Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery.

OBJECTIVES: Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined. METHODS: Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78). RESULTS: Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted). CONCLUSIONS: Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Utilising Discriminant Function Analysis (DFA) for Classifying Osteoarthritis (OA) Patients and Volunteers Based on Biomarker Concentration.

Osteoarthritis (OA) is a degenerative joint disease characterised by the breakdown of cartilage, causing pain, stiffness, and limited movement. Early diagnosis is crucial for effective management but remains challenging due to non-specific early symptoms. This study explores the application of Discriminant Function Analysis (DFA) to classify OA patients and healthy volunteers based on biomarker concentrations of Interleukin-6 (IL-6), Tumour necrosis factor-alpha (TNF-α), and Myeloperoxidase (MPO). DFA was employed to analyse biomarker data from 86 participants (58 patients, 28 volunteers) to evaluate the discriminatory power of these biomarkers in predicting OA. Significant differences were observed in MPO and TNF-α levels between groups, while IL-6 did not show a significant distinction. The iterative classification process improved model assumptions and classification accuracy, achieving a pre-classification accuracy of 71.8%, which adjusted to 57.1% post-classification. The results highlight DFA's potential in OA diagnosis, suggesting its utility in managing complex data and aiding personalised treatment strategies. The study underscores the need for larger sample sizes and additional biomarkers to enhance diagnostic robustness and provides a foundation for integrating DFA into clinical practice for early OA detection.

Three-dimensional geometric morphometric analyses of humerus ecomorphology: New perspectives for paleohabitat reconstruction in carnivorans and ungulates.

Long bone ecomorphology has proven effective for paleohabitat reconstructions across a wide range of mammalian clades. Still, there is no comprehensive framework to allow interpretation of long bone morphological variation within and between different monophyletic groups. Here, we investigated the use of humerus morphometry to classify living members of the orders Carnivora and ungulates based on their preferred habitats. Using geometric morphometrics, we extracted three different kinds of humerus shape data describing interspecific variation with and without accounting for evolutionary allometry and phylogenetic signal. The traditional a priori categorization of species in open, mixed, and closed habitats was employed in combination with selected subsets of shape variables to identify the best-predictive models for habitat adaptation. These were identified based on the statistical performance of phylogenetic and non-phylogenetic discriminant analyses and then applied to predict habitats on a subsample of fossil species. Size-free shape data combined with phylogenetic discriminant analyses showed the highest rate of accuracy in habitat classification for a combined sample of carnivorans and ungulates. Conversely, when the two groups were investigated separately, traditional shape data analyzed with phylogenetic discriminant function analyses provided models with the greatest predictive power. By combining carnivorans and ungulates within the same methodological framework we identified common adaptive features in closed habitat-adapted species that show compressed epiphyses, while open habitat-adapted species have expanded epiphyses. These morphologies evolved to allow significant degree of direction switches during locomotion in closed habitats compared to open habitat-adapted species whose forelimb joints evolved to stabilize articulations for increasing speed.

Sexual dimorphism of the twelfth thoracic vertebra for sex determination in the Central Thai population.

Analyzing skeletal remains is crucial for identifying individuals, and forensic anthropologists use this analysis to determine biological characteristics, particularly sex, aiding criminal investigations. Among thoracic vertebrae, the twelfth thoracic vertebra (T12) is highly sexually dimorphic in various populations. This study aims to establish a discriminant function equation (DFE) for sex determination based on T12 in the Central Thai population. A total of 15 parameters of T12 were examined in 69 bone samples (43 males and 26 females). Among the 15 parameters, 14 were significantly different between males and females. The discriminant function equation (DFE) was generated as DFE = -19.578 + 0.376(i) BDsm + 0.254(l) PW + 0.081TDm, with a cutoff value of -0.296 for males and females, showing 92.8 % accuracy. The evaluation of the DFE using 10 blind samples showed 90 % accuracy. These findings may offer an additional method for sex determination through T12, complementing the examination of other skeletal elements.

Selection of mungbean (Vigna radiata L.) mutants with respect to seasonal variation of summer and spring using discriminant function analysis.

The current study was carried out at Nuclear Institute of Agriculture, Tandojam to assess ten mungbean mutants together with one check cultivar in two separate cropping seasons. The findings revealed that all mutants, with the exception of the branches per plant, had significantly different examined traits. By characterizing ten variables, including plant height (cm), number of branches/plant, number of seeds per pod, grain yield per plot (g/plot), grain weight per plant, pod length (cm), pods per plant, days to flowering, above ground biological weight per plot (g/plot) and days to maturity, the results could considerably differentiate between low and high producing mutants. Discriminant analysis was used to choose high-yielding genotypes. The discriminant score demonstrated a significant canonical correlation of 0.994** and could account for 98.8 % of differences in mungbean production. According to the results of discriminant function analysis, the most significant features are pod length, days to flowering, plant height and above ground biological weight. The highest discriminant scores were displayed by the genotypes AEM66, AEM27, AEM25 and AEM14, identified as high yielding mutants. The low yielding mungbean mutants, designated Viz, AEM20, AEM30, AEM35, AEM-96, AEM29, AEM40 and AEM32 are those that exhibit the lowest values of the discriminant score. Mungbean cultivation is more successful in the summer than it is in the spring.

