Development and characterization of curcumin nanosuspension-embedded genipin-crosslinked chitosan/polyvinylpyrrolidone hydrogel patch for effective wound healing.

This study investigated the development of a genipin-crosslinked chitosan (CS)-based polyvinylpyrrolidone (PVP) hydrogel containing curcumin nanosuspensions (Cur-NSs) to promote wound healing in an excisional wound model. Cur-NSs were prepared, and a simplex centroid mixture design was employed to optimize hydrogel properties for high water absorption, degree of crosslinking, and sufficient toughness. The in vivo wound healing effect was tested in Wistar rats. The optimized hydrogel consisted of a 70:30 ratio of CS:PVP, crosslinked with a 2 % w/w genipin solution. It exhibited high swelling capability (486 %) while maintaining solidity, robustness, and durability. Incorporating 5 % w/w Cur-NSs resulted in a more compact structure, although with a reduction in swelling properties. The release kinetics of Cur from the hydrogel followed the Korsmeyer-Peppas Fickian diffusion model. In vitro biocompatibility studies demonstrated that the hydrogel was non-toxic to skin fibroblast cells. The in vivo experiment revealed a desirable wound healing rate with over 80 % recovery by day 7. Cur-NSs likely aided wound healing by reducing the inflammatory response and stimulating fibroblast proliferation. Additionally, the CS-based hydrogel provided a moist wound environment with hydration and gas transfer, further accelerating wound closure. These findings suggest that the Cur-NS-embedded hydrogel shows promise as a wound dressing material.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Impact of "3414" fertilization on the yield and quality of greenhouse tomatoes.

This study aimed to explore the effects of different nitrogen, phosphorus, and potassium ratios on the yield and nutritional quality of greenhouse tomatoes under a water and fertilizer integration model. Greenhouse tomatoes were used as the research object, and the "3414" fertilizer trial design was employed to assess tomato growth, yield, quality, and soil indicators across various treatment combinations. The goal was to determine the optimal fertilization scheme and recommend appropriate fertilizer quantities for tomato cultivation and production. The results revealed that different fertilizer ratios significantly affected both the quality and yield of tomatoes. Overall, the tomato yield tended to increase with higher fertilization amounts, with potassium exhibiting the most pronounced effect on yield increase, followed by phosphorus and nitrogen. The comprehensive analysis of principal components indicated that the N2P2K1 treatment yielded the highest nutritional quality and yield. Therefore, the best fertilization combination identified in this study consisted of nitrogen fertilizer at 197.28 kg hm-2, phosphorus fertilizer at 88.75 kg hm-2, and potassium fertilizer at 229.80 kg hm-2. These findings provided the scientific basis for optimizing fertilization practices in greenhouse tomato cultivation and production in the Jilin Province.

The Role of CMC Statisticians: Co-Practitioners of the Scientific Method.

Chemistry, manufacturing, and control (CMC) statisticians play a key role in the development and lifecycle management of pharmaceutical and biological products, working with their non-statistician partners to manage product quality. Information used to make quality decisions comes from studies, where success is facilitated through adherence to the scientific method. This is carried out in four steps: (1) an objective, (2) design, (3) conduct, and (4) analysis. Careful consideration of each step helps to ensure that a study conclusion and associated decision is correct. This can be a development decision related to the validity of an assay or a quality decision like conformance to specifications. Importantly, all decisions are made with risk. Conventional statistical risks such as Type 1 and Type 2 errors can be coupled with associated impacts to manage patient value as well as development and commercial costs. The CMC statistician brings focus on managing risk across the steps of the scientific method, leading to optimal product development and robust supply of life saving drugs and biologicals.

Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Green method for 17-hydroxyprogesterone extraction and determination using PDMS stir bar sorptive extraction coupled with HPLC: optimization by response surface methodology.

Quantifying small amounts of the 17-hydroxyprogesterone in various matrix is crucial for different purposes. In this study, a commercial polydimethylsiloxane stir bar was used to extract hormone from water and urine samples. Analysis was performed by high-performance liquid chromatography using a UV detector. The response surface methodology was used to optimize the desorption and extraction steps, with predicted optimal point relative errors of 1.25% and 6.40%, respectively. The optimized method was validated with a linear range of 1.21-1000.00 for aqueous and 2.43-2000.00 ng mL-1 for urine samples. The coefficient of determination was 0.9998 and 0.9967, and the detection limit of the proposed method was obtained to be 0.40 and 0.80 ng mL-1 for aqueous and urine samples, respectively. The recovery percentage and relative standard deviation within a day and between three days after the addition of three different concentration levels of the standard to the control sample were 87-103% and 0.4-3.6% for aqueous and 87.5-101% and 0.1-5.2% for urine samples, respectively. The results show that the proposed method can be appropriate and cost-effective for extracting and analyzing this hormone. In addition, using three different tools, the greenness of the proposed method was proven.

