Scaling up a targeted exposome LC-MS/MS biomonitoring method by incorporating veterinary drugs and pesticides.

Humans are exposed to a cocktail of food-related and environmental contaminants, potentially contributing to the etiology of chronic diseases. Better characterizing the "exposome" is a challenging task and requires broad human biomonitoring (HBM). Veterinary drugs (VDs)/antibiotics, widely used and regulated in food and animal production, however, are typically not yet included in exposomics workflows. Therefore, in this work, a previously established multianalyte liquid chromatography-tandem mass spectrometry (LC-MS/MS) method covering >80 diverse xenobiotics was expanded by >40 VDs/antibiotics and pesticides. It was investigated if the generic workflow allowed for the successful integration of a high number of new analytes in a proof-of-principle study. The expanded method was successfully in-house validated and specificity, matrix effects, linearity, intra- and inter-day precision, accuracy, limits of quantification, and detection were evaluated. The optimized method demonstrated satisfactory recovery (81-120%) for most of the added analytes with acceptable RSDs (<20%) at three spiking levels. The majority of VDs/antibiotics and pesticides (69%) showed matrix effects within a range of 50-140%. Moreover, sensitivity was excellent with median LODs and LOQs of 0.10 ng/mL and 0.31 ng/mL, respectively. In total, the expanded method can be used to detect and quantify more than 120 highly diverse analytes in a single analytical run. To the best of the authors' knowledge, this work represents the first targeted biomonitoring method integrating VDs with various other classes of pollutants including plasticizers, PFAS, bisphenols, mycotoxins, and personal care products. It demonstrates the potential to expand targeted multianalyte methods towards additional groups of potentially toxic chemicals.

Health-care-seeking behaviour in patients with hypertension: experience from a dedicated hypertension centre in Bangladesh.

Objective: The study aimed to assess health-seeking behaviour (HSB) and associated factors among hypertensive patients in Bangladesh.Methods: This cross-sectional study was conducted in the Hypertension & Research Centre, Rangpur, Bangladesh, between January 2022 and June 2022. A total of 497 hypertensive adults were recruited consecutively. A pre-tested structured questionnaire was deployed by the research team for data collection. Multivariable logistic regression analysis was used to explore the predictors of HSB.Results: The mean age of the hypertensive patients was 52 ± 11 (SD) years. Most of them were aged between 51 and 60 years (33%), female (55%), came from rural areas (57%), and belonged to middle socioeconomic class (68%). One-fourth of the patients (27%) had chosen informal healthcare providers for their first consultation. Fear of stroke (244, 45%), headache (170, 36%), and neck pain (81, 17%) were the three most common compelling causes of their visit to the hypertension centre. Age (aOR 0.78, 95% CI 0.68 - 0.89), male sex (aOR: 1.79, 95% CI 1.05 - 3.10), living in semi-urban (aOR 4.68, 95% CI 1.45 - 15.10) and rural area (aOR 1.68, 95% CI 1.01 - 2.80), farmers as occupation (aOR: 3.24, 95%CI: 1.31 - 8.06) and belonging to lower social economic class (aOR 4.24, 95% CI 1.68 - 10.69) were predictors of visiting informal providers of hypertensive patient. One-fourth of the hypertensive patients received consultation from informal healthcare providers.Conclusions: Raising awareness among patients and proper referral to specialised hypertension centres could promulgate the patients towards appropriate behaviour.

Annual risk of hepatitis E virus infection and seroreversion: Insights from a serological cohort in Sitakunda, Bangladesh.

Hepatitis E virus (HEV) is a major cause of acute jaundice in South Asia. Gaps in our understanding of transmission are driven by non-specific symptoms and scarcity of diagnostics, impeding rational control strategies. In this context, serological data can provide important proxy measures of infection. We enrolled a population-representative serological cohort of 2,337 individuals in Sitakunda, Bangladesh. We estimated the annual risks of HEV infection and seroreversion both using serostatus changes between paired serum samples collected 9 months apart, and by fitting catalytic models to the age-stratified cross-sectional seroprevalence. At baseline, 15% (95 CI: 14-17%) of people were seropositive, with seroprevalence highest in the relatively urban south. During the study, 27 individuals seroreverted (annual seroreversion risk: 15%, 95 CI: 10-21%), and 38 seroconverted (annual infection risk: 3%, 95CI: 2-5%). Relying on cross-sectional seroprevalence data alone, and ignoring seroreversion, underestimated the annual infection risk five-fold (0.6%, 95 CrI: 0.5-0.6%). When we accounted for the observed seroreversion in a reversible catalytic model, infection risk was more consistent with measured seroincidence. Our results quantify HEV infection risk in Sitakunda and highlight the importance of accounting for seroreversion when estimating infection incidence from cross-sectional seroprevalence data.

