Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures.

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Vestibulo-Ocular Reflex Suppression: Clinical Relevance and Assessment in the Digital Age.

Background: Visual acuity and image stability are crucial for daily activities, particularly during head motion. The vestibulo-ocular reflex (VOR) and its suppression (VORS) support stable fixation of objects of interest. The VOR drives a reflexive eye movement to counter retinal slip of a stable target during head motion. In contrast, VORS inhibits this countermovement when the target stimulus is in motion. The VORS allows for object fixation when it aligns with the direction of the head's movement, or when an object within or outside the peripheral vision needs to be focused upon. Summary: Deficits of the VORS have been linked to age-related diseases such as balance deficits associated with an increased fall risk. Therefore, the accurate assessment of the VORS is of particular clinical relevance. However, current clinical assessment methods for VORS are mainly qualitative and not sufficiently standardised. Recent advances in digital health technology, such as smartphone-based videooculography, offer a promising alternative for assessing VORS in a more accessible, efficient, and quantitative manner. Moreover, integrating mobile eye-tracking technology with virtual reality environments allows for the implementation of controlled VORS assessments with different visual inputs. These assessment approaches allow the extraction of novel parameters with potential pathomechanistic and clinical relevance. Key Messages: We argue that researchers and clinicians can obtain a more nuanced understanding of this ocular stabilisation reflex and its associated pathologies by harnessing digital health technology for VORS assessment. Further research is warranted to explore the technologies' full potential and utility in clinical practice.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

The Annual Wellness Visit Health Risk Assessment: Potential of Patient Portal-Based Completion and Patient-Oriented Education and Support.

Background and Objectives: Patient portals are secure online platforms that allow patients to perform electronic health management tasks and engage in bidirectional information exchange with their care team. Some health systems administer Medicare Annual Wellness Visit (AWV) health risk assessments through the patient portal. Scalable opportunities from portal-based administration of risk assessments are not well understood. Our objective is 2-fold-to understand who receives vs misses an AWV and health risk assessment and explore who might be missed with portal-based administration. Research Design and Methods: This is an observational study of electronic medical record and patient portal data (10/03/2021-10/02/2022) for 12 756 primary care patients 66+ years from a large academic health system. Results: Two-thirds (n = 8420) of older primary care patients incurred an AWV; 81.0% of whom were active portal users. Older adults who were active portal users were more likely to incur AWV than those who were not, though portal use was high in both groups (81.0% with AWV vs 76.8% without; p < .001). Frequently affirmative health risk assessment categories included falls/balance concerns (44.2%), lack of a documented advanced directive (42.3%), sedentary behaviors (39.9%), and incontinence (35.1%). Mean number of portal messages over the 12-month observation period varied from 7.2 among older adults affirmative responses to concerns about safety at home to 13.8 for older adults who reported difficulty completing activities of daily living. Portal messaging varied more than 2-fold across affirmative health risk categories and were marginally higher with greater number affirmative (mean = 13.8 messages/year no risks; 19.6 messages/year 10+ risks). Discussion and Implications: Most older adults were active portal users-a group more likely to have incurred a billed AWV. Efforts to integrate AWV risk assessments in the patient portal may streamline administration and scalability for dissemination of tailored electronically mediated preventive care but must attend to equity issues.

ChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice.

