Data-driven insights can transform women's reproductive health.

This perspective explores the transformative potential of data-driven insights to understand and address women's reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.g., next-generation sequencing techniques, electronic medical records (EMRs), computational power) provide unprecedented opportunities for research in women's reproductive health. Studies of molecular data, including large-scale meta-analyses, provide valuable insights into conditions like preterm birth and preeclampsia. Moreover, EMRs and other clinical data sources enable researchers to study populations of individuals, uncovering trends and associations in women's reproductive health conditions. Despite these advancements, challenges such as data completeness, accuracy, and representation persist. We emphasize the importance of holistic approaches, greater inclusion, and refining and expanding on how we leverage data and computational integrative approaches for discoveries so that we can benefit not only women's reproductive health but overall human health.

Perceptions and Information-Seeking Behavior Regarding COVID-19 Vaccination Among Patients With Chronic Kidney Disease in 2023: A Cross-Sectional Survey.

Background: People living with chronic kidney disease (CKD) face an increased risk of severe outcomes such as hospitalization or death from COVID-19. COVID-19 vaccination is a vital approach to mitigate the risk and severity of infection in patients with CKD. Limited information exists regarding the factors that shape COVID-19 vaccine uptake, including health information-seeking behavior and perceptions, within the CKD population. Objective: The objectives were to describe among CKD patients, (1) health information-seeking behavior on COVID-19, (2) their capacity to comprehend and trust COVID-19 information from different sources, and (3) their perceptions concerning COVID-19 infection and vaccination. Design/Setting: Cross-sectional web-based survey administered in British Columbia and Ontario from February 17, 2023, to April 17, 2023. Participants: Chronic kidney disease G3b-5D patients and kidney transplant recipients (CKD G1T-5T) enrolled in a longitudinal COVID-19 vaccine serology study. Methods and Measurements: The survey consisted of a questionnaire that included demographic and clinical data, perceived susceptibility of contracting COVID-19, the ability to collect, understand, and trust information on COVID-19, as well as perceptions regarding COVID-19 vaccination. Descriptive statistics were used to present the data with values expressed as count (%) and chi square tests were performed with a significance level set at P ≤ .05. A content analysis was performed on one open-ended response regarding respondents' questions surrounding COVID-19 infection and vaccination. Results: Among the 902 patients who received the survey via email, 201 completed the survey, resulting in a response rate of 22%. The median age was 64 years old (IQR 53-74), 48% were male, 51% were university educated, 32% were on kidney replacement therapies, and 57% had received ≥5 COVID-19 vaccine doses. 65% of respondents reported that they had sought out COVID-19-related information in the last 12 months, with 91% and 84% expressing having understood and trusted the information they received, respectively. Those with a higher number of COVID-19 vaccine doses were associated with having sought out (P =.017), comprehended (P < .001), and trusted (P =. 005) COVID-19-related information. Female sex was associated with expressing more concern about contracting COVID-19 (P = .011). Most respondents strongly agreed to statements regarding the benefits of COVID-19 vaccination. Respondents' questions about COVID-19 infection and vaccination centered on 4 major themes: COVID-19 vaccination strategy, vaccine effectiveness, vaccine safety, and the impact of COVID-19 infection and vaccination on kidney health. Limitations: This survey was administered within the Canadian health care context to patients with CKD who had at least 1 COVID-19 vaccine dose. Race/ethnicity of participants was not captured. Conclusions: In this survey of individuals with CKD, COVID-19 information-seeking behavior was high and almost all respondents understood and trusted the information they received. Perceptions toward the COVID-19 vaccine and booster were mostly favorable.

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method.

Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge-Kutta Fehlberg's fourth-fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.

Virtual staining for histology by deep learning.

In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.

Curated and Annotated Dataset of Lung US Images in Zambian Children with Clinical Pneumonia.

See also the commentary by Sitek in this issue. Supplemental material is available for this article.

Synergistic effect of silicon and arbuscular mycorrhizal fungi reduces cadmium accumulation by regulating hormonal transduction and lignin accumulation in maize.