Do community-level predictors have more impact than individual/family-level predictors on receiving the desired number of ANC services in Bangladesh?

Background: Bangladesh has improved maternal and child health, but healthcare indicators and access still need enhancement. Factors that contribute to increased antenatal care (ANC) need to be explored to inform healthcare policies. The study examined whether community-level (supply-side) predictors outperform individual/family-level (demand-side) predictors for the desired number of ANC services. Methods: This cross-sectional study collected primary data from 630 pregnant and lactating women (PLW) in seven upazilas in Rangpur and Nilphamari districts of Bangladesh in 2022. The individual/family and community-level factors as predictors of desired antenatal care services were investigated using a semi-structured questionnaire. Various statistical techniques including the Student t-test, z-test, Chi-square test, and logistic regression model were employed in analyzing the data. Results: Out of the total 630 participants, the majority were literate women who belong to higher pregnancy order and received benefits from SSNPs. In addition to this, these women did not earn and neither were the empowered. The outcome variable was the receiving status of 4+ ANC services by PLWs, which varied by different covariates. The desired 4+ ANC service received by 73 % PLWs. The significant (p < 0.05) predictors of receiving 4+ ANC services were secondary-level education (95 % CI:0.97-7.55), knowledge on danger signs (95 % CI:1.02-1.48), empowered women (95 % CI:0.99-2.69), community clinics as place of services (95 % CI:1.52-3.49), sources of information through SMS (95 % CI:2.63-7.04) and fully functional community clinic (95 % CI:1.0-2.347). The statistical evidence through the values of pseudo R2 of the reduced models of community level (0.09), individual level (0.03) and family level (0.01) revealed that the community level predictors are more influential than individual/family level predictors. Conclusion: The findings indicate that community level predictors played a dominant role in receiving the 4+ ANC services in Bangladesh. In short, the well-functioning of community clinics in tandem with government forums/bodies and awareness raising through SMS messages, are sufficient for ensuring the desired number of ANC services in rural areas of Bangladesh.

Clinical surveillance systems obscure the true cholera infection burden in an endemic region.

Our understanding of cholera transmission and burden largely relies on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serological surveillance provides a complementary approach to monitoring infections, although the link between serologically derived infections and medically attended disease incidence-shaped by immunological, behavioral and clinical factors-remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare-seeking and longitudinal serological data through statistical modeling. Combining the serological trajectories with a reconstructed incidence timeline of symptomatic cholera, we estimated an annual Vibrio cholerae O1 infection incidence rate of 535 per 1,000 population (95% credible interval 514-556), with incidence increasing by age group. Clinic-based surveillance alone underestimated the number of infections and reported cases were not consistently correlated with infection timing. Of the infections, 4 in 3,280 resulted in symptoms, only 1 of which was reported through the surveillance system. These results impart insights into cholera transmission dynamics and burden in the epicenter of the seventh cholera pandemic, where >50% of our study population had an annual V. cholerae O1 infection, and emphasize the potential for a biased view of disease burden and infection risk when depending solely on clinical surveillance data.

An open set model for pest identification.

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields. Numerous image-based machine learning (ML) models also exist to identify insect pests in agricultural fields. The models are significantly rely on a large, manually curated dataset and are close-set in nature. Our study aims to develop an open set pest identification approach by adding the capability of rejecting any irrelevant inputs. Tephritid fruit flies (Diptera:Tephritidae) are considered as an example since they are the most economically important agricultural pests worldwide. Images of the fruit flies were collected from a publicly available database and filtered to exclude uninformative images using a deep learning model (Inception-V3) and an unsupervised k-means clustering method. For the closed-set identification task, our EfficientNet-B2 model classified four major genera of notorious tephritid flies, namely, Anastrepha, Ceratitis, Rhagoletis, and Bactrocera with an accuracy of 89.65%. We further improvise our proposed model for open-set recognition tasks to leverage the identification beyond the trained datasets. The open set model achieved an overall accuracy of 86.48% and a macro F1-score of 94.44% on the four genera and an unknown class. Our proposed model can be a practical and effective pest identification tool for harmful fruit flies. In addition, the model is easy to implement with existing agricultural pest control systems in an open-world scenario.

A selective LSPR biosensor for molecular-level glycated albumin detection.