Metric analysis of the patella for sex estimation in a Portuguese sample.

The biological profile estimation is the first step toward positive identification. However, it is not always possible to access a complete and well-preserved skeleton due to postmortem damage and taphonomic changes. As such, there is a need to develop new alternatives to analyze different bones of the human skeleton. The present study aims to analyze the patellar osteometry, with attention to its degree of sexual dimorphism, to establish a simple method for estimating sex in Portuguese adults. Six measurements were taken from 222 patella pairs, including 117 females and 105 males from the XXI Century Identified Skeleton Collection of the University of Coimbra. Subsequently, this method was validated in a different sample of 50 individuals equally representing both sexes. Maximum height stands out with a 77.0% of correct sex estimation, reaching 98.0% when applied to the new sample. The linear discriminant function analysis containing all the six variables showed the best results, with 80.2% of correct classification after cross-validation and 96.0% when applied to the independent sample.

Morphological variation in Schizothorax oconnori, Schizothorax waltoni (Teleostei: Cyprinidae: Schizothoracinae), and their natural hybrids from the middle Yarlung Zangbo River, Tibet.

The morphological variation in Schizothorax oconnori, Schizothorax waltoni, and their natural hybrids was examined using conventional and image-based analysis approaches. In total, 38 specimens of S. oconnori, 35 of S. waltoni, and 37 natural hybrids were collected from the Shigatse to the Lhasa section of the Yarlung Zangbo River during June and July 2021. A total of 21 morphometric, 4 meristic, and 27 truss variables were employed for the classification of S. oconnori, S. waltoni, and natural hybrids. Principal component analysis (PCA) and factor analysis (FA), as well as discriminant function analysis (DFA) and cluster analysis (CA), were conducted to identify differences based on traditional and truss measurements. Four principal components explained 75.92% of the variation among the morphometric characters, while five principal components accounted for 79.69% of the variation among the truss distances. FA results showed that factor 1 was associated with head shape, and factor 2 was associated with fins based on morphometric characters. Among the truss characters, factor 1 was related to head shape, and factor 2 was related to chest shape. In DFA, morphometric measurements achieved higher accuracy (100%) compared to truss distances (94.55%). The head morphology of hybrids exhibited intermediate traits between S. oconnori and S. waltoni. Both morphometry-based and truss-based clustering indicated that the morphology of natural hybrids leaned toward S. oconnori. In conclusion, the combination of morphometric and truss analysis is beneficial for classifying S. oconnori, S. waltoni, and their natural hybrids. The presence of natural hybrids could be considered an evolutionary response to the differentiation of nutritional and spatial niches in the middle Yarlung Zangbo River.

Tea (Camellia sinensis) cultivated in three agro-ecological regions of Bangladesh: Unveiling the variability of methylxanthine, bioactive phenolic compound, and antioxidant activity.

Tea (Camellia sinensis) is a widely consumed beverage known for its numerous health benefits, largely attributed to its rich content of quality determining secondary metabolites such as methylxanthine compounds and bioactive phenolic compounds. The goal of this study was to find out variations of the levels of methylxanthines, bioactive phenolic compounds, and antioxidant activity in methanolic and hot water extracts of 129 tea samples grown in three different ecological regions of Bangladesh named Panchagar, Sylhet, and Chattogram. Methylxanthine and bioactive phenolic compounds were determined by using HPLC-DAD, and the antioxidant profile was analysed by UV-vis spectrophotometric methods for methanol and hot water extracts of tea leaves. The IC50 values showed the trend as Panchagar > Sylhet > Chattogram and Sylhet > Chattogram > Panchagar for water and methanol extract, respectively. The results revealed significant (p < 0.05) variations in the levels of methylxanthines content: Panchagar > Chattogram > Sylhet. Caffeine was significantly higher (103.02 ± 5.55 mg/g of dry extract) in the methanolic extract of tea leaves of Panchagar district and lower (53.33 ± 4.30 mg/g of dry extract) in the hot water extract of Sylhet district. Panchagar and Chattogram possessed significantly (p < 0.05) higher catechin content for methanol (57.01 ± 5.50 mg/g dry extract) and hot water (55.23 ± 4.11 mg/g dry extract) extracts, respectively. The utilization of canonical discriminant functions yielded highly favorable outcomes in the classification of tea from three distinct cultivation origins in Bangladesh, relying on their inherent features. This study demonstrated the potential effects of geographical variations on the bioactive compounds and antioxidant properties of tea, emphasizing the importance of regional differences in tea cultivation for optimizing its health benefits as well as dispersing tea cultivation across the country.

