Analysis of muscle synergies and muscle network in sling exercise rehabilitation technique.

The study assessed motor control strategies across the four sling exercises of supine sling exercise (SSE), prone sling exercise (PSE), left side-lying sling exercise (LLSE), and right side-lying sling exercise (RLSE) positions base on the muscle synergies and muscle network analyses. Muscle activities of bilateral transversus abdominis (TA), rectus abdominis, multifidus (MF), and erector spinae (ES) were captured via surface electromyography. Muscle synergies were extracted through principal components analysis (PCA) and non-negative matrix factorization (NNMF). Muscle synergies number, muscle synergies complexity, muscle synergies sparseness, muscle synergies clusters and muscle networks were calculated. PCA results indicated that SSE and PSE decomposed into 2.88 ± 0.20 and 2.82 ± 0.15 synergies respectively, while the LLSE and RLSE positions decomposed into 3.76 ± 0.14 and 3.71 ± 0.11 muscle synergies, respectively, which were more complex (P = 0.00) but less sparse (P = 0.01). Muscle synergies clusters analysis indicated common muscle synergies among different sling exercises. SSE position demonstrated specific muscle synergies with a strong contribution of the bilateral TA. LLSE-specific synergy has a strong contribution of the left erector spinae (ES). The RLSE-specific synergy has significant contributions from the right ES and multifidus. Muscle networks were functionally organized, with clustering coefficient (F(1.5, 24) = 6.041, P = 0.01) and global efficiency of the undirected network (F(1.5, 24) = 6.041, P = 0.01), and betweenness-centrality of the directed network (F(2.7, 44) = 6.453, P = 0.00). Our research highlights the importance of evaluating muscle synergies and network adaptation strategies in individuals with neuromuscular disorders and developing targeted therapeutic interventions accordingly.

A cogent technique to circumvent the use of (some) ratio measures in physiology.

Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value - if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. KEY POINTS: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known. A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values. A sample drawn from a population of these PCA-derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios. The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition.

Evaluating the Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) Scores Using Principal Components Analysis.

Introduction  The Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) is an extensive tool used to assess children with autism and other developmental disabilities who have language delays. Applied behavior analysis (ABA) professionals frequently use the VB-MAPP to create personalized intervention programs catering to each child's needs. The lack of studies examining the VB-MAPP at the pretest, posttest, and differential scores using principal components analysis (PCA) suggests an opportunity to conduct PCAs on these different VB-MAPP scores. In doing so, researchers could better understand the VB-MAPP's dimensionality and factor structure at these levels. This, in turn, could inform the development of more effective assessment strategies and intervention plans for individuals with language and social communication challenges.  Materials and methods From January 2018 to July 2021, The Oxford Center in Brighton and Troy, Michigan, treated autistic children using ABA therapy. A convenience sample of 13 children was retrospectively analyzed using VB-MAPP, which evaluates various behavioral milestones using a pretest-posttest design. Descriptive data analysis and internal consistency reliability estimates (using Cronbach's alpha) were calculated for pretest, posttest, and difference scores. A Wilcoxen signed-rank test was conducted to determine the statistical significance between the pretest and posttest. Correlation matrices were inspected for relevant relationships between VB-MAPP scales, and a PCA with orthogonal rotation was also performed on this pretest, posttest, and difference scores. Results The mean age for the children was 4.083 years ± 1.083 years, (95%CI 3.64, 4.36). Around 66.6% of the children had an autism severity level of three, 33.3% had a severity level of two, and none were at level one. Cronbach's alpha for internal consistency reliability of the pretest, posttest, and difference scores, indicating excellent reliability with values of 0.948 for the pretest and 0.937 for the posttest, respectively. The difference scores had a lower but acceptable reliability coefficient of 0.752. PCA on the pretest scores identified three factors that explain 85.584% of the total variation, indicating that these components capture most of the data's structure. The posttest PCA also identified three factors, accounting for 84.293% of the variance, suggesting a similar complexity and good model fit as the pretest. PCA revealed four factors explaining 82.317% of the variation for the difference scores. The increase in factors suggests that changes between pretest and posttest scores are complex, likely due to the ABA treatment, and require an additional component to represent the data adequately. There is a good model fit; the underlying structure is more complex than the pretest or posttest alone. Conclusions Robust coefficient alphas combined with the shift to a more detailed factor structure post-ABA treatment highlight ABA therapy's diverse and multi-faceted impact on children. The increase from three to four principal components indicates a richer and more nuanced pattern of improvements across different domains of verbal and social behavior. This detailed factor structure is a testament to the comprehensive and individualized nature of ABA treatment, reflecting the therapy's effectiveness in addressing specific needs and fostering broad developmental gains in children.

