Impact of a tailored exercise regimen on physical capacity and plasma proteome profile in post-COVID-19 condition.

Background: Individuals affected by the post-covid condition (PCC) show an increased fatigue and the so-called post-exertion malaise (PEM) that led health professionals to advise against exercise although accumulating evidence indicates the contrary. The goal of this study is to determine the impact of a closely monitored 8-week mixed exercise program on physical capacity, symptoms, fatigue, systemic oxidative stress and plasma proteomic profiles of PCC cases. Methods: Twenty-five women and men with PCC were assigned sequentially to exercise (n = 15) and non-exercise (n = 10) groups. Individuals with no PCC served as a control group. The exercise program included cardiovascular and resistance exercises. Physical capacity, physical activity level and the presence of common PCC symptoms were measured before and after the intervention. Fatigue was measured the day following each exercise session. Plasma and PBMC samples were collected at the beginning and end of the training program. Glutathione and deoxyguanosine levels in PBMC and plasma proteomic profiles were evaluated. Results: Bicep Curl (+15% vs 4%; p = 0.040) and Sit-to-Stand test (STS-30 (+31% vs +11%; p = 0.043)) showed improvement in the exercise group when compared to the non-exercise group. An interaction effect was also observed for the level of physical activity (p =0.007) with a positive effect of the program on their daily functioning and without any adverse effects on general or post-effort fatigue. After exercise, glutathione levels in PBMCs increased in women but remained unchanged in men. Discernable changes were observed in the plasma proteomics profile with certain proteins involved in inflammatory response decreasing in the exercise group. Conclusions: Supervised exercise adapted to the level of fatigue and ability is safe and effective in PCC patients in improving their general physical capacity and wellbeing. Systemic molecular markers that accompany physical improvement can be monitored by analyzing plasma proteomics and markers of oxidative stress. Large-scale studies will help identify promising molecular markers to objectively monitor patient improvement.

Effect of Deposition Parameters on Micromechanical Properties and Machining Performance of CrN Coating for Wet Finish Turning of Ti6Al4V Alloy.

This study aims to optimize the performance of CrN coatings deposited on WC cutting tools for machining Ti6Al4V alloy, where the formation of built-up edge (BUE) is a prevalent and critical issue. In-house CrN coatings were developed using the PVD (Physical Vapor Deposition) process, with variations in deposition parameters including nitrogen gas pressure, bias voltage, and coating thickness. A comprehensive experimental approach encompassing deposition, characterization, and machining performance evaluation was employed to identify the optimal deposition conditions. The results indicated that CrN coatings deposited at a nitrogen gas pressure of 4 Pa, a bias voltage of -50 V, and a thickness of 1.81 µm exhibited superior performance, significantly reducing BUE formation and tool wear. These optimized coatings demonstrated enhanced properties, such as a higher elastic modulus and a lower coefficient of friction, which contributed to improved tool life and machining performance. Comparative studies with commercial CrN coatings revealed that the in-house developed coatings outperformed the commercial variants by approximately 65% in tool life, owing to their superior mechanical properties and reduced friction. This research highlights the potential of tailored CrN coatings for advanced machining applications and emphasizes the importance of optimizing deposition parameters to achieve high-performance tool coatings.

The Health Information Seeking Behavior of Punjabi Elders During the COVID-19 Pandemic in Canada.

Health Information Seeking Behavior (HISB) refers to the behavior and strategies used to attain, clarify, or confirm health information. The uptake of health information depends on system-level and individual-level factors. The purpose of the present study is to understand the sources from which Punjabi elders obtain COVID-19 vaccine-related information and their information seeking behavior. A cross-sectional survey was conducted among 391 Punjabi elders aged 50+ years in the Greater Toronto Area (GTA), Ontario. The survey questions included the need for COVID-19 vaccine information, the type of information sought, sources of information, and barriers to seeking information. Descriptive analysis was conducted using frequencies and percentages, and logistic regression was performed to understand the associations between participants' sociodemographic characteristics and HISB. The results suggested that Punjabi elders are more likely to use informal sources and less likely to seek information from health professionals and government health and wellness websites. The results also suggested that most participants do not cross-check their information with other sources and are more likely to cross-check the information with family/friends, compared to credible care providers, across all demographics. Ultimately, there may be a need for stakeholders to collaborate to regulate the accuracy and type of health-information that is disseminated through media, and to tailor health communication to the health information seeking behavior of this population.

