Correct Adjustment Research Articles

The histological growth patterns in liver metastases from colorectal cancer display differences in lymphoid, myeloid, and mesenchymal cells.

Colorectal liver metastases grow following different histologic growth patterns (HGPs), classified as desmoplastic and nondesmoplastic (dHGP, non-dHGP), being the latter associated with worst prognosis. This study aimed to investigate the tumor microenvironment (TME) between HGPs supporting different survival. Multiplexed immunohistochemical staining was performed with the Opal7 system in a 100-patients cohort to evaluate the tumor-liver interface with three different cell panels: lymphoid, myeloid, and carcinoma-associated fibroblasts. Differences between HGPs were assessed by Mann-Whitney U test with Pratt correction and Holm-Bonferroni multitest adjustment. Cytotoxic T-cells were more abundant in tumoral areas of dHGP, while non-dHGP had higher macrophages infiltration, Th2, CD163+, and Calprotectin+ cells as well as higher pSMAD2 expression. Regarding carcinoma-associated fibroblasts, several subsets expressing COL1A1 were enriched in dHGP, while αSMAlow_single cells were present at higher densities in non-dHGP. Interestingly, Calprotectin+ cells confer better prognoses in non-dHGP, identifying a subgroup of good outcome patients that unexpectedly also show an enrichment in other myeloid cells. In summary, our results illustrate different TME landscapes with respect to HGPs. dHGP presents a higher degree of immunocompetence, higher amounts of Collagen 1 as well as lesser presence of myeloid cell populations, features that might be influencing on the better prognosis of encapsulated metastases.

An audit of the adherence to dosing and therapeutic monitoring of intravenous vancomycin in accordance with the trust’s antimicrobial guidelines

Abstract Introduction Inappropriate vancomycin dosing and monitoring have compromised patient safety, causing acute kidney injury and prolonged hospital stays. Misuse of antibiotics, like vancomycin, accelerates resistance, a global health threat. Following guidelines can prevent harm and curb the emergence of vancomycin-resistant enterococci and resistant Staphylococcus aureus1. Aim To assess the level of adherence to trust guidelines for the dosing and monitoring of intravenous vancomycin. Audit Standards Standard 1: Patients prescribed intravenous vancomycin should have documented indication Standard 2: Patients prescribed intravenous vancomycin should have correct loading dose Standard 3: Patients prescribed intravenous vancomycin should have correct maintenance dose Standard 4: Patients prescribed intravenous vancomycin should have trough levels taken at the right time Standard 5: Patients prescribed intravenous vancomycin should have doses accurately adjusted against trough level Results: Standard 6: Patients prescribed intravenous vancomycin should have all prescribed doses administered (i.e. no missed doses). Standard 7: Vancomycin prescriptions should be screened by a pharmacist. Methods This audit was approved by both the Trust Clinical Audit Committee and the Antimicrobial Stewardship Group. A retrospective audit using data extracted from EPMA. The data collected included: indication documented (Yes/No), correct loading dose? (Yes/No), missed doses? (Yes/No), correct maintenance dose? (Yes/No), correct first sampling? (Yes/No), correct subsequent sampling? (Yes/No), correct dose adjustments based on levels? (Yes/No) and prescription screened by a pharmacist? (Yes/No). Data was input into Microsoft Excel for analysis. The period covered was 01/12/2022 to 31/05/2023. Ethics approval was not required, as clinical audits do not require ethical review. The inclusion criterion was adult patients prescribed intravenous vancomycin. The exclusion criteria were patients in maternity, labour, and renal wards, due to different guidelines and ITU due to no EPMA. The population size was 243; sample size (n) =150 ensuring a 95% confidence level. Results In total, 150 patient drug charts were analysed, of which 96 % had their vancomycin indication documented. 65.3% of patients had the correct loading dose, 64.7% had the correct maintenance dose. 32.23% had the initial trough level done at the right time and 35% had correct subsequent trough levels measured. 44% had correct dose adjustments based on their trough levels and 57.33% had their prescriptions screened by a pharmacist. Discussion and conclusion None of the target compliance rates were met, highlighting significant issues with vancomycin dosing and therapeutic monitoring within the trust. Therapeutic drug monitoring (TDM) is crucial to ensure optimal patient outcomes2. Audit findings have been shared with the wider multidisciplinary team, education on TDM has taken place with junior pharmacists and digital prompts on EPMA have been included to help improve adherence. A limitation of this audit is its retrospective nature, useful for trend analysis but not for identifying causes. A prospective audit was not feasible due to staffing pressures. Conducting clinical audits are an important tool for assessing and ensuring safe clinical practice.