Association between dietary patterns and glycemic control in type II diabetes mellitus patients.

OBJECTIVE: To assess the association between dietary patterns and glycemic control among patients with type II diabetes mellitus (T2DM). DESIGN: A cross-sectional study. SITE: The 2015-2018 National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS: A total of 1646 T2DM patients were included, of whom 854 were hyperglycemia. METHODS: Main dietary patterns were identified using the sparse principal components analysis (SPCA). Logistic regression analysis was applied to investigate the association between each dietary pattern and the risk of hyperglycemia with odds ratios (OR) and 95% confidence intervals (CI). SPCA analysis yielded five significant principal components (PC), which represented five main dietary patterns. RESULTS: PC1, characterized by a high intake of sweets, red meat and processed meat, was associated with higher odds of hyperglycemia in patients who underwent hyperglycemic drug or insulin treatments (OR: 1.71, 95% CI: 1.10-2.64). PC5, characterized by high in red meat, while low in coffee, sweets, and high-fat dairy consumption. The relationship between the PC5 and hyperglycemia was marginal significance (OR: 0.63, 95% CI: 0.38-1.02). PC2 was characterized by a high consumption of green vegetables, other vegetables, and whole grains, and low intake of potatoes and processed meat. In patients with the hyperglycemic drug and insulin free, higher PC2 levels were related to lower odds of hyperglycemia (OR: 0.45, 95% CI: 0.21-0.96). CONCLUSIONS: High intake of sweets, red meat, and processed meat might be detrimental to glycemic control in patients with drug-treated T2DM. High in red meat, while low in coffee, sweets, and high-fat dairy consumption may be beneficial to glycemic control. In addition, high consumption of green vegetables, other vegetables, and whole grains, and low intake of potatoes and processed meat may be good for glycemic control in patients without drug-treated T2DM.

The principal components of meaning, revisited.

Osgood, Suci, and Tannebaum were the first to attempt to identify the principal components of semantics using dimensional reduction of a high-dimensional model of semantics constructed from human judgments of word relatedness. Modern word-embedding models analyze patterns of words to construct higher dimensional models of semantics that can be similarly subjected to dimensional reduction. Hollis and Westbury characterized the first eight principal components (PCs) of a word-embedding model by correlating them with several well-known lexical measures, such as logged word frequency, age of acquisition, valence, arousal, dominance, and concreteness. The results show some clear differentiation of interpretation between the PCs. Here, we extend this work by analyzing a larger word-embedding matrix using semantic measures initially derived from subjective inspection of the PCs. We then use quantitative analysis to confirm the utility of these subjective measures for predicting PC values and cross-validate them on two word-embedding matrices developed on distinct corpora. Several semantic and word class measures are strongly predictive of early PC values, including first-person and second-person verbs, personal relevance of abstract and concrete words, affect terms, and names of places and people. The predictors of the lowest magnitude PCs generalized well to word-embedding matrices constructed from separate corpora, including matrices constructed using different word-embedding methods. The predictive categories we describe are consistent with Wittgenstein's argument that an autonomous level of social interaction grounds linguistic meaning.

A triple-channel sensor array utilizing fluorescent carbon dots for simultaneous discrimination and detection of multiple fluoroquinolones.

The presence of fluoroquinolones (FQs) residues in food and the environment has prompted concerns regarding food safety and public health. Consequently, it is of great significance to analyze the types and levels of FQs present. However, the majority of studies have concentrated on the specific detection of individual FQs, with a notable absence of high-throughput and rapid analysis methods for the simultaneous detection of multiple FQs that may coexist in food and the environment. Hereon, a triple-channel sensor array was successfully constructed utilizing fluorescent carbon dots (TA-CDs), with the assistance of Cu2+ and Fe3+, for the qualitative discrimination and quantitative detection of eight types of FQs. The sensor array can distinguish between different concentrations of FQs and various mixtures of FQs, as well as 100 % accuracy in the discrimination of unknown samples. Impressively, the sensor platform can quantitatively detect FQs in animal-derived foods, such as honey, milk, eggs, and pork, as well as in water samples. This research has the potential to be extended to other analytes with similar chemical structures or properties.