Development of a multidomain gender norm attitude scale for youth in Bangladesh.

Objective: Gender norms shape individuals' perceptions and behaviours, particularly concerning health outcomes. However, the lack of comprehensive gender norm attitude measures in low- and middle-income countries, including Bangladesh, impedes gender-transformative efforts. This study introduces the Multidomain Gender Norm Attitude Scale (M-GNAS) to evaluate gender norm attitudes among Bangladeshi youths. Method: Three sequential studies were conducted in Bangladesh in 2022 to develop the M-GNAS. Study 1 engaged 124 participants in focus group discussions, generating a 40-item pool reflecting prevalent gender norms. Study 2 involved 1374 youths (mean age 26.82, SD 5.50) to finalise the M-GNAS items and explore its latent structure through exploratory factor analysis (EFA). Study 3, with 1416 participants of similar age, used confirmatory factor analysis (CFA) to assess structural validity and structural equation modelling to evaluate measurement invariance (MI) across genders. Results: EFA identified a four-domain solution with 13 items: gender-appropriate behaviour, family financial decisions, family responsibility, and career choice. CFA supported this four-domain solution (CFI=0.96, TLI=0.95; RMSEA=0.07; SRMR=0.04). MI across gender was well established (CFI & TLI>0.95, RMSEA≤0.06, SRMR<0.6). Higher education was associated with more egalitarian attitudes (F (5, 1408) = 7.25, p < 0.001), supporting the scale's construct validity. Conclusion: The M-GNAS is a psychometrically robust tool for assessing youths' attitudes toward prevalent gender norm domains in Bangladesh. It holds the potential for contributing to gender-transformative programmes and could be applied in similar initiatives across developing nations, contingent upon appropriate validation.

Advancements in MAX phase materials: structure, properties, and novel applications.

The MAX phase represents a diverse class of nanolaminate materials with intriguing properties that have received incredible global research attention because they bridge the divide separating metals and ceramics. Despite the numerous potential applications of MAX phases, their complex structure leads to a scarcity of readily accessible pure MAX phases. As a result, in-depth research on synthesis methods, characteristics, and structure is frequently needed for appropriate application. This review provides a comprehensive understanding of the recent advancements and growth in MAX phases, focusing on their complex crystal structures, unique mechanical, thermal, electrical, crack healing, corrosion-resistant properties, as well as their synthesis methods and applications. The structure of MAX phases including single metal MAX, i-MAX and o-MAX was discussed. Moreover, recent advancements in understanding MAX phase behaviour under extreme conditions and their potential novel applications across various fields, including high-temperature coatings, energy storage, and electrical and thermal conductors, biomedical, nanocomposites, etc. were discussed. Moreover, the synthesis techniques, ranging from bottom-up to top-down methods are scrutinized for their efficacy in tailoring MAX phase properties. Furthermore, the review explores the challenges and opportunities associated with optimizing MAX phase materials for specific applications, such as enhancing their oxidation resistance, tuning their mechanical properties, and exploring their functionality in emerging technologies. Overall, this review aims to provide researchers and engineers with a comprehensive understanding of MAX phase materials and inspire further exploration into their versatile applications in materials science and engineering.

Drilling mud contamination effect on wellbore cement strength: An experimental investigation.