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

PELATIHAN PERAWATAN KENDARAAN SEPEDA MOTOR BAGI MASYARAKAT DESA SIRNAJAYA

A motorcycle is an engine-driven two-wheeled vehicle composed of different parts that are subjected to different loads during operation, such as heat load, chemical load, impact, bump, twist, push-pull-bend force, friction, pressure. The precision of vehicle components will vary from their original and standard conditions over time such as becoming looser, weaker, or finer. To ensure it is always in top condition, cars require regular maintenance which can be done by a garage or by yourself. In improving the ability to maintain motorcycles and make an income through their own business, especially in the field of motorcycle maintenance services. Through this community service project, counseling on business management and training on motorcycle maintenance skills, including the correct adjustment and use of repair equipment, are provided. The service method applies socialization of motorcycle maintenance theory and direct assistance on how to perform motor vehicle maintenance for Sirnajaya Village youth. The results of this service activity are the increased knowledge, understanding, and abilities of the participants in the field of motorcycle maintenance and repair, besides that this service program supports the success of the community in establishing their own business.

No sex-based differences in odds of starting or time to treatment of generalized myasthenia gravis: A single center cohort study.

Females with generalized myasthenia gravis (gMG) report lower quality of life (QoL) and have more severe disease than males. Sex differences in disease characteristics exist, however whether there are sex differences in the treatment of gMG that may contribute to QoL disparities is unknown. Our objective is to determine whether there are sex differences in the treatment of gMG. We performed a single-center retrospective study of people diagnosed with gMG at the University of Calgary between 1997 and 2021. Primary outcome was proportion starting treatment and secondary outcome was time from diagnosis to treatment initiation. Treatments included pyridostigmine, prednisone, steroid sparing therapies (azathioprine, mycophenolate mofetil [MMF], methotrexate [MTX], or tacrolimus), intravenous immunoglobulin (IVIg), plasmapheresis, rituximab, eculizumab, cyclosporine, stem cell transplantation, and thymectomy. Multivariable logistic and Cox proportional hazards regression models were used to examine treatment associations with sex, adjusted for time from onset to diagnosis, age at diagnosis, presence of thymoma, and antibody status. A total of 179 people with gMG were included (41.9% female). Odds of starting treatment were not statistically associated with sex after adjustment for confounders and correction for multiple testing. Results of the secondary analysis using time to treatment initiation as the outcome were similar. We found no sex differences in odds of starting treatment or time to treatment initiation that might explain previously observed sex-based differences in QoL. Future work should capture physician and patient treatment preferences that may influence disease management.

From dim to glow: dynamic illuminance adjustment for simultaneous exposure correction and low-light image enhancement

Acquiring high-quality images in diverse lighting conditions is challenging, particularly in the constantly evolving driving landscape. From the harsh brilliance of daylight to the subtle shadows of nighttime scenarios, the complexity of capturing accurate images intensifies. Therefore, addressing exposure issues across various lighting conditions is imperative. This study introduces a novel multi-task end-to-end dynamic illumination adjustment approach termed CPGA-DIA. By incorporating feature selection and gamma factor correction techniques, our method effectively handles extreme variations in light conditions for both low-light image enhancement and exposure correction. Comprehensive experimental evaluations demonstrate superior performance to existing processes, showcasing our method’s adaptability and robustness in real-world driving scenarios. Furthermore, our approach extends its capabilities to include the control and estimation of explainable factors related to changing lightness, enabling our model to understand better the environmental conditions that influence image perception and clarity. This research contributes to advancing image capture methodologies, particularly in dynamic lighting conditions, with promising implications for applications ranging from photography to various other fields.

Kpop: a kernel balancing approach for reducing specification assumptions in survey weighting

Abstract With the precipitous decline in response rates, researchers and pollsters have been left with highly nonrepresentative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables X must be adjusted for, they rarely defend particular functional forms relating these variables to the response process or the outcome. Unfortunately, commonly used calibration weights—which make the weighted mean of X in the sample equal that of the population—only ensure correct adjustment when the portion of the outcome and the response process left unexplained by linear functions of X are independent. To alleviate this functional form dependency, we describe kernel balancing for population weighting (kpop). This approach replaces the design matrix X with a kernel matrix, K encoding high-order information about X. Weights are then found to make the weighted average row of K among sampled units approximately equal to that of the target population. This produces good calibration on a wide range of smooth functions of X, without relying on the user to decide which X or what functions of them to include. We describe the method and illustrate it by application to polling data from the 2016 US presidential election.