Distribution and functional perspective analysis of epiphytic and endophytic bacterial communities associated with marine seaweeds, Alexandria shores, Egypt.

There is an enormous diversity of life forms present in the extremely intricate marine environment. The growth and development of seaweeds in this particular environment are controlled by the bacteria that settle on their surfaces and generate a diverse range of inorganic and organic chemicals. The purpose of this work was to identify epiphytic and endophytic bacterial populations associated with ten common marine macroalgae from various areas along the Mediterranean Sea coast in Alexandria. This was done to target their distribution and possible functional aspects. Examine the effects of the algal habitat on the counting and phenotypic characterization of bacteria, which involves grouping bacteria based on characteristics such as shape, colour, mucoid nature, type of Gram stain, and their ability to generate spores. Furthermore, studying the physiological traits of the isolates under exploration provides insight into the optimum environmental circumstances for bacteria associated with the formation of algae. The majority of the bacterial isolates exhibited a wide range of enzyme activities, with cellulase, alginase, and caseinase being the most prevalent, according to the data. Nevertheless, 26% of the isolates displayed amylolytic activity, while certain isolates from Miami, Eastern Harbor, and Montaza lacked catalase activity. Geographical variations with the addition of algal extract may impact on the enumeration of the bacterial population, and this might have a relationship with host phylogeny. The most significant observation was that endophytic bacteria associated with green algae increased in all sites, while those associated with red algae increased in Abu Qir and Miami sites and decreased in Eastern Harbor. At the species level, the addition of algal extract led to a ninefold increase in the estimated number of epiphytic bacteria for Cladophora pellucida in Montaza. Notably, after adding algal extract, the number of presented endophytic bacteria associated with Codium sp. increased in Abu Qir while decreasing with the same species in Montaza. In addition to having the most different varieties of algae, Abu Qir has the most different bacterial isolates.

Clustering broiler farmers based on their behavioural differences towards biosecurity to prevent highly pathogenic avian influenza.

Highly pathogenic avian influenza (HPAI) is an important zoonotic disease. The study aims to identify farmer behaviour types to inform the design of behaviour change programmes for mitigating the transmission of HPAI. Therefore, the study utilised multivariate statistical analysis for gaining a better understanding of the relationships among farmers' 30 biosecurity behaviours, the risk of HPAI infection, and distinct features of commercial broiler farmers, which is different from using simple and few binary biosecurity measures. Convenience sampling was used to collect data from 303 Taiwan's farmers among which 40 farmers (13.2%) self-reported having had a HPAI outbreak in the study year while 16 farmers (5.3%) self-reported having had a HPAI outbreak in the past two years. Using categorical principal components analysis and a two-stage cluster analysis, four farmer clusters were identified with distinct features: 1)'Reserved' (4.6%) tended to choose 'No idea' for answering specific questions about HPAI; 2)'Secure' (76.3%) had a higher biosecurity status than the other farms; 3) 'Jeopardised' (16.8%) had a lower biosecurity status than the other farms; 4) 'No-response' (2.3%) tended to skip specific questions about HPAI. The biosecurity status of the 'Reserved' and 'No-response' clusters was undetermined, placing these farms at risk of HPAI infection. Compared to the 'Secure' cluster, the 'Jeopardised' cluster exhibited higher odds of self-reported HPAI in the study year (OR: 2.61, 95% CI: 1.22-5.58) and in the past two years (OR: 4.28, 95% CI: 1.39-13.19). Additionally, the 'Jeopardised' cluster showed increased odds of HPAI recurrence (OR: 4.01, 95% CI: 1.41-11.43). Our study demonstrates that inadequate biosecurity practices can elevate the occurrence or recurrence of HPAI outbreaks. The findings underscore the importance of distinguishing between these clusters to accurately assess the risk of HPAI infection across farms. Furthermore, understanding farmers' behaviours can inform the development of strategies aimed at behaviour change among farmers.

Sex differences in distributed error-related neural activation in problem-drinking young adults.

