: The primary objective of this review was to evaluate the effectiveness of mLearning interventions for delivering health professions education in terms of learners’ knowledge, skills, attitudes, and satisfaction. Methods: We performed a systematic review of the effectiveness of mLearning in health professions education using standard Cochrane methodology. We searched 7 major bibliographic databases from January 1990 to August 2017 and included randomized controlled trials (RCTs) or cluster RCTs. Results: A total of 29 studies, including 3175 learners, met the inclusion criteria. A total of 25 studies were RCTs and 4 were cluster RCTs. Interventions comprised tablet or smartphone apps, personal digital assistants, basic mobile phones, iPods, and Moving Picture Experts Group-1 audio layer 3 player devices to deliver learning content. A total of 20 studies assessed knowledge (n=2469) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning over traditional learning for knowledge (standardized mean difference [SMD]=0.43, 95% CI 0.05-0.80, N=11 studies, low-quality evidence). There was no difference between blended learning and traditional learning for knowledge (SMD=0.20, 95% CI –0.47 to 0.86, N=6 studies, low-quality evidence). A total of 14 studies assessed skills (n=1097) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning (SMD=1.12, 95% CI 0.56-1.69, N=5 studies, moderate quality evidence) and blended learning (SMD=1.06, 95% CI 0.09-2.03, N=7 studies, low-quality evidence) over traditional learning for skills. A total of 5 and 4 studies assessed attitudes (n=440) and satisfaction (n=327), respectively, with inconclusive findings reported for each outcome. The risk of bias was judged as high in 16 studies. Conclusions: The evidence base suggests that mLearning is as effective as traditional learning or possibly more so. Although acknowledging the heterogeneity among the studies, this synthesis provides encouraging early evidence to strengthen efforts aimed at expanding health professions education using mobile devices in order to help tackle the global shortage of health professionals

Reading behavior and the effect of embedded selfies in role-playing picture e-books: An eye-tracking investigation

Digital and interactive media platforms, such as e-books, are becoming important tools in reading and education. In particular, picture e-books can embed multimedia effects such as sound, animation or personalized images, with potential benefits for learning and engagement. However, little is known about how such e-books are read, and most designs remain untested. In this study, an innovative type of role-playing picture e-book entitled “The Prank in the Forest” was designed which provides three different role-playing approaches: emotive selfies that allow the reader to appear as one of the main characters and were varied according to the story development, a fixed selfie that was the same on each page, or no selfie. Sixty-five students were randomly assigned to the emotive selfie, fixed selfie, and no selfie groups. To understand students’ online reading processes, their eye movements were tracked. The results showed that the emotive selfies attracted attention to the main character and also promoted scanning between text and pictures, indicating a better integration of the written and pictorial information. The selfie design led to distinct scanpaths, and this was particularly true when emotive selfies were embedded which responded to the context. Self-report questionnaires of reading motivation and engagement demonstrated that this condition was also the most engaging design for readers. We conclude that emotive selfies can boost reading engagement since they encourage the reader to observe the development of the story from the role he/she chose to play, and we discuss how embodying the reader within the graphics can enhance role-play activities with the potential for improved educational outcomes. We suggest this emotive selfie role-playing design could be applied in e-book user interfaces to create more interaction and personal meaning for the readers

Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors

Effectiveness of interventions for people living with dementia and their carers in Chinese communities: protocol for a systematic review and meta-analysis of randomised controlled trials

Introduction As the largest and most rapidly ageing population, Chinese people are now the major driver of the continued growth in dementia prevalence globally. The need for evidence-based interventions in Chinese communities is urgent. Although a wide range of pharmacological and non-pharmacological interventions for dementia have been trialled in Chinese populations, the evidence has not been systematically synthesised. This systematic review and meta-analysis aims to map out the interventions for people living with dementia and their carers in Chinese communities worldwide and compare the effectiveness of these interventions. Methods and analysis This protocol followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols checklist. We will search Chinese (China National Knowledge Infrastructure, WanFang DATA) and English bibliographical databases (MEDLINE, EMBASE, PsycINFO, CINAHL Plus, Global Health, WHO Global Index Medicus, Virtual Health Library, Cochrane CENTRAL, Social Care Online, BASE, MODelling Outcome and cost impacts of interventions for DEMentia (MODEM) Toolkit, Cochrane Database of Systematic Reviews), complemented by hand searching of reference lists. We will include studies evaluating the effectiveness of interventions for dementia or mild cognitive impairment in Chinese populations, using a randomised controlled trial design, and published between January 2008 and June 2020. We will use a standardised form to extract data and Version 2 of the Cochrane risk-of-bias tool for randomised trials to assess the risk of bias of the included studies. Collected data will be fully interpreted with narrative synthesis and analysed using pairwise and network meta-analyses to pool intervention effects where sufficient information is available. We will perform subgroup analysis and meta-regression to explore potential reasons for heterogeneity. Ethics and dissemination No formal ethics approval is required for this protocol. The findings will facilitate the development of studies on interventions for dementia and timely inform dementia policymaking and practice. Planned dissemination channels include peer-reviewed publications, conference presentations, public events and websites. PROSPERO registration number CRD42019134135

