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Background: Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out., Objective: This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers., Methods: We evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models' ability to distinguish between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model., Results: The best model achieves an average area under the receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and images depicting multiple conditions in the same breast., Conclusions: This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes., (©Jessica De Souza, Varun Kumar Viswanath, Jessica Maria Echterhoff, Kristina Chamberlain, Edward Jay Wang. Originally published in JMIR AI (https://ai.jmir.org), 24.06.2024.)

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night's sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual's sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e-100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e-100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2-10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses., (© 2024. The Author(s).)

Background: Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales., Methods: Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments., Results: Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales., Conclusions: These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them., (© 2023. The Author(s).)

Pfs25, a vaccine candidate, expressed on the surface of the malarial parasite, plays an important role in the development of Plasmodium falciparum. 1269, a monoclonal antibody targeting the epidermal growth factor-like domain 1 and epidermal growth factor-like domain 3 of Pfs25, blocks the transmission of parasites in mosquitoes. In this study, we refolded 1269-Db, a dimeric antibody fragment referred as diabody, designed from 1269, with a yield of 3 mg/litre of bacterial culture. Structural integrity of the protein was validated with thermal stability, disulphide bond analysis and glutaraldehyde crosslinking experiments. To evaluate the functionality of 1269-Db, recombinant monomeric MBP-Pfs25 was produced from bacteria. Qualitative binding assays demonstrated that 1269-Db recognized the epitopes on Pfs25 in its native, but not the denatured state. An apparent K D of 2.6 nM was determined for 1269-Db with monomeric MBP-Pfs25, using isothermal titration calorimetry. 1269-Db recognized the periphery of zygotes/ookinetes, demonstrating recognition of Pfs25, expressed on the surface of the parasite. As the established refolding method resulted in a functional diabody, the optimized method pipeline for 1269-Db can potentially facilitate engineering of antibody fragments with desired properties., Competing Interests: Conflict of interest The authors declare that there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO 2 ) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO 2 sensing system using a varied fraction of inspired oxygen (FiO 2 ) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO 2 values (70-100%) than prior studies (85-100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO 2 while identifying positive cases of low SpO 2  < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work., (© 2022. The Author(s).)

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables., (© 2022. The Author(s).)

Malaria is a life-threatening disease caused by one of the five species of Plasmodium, among which Plasmodium falciparum cause the deadliest form of the disease. Plasmodium species are dependent on a vertebrate host and a blood-sucking insect vector to complete their life cycle. Plasmodium chitinases belonging to the GH18 family are secreted inside the mosquito midgut, during the ookinete stage of the parasite. Chitinases mediate the penetration of parasite through the peritrophic membrane, facilitating access to the gut epithelial layer. In this review, we describe Plasmodium chitinases with special emphasis on chitinases from P. falciparum and P. vivax, the representative examples of the short and long forms of this protein. In addition to the chitinase domain, chitinases belonging to the long form contain a pro-domain and chitin-binding domain. Amino acid sequence alignment of long and short form chitinase domains reveals multiple positions containing variant residues. A subset of these positions was found to be conserved or invariant within long or short forms, indicating the role of these positions in attributing form-specific activity. The reported differences in affinities to allosamidin for P. vivax and P. falciparum were predicted to be due to different residues at two amino acid positions, resulting in altered interactions with the inhibitor. Understanding the role of these amino acids in Plasmodium chitinases will help us elucidate the mechanism of catalysis and the mode of inhibition, which will be the key for identification of potent inhibitors or antibodies demonstrating transmission-blocking activity., (© 2021 The Protein Society.)