A biosensor specifically engineered to detect glycated albumin (GA), a critical biomarker for diabetes monitoring, is presented. Unlike conventional GA monitoring methods, the biosensor herein uniquely employs localised surface plasmon resonance (LSPR) for signal transduction, leveraging a novel fabrication process where gold nanoparticles are deposited on a quartz substrate using flame spray pyrolysis. This enables the biosensor to provide mean glucose levels over a three-week period, correlating with the glycation status of diabetes patients. The sensor's DNA aptamer conjugation selectively binds GA, inducing a plasmonic wavelength shift; resulting in a detection limit of 0.1 µM, well within the human GA range of 20-240 µM. Selectivity experiments with diverse molecules and an exploration of sensor reusability were carried out with positive results. The novelty of the biosensor presented includes specificity, sensitivity and practical applicability; which is promising for enhanced diabetes diagnosis using a rapid and inexpensive process.

Estimating water levels caused by a tropical storm along the Bangladesh coast: A numerical approach.

In this study, water levels resulting from the dynamic interaction of tide and surge are estimated by solving a 2-D vertically integrated shallow water equations numerically. To solve the equations on the specific 2-D grid, the explicit Leapfrog scheme is implemented, adopting a staggered Arakawa C-grid. The domain's complex land-sea interface is approximated through the stair-step method in order to employ the finite difference technique. To incorporate the complexity of the domain with a considerably high accuracy and to reduce computational cost, one-way nested grid models are embraced. The Meghna River freshwater discharge is incorporated into the innermost child model. A stable tidal regime over the region of interest is generated by applying the four vital tidal constituents, namely M2 (principal lunar semidiurnal), S2 (principal solar semidiurnal), O1 (principal lunar diurnal) and K1 (luni-solar diurnal) in the southern open boundary of the outermost model. This previously effectuated tidal regime is used as the initial state of the sea in getting total water levels due to the dynamic interaction of tide and surge. Numerical experiments are performed with the storm AILA that hit the coast of Bangladesh on May 25, 2009. The simulated results are found to closely match observed and reported data.

Dissecting the role of SWI/SNF component ARID1B in steady-state hematopoiesis.

The adenosine triphosphate (ATP)-dependent chromatin remodeling complex, SWItch/Sucrose Non-Fermentable (SWI/SNF), has been implicated in normal hematopoiesis. The AT-rich interaction domain 1B (ARID1B) and its paralog, ARID1A, are mutually exclusive, DNA-interacting subunits of the BRG1/BRM-associated factor (BAF) subclass of SWI/SNF complex. Although the role of several SWI/SNF components in hematopoietic differentiation and stem cell maintenance has been reported, the function of ARID1B in hematopoietic development has not been defined. To this end, we generated a mouse model of Arid1b deficiency specifically in the hematopoietic compartment. Unlike the extensive phenotype observed in mice deficient in its paralog, ARID1A, Arid1b knockout (KO) mice exhibited a modest effect on steady-state hematopoiesis. Nonetheless, transplantation experiments showed that the reconstitution of myeloid cells in irradiated recipient mice was dependent on ARID1B. Furthermore, to assess the effect of the complete loss of ARID1 proteins in the BAF complex, we generated mice lacking both ARID1A and ARID1B in the hematopoietic compartment. The double-KO mice succumbed to acute bone marrow failure resulting from complete loss of BAF-mediated chromatin remodeling activity. Our Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) analyses revealed that >80% of loci regulated by ARID1B were distinct from those regulated by ARID1A; and ARID1B controlled expression of genes crucial in myelopoiesis. Overall, loss of ARID1B affected chromatin dynamics in murine hematopoietic stem and progenitor cells, albeit to a lesser extent than cells lacking ARID1A.

Estimating the gap between clinical cholera and true community infections: findings from an integrated surveillance study in an endemic region of Bangladesh.

Our understanding of cholera transmission and burden largely rely on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serologic surveillance provides a complementary approach to monitoring infections, though the link between serologically-derived infections and medically-attended disease - shaped by immunological, behavioral, and clinical factors - remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare seeking, and longitudinal serological data through statistical modeling. We found >50% of the study population had a V. cholerae O1 infection annually, and infection timing was not consistently correlated with reported cases. Four in 2,340 infections resulted in symptoms, only one of which was reported through the surveillance system. These results provide new insights into cholera transmission dynamics and burden in the epicenter of the 7th cholera pandemic and provide a framework to synthesize serological and clinical surveillance data.

Machine learning for detecting DNA attachment on SPR biosensor.

Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

BACKGROUND: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS: On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS: This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Evacuation dilemmas of coastal households during cyclone Amphan and amidst the COVID-19 pandemic: a study of the Southwestern region of Bangladesh.