Artificial intelligence (AI) chatbots, such as ChatGPT, have widely invaded all domains of human life. They have the potential to transform healthcare future. However, their effective implementation hinges on healthcare workers' (HCWs) adoption and perceptions. This study aimed to evaluate HCWs usability of ChatGPT three months post-launch in Saudi Arabia using the System Usability Scale (SUS). A total of 194 HCWs participated in the survey. Forty-seven percent were satisfied with their usage, 57 % expressed moderate to high trust in its ability to generate medical decisions. 58 % expected ChatGPT would improve patients' outcomes, even though 84 % were optimistic of its potential to improve the future of healthcare practice. They expressed possible concerns like recommending harmful medical decisions and medicolegal implications. The overall mean SUS score was 64.52, equivalent to 50 % percentile rank, indicating high marginal acceptability of the system. The strongest positive predictors of high SUS scores were participants' belief in AI chatbot's benefits in medical research, self-rated familiarity with ChatGPT and self-rated computer skills proficiency. Participants' learnability and ease of use score correlated positively but weakly. On the other hand, medical students and interns had significantly high learnability scores compared to others, while ease of use scores correlated very strongly with participants' perception of positive impact of ChatGPT on the future of healthcare practice. Our findings highlight the HCWs' perceived marginal acceptance of ChatGPT at the current stage and their optimism of its potential in supporting them in future practice, especially in the research domain, in addition to humble ambition of its potential to improve patients' outcomes particularly in regard of medical decisions. On the other end, it underscores the need for ongoing efforts to build trust and address ethical and legal concerns of AI implications in healthcare. The study contributes to the growing body of literature on AI chatbots in healthcare, especially addressing its future improvement strategies and provides insights for policymakers and healthcare providers about the potential benefits and challenges of implementing them in their practice.

[Bacterial Community Structure of Typical Lake Sediments in Yinchuan City and Its Response to Heavy Metals].

Lake wetlands are extremely important and special ecosystems, which are important for regional water resource storage, environmental protection, and biodiversity maintenance. Sediment bacteria are an important component of lake ecosystems and are a major driver of biogeochemical cycling in lakes. In order to investigate the community structure of bacteria in typical lake sediments in Yinchuan City and their influencing factors, three typical lakes in Yinchuan City (Yuehai Lake, Mingcui Lake, and Xiniu Lake) were selected for the study and surface sediments were collected in January, April, July, and October 2021. The composition of the sediment bacterial community was examined using 16S rDNA high-throughput sequencing technology, and the response relationships between them and heavy metals were explored. The results showed that the ecological hazard coefficient for heavy metals in the sediments of three typical lakes in Yinchuan City was far less than 40, and the ecological hazard index was far less than 150, all of which indicated a minor ecological hazard. There were no significant differences in bacterial community diversity among the three lakes, but there were significant variations in diversity among the lakes in different seasons and significant differences in community composition. The dominant phyla (top three in terms of relative abundance) in Yuehai Lake, Mingcui Lake, and Xiniu Lake were Proteobacteria, Bacteroidetes, and Chloroflexi. The dominant lower orders were Gammaproteobacteria, Alphaproteobacteria, and Deltaproteobacteria. The main divergent species that occurred at the phylum level in typical lakes in Yinchuan were Proteobacteria, Bacteroidetes, Euryarchaeota, Firmicutes, Actinobacteria, and Acidobacteria. The sediment bacterial community structure of Yuehai Lake was significantly correlated with Cu, Fe, Mn, Zn, As, and Pb; the sediment bacterial community structure of Lake Mingcui was significantly correlated with Fe, Pb, and Cr; and the sediment bacterial community structure of Xiniu Lake was not significantly correlated with heavy metals. The types and contents of sediment heavy metals had a significant effect on the bacterial community structure of sediments in Yinchuan Yuehai Lake and Mingcui Lake and were important environmental factors that caused changes in the bacterial community structure of lake sediments.

Service Quality and Residents' Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study.

BACKGROUND: Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents' preference for this new pattern are unclear. OBJECTIVE: This study aimed to compare the service quality and residents' preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service's quality and residents' preferences. METHODS: We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants' preferences. Differences in service quality and the participants' preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service's quality and residents' preference were conducted using generalized logit models. RESULTS: A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents' basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P=.79; average screening time: P=.57; no physiological discomfort rate: P=.92; safety rate: P=.78; convenience rate: P=.95; trustworthiness rate: P=.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group (P<.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening (P=.02). CONCLUSIONS: This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents' preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents' individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.