Cementing is an essential downhole operation during the drilling of oil, gas, and geothermal wells, with the primary objective of providing structural support and a seal in the wellbore. It is important to attain adequate cement strength to preserve well integrity, inhibit fluid movement, enhance zonal isolation, and hold the wellbore casing in place. However, a significant challenge arises from the potential contamination of the cement by drilling mud (DM) during the placement process. This study aims to assess the impact of both water-based drilling mud (WBDM) and oil-based drilling mud (OBDM) contamination on the strength of API Class G wellbore cement by comprehensively investigating the underlying causes and the mechanism of strength degradation. Following a systematic methodological approach and the guidelines provided by API and ASTM, cement slurries were prepared and contaminated with DM at 10 %, 20 %, and 30 % concentrations by volume. Subsequently, both neat and contaminated samples were cured for 1, 3, 7, and 14 days, after which a series of uniaxial compressive strength (UCS) tests were conducted, and the results were further reinforced by microstructural and petrophysical property analysis. The results demonstrate that both WBDM and OBDM contamination significantly reduce cement strength, irrespective of curing time. Though the effect of OBDM contamination appears to be more pronounced, the effect of WBDM contamination is also remarkable. After final curing of 14 days, WBDM-contaminated cement samples exhibited an average strength reduction of 45.06 %, while OBDM contamination led to a 66.32 % reduction in strength compared to the neat cement with 30 % contamination. Additionally, for the same curing time and contamination percentage, porosity was found to be increased by 71.18 % and 91.33 %, respectively. The mechanism of neat and contaminated cement hydration is explained with the support of SEM-EDS tests, which confirmed the presence of contamination and revealed how two different drilling muds generate dissimilar pore structures within the cement sheath, affecting the hydration and ultimately contributing to the observed reduction in strength. The findings quantify the negative impact of WBDM and OBDM contamination on wellbore cement, emphasizing the importance of mitigating mud contamination during cementing operations.

Prevalence and determinants of fetal macrosomia in Bangladesh.

Background: Fetal macrosomia, marked by excessive birth weight, is a significant public health issue in developing countries, yet it has received less attention compared to low birth weight. This study aims to determine the prevalence of fetal macrosomia in Bangladesh and its associated factors. Methods: The study utilized data from 4,754 women with complete birth weight information of their children from the Bangladesh Multiple Indicator Cluster Survey (MICS) -2019, defining fetal macrosomia as newborns with a birth weight ≥4,000 g regardless of gestational age. Bivariate logistic regression assessed associations between independent variables and fetal macrosomia, presenting adjusted odds ratios (AOR) and a 95% confidence interval (CI), while controlling for potential confounders such as women's age, wealth index, education, healthcare utilization, comorbidities, newborn sex, and place of residence. Results: The prevalence of fetal macrosomia was 11.6%. Significant associations with fetal macrosomia included higher maternal age group (30-34 years) (AOR = 1.36, 95% CI = 1.07-1.74), secondary level of mother's education (AOR = 1.95, 95% CI = 1.43-2.66), experienced physical attacks (AOR = 1.41, 95% CI = 1.06-1.88), hypertension during pregnancy (AOR = 1.54, 95% CI = 1.15-2.07), and rural residence (AOR = 1.25, 95% CI = 1.15-1.49). Female infants had 18% lower odds of being macrosomic compared to male infants (AOR = 0.82, 95% CI = 0.72-0.93). Conclusion: One in ten infants in Bangladesh are born with macrosomia, necessitating a multi-faceted approach involving improving maternal nutrition, promoting healthy lifestyles, enhancing access to quality prenatal care, and addressing socioeconomic, residential, and healthcare system challenges, underlining the importance of further community-based research to expand the study's scope.

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems.

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

Organic Molecular Glues to Design Three-Dimensional Cubic Nano-assemblies of Magnetic Nanoparticles.

Self-assembled magnetic nanoparticles offer next-generation materials that allow harnessing of their physicochemical properties for many applications. However, how three-dimensional nanoassemblies of magnetic nanoparticles can be synthesized in one-pot synthesis without excessive postsynthesis processes is still a bottleneck. Here, we propose a panel of small organic molecules that glue nanoparticle crystallites during the growth of particles to form large nanoassembled nanoparticles (NANs). We find that both carbonyl and carboxyl functional groups, presenting in benzaldehyde and benzoic acid, respectively, are needed to anchor with metal ions, while aromatic rings are needed to create NANs through π-π stacking. When benzyl alcohol, lacking carbonyl and carboxyl groups, is employed, no NANs are formed. NANs formed by benzoic acid reveal a unique combination of high magnetization and coercivity, whereas NANs formed by benzaldehyde show the largest exchange bias reported in nanoparticles. Surprisingly, our NANs show unconventional colloidal stability due to their unique nanoporous architectures.

