Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

Background: Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major driver of new infections. People who use web-based venues to meet sex partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections. To our knowledge, no machine learning (ML) interventions have been developed that use web-based and digital technologies to inform and personalize HIV and substance use prevention efforts for SGM young people.

Data-driven prediction of continuous renal replacement therapy survival.

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families.

Biomass and hydrocarbon production from Botryococcus braunii: A review focusing on cultivation methods.

Botryococcus braunii has garnered significant attention in recent years due to its ability to produce high amounts of renewable hydrocarbons through photosynthesis. As the world shifts towards a greener future and seeks alternative sources of energy, the cultivation of B. braunii and the extraction of its hydrocarbons can potentially provide a viable solution.

Flow reversal during stroke thrombectomy.

When performing mechanical thrombectomy for stroke patients, some physicians use balloon guide catheters (BGCs) in order to achieve flow reversal and thereby improve reperfusion quality. There is substantial evidence favoring the use of BGCs to improve reperfusion rates and clinical outcomes for thrombectomy patients; however, as we will outline in this review, there is also evidence that BGCs do not achieve reliable flow reversal in many circumstances. Therefore, if we are able to modify our techniques to improve the likelihood of flow reversal during thrombectomy maneuvers, we may be able to further improve reperfusion quality and clinical outcomes.

Cationic cellulose filter papers modified with ZnO/Ag/GO nanocomposite as point of use gravity-driven filters for bacterial removal from water.

The surface modification of filters with large pore sizes for the development of low-cost gravity-driven point-of-use (POU) technologies for water disinfection can be an effective strategy to empower people to access safe water instantly, especially in low- and middle-income countries. In this study, the surface of commercial cellulose filter papers, as cheap and bio-based filters, was modified with polydopamine (PDA), polyethyleneimine (PEI) and ZnO/Ag/GO nanocomposite (ZnO/Ag/GO@PDA/PEI papers) for bacterial removal from water. PDA/PEI incorporation introduced a cationic functional layer, which can entrap negative bacteria and make a stable chemical bond with the nanocomposite.

Data-driven prediction of continuous renal replacement therapy survival.

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty as to whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families.

Analysis of Smartphone Text Data Related to mpox from a U.S. Sample of Gay, Bisexual, and Other Men Who Have Sex with Men During the 2022 Outbreak.

We sought to understand technology-based communication regarding mpox (monkeypox) among gay, bisexual, and other men who have sex with men (GBMSM) during the global outbreak in 2022. Forty-four GBMSM (M = 25.3 years, 68.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

Background: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility.

Minimal handgrip force is needed for transcutaneous electrical stimulation to improve hand functions of patients with severe spinal cord injury.

Spinal cord stimulation enhanced restoration of motor function following spinal cord injury (SCI) in unblinded studies. To determine whether training combined with transcutaneous electrical spinal cord stimulation (tSCS), with or without systemic serotonergic treatment with buspirone (busp), could improve hand function in individuals with severe hand paralysis following SCI, we assessed ten subjects in a double-blind, sham-controlled, crossover study. All treatments-busp, tSCS, and the busp plus tSCS-reduced muscle tone and spasm frequency.

Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma.

Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

Introduction: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best-performing candidate model to determine whether the model's predictions were susceptible to bias or confounding.

Method: A CANDID-PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation.

Adequacy of Existing Surveillance Systems to Monitor Racism, Social Stigma and COVID Inequities: A Detailed Assessment and Recommendations.

The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and best practices that might inform the development of a novel COVID surveillance system that achieves these aims. We used subject area expertise, an expert panel and CDC guidance to generate an initial list of N > 50 existing surveillance systems as of 29 October 2020, and systematically excluded those not advancing the project aims.

Autopopulus: A Novel Framework for Autoencoder Imputation on Large Clinical Datasets.

The adoption of electronic health records (EHRs) has made patient data increasingly accessible, precipitating the development of various clinical decision support systems and data-driven models to help physicians. However, missing data are common in EHR-derived datasets, which can introduce significant uncertainty, if not invalidating the use of a predictive model. Machine learning (ML)-based imputation methods have shown promise in various domains for the task of estimating values and reducing uncertainty to the point that a predictive model can be employed.