A systematic review about the importance of neuropsychological features in heart failure: is at heart the only failure?

Heart failure can lead to cognitive impairment that is estimated to be present in over a quarter of patients. It is important to intervene at a cognitive level to promote brain plasticity through cognitive training programs. Interventions transformed by technology offer the promise of improved cognitive health for heart failure patients. This review was conducted on studies evaluating the role of cognitive rehabilitation in patients with heart failure. We examined clinical trials involving patients with heart failure. Our search was performed on Pubmed, Web of Science and Cochrane library databases. Of the initial 256 studies, 10 studies met the inclusion criteria. Cognitive rehabilitation training has important implications for the treatment and prevention of cognitive decline in heart failure patients with significant recovery for delayed recall memory and a significant time effect for total recall memory and delayed, psychomotor speed and IADL performance. It is important to include the assessment of cognitive functioning in the routine clinical examinations of patients with heart failure, discover the relationship between cognitive function and heart failure, and target cognitive rehabilitation programs that promote brain plasticity.

Head Impact Exposure in Hawaiian High School Football: Influence of Adherence Rates on a Helmetless Tackling and Blocking Training Intervention.

CONTEXT: High school football remains a popular, physically demanding sport despite the known risks for acute brain and neck injury. Impacts to the head also raise concerns about their cumulative effects and long-term health consequences. OBJECTIVE: To examine the effectiveness of a helmetless tackling training program to reduce head impact exposure in football participants. DESIGN: A three-year, quasi-experimental, prospective cohort (clinicaltrials.gov #NCTXXX) study. SETTING: Honolulu (XXX, XXX) area public and private secondary schools with varsity and junior varsity football. PATIENTS OR OTHER PARTICIPANTS: Football participants (n=496) ages 14 to 18 years old. Intervention(s) Participants wore new football helmets furnished with head impact sensor technology. Teams employed a season-long helmetless tackling and blocking intervention in Years 2 and 3 consisting of a 3-phase, systematic progression of 10 instructional drills. MAIN OUTCOME MEASURE(S): Head impact frequency per athlete exposure (ImpAE), location, and impact magnitude per participant intervention adherence levels (60% and 80%). RESULTS: An overall regression analysis revealed a significant negative association between ImpAE and adherence (p=0.003, beta=-1.21, SE=0.41). In year 3, a longitudinal data analysis of weekly ImpAE data resulted in an overall difference between the adherent and non-adherent groups (p=0.040 at 80%; p=0.004 at 60%), mainly due to decreases in top and side impacts. Mean cumulative impact burden for the adherent group (n=131: 2,105.84g ± 219.76,) was significantly (p=0.020) less than the non-adherent group (n=90: 3,158.25g ± 434.80) at the 60% adherence level. CONCLUSIONS: Participants adhering to the intervention on at least a 60% level experienced a 34% to 37% significant reduction in the number of head impacts (per exposure) through the season. These results provide additional evidence that a helmetless tackling and blocking training intervention (utilizing the HuTT® program) reduces head impact exposure in high school football players. Adherence to an intervention is crucial for achieving intended outcomes.

Levels of work stress among information technology professionals during COVID-19 pandemic in an Indian metropolis.

