Copper-Based Two-Dimensional Metal-Organic Frameworks for Fenton-like Photocatalytic Degradation of Methylene Blue under UV and Sunlight Irradiation.

To efficiently degrade organic pollutants, photocatalysts must be effective under both ultraviolet (UV) radiation and sunlight. We synthesized a series of new metal-organic frameworks by using mild hydrothermal conditions. These frameworks incorporate three distinct bipyridyl ligands: pyrazine (pyr), 4,4'-bipyridine (bpy), and 1,2-bis(4-pyridyl)ethane (bpe). The resulting compounds are denoted as [Cu(pyz)(H2O)2MF6], [Cu(bpy)2(H2O)2]·MF6, and [Cu(bpe)2(H2O)2]·MF6·H2O [M = Zr (1, 3, and 5) and Hf (2, 4, and 6)]. All six compounds exhibited a two-dimensional crystal structure comprising infinitely nonintersecting linear chains. Compound 3 achieved 100% degradation of methylene blue (MB) after 8 min under UV irradiation and 100 min under natural sunlight in the presence of H2O2 as the electron acceptor. For compound 5, 100% MB degradation was achieved after 120 min under sunlight and 10 min under UV light. Moreover, reactive radical tests revealed that the dominant species involved in photocatalytic degradation are hydroxyl (•OH), superoxide radicals (•O2-), and photogenerated holes (h+). The photodegradation process followed pseudo-first-order kinetics, with photodegradation rate constants of 0.362 min-1 (0.039 min-1) for 3 and 0.316 min-1 (0.033 min-1) for 5 under UV (sunlight) irradiation. The developed photocatalysts with excellent activity and good recyclability are promising green catalysts for degrading organic pollutants during environmental decontamination.

Designing a Placebo Microneedle Stamp: Modeling and Validation in a Clinical Control Trial.

Recently, several clinical studies have been conducted using microneedles (MNs), and various devices have been developed. This study aimed to propose and confirm the feasibility of a placebo control for activating MN clinical research. A 0.5 mm MN stamp with 42 needles was used as a treatment intervention, and a placebo stamp with four acupressure-type needles that did not penetrate was proposed and designed as a control for comparison. First, to check whether the placebo stamp did not invade the skin and to set an appropriate level of pressure to be provided during skin stimulation, two participants were stimulated with five different forces on the forearm, and then the skin was dyed. Secondly, to evaluate the validity of the placebo control group, a blinded study between the MN and placebo stamps was performed on 15 participants. We confirmed that the placebo stamp did not penetrate the skin at any intensity or location. Both types of stamps reported relatively low pain levels, but the MN stamp induced higher pain compared to the placebo stamp. Based on the speculation regarding the type of intervention received, the MN stamp was successfully blinded (random guess), whereas the placebo stamp was unblinded. However, according to a subgroup analysis, it was confirmed that the group with low skin sensitivity was completely blind. Blinding the placebo MN stamp had limited success in participants with low skin sensitivity. Future research on suitable placebo controls, considering the variations in MN stamp length and needle count, is warranted.

SleepMI: An AI-based screening algorithm for myocardial infarction using nocturnal electrocardiography.

Myocardial infarction (MI) is a common cardiovascular disease, the early diagnosis of which is essential for effective treatment and reduced mortality. Therefore, novel methods are required for automatic screening or early diagnosis of MI, and many studies have proposed diverse conventional methods for its detection. In this study, we aimed to develop a sleep-myocardial infarction (sleepMI) algorithm for automatic screening of MI based on nocturnal electrocardiography (ECG) findings from diagnostic polysomnography (PSG) data using artificial intelligence (AI) models. The proposed sleepMI algorithm was designed using representation and ensemble learning methods and optimized via dropout and batch normalization. In the sleepMI algorithm, a deep convolutional neural network and light gradient boost machine (LightGBM) models were mixed to obtain robust and stable performance for screening MI from nocturnal ECG findings. The nocturnal ECG signal was extracted from 2,691 participants (2,331 healthy individuals and 360 patients with MI) from the PSG data of the second follow-up stage of the Sleep Heart Health Study. The nocturnal ECG signal was extracted 3 h after sleep onset and segmented at 30-s intervals for each participant. All ECG datasets were divided into training, validation, and test sets consisting of 574,729, 143,683, and 718,412 segments, respectively. The proposed sleepMI model exhibited very high performance with precision, recall, and F1-score of 99.38%, 99.38%, and 99.38%, respectively. The total mean accuracy for automatic screening of MI using a nocturnal single-lead ECG was 99.387%. MI events can be detected using conventional 12-lead ECG signals and polysomnographic ECG recordings using our model.