Evaluation of morphological traits using multivariate techniques in Kotdhar goat native to Shivalik hill region of Himachal Pradesh.

The objective of this study was to analyze the diverse morphological attributes of Kotdhar goat native to Shivalik hill region of Himachal Pradesh. A total number of 180 goats, comprising 150 females and 30 males aged between 1 and 3 years, were randomly selected to estimate15 distinct morphological traits and ten body indices. Mean values of 17.66 cm, 11.68 cm, 8.73 cm, 71.94 cm, 69.83 cm, 74.48 cm, 38.10 cm, 73.38 cm, 20.55 cm, 16.48 cm, 14.89 cm, 13.57 cm, 19.20 cm, 7.71 cm and 26.12 kg were estimated for head length, head width, face length, body length, wither height, heart girth, chest depth, rump height, rump length, rump width, haunch width, bicoastal width, shoulder width, cannon bone and body weight respectively. In addition, morphological indices viz. body index, length index, body ratio, thoracic development, pelvic index, longitudinal pelvic index, transverse pelvic index, proportionality index, dactyl thorax index and relative depth of thorax were estimated from morphological traits. Results of morphological indices indicated that Kotdhar goat are medium bodied, longilinear, tall animals and well-suited for meat production. Phenotypic correlation among most of traits was positive and significant. Among all correlations, correlation between head length and head width was found lowest (- 0.270), while the correlation between heart girth and chest depth was observed highest (0.861). Canonical discriminant function analysis showed that there was no considerable level of intermixing for morphological traits in Kotdhar goat. Employing principal component analysis with varimax rotation, three principal components were observed, accounting for 68.47% of the total variation. First principal component (PC1) encapsulated the highest proportion of variance (44.39), revealing the high loadings on head length (0.777), body length (0.747), wither height (0.782), heart girth (0.785), chest depth (0.706), rump height (0.762), haunch width (0.734) and bicoastal width (0.793). Hence, it is possible to utilize these eight variables out of the 15 morphological traits for the characterization of the Kotdhar goat.

Estimation of sex using dimensions around the metatarsal diaphyseal nutrient foramen: Application of discriminant function analysis and logistic regression models.

Sex estimation equations are population-specific, and a wider use of multiple bones to generate equations will increase the accuracy of sex estimation in forensic settings. The metatarsal bones have been used previously, however the dimensions around the diaphyseal nutrient foramen have not been utilised in sex estimation. The current study aimed to determine the utility of the dimensions around the nutrient foramen of metatarsal bones in estimating sex in the South Africans of European descent (SAED). Five measurements around the nutrient foramen were taken from a total of 876 metatarsal bones (first to fifth) from 186 individual skeletons (99 males, 87 females) obtained from the Raymond A. Dart Modern Skeletal Collection. Measurements subjected to direct and stepwise discriminant function (DFA) and logistic regression (LRA) analyses included total length, distance from proximal end to nutrient foramen, circumference, and mediolateral and dorsoplantar diameters at the level of the nutrient foramen. The original classification accuracies for multivariable functions of the stepwise and direct DFA ranged from 83.1-88.3% to 85.5-88.3%, respectively. The original classification accuracies for multivariable functions of the stepwise and direct LRA ranged from 83.3%-88.7% to 86.2%-88.3%, respectively. The cross-validation classifications showed a drop of 0-2.4% for DFA and 0.2-1.1% for LRA. The width measurements were better predictors of sex than length. The dimensions around the metatarsal bone nutrient foramen exhibit sexual dimorphism in the SAED. The generated DFA and LRA functions produced high average classification accuracies which are useful in sex estimation during forensic human identification.

Maximum margin and global criterion based-recursive feature selection.

In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the feature ranking criterion utilized in these approaches is inconsistent with the maximum-margin theory. Secondly, the computation of the criterion is performed locally, lacking the ability to measure the importance of features globally. To overcome these challenges, we propose a novel feature ranking criterion called Maximum Margin and Global (MMG) criterion. This criterion utilizes the classification margin to determine the importance of features and computes it globally, enabling a more accurate assessment of feature importance. Moreover, we introduce an optimal feature subset evaluation algorithm that leverages the MMG criterion to determine the best subset of features. To enhance the efficiency of the proposed algorithms, we provide two alpha seeding strategies that significantly reduce computational costs while maintaining high accuracy. These strategies offer a practical means to expedite the feature selection process. Through extensive experiments conducted on ten benchmark datasets, we demonstrate that our proposed algorithms outperform current state-of-the-art methods. Additionally, the alpha seeding strategies yield significant speedups, further enhancing the efficiency of the feature selection process.