Introduction: Amid the ongoing Non-Communicable Disease and COVID-19 pandemic, understanding prevalence and characteristics associated with work stress is vital from a health and economic perspective, more so among information technology (IT) professionals. Objective: To estimate the prevalence and factors associated with work stress among IT professionals during the coronavirus disease 2019 (COVID-19) pandemic in Bengaluru, India. Materials and Methods: A cross-sectional study was undertaken on a convenient sample of 356 IT professionals in Bengaluru, India, between September 2020 and March 2021 to assess work stress. The weblink to TAWS-16 (Tool to Assess and classify Work Stress) was provided for IT employees to self-report their experience of work-related stressors and coping abilities in the past 6 months. The prevalence and specific prevalence of work stress were calculated. Multi-variate logistic regression was conducted to identify factors associated with work stress. Ethical clearance was obtained from the Institutional Ethics committee, National Institute of Mental Health and Neurosciences, Bengaluru. Informed consent was obtained from study subjects. Results and Conclusion: The prevalence of work stress among IT professionals was 17.7% during the COVID19 pandemic, with higher rates among employees aged 31 years and above, among female employees, and among employees with 4-7 years of work experience. More than 80% of the professionals experienced deadline pressures, long working hours, regular multi-tasking, and difficulty in maintaining work-life balance. Based on the results, it is recommended to integrate work-stress assessment in periodical medical examination of IT employees from a health promotion and productivity improvement perspective.

Assessment of Reader Technologies for Over-the-Counter Diagnostic Testing.

Background: Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. Methods: As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. Results: Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. Conclusions: Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.

Enzyme-Responsive Fluorescent Labeling Strategy for In Vivo Imaging of Gut Bacteria.

Myrosinase (Myr), as a unique ß-thioglucosidase enzyme capable of converting natural and gut bacterial metabolite glucosinolates into bioactive agents, has recently attracted a great deal of attention because of its essential functions in exerting homeostasis dynamics and promoting human health. Such nutraceutical and biomedical significance demands unique and reliable strategies for specific identification of Myr enzymes of gut bacterial origin in living systems, whereas the dearth of methods for bacterial Myr detection and visualization remains a challenging concern. Herein, we present a series of unique molecular probes for specific identification and imaging of Myr-expressing gut bacterial strains. Typically, an artificial glucosinolate with an azide group in aglycone was synthesized and sequentially linked with the probe moieties of versatile channels through simple click conjugation. Upon gut bacterial enzymatic cleavage, the as-prepared probe molecules could be converted into reactive isothiocyanate forms, which can further act as reactive electrophiles for the covalent labeling of gut bacteria, thus realizing their localized fluorescent imaging within a wide range of wavelength channels in live bacterial strains and animal models. Overall, our proposed method presents a novel technology for selective gut bacterial Myr enzyme labeling in vitro and in vivo. We envision that such a rational probe design would serve as a promising solution for chemoprevention assessment, microflora metabolic mechanistic study, and gut bacterium-mediated physiopathological exploration.

Shear wave elastography to assess stiffness of the human ovary and other reproductive tissues across the reproductive lifespan in health and disease†.

The ovary is one of the first organs to show overt signs of aging in the human body, and ovarian aging is associated with a loss of gamete quality and quantity. The age-dependent decline in ovarian function contributes to infertility and an altered endocrine milieu, which has ramifications for overall health. The aging ovarian microenvironment becomes fibro-inflammatory and stiff with age, and this has implications for ovarian physiology and pathology, including follicle growth, gamete quality, ovulation dynamics, and ovarian cancer. Thus, developing a non-invasive tool to measure and monitor the stiffness of the human ovary would represent a major advance for female reproductive health and longevity. Shear wave elastography is a quantitative ultrasound imaging method for evaluation of soft tissue stiffness. Shear wave elastography has been used clinically in assessment of liver fibrosis and characterization of tendinopathies and various neoplasms in thyroid, breast, prostate, and lymph nodes as a non-invasive diagnostic and prognostic tool. In this study, we review the underlying principles of shear wave elastography and its current clinical uses outside the reproductive tract as well as its successful application of shear wave elastography to reproductive tissues, including the uterus and cervix. We also describe an emerging use of this technology in evaluation of human ovarian stiffness via transvaginal ultrasound. Establishing ovarian stiffness as a clinical biomarker of ovarian aging may have implications for predicting the ovarian reserve and outcomes of Assisted Reproductive Technologies as well as for the assessment of the efficacy of emerging therapeutics to extend reproductive longevity. This parameter may also have broad relevance in other conditions where ovarian stiffness and fibrosis may be implicated, such as polycystic ovarian syndrome, late off target effects of chemotherapy and radiation, premature ovarian insufficiency, conditions of differences of sexual development, and ovarian cancer. Summary sentence:  Shear Wave Elastography is a non-invasive technique to study human tissue stiffness, and here we review its clinical applications and implications for reproductive health and disease.