Diagnostic and Management Challenges of Esophageal Rupture with Concomitant Cervical Abscess in Chronic High Cervical Tetraplegia.

A 65-year-old with a history of spinal cord injury and previous cervical surgery presented with persistent fever despite antibiotic treatment. MRI scans revealed an abscess in the neck extending from C3 to C6, with associated osteomyelitis. After an initial discharge following antibiotic therapy, the patient was readmitted due to recurrent systemic infection symptoms and another abscess. A subsequent endoscopy showed esophageal rupture with protruding cervical fusion metal. Due to operative risks, a percutaneous endoscopic gastrostomy was performed without further infection recurrence. The absence of typical imaging signs of esophageal rupture made diagnosis difficult. The infection spread through the cervical fascia from superficial to deep cervical areas. Esophageal rupture, a rare complication of cervical surgery, presents with varying symptoms depending on its location and was particularly challenging to diagnose in this patient due to high cervical tetraplegia, which masked typical pain responses. Therefore, this case highlights the need to consider esophageal rupture in differential diagnoses for chronic ACDF patients, even when typical symptoms are absent.

A DNA vaccine against GII.4 human norovirus VP1 induces blocking antibody production and T cell responses.

Human noroviruses (HuNoVs) are highly contagious and a leading cause of epidemics of acute gastroenteritis worldwide. Among the various HuNoV genotypes, GII.4 is the most prevalent cause of outbreaks. However, no vaccines have been approved for HuNoVs to date. DNA vaccines are proposed to serve as an ideal platform against HuNoV since they can be easily produced and customized to express target proteins. In this study, we constructed a CMV/R vector expressing a major structural protein, VP1, of GII.4 HuNoV (CMV/R-GII.4 HuNoV VP1). Transfection of CMV/R-GII.4 HuNoV VP1 into human embryonic kidney 293T (HEK293T) cells resulted in successful expression of VP1 proteins in vitro. Intramuscular or intradermal immunization of mice with the CMV/R-GII.4 HuNoV VP1 construct elicited the production of blocking antibodies and activation of T cell responses against GII.4 HuNoV VP1. Our collective data support the utility of CMV/R-GII.4 HuNoV VP1 as a promising DNA vaccine candidate against GII.4 HuNoV.

Immediate-start peritoneal dialysis without break-in-period: an 18-year retrospective cohort study on patient survival.

Background: Immediate-start peritoneal dialysis (ISPD) is an effective renal replacement therapy that can prevent central venous catheterization due to its immediate initiation of peritoneal dialysis (PD) after catheter insertion without a break-in period. This study aimed to investigate the effect of ISPD on long-term patient survival. Methods: In this retrospective single-center cohort study, 178 consecutive patients who started PD from August 2005 to March 2023 were enrolled, from whom 144 patients with ISPD were analyzed. PD was initiated without a break-in period within 24 hours of catheter insertion using percutaneous needle-guidewire technique. The primary outcome was patient survival, estimated using the Kaplan-Meier method. A Cox proportional hazard regression model was used to identify factors independently associated with patient survival. Results: The median follow-up period was 4.00 years (interquartile range, 1.23‒5.75 years). The mean age of patients was 61.6 ± 13.6 years; 58 patients (40.3%) were male and 93 patients (64.6%) were diabetic. Overall patient survival rates at 1, 3, 5, and 10 years were 98.5%, 93.5%, 92.1%, and 65.6%, respectively. The technique survival rates at 1, 3, 5, and 10 years were 88.1%, 74.9%, 63.2%, and 40.2%, respectively. The peritonitis-free survival rates at 1, 3, 5, and 10 years were 92.3%, 76.0%, 59.4%, and 28.0%, respectively. In the multivariate analysis, diabetes was the only factor associated with patient survival and technique survival. Conclusion: Our study demonstrated that patient survival and technique survival rates were relatively high in ISPD patients who were catheterized using percutaneous needle-guidewire technique.

A study on the fabrication of metal microneedle array electrodes for ECG detection based on low melting point Bi-In-Sn alloys.

This study describes the fabrication and characteristics of microneedle array electrodes (MAEs) using Bismuth-Indium-Tin (Bi-In-Sn) alloys. The MAEs consist of 57 pyramid-shaped needles measuring 340 µm wide and 800 µm high. The fabrication process involved micromolding the alloys in a vacuum environment. Physical tests demonstrated that Bi-In-Sn MAEs have good mechanical strength, indicating their suitability for successful skin penetration. The electrode-skin interface impedance test confirmed that Bi-In-Sn MAEs successfully penetrated the skin. Impedance measurements revealed the importance of insulating the microneedle electrodes for optimal electrical performance, and a UV-curable Polyurethane Acrylate coating was applied to enhance insulation. Electrocardiogram measurements using the Bi-In-Sn MAEs demonstrated performance comparable to that of traditional Ag/AgCl electrodes, which shows promise for accurate data collection. Overall, the study demonstrates successful, minimally-invasive skin insertion, improved electrical insulation, and potential applications of Bi-In-Sn microneedle array. These findings contribute to advancements in microneedle technology for biomedical applications.