Influence of multi-stressor combinations of pCO2, temperature, and salinity on the toxicity of Heterosigma akashiwo (Raphidophyceae), a fish-killing flagellate.

Climate change and global warming have led to more frequent harmful algal blooms in the last decade. Among these blooms, Heterosigma akashiwo, a golden-brown phytoflagellate, is one of the 40 species with a high potential to form harmful blooms, leading to significant fish mortality. Climate change leads to rising atmospheric and ocean temperatures. These changes, along with altered rainfall patterns and meltwater input, can cause fluctuations in ocean salinity. Elevated atmospheric carbon dioxide (CO2) levels increase water acidity as oceans absorb CO2. This study investigated the effects of temperature, salinity, and CO2 levels on lipid production, hemolytic activity, and toxicity of H. akashiwo using the design of experiment approach, which can be used to investigate the effect of two or more factors on the same response simultaneously in a precise manner with fewer experiments and materials but in a larger region of the factor space. The lipid content was measured using a high-throughput Nile Red method, and the highest level of lipid content was detected at 25°C, a salinity of 30, and a CO2 concentration of 400 ppm. Hemolytic activity was assessed using rabbit blood erythrocytes in a 96-well plate, and the optimal conditions for achieving the highest hemolytic activity were determined at 15°C, a salinity of 10, and a CO2 concentration of 400 ppm. As the chemical structure of the toxin is not known, we used the toxicity against the cell line RTgill-W1 as the cell toxicity proxy. The maximum toxicity was identified at 15°C, a salinity of 10, and a CO2 level of 700 ppm.

Activated Carbon Prepared from Waste Coffee Grounds: Characterization and Adsorption Properties of Dyes.

Spent coffee grounds (SCGs) have great potential as a useful, value-added biological material. In this context, activated carbon (AC) was prepared from SCGs by an activation process using H3PO4 at 600 °C in the air and used as an adsorbent for the azo dye AO7, a model molecule for dye colorants found in textile industry effluents. X-ray diffraction, SEM and BET revealed that the AC was predominantly amorphous, consisting of a powder of 20-100 µm particles with mesopores averaging 5.5 nm in pore size. Adsorption kinetics followed a pseudo-second-order law, while the Langmuir model best fitted the experimental isotherm data (maximum capacity of 119.5 mg AO7 per AC g). The thermodynamic parameters revealed that adsorption was endothermic and spontaneous. All the characterizations indicated that adsorption occurred by physisorption via mainly π-π interactions. The best experimental removal efficiency optimized by means of a Box-Behnken design and response surface methodology was 98% for an initial AO7 concentration of 20 mg·L-1 at pH 7.5 with a dose of 0.285 g·L-1 of AC and a contact time of 40 min. These results clearly show that activated carbon prepared from SCGs can be a useful material for efficiently removing organic matter from aqueous solutions.

Influence of Food Environments on Dietary Habits: Insights from a Quasi-Experimental Research.

Nutrition is a vital factor that exerts a profound and direct impact on health. Food environments significantly influence individuals' dietary behaviors, health outcomes, and overall food security. Individuals in food deserts and food swamps do not have access to healthier food options. And in both cases, the emphasis is primarily on the physical configuration of the environment as it relates to food availability. This quasi-experimental study aims to investigate the impact of two different food environments (defined to include a social component) on food choices. A total of 246 participants were surveyed by telephone, half of whom were primed with Scenario A (a food environment deficient in healthy options and cues that support and reinforce healthier choices) and half were primed with Scenario B (an environment with an abundance of healthy food options and cues that support and reinforce healthy eating). Ordered logit regression was used for analysis. The results show significant differences in likely food consumption between the groups. Individuals in Scenario B were found to be 4.48 times more likely to consume fruits and vegetables. In addition, it was determined that being a woman increases the probability of consuming more fruits and vegetables by 0.92 times (1/0.52-1), and adherence to a healthy diet increases by 3.64 times. Age and race were not significant predictors. This study highlights the crucial role of environmental factors in shaping dietary habits and underscores the importance of the social components of the food environment in promoting the adoption of healthier dietary habits. Based on these findings, policymakers should prioritize developing strategies that go beyond providing physical access and consider social aspects of the environment in promoting healthy eating habits to improve public health and bolster the food security of communities.

A Novel Method for the Preparation of Casein-Fucoidan Composite Nanostructures.