On the Clinimetrics of the Montreal Cognitive Assessment: Cutoff Analysis in Patients with Mild Cognitive Impairment due to Alzheimer's Disease.

In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial. Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD). Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated. The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06). We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study

BACKGROUNDIntraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. PURPOSEThe objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. STUDY DESIGNExperimental cadaver study. METHODSA human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (−30° to +30°) and swivel (−25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1–5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. RESULTSThe angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). CONCLUSIONSIn this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. CLINICAL SIGNIFICANCEThe knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.

Genetic prediction of blood metabolites mediating the relationship between gut microbiota and Alzheimer's disease: a Mendelian randomization study.

Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive. To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators. Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy. After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk. Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD.

The Key Role of Nutritional Intervention in Delaying Disease Progression and the Therapeutic Management of Diabetic Kidney Disease-A Challenge for Physicians and Patients.

Chronic kidney disease (CKD) represents an increasingly common pathology that affects patients' quality of life, and it is frequently associated with a high mortality rate, especially in the final stages of the disease. At the same time, diabetes mellitus is a chronic disease that contributes to the increased number of patients with CKD through diabetic kidney disease (DKD). The alternation of hypoglycemia with hyperglycemia is a condition in the occurrence of microvascular complications of diabetes, including DKD, which involves structural and functional changes in the kidneys. The therapeutic management of diabetic nephropathy is a much-discussed topic, both from nutritional medical recommendations and a pharmacotherapy perspective. The diet starting point for patients with DKD is represented by a personalized and correct adjustment of macro- and micronutrients. The importance of nutritional status in DKD patients is given by the fact that it represents a modifiable factor, which contributes to the evolution and prognosis of the disease. Since, in most cases, it is necessary to restrict many types of food, malnutrition must be considered and avoided as much as possible.

Introducing emissivity directionality to the temperature-emissivity separation algorithm

Natural surfaces are mostly anisotropic emitters, contributing to the anisotropic behavior of Land Surface Temperature (LST). This characteristic of thermal infrared emissivity is well known and several studies have tried to simulate this behavior either with physical or empirical models. However, given the high heterogeneity of land surfaces, the translation of the angular dependence of emissivity as provided from measurements or models into satellite pixel scale anisotropy is generally very difficult. Here we propose a reformulation of the Mean Minimum-Maximum Difference (MMD) curve of the Temperature-Emissivity Separation (TES) algorithm to allow a correct adjustment of the TES retrievals by taking into account the emissivity angular distribution. For that purpose, the Multi-Sensor method is used to obtain directional emissivities at different sites in the Saharan and Namib desert. The data is then used to calibrate the view-angle dependence of the new MMD formulation. The TES retrievals obtained with the new formulation show a good agreement with the Multi-Sensor data. Results also suggest that the new coefficients of the MMD can be applied to other sensors with similar spectral channels. The new angle-dependent emissivities may lead to a reduction of LST bias as high as 2 K for view angles above 50o. The proposed formulation is currently only valid over barren surfaces.

A modified mechanistic approach for predicting ribbon solid fraction at different roller compaction speeds

This research investigates the modeling of the pharmaceutical roller compaction process, focusing on the application of the Johanson model and the impact of varying roll speeds from 1 to 15 RPM on predictive accuracy of ribbon solid fraction. The classical Johanson’s model was integrated with a dwell time parameter leading to an expression of a floating correction factor as a function of roll speed. Through systematic analysis of the effect of different roll speeds on the solid fraction of ribbons composed of microcrystalline cellulose, lactose, and their blends, corrective adjustment to the Johanson model was found to depend on both roll speed and formulation composition. Interestingly, the correction factor demonstrated excellent correlation with the blend’s mechanical properties, namely yield stress (Py) and elastic modulus (E0), representative of the deformability of the powder. Validated by a multicomponent drug formulation with ±0.4–1.3 % differences, the findings underscore the utility of this modified mechanistic approach for precise prediction of ribbon solid fraction when Py or E0 is known for a given blend. Hence, this work advances the field by offering early insights for more accurate and controllable roller compaction operations during late-stage pharmaceutical manufacturing.