BACKGROUND: Detecting and responding to errors is central to goal-directed behavior and cognitive control and is thought to be supported by a network of structures that includes the anterior cingulate cortex and anterior insula. Sex differences in the maturational timing of cognitive control systems create differential periods of vulnerability for psychiatric conditions, such as substance use disorders. METHODS: We examined sex differences in error-related activation across an array of distributed brain regions during a Go/No-Go task in young adults with problem alcohol use (N=69; 34 females; M=19.4 years). Regions of interest previously linked to error-related activation, including anterior cingulate cortex, insula, and frontoparietal structures, were selected in a term-based meta-analysis. Individual differences in their responses to false alarm (FA) inhibitory errors relative to "go" trials (FA>GO) and correct rejections (FA>CR) were indexed using multivariate summary measures derived from principal components analysis. RESULTS: FA>GO and FA>CR activation both revealed a first component that explained the majority of the variance across error-associated regions and displayed the strongest loadings on salience network structures. Compared to females, males exhibited significantly higher levels of the FA>GO component but not the FA>CR component. CONCLUSIONS: Males exhibit greater salience network activation in response to inhibitory errors, which could be attributed to sex differences in error-monitoring processes or to other functions (e.g., novelty detection). The findings are relevant for the further characterization of sex differences in cognitive control and may have implications for understanding individual differences in those at risk for substance use or other cognitive control disorders.

Development and psychometric testing of a scale to measure effective rural emergency transfer (RET).

BACKGROUND: The process of transferring patients from small rural primary care facilities to referral facilities impacts the quality of care and effectiveness of the referral healthcare system. The study aimed to develop and evaluate the psychometric properties of a scale measuring requirements for effective rural emergency transfer. METHODS: An exploratory sequential design was utilized to develop a scale designed to measure requirements for effective emergency transport. Phase one included a qualitative, interview study with 26 nursing transport providers. These transcripts were coded, and items developed for the proposed scale. Phase two included a content validity review by these 16 transport providers of the domains and items developed. Phase three included development and evaluation of psychometric properties of a scale designed to measure requirements for effective emergency transport. This scale was then tested initially with 84 items and later reduced to a final set of 58 items after completion by 302 transport nurses. The final scale demonstrated three factors (technology & tools; knowledge & skills; and organization). Each factor and the total score reported excellent scale reliability. RESULTS: The initial item pool consisted of 84 items, generated, and synthesized from an extensive literature review and the qualitative descriptive study exploring nurses' experiences in rural emergency patient transportation. A two-round modified Delphi method with experts generated a scale consisting of 58 items. A cross-sectional study design was used with 302 nurses in rural clinics and health in four rural health districts. A categorical principal components analysis identified three components explaining 63.35% of the total variance. The three factors, technology, tools, personal knowledge and skills, and organization, accounted for 27.32%, 18.15 and 17.88% of the total variance, respectively. The reliability of the three factors, as determined by the Categorical Principal Component Analysis (CATPCA)'s default calculation of the Cronbach Alpha, was 0.960, 0.946, and 0.956, respectively. The RET Cronbach alpha was 0.980. CONCLUSIONS: The study offers a three-factor scale to measure the effectiveness of emergency patient transport in rural facilities to better understand and improve care during emergency patient transport.

Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity.

Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity.

Determining the relationship between oral health and physical function in elderly people.

BACKGROUND: Poor oral conditions in the elderly may have numerous effects on general health, including physical fitness and performance. OBJECTIVES: This study aimed to determine the relationship between oral health and physical function in elderly people. METHODS: Physical function and oral health parameters were compared using parametric comparison tests and Pearson correlation analyses. In addition, principal components analysis, hierarchical clustering and multidimensional scaling analysis clustered the patients' physical and oral health scores. The relationship between the groups was also determined using decision tree analysis. RESULTS: A total of 112 elderly patients participated in the study. Grip strength (GS) was higher in patients with high chewing ability, and Timed Up and Go (TUG) scores were lower in the high oral health group (p < .05). GS was correlated with Decay, Missing, and Filled Teeth Index (DMFT) and the number of remaining and functional teeth (p < .05). According to principal component analysis, it was seen that there were three components (oral, functional and quality of life (QoL) parameters), and the features that were related to each other were gathered together. TUG and GS showed the highest relative importance among physical function criteria in the classification based on chewing ability. They were GS and physical activity for oral health-related QoL. CONCLUSION: In the elderly, higher physical function parameters, especially GS may be an indicator of a better oral health and oral health-related QoL. Preventive physical rehabilitation practices, in addition to oral treatments, may be effective in improving oral health in the elderly.