The aim of the study was to develop casein-fucoidan composite nanostructures through the method of polyelectrolyte complexation and subsequent spray drying. To determine the optimal parameters for the preparation of the composite structures and to investigate the influence of the production and technological parameters on the main structural and morphological characteristics of the obtained structures, 3(k-p) fractional factorial design was applied. The independent variables (casein to fucoidan ratio, glutaraldehyde concentration, and spray intensity) were varied at three levels (low, medium, and high) and their effect on the yield, the average particle size, and the zeta potential were evaluated statistically. Based on the obtained results, models C1F1G1Sp.30, C1F1G2Sp.40, and C1F1G3Sp.50, which have an average particle size ranging from (0.265 ± 0.03) µm to (0.357 ± 0.02) µm, a production yield in the range (48.9 ± 2.9) % to (66.4 ± 2.2) %, and a zeta potential varying from (-20.12 ± 0.9) mV to (-25.71 ± 1.0) mV, were selected as optimal for further use as drug delivery systems.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Effect of information and communication technology-based smart care services for physical and cognitive functions in older adults living alone: A quasi-experimental study.

BACKGROUND: As the global population ages and the number of older adults living alone increases, societies face the responsibility of building new support systems and providing novel forms of care to ensure the independence and happiness of sick or frail older individuals. This quasi-experimental study examined the association between information and communication technology-based smart care services and the physical and cognitive functions of older individuals living alone. METHODS: This study used a suite of smart technologies (artificial intelligence speaker, radar sensor, and personalized exercise App.) and interventions tailored to the initial physical functional scores of the participants. A total of 176 participants were recruited and assigned, with 88 participants in the intervention group and 88 in the control group. The short physical performance battery (SPPB), the digit span test (DST), and the Korean mini-mental state examination (K-MMSE) were used to assess participants before and after 12 weeks. RESULTS: No significant differences in gender, age, or educational levels were observed between the intervention and control groups. After adjusting for baseline performance, analysis of covariance revealed that the intervention group exhibited better outcomes in the SPPB five-time chair stand score (adjusted score difference: 0.329; P = 0.044) and the backward DST (adjusted score difference: 0.472; P = 0.007), but had lower score of K-MMSE (adjusted score difference: -0.935; P = 0.021), indicating enhanced lower limb muscle strength and cognitive function in working memory. CONCLUSION: ICT-based smart care services, combined with personalized exercise interventions, significantly support the physical and cognitive health of solitary older individuals. This approach highlights the potential of integrating smart technology and targeted physical activity to foster the well-being of the aging population living alone.

Erratum: Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis.

[This corrects the article DOI: 10.3389/fpubh.2024.1393677.].

Insights on the role of blocking agent on the properties of the lipase from Thermomyces lanuginosus immobilized on heterofunctional support for hydroesterification reactions.

Here, we report a study of the effect of the blocking agent on the properties of the lipase from Thermomyces lanuginosus (TLL) immobilized on a heterofunctional support (Purolite C18-ethylnediamina (EDA)- vinyl sulfone (VS)-TLL-blocking agent) in different reactions. The performance of the biocatalysts was compared to those immobilized on standard hydrophobic support (Purolite C18-TLL) and the commercial one (TLL-IM). The nature of the blocking agent (Cys, Gly and Asp) altered the enzyme features. TLL-IM always gave a comparatively worse performance, with its specificity for the oil being very different to the Purolite biocatalysts. Under optimized conditions, Purolite C18-TLL yielded 97 % of hydrolysis conversion after 4 h using a water/waste cooking soybean oil (WCSO) mass ratio of 4.3, biocatalyst load of 6.5 wt% and a temperature of 44.2 °C (without buffer or emulsification agent). In esterification reactions of the purified free fatty acids (FFAs) obtained from WCSO, the best TLL biocatalysts depended on the utilized alcohol: linear amyl alcohol was preferred by Purolite C18-TLL and Purolite C18-EDA-VS-TLL-Gly, while higher activity was achieved utilizing isoamyl alcohol as nucleophile by Purolite C18-EDA-VS-TLL-Cys, Purolite C18-EDA-VS-TLL-Asp and IM-TLL as catalysts. All the results indicate the influence of the blocking step on the final biocatalyst features.

Improvement of Bacillus subtilis PI agarase production, hydrolysate scavenging capability assessment, and saccharification of algal biomass for green ethanol generation.