Projecting the 10-year costs of care and mortality burden of depression until 2032: a Markov modelling study developed from real-world data

Background Based on real-world data, we developed a 10-year prediction model to estimate the burden among patients with depression from the public healthcare system payer's perspective to inform early resource planning in Hong Kong. Methods We developed a Markov cohort model with yearly cycles specifically capturing the pathway of treatment-resistant depression (TRD) and comorbidity development along the disease course. Projected from 2023 to 2032, primary outcomes included costs of all-cause and psychiatric care, and secondary outcomes were all-cause deaths, years of life lived, and quality-adjusted life-years. Using the territory-wide electronic medical records, we identified 25,190 patients aged ≥10 years with newly diagnosed depression from 2014 to 2016 with follow-up until 2020 to observe the real-world time-to-event pattern, based on which costs and time-varying transition inputs were derived using negative binomial modelling and parametric survival analysis. We applied the model as both closed cohort, which studied a fixed cohort of incident patients in 2023, and open cohort, which introduced incident patients by year from 2014 to 2032. Utilities and annual new patients were from published sources. Findings With 9217 new patients in 2023, our closed cohort model projected the 10-year cumulative costs of all-cause and psychiatric care to reach US$309.0 million and US$58.3 million, respectively, with 899 deaths (case fatality rate: 9.8%) by 2032. In our open cohort model, 55,849–57,896 active prevalent cases would cost more than US$322.3 million and US$60.7 million, respectively, with more than 943 deaths annually from 2023 to 2032. Fewer than 20% of cases would live with TRD or comorbidities but contribute 31–54% of the costs. The greatest collective burden would occur in women aged above 40, but men aged above 65 and below 25 with medical history would have the highest costs per patient-year. The key cost drivers were relevant to the early disease stages. Interpretation A limited proportion of patients would develop TRD and comorbidities but contribute to a high proportion of costs, which necessitates appropriate attention and resource allocation. Our projection also demonstrates the application of real-world data to model long-term costs and mortality, which aid policymakers anticipate foreseeable burden and undertake budget planning to prepare for the care need in alternative scenarios

The CAR‐HEMATOTOX score identifies patients at high risk for hematological toxicity, infectious complications, and poor treatment outcomes following brexucabtagene autoleucel for relapsed or refractory MCL

CD19-directed CAR T-cell therapy with brexucabtagene autoleucel (brexu-cel) has substantially improved treatment outcomes for patients with relapsed/refractory mantle cell lymphoma (r/r MCL). Prolonged cytopenias and infections represent common and clinically relevant side effects. In this multicenter observational study, we describe cytopenias and infections in 103 r/r MCL patients receiving brexu-cel. Furthermore, we report associations between the baseline CAR-HEMATOTOX (HT) score and toxicity events, non-relapse mortality (NRM), and progression-free/overall survival (PFS/OS). At lymphodepletion, 56 patients were HTlow (score 0–1) while 47 patients were HThigh (score ≥2). The HThigh cohort exhibited prolonged neutropenia (median 14 vs. 6 days, p < .001) and an increased rate of severe infections (30% vs. 5%, p = .001). Overall, 1-year NRM was 10.4%, primarily attributed to infections, and differed by baseline HT score (high vs. low: 17% vs. 4.6%, p = .04). HThigh patients experienced inferior 90-day complete response rate (68% vs. 93%, p = .002), PFS (median 9 months vs. not-reached, p < .0001), and OS (median 26 months vs. not-reached, p < .0001). Multivariable analyses showed that high HT scores were independently associated with severe hematotoxicity, infections, and poor PFS/OS. In conclusion, infections and hematotoxicity are common after brexu-cel and contribute to NRM. The baseline HT score identified patients at increased risk of poor treatment outcomes

A Study of Generative Large Language Model for Medical Research and Healthcare

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare

Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead.

Lifestyle factors are responsible for a considerable portion of cancer incidence worldwide, but credible estimates from the World Health Organization and the International Agency for Research on Cancer (IARC) suggest that the fraction of cancers attributable to toxic environmental exposures is between 7% and 19%. To explore the hypothesis that low-dose exposures to mixtures of chemicals in the environment may be combining to contribute to environmental carcinogenesis, we reviewed 11 hallmark phenotypes of cancer, multiple priority target sites for disruption in each area and prototypical chemical disruptors for all targets, this included dose-response characterizations, evidence of low-dose effects and cross-hallmark effects for all targets and chemicals. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). Our analysis suggests that the cumulative effects of individual (non-carcinogenic) chemicals acting on different pathways, and a variety of related systems, organs, tissues and cells could plausibly conspire to produce carcinogenic synergies. Additional basic research on carcinogenesis and research focused on low-dose effects of chemical mixtures needs to be rigorously pursued before the merits of this hypothesis can be further advanced. However, the structure of the World Health Organization International Programme on Chemical Safety 'Mode of Action' framework should be revisited as it has inherent weaknesses that are not fully aligned with our current understanding of cancer biology