Cancer screening research in Bangladesh: Insights from a scoping review.

This scoping review summarises the findings of research conducted on cancer screening in Bangladesh, including the prevalence, awareness, barriers, and evaluation of screening programmes, by performing a comprehensive search of electronic databases and gray literature. 25 studies that met inclusion criteria were included in the final analysis. Most of the studies were about screening for cervical cancer, were quantitative, were cross-sectional, and were conducted in hospital settings. The main challenges to screening uptake were shyness, fear, a lack of knowledge, and an inadequate understanding of the concept of screening. Visual inspection with acetic acid (VIA) was found to be a simple and cost-efficient way to detect early-stage cervical cancer. However, breast self-examination (BSE) was reported to be insufficient. Education was found to have a positive impact on cancer screening knowledge and practice, but more needs to be done to improve screening rates, such as the utilisation of media, particularly in rural areas. The results of this scoping review highlight Bangladesh's low cancer screening prevalence and uptake and suggest that targeted awareness campaigns and enhanced access to screening services are required to increase cancer screening uptake and reduce the cancer burden in Bangladesh.

Amplification- and Enzyme-Free Magnetic Diagnostics Circuit for Whole-Genome Detection of SARS-CoV-2 RNA.

Polymerase chain reaction (PCR) requires thermal cycling and enzymatic reactions for sequence amplification, hampering their applications in point-of-care (POC) settings. Magnetic bioassays based on magnetic particle spectroscopy (MPS) and magnetic nanoparticles (MNPs) are isothermal, wash-free, and can be quantitative. Realizing them amplification- and enzyme-free on a benchtop device, they will become irreplaceable for POC applications. Here we demonstrate a first-in-class magnetic signal amplification circuit (MAC) that enables detection of whole genome of SARS-CoV-2 by combining the specificity of toehold-mediated DNA strand displacement with the magnetic response of MNPs to declustering processes. Using MAC, we detect the N gene of SARS-CoV-2 samples at a concentration of 104 RNA copies/µl as determined by droplet digital PCR. Further, we demonstrate that MAC can reliably distinguish between SARS-CoV-2 and other human coronaviruses. Being a wash-, amplification- and enzyme-free biosensing concept and working at isothermal conditions (25 °C) on a low-cost benchtop MPS device, our MAC biosensing concept offers several indispensable features for translating nucleic acid detection to POC applications.

Combining species distribution models and big datasets may provide finer assessments of snakebite impacts.

BACKGROUND: Snakebite is a major poverty-related neglected tropical disease. An integrated scientific approach is needed to understand the dynamics of this important health issue. Our objective was to estimate snakebite occurrence in a tropical area by using a blend of ecological modelling and robust statistical analysis. METHODS: The present study used climatic, environmental, and human population density data to determine the area with snakebite occurrence-probability for the first time in Bangladesh. We also analysed a large, 16-year dataset of hospitalized snakebite cases to reveal the epidemiology of snakebite in the south-eastern zone of the country. FINDINGS: Our results show that cobra bite is the most commonly occurring venomous snakebite in humans (around ~12% of the total yearly snakebite records), and men are more frequently bitten than women (2/3 of human victims are men). Most bites occur during the rainy season for cobra and green pit viper, while krait bites are not restricted to any particular season. As snakebite incidents are closely related to climate conditions, we can model snakebite risk using temperature and precipitation variables. Whereas there is a lack of snakebite reports from several parts of the study area in official records, our models predict that the entire study area is favourable for snakebite incidents. Based on the combined evidence we estimate that about 200,000 snakebite events occur every year in the south-eastern part of Bangladesh alone. Considering future global climate change, our model projections show that snakebite incidence in Bangladesh might not significantly decrease in the future (- 2070-); however, the distribution of probabilities might change, with a predicted increase of snakebite incidence in the hilly areas of the country. CONCLUSIONS: Using climatic data to predict snakebite incidence in Bangladesh allowed us to provide estimations of the total annual number of snakebite cases in the study area. As in most countries, the scarcity of accurate epidemiological data in official records might have masked the real magnitude of this problem. Our analysis suggests that the problem of snakebite envenoming in Bangladesh might be worse than currently perceived. A long-term sustainable snakebite program plan should be designed and institutionalized, considering climatic, geographical and human demographic variables, to obtain better data and facilitate the implementation of accurate snakebite management programs for this country.