The contribution of different sperm parameters to better explain ram semen cryoresistance.

In order to determine an effective procedure for explaining ram sperm cryoresistance and develop a new model for breeders classification, a retrospective study was conducted using sperm analysis data obtained over two consecutive years from a total of 82 sessions of ram semen cryopreservation. In each session, fresh ejaculates from eight males were collected via artificial vagina, pooled and frozen in liquid nitrogen vapors. After thawing, a total of 19,084 sperm tracks and 11,319 morphometric measurements were analysed. Clustering analyses were applied to establish motile and morphometric sperm subpopulations. Additionally, plasma and acrosome membrane integrity, as well mitochondrial activity using flow cytometry immediately after sperm thawing and following hypoosmotic shock test (HOST) was assessed. To develop a Ram Sperm Cryoresistance Index, Principal Component Analyses (PCA) using 22 variables were conducted. In the first PCA, the parameters that best explain cryoresistance include total motility (TM), motile subpopulation 2 (motSP2, which groups slow, very linear spermatozoa with low lateral head displacement), morphometric subpopulation 1 (morphSP1, grouping spermatozoa with the smallest head size and lowest shape values), sperm plasma membrane integrity immediately after thawing and following hypoosmotic shock test. These parameters collectively account for 77.34 % of the accumulated variance. To emphasize their importance, a second PCA was performed, revealing significant higher weighting coefficients for the quantity (TM) and quality (motSP2) of sperm movement after thawing, compared to the head size and shape of the thawed sperm (morphSP1). Furthermore, HOST Viability played a more decisive role than what was observed under isotonic conditions.

A novel two-way functional linear model with applications in human mortality data analysis.

Recently, two-way or longitudinal functional data analysis has attracted much attention in many fields. However, little is known on how to appropriately characterize the association between two-way functional predictor and scalar response. Motivated by a mortality study, in this paper, we propose a novel two-way functional linear model, where the response is a scalar and functional predictor is two-way trajectory. The model is intuitive, interpretable and naturally captures relationship between each way of two-way functional predictor and scalar-type response. Further, we develop a new estimation method to estimate the regression functions in the framework of weak separability. The main technical tools for the construction of the regression functions are product functional principal component analysis and iterative least square procedure. The solid performance of our method is demonstrated in extensive simulation studies. We also analyze the mortality dataset to illustrate the usefulness of the proposed procedure.

Modulation of reaching by spatial attention.

Attention is needed to perform goal-directed vision-guided movements. We investigated whether the direction of covert attention modulates movement outcomes and dynamics. Right-handed and left-handed volunteers attended to a spatial location while planning a reach toward the same hemifield, the opposite one, or planned a reach without constraining attention. We measured behavioral variables as outcomes of ipsilateral and contralateral reaching and the tangling of behavioral trajectories obtained through principal component analysis as a measure of the dynamics of motor control. We found that the direction of covert attention had significant effects on the dynamics of motor control, specifically during contralateral reaching. Data suggest that motor control was more feedback-driven when attention was directed leftward than when attention was directed rightward or when it was not constrained, irrespectively of handedness. These results may help to better understand the neural bases of asymmetrical neurological diseases like hemispatial neglect.

New evidence for the sensorimotor mismatch theory of weight perception and the size-weight illusion.

The size-weight illusion is a phenomenon where a smaller object is perceived heavier than an equally weighted larger object. The sensorimotor mismatch theory proposed that this illusion occurs because of a mismatch between efferent motor commands and afferent sensory feedback received when lifting large and small objects (i.e., the application of too little and too much lifting force, respectively). This explanation has been undermined by studies demonstrating a separation between the perceived weight of objects and the lifting forces that are applied on them. However, this research suffers from inconsistencies in the choice of lifting force measures reported. Therefore, we examined the contribution of sensorimotor mismatch in the perception of weight in the size-weight illusion and in non-size-weight illusion stimuli and evaluated the use of a lifting force aggregate measure comprising the four most common lifting force measures used in previous research. In doing so, the sensorimotor mismatch theory was mostly supported. In a size-weight illusion experiment, the lifting forces correlated with weight perception and, contrary to some earlier research, did not adapt over time. In a non-size-weight illusion experiment, switches between lifting light and heavy objects resulted in perceiving the weight of these objects differently compared to no switch trials, which mirrored differences in the manner participants applied forces on the objects. Additionally, we reveal that our force aggregate measure can allow for a more sensitive and objective examination of the effects of lifting forces on objects.