Precise Relative Geometric Correction for Multi-Sensor Satellite Images

Abstract. Rapid progress in satellite technology has led to a noticeable surge in availability of Earth observation satellite images, which are being collected daily from satellites deployed worldwide. However, even with advanced satellite positioning equipment, there are still diverse level of remaining positional errors. This is a hindrance to satellite image utilization. Therefore, positional errors between satellite images must be corrected before utilization. Relative geometric correction of satellite images is a technique that adjusts geometric displacements based on their relative positional relationships in image or object space. In this study, we propose homography-based bundle adjustment for relative geometric correction of multi-sensor satellite images. Our method aims to estimate optimal ground plane on which images are projected and quickly generate result image. For experiments, orthorectified satellite images with various resolutions and georeferencing information were employed as input data. The experiment results showed that the average error, which was initially 4.96 pixels before relative geometric correction, was decreased to 1.73 pixels after applying the proposed method.

Prediction of brain metastases of breast cancer using next generation sequencing genomics.

e14004 Background: Breast cancer was the most common malignancy diagnosed in 2023 with over 300,000 new diagnoses. Though it is highly treatable when caught in early stages, metastatic disease is highly morbid. Approximately 0.4% of all cases and 7.2% of cases of metastatic breast cancer will develop brain metastases. Hormonal receptors correlate poorly with risk of brain metastasis. Predictive factors for breast cancer brain metastases remain elusive. This project aimed to identify genomic risk factors for the metastasis of breast cancer to the brain. Methods: We created a retrospective cohort of all breast cancer patients with next generation sequencing (NGS) at a comprehensive cancer center and its associated clinics. All NGS in the cohort was performed by FoundationOne. Brain metastases were defined via ICD-10 codes. Univariate analysis was performed using Fisher’s Exact test. Significance was defined as p&lt;0.05 after adjustment of multiple testing corrections performed using the Benjamini-Hockberg procedure. A multivariable logistic regression model was fit; to avoid overfitting, the maximum number of covariables was defined by requiring 10 cases for every variable included. Model fit was compared by the Akaike information criterion (AIC) statistic. SAS v9.4 was used for all analyses. Results: This final cohort included 242 patients, of which 29 (12%) developed brain metastases. Among the standard panel of 223 tested genes, 11 were significantly associated with brain metastases after multiple testing correction. Significant genes included: TP53, GATA3, PIK3CA, EP300, MSH3, NTRK1, BRCA1, CBFB, FGFR4, MAP2K2, and NOTCH2. TP53 was the most prevalent gene mutation amongst brain metastases patients. The gene was present in 79% (23/29) of all brain metastases cases and 96% (23/24) of patients with TP53 had brain metastasis. PIK3CA and GATA3 were the next most common in 10 (34%) and 6 (21%) cases respectively. In building predictive models, the optimal model included TP53, GATA3, and CBFB which presented an AIC of 33.15 and an AUC of 0.98. The best two variable model included TP53 and PIK3CA with AIC of 49.67 and AUC of 0.95. Conclusions: Among this cohort, 11 genes were found to be significantly associated with the development of brain metastases. Among them, TP53 was the most prevalent while GATA3, PIK3CA, and CBFB also contributed to predictive ability. These variables suggest high levels of predictability, though given the limited sample thorough validation will be required. However, given the numerous genes found to be associated and the significant starting predictivity, there is potential for NGS to identify those at higher risk of brain metastases. Further study and expansion of NGS will be needed to validate these results and investigate the timing of brain metastases for the potential of early detection.

Application of Linear Regression in GDP Forecasting

Gross Domestic Product (GDP) is the core indicator of national economic accounts, which reflects the economic capacity of a region. It is necessary to have an accurate forecast of GDP. GDP prediction plays an important role in formulating economic policies. By predicting the growth trend of GDP, the government can make correct adjustments and regulate economic activities. This paper takes Guangdong Province and Shandong Province as examples, aiming to establish their GDP models respectively, and obtains their important economic factors on the basis of the models. Firstly, the correlation test and stepwise regression method were used to screen out the variables with significant impact on GDP, and then ridge regression was used to solve the multicollinearity to make the model more accurate. Finally, the similarities and differences of important economic factors in the two provinces were compared. The important economic factors of these two provinces can reflect the characteristics of the economic development of China's north and south, which is conducive to promoting the coordinated development of regional economy and win-win cooperation.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Mitigating against relative age effects in youth Track & Field: Validating corrective adjustment procedures across multiple events