Determinants of low birth weight and its effect on childhood health and nutritional outcomes in Bangladesh.

BACKGROUND: The high incidence of low birth weight (LBW) is associated with an increased risk of infant mortality, adverse pregnancy outcomes for mothers, and a decline in overall health and well-being. The current study aimed to identify the various determinants of LBW and its effect on adverse health and nutritional outcomes of children aged 0-23 months in Bangladesh. METHODS: Bangladesh Demography and Health Survey (BDHS) 2017-18 data was used. A chi-square test and multivariable logistic regression analysis were used to find out the associations between independent variables and outcomes (e.g., LBW, child illness and undernutrition). RESULTS: The overall prevalence of LBW among was 16.3%. Mother with no formal education (AOR = 2.64, 95% CI = 0.55-3.30, p = 0.01), female child (AOR = 1.31, 95% CI = 1.04-1.65, p = 0.023); and poorest economic status (AOR = 1.69, 95% CI = 1.13-2.51, p = 0.010), were identified significant determinants of LBW. Of home environment and hygiene factors, unimproved toilet facilities (AOR = 1.38, 95% CI = 1.03-1.84, p = 0.030) had a significant effect on LBW. In addition, children born with LBW were more likely to suffer fever (AOR = 1.26, 95% CI = 1.05-1.60, p = 0.050), stunting (AOR = 2.42, 95% CI = 1.86-3.15, p = < 0.001), wasting (AOR = 1.47, 95% CI = 1.02-2.25 p = 0.049), and underweight (AOR = 3.19, 95% CI = 2.40-4.23, p = < 0.001). CONCLUSION: One out of five children was LBW in Bangladesh. Maternal education, sex of child, wealth index, and toilet facilities had significant effects on LBW. In addition, LWB contributed to children's poor health and nutritional outcomes. Enhancing maternal pregnancy, and child health outcomes necessitates policies addressing poverty, gender inequality, and social disparities. Key strategies include promoting regular prenatal care, early medical intervention, reproductive health education, and safe hygiene practices. To combat the negative impacts of LBW, a comprehensive strategy is vital, encompassing exclusive breastfeeding, nutritional support, growth monitoring, accessible healthcare, and caregiver education.

Superior Induced Pluripotent Stem Cell Generation through Phactr3-Driven Mechanomodulation of Both Early and Late Phases of Cell Reprogramming.

Human cell reprogramming traditionally involves time-intensive, multistage, costly tissue culture polystyrene-based cell culture practices that ultimately produce low numbers of reprogrammed cells of variable quality. Previous studies have shown that very soft 2- and 3-dimensional hydrogel substrates/matrices (of stiffnesses ≤ 1 kPa) can drive ~2× improvements in human cell reprogramming outcomes. Unfortunately, these similarly complex multistage protocols lack intrinsic scalability, and, furthermore, the associated underlying molecular mechanisms remain to be fully elucidated, limiting the potential to further maximize reprogramming outcomes. In screening the largest range of polyacrylamide (pAAm) hydrogels of varying stiffness to date (1 kPa to 1.3 MPa), we have found that a medium stiffness gel (~100 kPa) increased the overall number of reprogrammed cells by up to 10-fold (10×), accelerated reprogramming kinetics, improved both early and late phases of reprogramming, and produced induced pluripotent stem cells (iPSCs) having more naïve characteristics and lower remnant transgene expression, compared to the gold standard tissue culture polystyrene practice. Functionalization of these pAAm hydrogels with poly-l-dopamine enabled, for the first-time, continuous, single-step reprogramming of fibroblasts to iPSCs on hydrogel substrates (noting that even the tissue culture polystyrene practice is a 2-stage process). Comparative RNA sequencing analyses coupled with experimental validation revealed that a novel reprogramming regulator, protein phosphatase and actin regulator 3, up-regulated under the gel condition at a very early time point, was responsible for the observed enhanced reprogramming outcomes. This study provides a novel culture protocol and substrate for continuous hydrogel-based cell reprogramming and previously unattained clarity of the underlying mechanisms via which substrate stiffness modulates reprogramming kinetics and iPSC quality outcomes.

Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities.

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.

Creation of a boron carbide-based Ti3AlBC (312) MAX phase: a route to novel MXenes for energy storage.

A novel boron carbide (B4C)-based Ti3AlBC (312) MAX phase was predicted for the first time via density functional theory (DFT). The stability of the MAX phase was confirmed by mechanical and thermal property analyses. The computational details revealed the attractive properties of Ti3AlBC, indicating its potential as an advanced material with improved characteristics. Its thermodynamic properties are reported as a function of temperature, indicating its potential for energy storage applications.

Guideline Recommendations on Behavioral and Psychological Symptoms of Dementia: A Systematic Review.

OBJECTIVE: To synthesize recommendations on assessing and managing behavioral and psychological symptoms of dementia (BPSDs) in existing clinical practice guidelines on dementia care to learn from and adapt recommendations to a Canadian context and language for describing BPSDs. DESIGN: Systematic review. SETTING AND PARTICIPANTS: Moderate to high-quality clinical practice guidelines on dementia care that made 1 or more recommendations on BPSD assessment or management. METHODS: We searched MEDLINE, Embase, JBI EBM, PsycINFO, AgeLine, and gray literature for clinical practice guidelines on dementia care making recommendations on BPSD, published between January 1, 2011, and October 13, 2022. Two independent reviewers conducted study screening and data abstraction. Four independent reviewers completed quality appraisal using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool; included guidelines had a mean overall AGREE II score ≥4. RESULTS: Our systematic review identified 23 moderate to high-quality clinical practice guidelines (264 recommendations). The mean overall quality score on the AGREE II tool ranged from 4 to 6.5. Recommendations were clearly presented (mean clarity of presentation score 73.5%), but guideline applicability was not consistently addressed (mean applicability score 39.3%). BPSD was the most prevalent term describing neuropsychiatric symptoms (number of guidelines [n] = 14). People with lived experience contributed to 6 guidelines (26.1%). Ten guidelines (43.5%) described 1 or more health equity considerations. Guidelines made recommendations for assessing and managing agitation (n = 12), aggression (n = 10), psychosis (n = 11), depression (n = 9), anxiety (n = 5), apathy (n = 6), inappropriate sexual behavior (n = 3), nighttime behavior (n = 5), and eating disturbances (n = 3). There was substantial variability in recommendation statements, evidence quality assigned to each statement, and strength of recommendations. CONCLUSIONS AND IMPLICATIONS: There are several moderate to high-quality clinical practice guidelines making recommendations on BPSD assessment and management, but variability in recommendation statements across guidelines and insufficient consideration of guideline applicability may hamper guideline dissemination and implementation in clinical practice.

Preoperative Variables of 30-Day Mortality in Adults Undergoing Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND AND AIM: Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments. METHOD: The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment. RESULTS: The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472-0.530) to 0.928 (95% CI 0.900-0.956), and four studies (9.5%) validated the model on external/out-of-sample data. CONCLUSIONS: Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.

BACKGROUND: Hair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis. METHODS: The research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders. RESULTS: The model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) and Saliency Map provided deeper insights into the model's decision-making process. CONCLUSION: This study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.