Cadmium (Cd) stress causes serious damage to plants, inducing various physiological and biochemical disruptions that lead to reduced plant biomass and compromised growth. The study investigated the combined effects of silicon (Si) and arbuscular mycorrhizal fungi (AMF) on mitigating Cd stress in plants, revealing promising results in enhancing plant tolerance to Cd toxicity. Under Cd stress, plant biomass was significantly reduced (-33% and -30% shoot and root dry weights) as compared to control. However, Si and AMF application ameliorated this effect, leading to increased shoot and root dry weights (+47% and +39%). Furthermore, Si and AMF demonstrated their potential in reducing the relative Cd content (-43% and -36% in shoot and root) in plants and positively influencing plant colonization (+648%), providing eco-friendly and sustainable strategies to combat Cd toxicity in contaminated soils. Additionally, the combined treatment in the Cd-stressed conditions resulted in notable increases in saccharide compounds and hormone levels in both leaf and root tissues, further enhancing the plant's resilience to Cd-induced stress. Si and AMF also played a vital role in positively regulating key lignin biosynthesis genes and altering lignin-related metabolites, shedding light on their potential to fortify plants against Cd stress. These findings underscore the significance of Si and AMF as promising tools in addressing Cd toxicity and enhancing plant performance in Cd-contaminated environments.

Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis.

This study investigates the construct of sisu, a Finnish attribute representing mental resilience and fortitude when confronted with difficult situations. By leveraging advanced analytical methods and explainable Artificial Intelligence, we gain insights into how sisu factors influence well-being, work efficiency, and overall health. We investigate how the beneficial aspects of sisu contribute significantly to mental and physical health, satisfaction, and professional accomplishments. Conversely, we analyze the harmful sisu and its adverse impacts on the same domains. Our findings, including intriguing trends related to age, educational level, emotional states, and gender, pave the way for developing tailored solutions and initiatives to nurture the beneficial aspects of sisu and curtail the damaging consequences of sisu within professional settings and personal welfare.

Two-phase simulation of entropy optimized mixed convection flow of two different shear-thinning nanomaterials in thermal and mass diffusion systems with Lorentz forces.

This research compares the momentum, thermal energy, mass diffusion and entropy generation of two shear thinning nanofluids in an angled micro-channel with mixed convection, nonlinear thermal radiation, temperature jump boundary condition and variable thermal conductivity effects. The [Formula: see text] approach was used to solve the Buongiorno nonlinear governing model. The effect of different parameters on the flow, energy, concentration, and entropy generating fields have been graphically illustrated and explained. The hyperbolic tangent nanoliquid has a better velocity than the Williamson nanofluid. The Williamson nanofluid has higher thermal energy and concentration than the hyperbolic tangent nanoliquid in the microchannel. The Grashof number, both thermal and solutal, increases the fluid flow rate throughout the flow system. The energy of the nanoliquid is reduced by the temperature jump condition, while the energy field of the nanoliquid is enhanced by the improving thermal conductivity value. The nanoliquids concentration rises as the Schmitt number rises. The irreversibility rate of the channel system is maximized by the variable thermal conductivity parameter.

Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression.

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.

Frequentmers - a novel way to look at metagenomic next generation sequencing data and an application in detecting liver cirrhosis.

Early detection of human disease is associated with improved clinical outcomes. However, many diseases are often detected at an advanced, symptomatic stage where patients are past efficacious treatment periods and can result in less favorable outcomes. Therefore, methods that can accurately detect human disease at a presymptomatic stage are urgently needed. Here, we introduce "frequentmers"; short sequences that are specific and recurrently observed in either patient or healthy control samples, but not in both. We showcase the utility of frequentmers for the detection of liver cirrhosis using metagenomic Next Generation Sequencing data from stool samples of patients and controls. We develop classification models for the detection of liver cirrhosis and achieve an AUC score of 0.91 using ten-fold cross-validation. A small subset of 200 frequentmers can achieve comparable results in detecting liver cirrhosis. Finally, we identify the microbial organisms in liver cirrhosis samples, which are associated with the most predictive frequentmer biomarkers.