Safety tests and clinical research on buccal and nasal microneedle swabs for genomic analysis.

Conventional swabs have been used as a non-invasive method to obtain samples for DNA analysis from the buccal and the nasal mucosa. However, swabs may not always collect pure enough genetic material. In this study, buccal and nasal microneedle swab is developed to improve the accuracy and reliability of genomic analysis. A cytotoxicity test, a skin sensitivity test, and a skin irritation test are conducted with microneedle swabs. Polymer microneedle swabs meet the safety requirements for clinical research and commercial use. When buccal and nasal microneedle swabs are used, the amount of genetic material obtained is greater than that from commercially available swabs, and DNA purity is also high. The comparatively short microneedle swab (250 µm long) cause almost no pain to all 25 participants. All participants also report that the microneedle swabs are very easy to use. When genotypes are compared at five SNP loci from blood of a participant and from that person's buccal or nasal microneedle swab, the buccal and nasal microneedle swabs show 100% concordance for all five SNP genotypes. Microneedle swabs can be effectively used for genomic analysis and prevention through genomic analysis, so the utilization of microneedle swabs is expected to be high.

Pharmaceutical and Immunological Evaluation of Cholera Toxin A1 Subunit as an Adjuvant of Hepatitis B Vaccine Microneedles.

PURPOSE: For successful delivery of a solid vaccine formulation into the skin using microneedles, the solubility of an adjuvant should be considered because the decrease in the dissolution rate by the addition of adjuvant decreases the delivery efficiency of the vaccine. METHODS: In this study, cholera toxin A subunit 1 (CTA1) was examined as an adjuvant to Hepatitis B vaccine (HBV) microneedles because of its good water solubility, improved safety, and positive effect as shown in intramuscular administration of a liquid vaccine. RESULTS: All solid formulations with CTA 1 dissolved in in vivo mouse skin within 30 min, and they were successfully delivered into the skin. In experiments with mice, the addition of CTA1 led to improved IgG immune response compared to the use of an aluminum hydroxide-based formulation and intramuscular administration of HBV. In addition, CTA1 induced CD8 + T cell response as much as in which the aluminum hydroxide-based formulation induced. CONCLUSIONS: CTA1 is an adjuvant that satisfies both the delivery efficiency and the immunological characteristics required for vaccine microneedles. CTA1 will be used as a potential adjuvant through vaccine microneedles.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions.

BACKGROUND: Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. OBJECTIVE: The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). METHODS: We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. RESULTS: As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. CONCLUSIONS: This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52161.

Assessing the risk of a clinically significant infection from a Microneedle Array Patch (MAP) product.

Microneedle Array Patches (MAPs) are an emerging dosage form that creates transient micron-sized disruptions in the outermost physical skin barrier, the stratum corneum, to facilitate delivery of active pharmaceutical ingredients to the underlying tissue. Numerous MAP products are proposed and there is significant clinical potential in priority areas such as vaccination. However, since their inception scientists have hypothesized about the risk of a clinically significant MAP-induced infection. Safety data from two major Phase 3 clinical trials involving hundreds of participants, who in total received tens of thousands of MAP applications, does not identify any clinically significant infections. However, the incumbent data set is not extensive enough to make definitive generalizable conclusions. A comprehensive assessment of the infection risk is therefore advised for MAP products, and this should be informed by clinical and pre-clinical data, theoretical analysis and informed opinions. In this article, a group of key stakeholders identify some of the key product- and patient-specific factors that may contribute to the risk of infection from a MAP product and provide expert opinions in the context of guidance from regulatory authorities. Considerations that are particularly pertinent to the MAP dosage form include the specifications of the finished product (e.g. microbial specification), it's design features, the setting for administration, the skill of the administrator, the anatomical application site, the target population and the clinical context. These factors, and others discussed in this article, provide a platform for the development of MAP risk assessments and a stimulus for early and open dialogue between developers, regulatory authorities and other key stakeholders, to expedite and promote development of safe and effective MAP products.

TOPK inhibits TNF-α-induced granulosa cell apoptosis via regulation of SIRT1/p53.