Cultivation of Mixed Microalgae Using Municipal Wastewater: Biomass Productivity, Nutrient Removal, and Biochemical Content.

Background: Microalgal biotechnology has gained much attention previously. Monoculture algae cultivation has been carried out extensively in the last decades. However, although the mixed microalgae cultivation has some advantageous over pure cultures, there is still a lack of knowledge about the performance of mixed cultures.

Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study.

Background: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic.

Development of Digital Image Processing as an Innovative Method for Activated Sludge Biomass Quantification.

Activated sludge process is the most common method for biological treatment of industrial and municipal wastewater. One of the most important parameters in performance of activated sludge systems is quantitative monitoring of biomass to keep the cell concentration in an optimum range. In this study, a novel method for activated sludge quantification based on image processing and RGB analysis is proposed.

Generative Imputation and Stochastic Prediction.

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration.

Biomedical REAl-Time Health Evaluation (BREATHE): toward an mHealth informatics platform.

Objective: To describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma.

Materials And Methods: The Biomedical REAl-Time Health Evaluation (BREATHE) platform connects different sensors and data streams, contextualizing an individual's symptoms and daily activities over time to understand pediatric asthma's presentation and its management. A smartwatch/smartphone combination serves as a hub for personal/wearable sensing devices collecting data on health (eg, heart rate, spirometry, medications), motion, and personal exposures (eg, particulate matter, ozone); securely transmitting information to BREATHE's servers; and interacting with the user (eg, ecological momentary assessments).

Autotrophic granulation of hydrogen consumer denitrifiers and microalgae for nitrate removal from drinking water resources at different hydraulic retention times.

To avoid hydrogen injection and to enhance the settleability of microbial biomass in biological treatment of nitrate-contaminated drinking water resources, a new method based on granulation of a mixture of hydrogen consumer denitrifiers (HCD) and microalgae is introduced. Decreasing hydraulic retention time (HRT) was applied as the selection pressure in an up-flow photobioreactor to increase the speed of granulation and nitrate removal under autotrophic condition during a 50-day operation. Formation of granules occurred at three phases including granule nucleation, growth of granule, and mature granule, with decreasing the values of ζ-potential from -19 mV to -4 mV.

TAPER: Time-Aware Patient EHR Representation.

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical conditions the patient may have, and are typically recorded by medical staff.

A pH-sensitive delivery system based on N-succinyl chitosan-ZnO nanoparticles for improving antibacterial and anticancer activities of curcumin.

Inherent selective cytotoxicity, antibacterial activity and unique physicochemical properties of ZnO nanostructures and chitosan (CS) make them promising candidates for drug delivery. In this study, ZnO nanoparticles functionalized by N-succinyl chitosan as a pH-sensitive delivery system were synthesized to enhance the therapeutic potential of curcumin (CUR). CS coated-ZnO nanoparticles were synthesized by a co-precipitation method in the presence of CS.

Domain Adaptation in Children Activity Recognition.

Among the major challenges in training predictive models in wireless health, is adapting them to new individuals or groups of people. This is not trivial largely due to possible differences in the distribution of data between a new individual in a real-world deployment and the training data used for building the model. In this study, we aim to tackle this problem by employing recent advancements in deep Domain Adaptation which tries to transfer a model trained on a labeled dataset to a new unlabeled one that follows a different distribution as well.

Flower-like curcumin-loaded folic acid-conjugated ZnO-MPA- βcyclodextrin nanostructures enhanced anticancer activity and cellular uptake of curcumin in breast cancer cells.

Non-spherical structures are beneficial to advance drug delivery effectiveness compared with common spherical ones, due to increased drug loading capability, improved bonding to a vascular wall, enhanced cellular uptake efficacy and prolonged circulation times. In this study, flower-like Zinc oxide-βcyclodextrin (βCD) nanostructures functionalized by 3-mercaptopropionic acid (MPA) as a non-spherical delivery system was successfully synthesized for aqueous delivery of curcumin (CUR) to enhance its targeting, bioavailability, and release profile. Terminal carboxyl functional groups were used for the conjugation of folic acid (FA) with the aim of active targeting to folate overexpressing breast cancer cells.