The goal of the current work was to optimize the growth parameters needed to manufacture agarase enzyme from a non-marine PI strain of Bacillus subtilis on an agar-based medium. Using Plackett-Burman design (PBD), nine process parameters were evaluated, and agar, peptone, and yeast-extract were identified as the most significant independent factors influencing agarase production with confidence levels more than 90%. To evaluate the optimal concentrations of the indicated process parameters on agarase production, the Box-Behnken design (BBD) was applied. After optimization, B. subtilis strain PI produced 119.8 U/ml of agarase, representing a 1.36-fold increase. In addition the agar hydrolysate fermented products contain the liberated oligosaccharide acts as strong antioxidant which has 62.4% scavenging activity. Also, the agarase yields increased (1141.12, 1350.253, 1684.854 and 1921.863 U/ml) after substitution the agar with algal biomass of Carolina officinalis at different concentrations (2, 5, 10 and 15%), respectively. After completing the saccharification process, the resulted hydrolysate was used to produce ethanol through fermentation with Pichia pastoris yeast strain as an economical method giving yields (6.68317, 7.09748, 7.75648 and 8.22332 mg/ml), that are higher than using yeast extract peptone dextrose (YPD) medium (4.461 mg/ml).

ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks.

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive. Methods: In this study, we propose a novel approach utilizing experimental design methodologies, specifically 2-Factorial Analysis for Screening, and Response Surface Methodology (RSM) for parameter adjustment. We apply this methodology to the task of detecting cancer driver genes in colorectal cancer. Result: Through experimental validation of colorectal cancer data, we demonstrate the effectiveness of our proposed methodology. Compared with existing methods, our approach offers several advantages, including reduced computational overhead, systematic parameter selection grounded in statistical theory, and improved performance in detecting cancer driver genes. Conclusion: This study presents a significant advancement in the field of network analysis by providing a practical and systematic approach to hyperparameter tuning. By optimizing parameter settings, our methodology offers promising implications for critical biomedical applications such as cancer driver gene detection.

Experimental design considerations and statistical analyses in preclinical tumor growth inhibition studies.

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.

Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: a comprehensive methodological approach.

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

A Methodology for Robust Multislice Ptychography.

While multislice electron ptychography can provide thermal vibration limited resolution and structural information in 3D, it relies on properly selecting many intertwined acquisition and computational parameters. Here, we outline a methodology for selecting acquisition parameters to enable robust ptychographic reconstructions. We develop two physically informed metrics, areal oversampling and Ronchigram magnification, to describe the selection of these parameters in multislice ptychography. Through simulations, we comprehensively evaluate the validity of these two metrics over a broad range of conditions and show that they accurately guide reconstruction success. Further, we validate these conclusions with experimental ptychographic data and demonstrate close agreement between trends in simulated and experimental data. Using these metrics, we achieve experimental multislice reconstructions at a scan step of 2.1Å/px, enabling large field-of-view, data-efficient reconstructions. These experimental design principles enable the routine and robust use of multislice ptychography for 3D characterization of materials at the atomic scale.

The effect of an online acceptance and commitment intervention on the meaning-making process in cancer patients following hematopoietic cell transplantation: study protocol for a randomized controlled trial enhanced with single-case experimental design.

BACKGROUND: Hematopoietic cell transplantation (HCT) is a highly invasive and life-threatening treatment for hematological neoplasms and some types of cancer that can challenge the patient's meaning structures. Restoring meaning (i.e., building more flexible and significant explanations of the disease and treatment burden) can be aided by strengthening psychological flexibility by means of an Acceptance and Commitment Therapy (ACT) intervention. Thus, this trial aims to examine the effect of the ACT intervention on the meaning-making process and the underlying mechanisms of change in patients following HCT compared to a minimally enhanced usual care (mEUC) control group. The trial will be enhanced with a single-case experimental design (SCED), where ACT interventions will be compared between individuals with various pre-intervention intervals. METHODS: In total, 192 patients who qualify for the first autologous or allogeneic HCT will be recruited for a two-armed parallel randomized controlled trial comparing an online self-help 14-day ACT training to education sessions (recommendations following HCT). In both conditions, participants will receive once a day a short survey and intervention proposal (about 5-10 min a day) in the outpatient period. Double-blinded assessment will be conducted at baseline, during the intervention, immediately, 1 month, and 3 months after the intervention. In addition, 6-9 participants will be invited to SCED and randomly assigned to pre-intervention measurement length (1-3 weeks) before completing ACT intervention, followed by 7-day observations at the 2nd and 3rd post-intervention measure. The primary outcome is meaning-related distress. Secondary outcomes include psychological flexibility, meaning-making coping, meanings made, and well-being as well as global and situational meaning. DISCUSSION: This trial represents the first study that integrates the ACT and meaning-making frameworks to reduce meaning-related distress, stimulate the meaning-making process, and enhance the well-being of HCT recipients. Testing of an intervention to address existential concerns unique to patients undergoing HCT will be reinforced by a statistically rigorous idiographic approach to see what works for whom and when. Since access to interventions in the HCT population is limited, the web-based ACT self-help program could potentially fill this gap. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT06266182. Registered on February 20, 2024.