Numerical study of thermal and solutal advancements in ZnO-SAE50 nanolubricant flow past a convergent/divergent channel with the effects of thermophoretic particle deposition.

The present research focuses on the significance of thermophoretic particle deposition on a ZnO-SAE50 nanolubricant flow in a stretchable/shrinkable convergent/divergent channel in the presence of an applied magnetic field and nonlinear heat radiation. A parameter in the governing differential equations and wall boundary conditions defines the physical mechanism of the model. The Galerkin finite element method, combined with similarity transformation, is adopted to solve the governing equations. The Levenberg-Marquardt backpropagating algorithm of an artificial neural network model forecasts heat and mass transfer properties. The results reveal that by stretching/shrinking the walls enough, the classical flow and heat properties are significantly affected. The stretching of the convergent or divergent channel is observed to increase the velocity profiles, whilst shrinking results in backflow regions. In terms of the temperature field, stretching causes more heat to be produced in the flow; nevertheless, the thermal layer is decreased and cooling is attained by channel shrinkage, which may have important technical implications.

Impact of COVID-19 on Pregnancy Outcomes across Trimesters in the United States.

BACKGROUND: Current knowledge regarding the association between trimester-specific changes during pregnancy and COVID-19 infection is limited. We utilized the National Inpatient Sample (NIS) database to investigate trimester-specific outcomes among hospitalized pregnant women diagnosed with COVID-19. RESULTS: Out of 3,447,771 pregnant women identified, those with COVID-19 exhibited higher in-hospital mortality rates in their third trimester compared with those without the virus. Notably, rates of mechanical ventilation, acute kidney injury, renal replacement therapy, and perinatal complications (preeclampsia, HELLP syndrome, and preterm birth) were significantly elevated across all trimesters for COVID-19 patients. COVID-19 was found to be more prevalent among low-income, Hispanic pregnant women. CONCLUSIONS: Our findings suggest that COVID-19 during pregnancy is associated with increased risk of maternal mortality and complications, particularly in the third trimester. Furthermore, we observed significant racial and socioeconomic disparities in both COVID-19 prevalence and pregnancy outcomes. These findings emphasize the need for equitable healthcare strategies to improve care for diverse and socioeconomically marginalized groups, ultimately aiming to reduce adverse COVID-19-associated maternal and fetal outcomes.

The role of internal auditing on corporate governance: its effects of economic and environmental performance.

Internal auditing has been an elementary and powerful component of corporate governance in business strategies. It is highly acknowledged as an important business control system in the realm of quality management. Business performance does not come by itself; it needs plenty of aids and capabilities. Existing studies have investigated the business performance with fewer outcomes in corporate sustainability, firm, and environmental performance. We present analysis and validated improvised hypothesis through structural equation modeling (SEM), using analysis of a moment structures (AMOS), on empirical evidence gathered from 304 Chief Executive Officer (CEOs) in Top Multinational Companies of Pakistan (TMCP). In this paper, we examine the role of internal audit functionality as an influencing factor via corporate governance in corporate sustainability, firm performance, and environmental performance. Moreover, business quality and performance are maintained using functions such as interests of shareholders, personnel and finance. Results suggest that internal audit functionality and corporate governance significantly influence corporate sustainability, firm performance, and environmental performance. Additionally, CEOs and top managers of TMCP are advised to concentrate on internal audit functionality with a mediating role of corporate governance that in turn encourages corporate sustainability, firm performance, and environmental performance.

Thermal performance of Fe3O4, SWCNT, MWCNT and H2O based on magnetohydrodynamic nanofluid flow across a wedge with significant impacts of Soret and Dufour.