T-LAK cell originated protein kinase (TOPK) has been shown to regulate proliferation, invasion or migration of various cancer cells. However, the role of TOPK in follicle environments remains unknown. Here we reveal that TOPK inhibits TNF-α-induced human granulosa COV434 cell apoptosis. The expression of TOPK were increased in COV434 cells in response to TNF-α. TOPK inhibition also decreased TNF-α-induced SIRT1 expression but promoted TNF-α-induced p53 acetylation and expression of PUMA or NOXA. Accordingly, TOPK inhibition attenuated TNF-α-mediated SIRT1 transcriptional activity. In addition, SIRT1 inhibition augmented acetylation of p53 or expression of PUMA and NOXA in response to TNF-α, leading to COV434 cell apoptosis. We conclude that TOPK suppresses TNF-α-induced COV434 granulosa cell apoptosis via regulation of p53/SIRT1 axis, suggesting a potential role of TOPK in regulation of ovarian follicular development.

Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.

BACKGROUND: Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times. OBJECTIVE: The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data. METHODS: We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity. RESULTS: The total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3). CONCLUSIONS: Walking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a "smart" intervention for promoting physical activity.

Effect of Annealing Temperature on the Structural and Electrochemical Properties of Hydrothermally Synthesized NiCo2O4 Electrodes.

In this study, a porous Ni-foam support was employed to enhance the capacitance of nickel cobaltite (NiCo2O4) electrodes designed for supercapacitors. The hydrothermal synthesis method was employed to grow NiCo2O4 as an active material on Ni-foam. The NiCo2O4 sample derived from hydrothermal synthesis underwent subsequent post-heat treatment at temperatures of 250 °C, 300 °C, and 350 °C. Thermogravimetric analysis of the NiCo2O4 showed that weight loss due to water evaporation occurs after 100 °C and enters the stabilization phase at temperatures above 400 °C. The XRD pattern indicated that NiCo2O4 grew into a spinel structure, and the TEM results demonstrated that the diffraction spots (DSs) on the (111) plane of the sample annealed at 350 °C were more pronounced than those of other samples. The specific capacitance of the NiCo2O4 electrodes exhibited a decrease with increasing current density across all samples, irrespective of the annealing temperature. The electrode annealed at 350 °C recorded the highest specific capacitance value. However, the capacity retention rate of the NiCo2O4 electrode revealed a deteriorating trend, declining to 88% at 250 °C, 75% at 300 °C, and 63% at 350 °C, as the annealing temperature increased.

Applications of deep learning methods in digital biomarker research using noninvasive sensing data.

Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.

Enhanced Social Cognitive Theory Dynamic Modeling and Simulation Towards Improving the Estimation of "Just-In-Time" States.

Insufficient physical activity (PA) is commonplace in society, in spite of its significant impact on personal health and well-being. Improved interventions are clearly needed. One of the challenges faced in behavioral interventions is a lack of understanding of multi-timescale dynamics. In this paper we rely on a dynamical model of Social Cognitive Theory (SCT) to gain insights regarding a control-oriented experimental design for a behavioral intervention to improve PA. The intervention (Just Walk JITAI) is designed with the aim to better understand and estimate ideal times for intervention and support based on the concept of "just-in-time" states. An innovative input signal design strategy is used to study the just-in-time state dynamics through the use of decision rules based on conditions of need, opportunity and receptivity. Model simulations featuring within-day effects are used to assess input signal effectiveness. Scenarios for adherent and non-adherent participants are presented, with the proposed experimental design showing significant potential for reducing notification burden while providing informative data to support future system identification and control design efforts.

Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity.

Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC's potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.

Corrigendum: Development of a microneedle swab for acquisition of genomic DNA from buccal cells.

[This corrects the article DOI: 10.3389/fbioe.2022.829648.].

Preparation of particle-attached microneedles using a dry coating process.

The standard process for manufacturing microneedles containing API requires a way to process the API, including dissolving the API in a co-solvent and a drying process. In this study, the authors introduce a novel microneedle system that involves physically attaching API particles to the biocompatible adhesive surface of the microneedles. To manufacture particle-attached microneedles, an adhesive surface was prepared by coating polydimethylsiloxane (PDMS) mixed with an elastomer base and a curing agent at a ratio of 40:1 (PDMS40) onto polylactic acid microneedles (PLA), and then attaching ovalbumin (OVA) particles with a mean diameter of 10 µm to the PDMS adhesive layer. The OVA particles were delivered for 5 min into porcine skin with a delivery efficiency of 93% ex vivo and into mouse skin with a delivery efficiency of over 90% in vivo. Finally, mouse experiments with OVA particle-attached microneedles showed a value of OVA antibody titer similar to that produced by intramuscular administration. Particle-attached microneedles are a novel microneedle system with a dry coating process and rapid API delivery into the skin. Particle-attached microneedles can provide a wide range of applications for administering drugs and vaccines.