Evaluation of Biochemical Characteristics in a Retrospective Cohort of COVID-19 Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had a significant impact on global health and healthcare systems. This retrospective study aimed to assess the association between biochemical parameters and outcomes in COVID-19 patients in Jazan, Saudi Arabia. METHODS: After establishing the inclusion criteria and obtaining ethical approval, data from 156 reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients were collected from electronic medical records from a general hospital in Samtah, Jazan, from April 2020 to October 2021. The collected data included patient demographics and liver, kidney, heart, and electrolyte function marker levels. Descriptive, inferential, and principal component analyses were conducted. RESULTS: Survival rates varied according to age and body mass index (BMI). Statistical analysis demonstrated that the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), sodium (Na), potassium (K), blood urea nitrogen (BUN), creatinine (Cr), creatine kinase (CK), CK myocardial band (MB), and lactate dehydrogenase (LDH) were significantly higher (P < 0.05) than the reference values, as assessed using the one-sample t-test. Principal component analysis (PCA) also revealed an underlying pattern in the variation of these biochemical markers. These findings suggest that certain biochemical parameters may serve as useful indicators for monitoring the condition of COVID-19 patients. CONCLUSION: This retrospective study in Jazan, Saudi Arabia highlights the association between biochemical parameters and outcomes in COVID-19 patients. Elevated levels of markers of liver, kidney, heart, and electrolyte function suggest organ damage and dysregulation. The pattern identified through PCA provides insights into disease severity. Monitoring these parameters may serve as valuable indicators for assessing COVID-19 patients. Further research is needed to validate these findings, explore their potential for personalized treatment strategies, and improve patient outcomes during the ongoing pandemic.

Per- and poly-fluoroalkyl substances in agricultural contexts and mitigation of their impacts using biochar: A review.

Growing concern over the presence of per- and polyfluoroalkyl substances (PFAS) in agricultural compartments (e.g., soil, water, plants, soil fauna) has led to an increased interest in scalable and economically feasible remediation technologies. Biochar is the product of pyrolyzing organic materials (crop waste, wood waste, manures, grasses) and has been used as a low-cost adsorbent to remove contaminants including PFAS. This review frames biochar as a strategy for mitigating the detrimental impacts of PFAS in agricultural systems and discusses the benefits of this strategy within the framework of the needs and challenges of contaminant remediation in agriculture. To gauge the optimal physicochemical characteristics of biochar in terms of PFAS adsorption, principal component analysis using >100 data points from the available literature was performed. The main biochar-based PFAS treatment strategies (water filtration, soil application, mixing with biosolids) were also reviewed to highlight the benefits and complications of each. Life cycle analyses on the use of biochar for contaminant removal were summarized, and data from selected studies were used to calculate (for the first time) the global warming potential and net energy demand of various agriculturally important biochar classes (crop wastes, wood wastes, manures) in relation to their PFAS adsorption performance. This review serves to identify key gaps in our knowledge of (i) PFAS adsorption by biochars in agricultural remediation applications and (ii) environmental costs/benefits of biochars in relation to their adsorptive properties toward PFAS. The concepts introduced in this review may assist in developing large-scale biochar-based PFAS remediation strategies to help protect the agricultural food production environment.

Sugar detection in adulterated honey using hyper-spectral imaging with stacking generalization method.

This paper develops a new hybrid, automated, and non-invasive approach by combining hyper-spectral imaging, Savitzky-Golay (SG) Filter, Principal Components Analysis (PCA), Machine Learning (ML) classifiers/regressors, and stacking generalization methods to detect sugar in honey. First, the 32 different sugar concentration levels in honey were predicted using various ML regressors. Second, the six ranges of sugar were classified using various classifiers. Third, the 11 types of honey and 100% sugar were classified using classifiers. The stacking model (STM) obtained R2: 0.999, RMSE: 0.493 ml (v/v), RPD: 40.2, a 10-fold average R2: 0.996 and RMSE: 1.27 ml (v/v) for predicting 32 sugar concentrations. The STM achieved a Matthews Correlation Coefficient (MCC) of 99.7% and a Kappa score of 99.7%, a 10-fold average MCC of 98.9% and a Kappa score of 98.9% for classifying the six sugar ranges and 12 categories of honey types and a sugar.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.