The Soret and Dufour effects have significant importance in several practical scenarios, especially in the domain of fluidic mass and temperature transfer. Nanofluidics, biological systems, and combustion processes are all areas where these consequences are crucial. Because of its distinct geometry, a wedge-shaped structure has aerodynamics, production, and engineering applications. Wedge shapes are used in aerodynamics for analyzing and improving airflow across various objects. Nanofluids increase thermal conductivity over traditional fluids making them ideal for cooling high-power electronics, boosting temperature transfer efficiencies, and boosting the solar energy system output. This work is of critical importance since it examines the consequences of a heat source/sink, the Soret impact and the Dufour impact, on the movement of a ternary nanofluid over a wedge. This work uses appropriate similarity constraints to reduce the complexity of the underlying governing equations, allowing for fast computational solutions with the Runge-Kutta-Fehlberg 4-5th order method (RKF-45). Analysis of these phenomena helps determine their possible real-world applications across various engineering fields, by presenting numerical results through plots. The results reveal that adjusting the moving wedge factor lessens the temperature profile, improving the magnetic constraint increases the velocity, and modifying the heat source/sink, Dufour, and Soret factors increases the temperature and concentration profiles. Dufour and heat source/sink constraints speed-up the heat transmission rate. In all cases, ternary nano liquids show significant performance over hybrid nano liquids.

Heat transfer analysis in magnetohydrodynamic nanofluid flow induced by a rotating rough disk with non-Fourier heat flux: aspects of modified Maxwell-Bruggeman and Krieger-Dougherty models.

Non-Newtonian fluids have unique heat transfer properties compared to Newtonian fluids. The present study examines the flow of a Maxwell nanofluid across a rotating rough disk under the effect of a magnetic field. Furthermore, the Cattaneo-Christov heat flux model is adopted to explore heat transport features. In addition, a comparison of fluid flow without and with aggregation is performed. Using similarity variables, the governing partial differential equations are transformed into a system of ordinary differential equations, and this system is then solved by employing the Runge-Kutta Fehlberg fourth-fifth order method to obtain the numerical solution. Graphical depictions are used to examine the notable effects of various parameters on velocity and thermal profiles. The results reveal that an increase in the value of Deborah number decreases the velocity profile. An increase in the thermal relaxation time parameter decreases the thermal profile. An artificial neural network is employed to calculate the rate of heat transfer and surface drag force. The R values for skin friction and Nusselt number were computed. The results demonstrate that artificial neural networks accurately predicted skin friction and Nusselt number values.

Stability analysis for heat transfer flow in micropolar hybrid nanofluids.

Objective: hybrid nanofluids have superior thermal efficiency and physical durability in contrast to regular nanofluids. The stagnation point flow of MHD micropolar hybrid nanofluids over a deformable sheet with viscous dissipation is investigated. Methodology: the controlling partial differential equations are converted to nonlinear ordinary differential equations using the transmuted similarity, and are subsequently solved using the bvp4c solver in MATLAB. The hybrid nanofluids consist of aluminum and copper nanoparticles, dispersed in a base fluid of water. Results: multiple solutions are obtained in the given problem for the case of shrinking as well as for the stretching sheet due to the variation in several influential parameters. Non-unique solutions, generally, exist for the case of shrinking sheets. In addition, the first branch solution is physically stable and acceptable according to the stability analysis. The friction factor is higher for the branch of the first solution and lower in the second branch due to the higher magnetic parameters, while the opposite behavior is seen in the case of the local heat transfer rate. Originality: the novelty of this model is that it finds multiple solutions in the presence of Cu and Al2O3 nanoparticles and also performs the stability analysis. In general, non-unique solutions exist for the phenomenon of shrinking sheets.

Investigation of a two-dimensional photovoltaic thermal system using hybrid nanofluids and a rotating cylinder.