Hemodynamic goals in sepsis and septic shock resuscitation: An umbrella review of systematic reviews and meta-analyses with trial sequential analysis.

OBJECTIVE: The objective of this study was to verify whether any parameter among those used as the target for haemodynamic optimisation (e.g., mean arterial pressure, central venous oxygen saturation, systolic or diastolic dysfunction, CO2 gap, lactates, right ventricular dysfunction, and PvaCO2/CavO2 ratio) is correlated with mortality in an undifferentiated population with sepsis or septic shock. METHODS: An umbrella review, searching MEDLINE, the Cochrane Database of Systematic Reviews, Health Technology Assessment Database, and the JBI Database of Systematic Reviews and Implementation Reports, was performed. We included systematic reviews and meta-analyses enrolling a population of unselected patients with sepsis or septic shock. The main outcome was mortality. Two authors conducted data extraction and risk-of-bias assessments independently. We used a random-effects model to pool binary and continuous data and summarised estimates of effect using equivalent odds ratios (eORs). We used the ROBIS tool to assess risk of bias and the assessment of multiple systematic reviews 2 score to assess global quality. DATA SYNTHESIS: 17 systematic reviews and meta-analyses (15 828 patients) were included in the quantitative analysis. Diastolic dysfunction (eOR: 1.42; 95% confidence interval [CI]: 1.14-1.76), PvaCO2/CavO2 ratio (eOR: 2.15; 95% CI: 1.37-3.37), and CO2 gap (eOR: 1.86; 95% CI: 1.07-3.25) showed a significant correlation with mortality. Lactates were the parameter with highest inconsistency (I2 = 92%). Central venous oxygen saturation and right ventricle dysfunction showed significant statistical excess test of significance (p-value = 0.009 and 0.005, respectively). None of the considered parameters showed statistically significant publication bias. CONCLUSIONS: According to this umbrella review, diastolic dysfunction is the haemodynamic variable that is most closely linked to the prognosis of septic patients. The PvaCO2/CavO2 ratio and the CO2gap are significantly related to the mortality of septic patients, but the poor quality of evidence or the low number of cases, studied so far, limit their clinical applicability. CLINICAL TRIAL REGISTRATION: PROSPERO: International prospective register of systematic reviews, 2023, CRD42023432813 (Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432813).

Gluten-Free Diet and Other Celiac Disease Therapies: Current Understanding and Emerging Strategies.

A lifelong gluten-free diet (GFD) is the only treatment for celiac disease and other gluten-related disorders. Nevertheless, strict adherence to the GFD is often challenging due to concerns about social isolation, risk of gluten contaminations, high cost, poor quality and the taste of gluten-free products. Moreover, although the GFD is effective in achieving mucosal healing, it may lead to dietary imbalances due to nutrient deficiencies over a long period of time. To overcome these issues, several gluten-free wheat flours have been developed to create products that closely resemble their gluten-containing counterparts. Furthermore, given the critical importance of adhering to the GFD, it becomes essential to promote adherence and monitor possible voluntary or involuntary transgressions. Various methods, including clinical assessment, questionnaires, serology for celiac disease, duodenal biopsies and the detection of Gluten Immunogenic Peptides (GIPs) are employed for this purpose, but none are considered entirely satisfactory. Since adherence to the GFD poses challenges, alternative therapies should be implemented in the coming years to improve treatment efficacy and the quality of life of patients with celiac disease. The aim of this narrative review is to explore current knowledge of the GFD and investigate its future perspectives, focusing on technology advancements, follow-up strategies and insights into a rapidly changing future.