ObjectivesWith the aim to better identify talented Track & Field performance development, this study estimated the relationships between chronological (decimal) age with 60-m sprint, high jump, triple jump, and pole vault performance. Then, to mitigate against expected Relative Age Effects (RAEs), Corrective Adjustment Procedures (CAPs) were applied to an independent sample. DesignMixed-longitudinal design examining public data between 2005 and 2019. MethodsThe performances of 5339 Italian sprinters and jumpers (53.1 %) spanning 11.01–17.99 years of age were examined, with trendlines between chronological age and performance established. Related to an independent sample (N = 40,306; female 45.5 %), trendlines were then utilised to apply CAPs and adjust individual performance. Considering raw and adjusted performance data, RAE distributions were examined for the top 25 % and 10 % performers. ResultsFor all male and female events, quadratic models best summarised the relationships between chronological age and performance (R2 = 0.74–0.89). When examining independent athletes in similar event, RAEs were more pronounced in males (Cramer's V = 0.35–0.14) than females (Cramer's V = 0.29–0.07). For both sexes, RAE magnitude decreased with age and increased according to performance level (i.e., Top25%–Top10%). However, following CAP applications, RAEs were reduced or removed within annual age groups and performance levels. ConclusionsWith RAEs prevalent across Italian youth Track & Field events, findings validate CAPs as a strategy to account for the influence of relative age differences on athletic performance. CAPs help establish a more equitable strategy for performance evaluation and could help improve the efficacy of long-term athlete development programming.

Optimization of number and range of shunt valve performance levels in infant hydrocephalus: a machine learning analysis.

Shunt surgery is the main treatment modality for hydrocephalus, the leading cause of brain surgery in children. The efficacy of shunt surgery, particularly in infant hydrocephalus, continues to present serious challenges in achieving improved outcomes. The crucial role of correct adjustments of valve performance levels in shunt outcomes has been underscored. However, there are discrepancies in the performance levels of valves from different companies. This study aims to address this concern by optimizing both the number and range of valve performance levels for infant hydrocephalus, aiming for improved shunt surgery outcomes. We conducted a single-center cohort study encompassing infant hydrocephalus cases that underwent initial shunt surgery without subsequent failure or unimproved outcomes. An unsupervised hierarchical machine learning method was utilized for clustering and reporting the valve drainage pressure values for all patients within each identified cluster. The optimal number of clusters corresponds to the number of valve performance levels, with the valve drainage pressure ranges within each cluster indicating the pressure range for each performance level. Comparisons based on the Silhouette coefficient between 3-7 clusters revealed that this coefficient for the 4-cluster (4-performance level) was at least 28.3% higher than that of other cluster formations in terms of intra-cluster similarity. The Davies-Bouldin index for the 4-performance level was at least 37.2% lower than that of other configurations in terms of inter-cluster dissimilarity. Cluster stability, indicated by a Jaccard index of 71% for the 4-performance level valve, validated the robustness, reliability, and repeatability of our findings. Our suggested optimized drainage pressure ranges for each performance level (1.5-5.0, 5.0-9.0, 9.0-15.0, and 15.0-18.0cm H2O) may potentially assist neurosurgeons in improving clinical outcomes for patients with shunted infantile hydrocephalus.

Desalination and nutrient removal from tertiary treated urban wastewater by a radial capacitive deionizer

This study focused on removing salts and nutrients (nitrogen and phosphorus) from tertiary treated urban wastewater by capacitive deionization. The performance of a pilot-scale radial capacitive deionizer subjected to varying feed flow rates during the purification stage and constant flow rates during the reject stage was evaluated. This operational strategy aimed to maximize the water recovery rates of the system, with electrical adjustments being made proportionally to the water recovery rate. Lower hydraulic loads resulted in the radial capacitive deionizer removing more ions from the sewage, albeit with higher specific energy consumption. Optimal operational conditions reduced conductivity by 92.8 %, with a specific energy consumption of 2.17 kWhm−3. Significant removals of ammonium, nitrite, nitrate, and orthophosphate ions were observed, with average specie reductions of up to 84, 91.4, 100, and 60.5 %, respectively. Higher rates of treated water recovery resulted in lower ion removal reduction and energy consumption. The limiting step of the process referred to ion desorption, requiring the correct adjustment of the time lapse, flow rate, and applied electrical voltage to ensure the efficiency of the subsequent purification stage. A loss of efficiency was observed when the process was run continuously, thus emphasizing the strategic significance of capacitor rinsing during the rejection phase across multiple subsequent treatment cycles.