This article focuses on a numerical investigation aimed at enhancing the electrical performance of a two-dimensional photovoltaic thermal system (PV/T) through the application of cooling using hybrid nanofluids. The hybrid nanofluids consist of titanium oxide and silver nanoparticles suspended in water, while the PV/T system is based on polycrystalline silicon, copper, and a flow channel with a rotating cylinder. PV/T devices generate electricity from sunlight, but their performance degrades over time due to the heat generated by solar radiation. Therefore, nanofluids can be circulated through the bottom flow channel to cool the device. This study utilizes 2D incompressible Navier-Stokes equations to control fluid flow and energy equations to manage energy distribution. The COMSOL 6.0 finite element software is employed for comprehensive modeling and simulation. To enhance the performance of the PV/T system, a parametric study is conducted by varying the Reynolds number (ranging from 100 to 1000), cylinder rotational speed (varying from 0.01 to 0.2 m s-1), and silver volume fraction (ranging from 0.01 to 0.2). The results show that increasing the Reynolds number and the volume fraction of silver leads to a reduction in the maximum temperature of the cell. The maximum temperature of the cell also decreases with the rotational speed of the cylinder but only for high Reynolds numbers. By applying the present model, the cell's efficiency is improved by 5.93%.

Heat and mass transfer analysis of assisting and opposing radiative flow conveying ternary hybrid nanofluid over an exponentially stretching surface.

Access to dependable and environmentally friendly energy sources is critical to a country's economic growth and long-term development. As countries seek greener energy alternatives, the interaction of environmental elements, temperature, and sunlight becomes more critical in utilizing renewable energy sources such as wind and bioenergy. Solar power has received much attention due to extraordinary efficiency advances. under this context, the present work focus on solar radiation and chemical processes in the presence of modified ternary hybrid nanofluids (THNFs) circulating over an exponentially stretched surface in both aiding flow (A-F) and opposing flow (O-F) circumstances. The primary objective of this investigation is to dive into the complicated dynamics of these structures, which are distinguished by complex interactions involving radiation, chemical reactions, and the movement of fluids. We construct reduced ordinary differential equations from the governing equations using suitable similarity transformations, which allows for a more in-depth examination of the liquid's behavior. Numerical simulations using the Runge-Kutta Fehlberg (RKF) approach and shooting techniques are used to understand the underlying difficulties of these reduced equations. The results show that thermal radiation improves heat transmission substantially under O-F circumstances in contrast to A-F conditions. Furthermore, the reaction rate parameter has an exciting connection with concentration levels, with greater rates corresponding to lower concentrations. Furthermore, compared to the O-F scenario, the A-F scenario promotes higher heat transfer in the context of a modified nanofluid. Rising reaction rate and solid fraction volume enhanced mass transfer rate. The rate of thermal distribution in THNFs improves from 0.13 to 20.4% in A-F and 0.16 to 15.06% in O-F case when compared to HNFs. This study has real-world implications in several fields, including developing more efficient solar water heaters, solar thermal generating plants, and energy-saving air conditioners.

Deformation equivariant cross-modality image synthesis with paired non-aligned training data.

Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.

Numerical analysis of magnetohydrodynamics in an Eyring-Powell hybrid nanofluid flow on wall jet heat and mass transfer.

The customization of hybrid nanofluids to achieve a particular and controlled growth rate of thermal transport is done to meet the needs of applications in heating and cooling systems, aerospace and automotive industries, etc. Due to the extensive applications, the aim of the current paper is to derive a numerical solution to a wall jet flow problem through a stretching surface. To study the flow problem, authors have considered a non-Newtonian Eyring-Powell hybrid nanofluid with water and CoFe2O4and TiO2nanoparticles. Furthermore, the impact of a magnetic field and irregular heat sink/source are studied. To comply with the applications of the wall jet flow, the authors have presented the numerical solution for two cases; with and without a magnetic field. The numerical solution is derived with a similarity transformation and MATLAB-based bvp4c solver. The value of skin friction for wall jet flow at the surface decreases by more than 50% when the magnetic fieldMA=0.2is present. The stream function value is higher for the wall jet flow without the magnetic field. The temperature of the flow rises with the dominant strength of the heat source parameters. The results of this investigation will be beneficial to various applications that utilize the applications of a wall jet, such as in car defrosters, spray paint drying for vehicles or houses, cooling structures for the CPU of high-processor laptops, sluice gate flows, and cooling jets over turbo-machinery components, etc.