Cyclone Amphan battered the coastal communities in the southwestern part of Bangladesh in 2020 during the COVID-19 pandemic. These coastal communities were experiencing such a situation for the first time and faced the dilemma of whether to stay at home and embrace the cyclone or be exposed to the COVID-19 virus in the cyclone shelters by evacuating. This article intends to explore individuals' decisions regarding whether to evacuate in response to cyclone Amphan and in light of the risks of the COVID-19 pandemic. Consequently, this study investigated evacuation behaviors among the households and explored the impacts of COVID-19 during the evacuation procedures. We conducted household surveys to collect primary information and undertook 378 samples for interviews at a precision level of 0.05 in fourteen villages. Despite the utmost effort of the government, the results demonstrated that 96.6% of people in the coastal area received a cyclone evacuation order before the cyclone's landfall, and only 42% of people followed the evacuation order. The majority of households chose to stay at home because of fear of COVID-19 exposure in the crowded shelters. Although half of the evacuees were housed in cyclone shelters, COVID-19 preventive measures were apparently not set in place. Thus, this study will assist in crafting future government policies to enhance disaster evacuation plans by providing insights from the pandemic that can inform disaster management plans in the Global South.

Appearance of tolerance-induction and non-inflammatory SARS-CoV-2 spike-specific IgG4 antibodies after COVID-19 booster vaccinations.

Background: Understanding the characteristics of the humoral immune responses following COVID-19 vaccinations is crucial for refining vaccination strategies and predicting immune responses to emerging SARS-CoV-2 variants. Methods: A longitudinal analysis of SARS-CoV-2 spike receptor binding domain (RBD) specific IgG antibody responses, encompassing IgG subclasses IgG1, IgG2, IgG3, and IgG4 was performed. Participants received four mRNA vaccine doses (group 1; n=10) or two ChAdOx1 nCoV-19 and two mRNA booster doses (group 2; n=19) in Bangladesh over two years. Results: Findings demonstrate robust IgG responses after primary Covishield or mRNA doses; declining to baseline within six months. First mRNA booster restored and surpassed primary IgG responses but waned after six months. Surprisingly, a second mRNA booster did not increase IgG levels further. Comprehensive IgG subclass analysis showed primary Covishield/mRNA vaccination generated predominantly IgG1 responses with limited IgG2/IgG3, Remarkably, IgG4 responses exhibited a distinct pattern. IgG4 remained undetectable initially but increased extensively six months after the second mRNA dose, eventually replacing IgG1 after the 3rd/4th mRNA doses. Conversely, initial Covishield recipients lack IgG4, surged post-second mRNA booster. Notably, mRNA-vaccinated individuals displayed earlier, robust IgG4 levels post first mRNA booster versus Covishield counterparts. IgG1 to IgG4 ratios decreased with increasing doses, most pronounced with four mRNA doses. This study highlights IgG response kinetics, influenced by vaccine type and doses, impacting immunological tolerance and IgG4 induction, shaping future vaccination strategies. Conclusions: This study highlights the dynamics of IgG responses dependent on vaccine type and number of doses, leading to immunological tolerance and IgG4 induction, and shaping future vaccination strategies.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

BACKGROUND: Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. METHODS: Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. RESULTS: Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Factors associated with Facebook addiction among university students amid the COVID-19 pandemic: Findings from an online cross-sectional survey.

BACKGROUND: Facebook addiction (FA) has been suggested as a potential behavioral addiction. There is a severe lack of research evidence regarding the Facebook addiction behavior among university students during the ongoing COVID-19 pandemic. The aim of this study was to determine factors associated with Facebook addiction among Bangladeshi university students. METHODS: A cross-sectional online survey was conducted among 2,161 Bangladeshi university students during the COVID-19 pandemic from June 2021 to September 2021. A well fitted regression model in R programming language was used for this study. RESULTS: Female respondents and those whose family monthly income was <25,000 BDT were more addicted to Facebook than other respondents. Respondents who lost a family member or a relative to COVID-19, engaged in physical activities (exercise) during the pandemic, used Facebook for work purposes or used Facebook to relieve daily stress were more addicted to Facebook. CONCLUSION: Overuse of social media is problematic as it can trigger several mental health symptoms, especially among students. Adequate and effective interventions are required to educate students about the dangers of Facebook addiction and to provide an alternative, healthy options.

A ResNet attention model for classifying mosquitoes from wing-beating sounds.

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely 'Wing-beating Network' or 'WbNet', combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey.

BACKGROUND: Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter-glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. OBJECTIVE: The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. METHODS: A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. RESULTS: On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. CONCLUSIONS: Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.