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

Assessment of the potential threats to brain health posed by the radiation from 5G sub-6 GHz base stations in China using dosimetric methods.

The 5G sub-6 GHz radio frequency (RF) electromagnetic fields (EMF) are the most widely used in China's communications. The public has expressed concerns about possible brain health effects of the higher frequency bands in 5G compared to 2G, 3G, and 4G bands. It is imperative to empirically investigate the potential health hazards of these novel frequency bands in 5G communication technology. This study evaluates the assessment of brain tissue dose coupling from sub-6 GHz band EMF emitted by base stations in China. Based on the 3D virtual human body model, the simulation environment was established. Dose including specific absorption rate (SAR) and internal electric field (IEF) between 2G, 3G, and 4G bands and 5G sub-6 GHz was investigated using normalized exposure values and exposure limits. The results indicate that the sub-6 GHz high-frequency band of 5G has the lowest dose value. It can be concluded that high-frequency electromagnetic radiation in 5G sub-6 GHz reduces the dose and health threats to the brain. This provides strong support for the promotion of 5G commutation in China and other regions.

How should artificial intelligence be used in Australian health care? Recommendations from a citizens' jury.

OBJECTIVE: To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care. STUDY DESIGN: Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?" SETTING, PARTICIPANTS: Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting. MAIN OUTCOME MEASURES: Jury recommendations, with reasons. RESULTS: The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication. CONCLUSIONS: The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.

[Association of polymorphisms in SEC16B, DNAJC27, FTO and MC4R genes with overweight and obesity in Han preschool children].

OBJECTIVE: To investigate the association of polymorphisms in SEC16B rs633715, DNAJC27 rs713586, FTO rs11642015 and MC4R rs6567160 with overweight and obesity in Han Chinese preschool children. METHODS: A total of 749 Han Chinese preschool children from Henan and Guizhou Province of Long-term Health Effects Assessment Project of Infants and Toddlers Nutritional Pack were selected for the study and divided into an overweight and obese group and a normal control group in 2022. rs633715, rs713586, rs11642015 and rs6567160 were genotyped using Kompetitive allele-specific PCR(KASP) technology. The distribution of genotypic polymorphisms was compared using the χ~2 test. The association between the four loci and overweight and obesity in preschool children was analyzed using a multifactorial logistic regression model. RESULTS: The statistical analysis revealed a significant disparity(P<0.05) in the distribution of genotypic polymorphisms of rs633715 and rs6567160 among preschoolers in Henan and Guizhou Province. CC heterozygous mutant and recessive models at rs633715 locus were associated with susceptibility to overweight and obesity in preschool children [OR and 95% CI 2.915(1.163-7.305), and 2.997(1.226-7.323), respectively, both P<0.05]. TC heterozygous mutant and dominant models at rs713586 locus were also associated susceptibility to overweight and obesity in preschool children(OR and 95% CI were 2.362(1.054-5.289)and 2.362(1.054-5.289), respectively, both P<0.05). rs11642015 and rs6567160 loci were not associated with susceptibility to overweight and obesity in preschool children(P>0.05). The result of the analysis of the cumulative effect of rs633715 and rs713586 showed that the number of genotypes carrying the risk genotype was positively associated with the risk of overweight and obesity in preschool children(P_(trend)<0.01). CONCLUSION: Among Han Chinese preschool children, SEC16B rs633715 and DNAJC27 rs713586 were associated with susceptibility to overweight and obesity in preschool children. Moreover, rs633715 and rs713586 had a cumulative effect on susceptibility to overweight and obesity in preschool children, the number of risk genotypes carried was positively associated with childhood overweight and obesity risk.

Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports.

INTRODUCTION: Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies. OBJECTIVES: The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP). METHODS: Data encompassing CONITEC's official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected). RESULTS: The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies' experiences and government as demandant, potentially influencing CONITEC's decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester. CONCLUSIONS: NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.