New prognostic model for hospitalized patients with alcoholic cirrhosis and Maddrey's discriminant function <32.

BACKGROUND & AIMS: Few studies have investigated the prognosis of patients with non-severe alcoholic hepatitis (Non-SAH). The study aimed to develop a new prognostic model for patients with especially Non-SAH. METHODS: We extracted 316 hospitalized patients with alcoholic cirrhosis without severe alcoholic hepatitis, defined as Maddrey's discriminant function score lower than 32, from the retrospective Korean Acute-on-Chronic Liver Failure (KACLiF) cohort to develop a new prognostic model (training set), and validated it in 419 patients from the prospective KACLiF cohort (validation set). Prognostic factors for death and liver transplantation were analyzed to construct a prognostic model. RESULTS: Twenty-one and 24 patients died within 6 months in both sets, respectively. In the training set, the highest area under the curve (AUC) of conventional prognostic models was 0.765, 0.732, and 0.684 for 1-, 3-, and 6-month mortality, respectively. Refractory ascites, vasopressor use, and hyponatremia were independently associated with mortality of cirrhotic patients with Non-SAH. The new model consisted of four variables: past deterioration, neutrophil proportion > 70%, Na < 128 mmol/L, and vasopressor use. It showed the highest accuracy for short-term mortality in the training and validation sets (0.803 and 0.786; 0.797 and 0.776; and 0.789 and 0.721 for 1-, 3-, and 6-month mortality, respectively). CONCLUSION: There is a group of patients with high risk among those classified as Non-SAH. The new model will help stratifying cirrhotic patients with Non-SAH more accurately in terms of prognosis. The patients with high Non-SAH score need to monitor closely and might be considered for preemptive liver transplantation. TRIAL REGESTRATION: ClinicalTrials.gov identifier: NCT02650011.

Sex estimation from the hyoid bone measurements in an adult Eastern Turkish population using 3D CT images, discriminant function analysis, support vector machines, and artificial neural networks☆.

The hyoid bone is one of the bones in the human body that shows sexual dimorphism. The anthropological and anthropometric characteristics that determine sexual dimorphism are influenced by demographic differences. The aim of this study was to investigate the rate of sexual dimorphism of the hyoid bone in the adult Eastern Turkish population from the examination of the 3D computed tomography images of 240 patients, using discriminant function analysis (DFA), support vector machines (SVM), and artificial neural networks (ANN). These evaluations were based on eight hyoid measurements that have been frequently used in previous CT studies. The results showed that all eight measurements were higher in males than in females (p = 0.000). It was determined that sex could be estimated accurately at up to 93.3 % using DFA, 93.8 % using SVM and 95.4 % using ANN. The maximum accuracy rate achieved to 94.2 % in males using SVM, and 95.8 % in females using ANN. These high rates of sexual dimorphism found using DFA, SVM, and ANN in this study indicate that characteristics of the hyoid bone can be utilized to determine sex in the Eastern Turkish population.

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category's cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

Assessment of sex and sexual dimorphism in children from Tamilnadu, India through odontometrics in primary dentition - a cross sectional study with discriminant function analysis.

One of the crucial steps in the process of identification of human remains is sex determination of an unknown individual. Sex can be determined using the human skeleton, especially by examining the pelvis and skull. Odontometric analysis and assessment of sexual dimorphism of the human dentition has been less investigated, especially in primary dentition. Hence, this research was designed to verify the presence of sexual dimorphism through odontometric approach in paediatric population of Tamil Nadu, India. The present study was performed on 229 females and 244 males between 3 and 6 years of age. Linear measurements such as maximum mesio distal width, maximum bucco lingual width and clinical crown length were measured in all the primary teeth. Statistical analysis was performed using the Statistical Package for Social Science version 20.0 software (SPSS Inc., Chicago, IL, USA). Mann-Whitney-U test was used to check the statistical significance of the differences observed between males and females. Six equations were derived by discriminant function analysis for practical use. Sexual dimorphism existed in odontometric parameters of primary dentition. The maximum sexual dimorphism was observed in maximum bucco lingual width of 51 and maximum bucco lingual width of 75. The equations derived using discriminant function analysis yielded a jackknife accuracy ranging between 87.5% and 99.8%. These equations may be used to assess the sex from the children of Tamil Nadu, India.

Prognostic and diagnostic scoring models in acute alcohol-associated hepatitis: A review comparing the performance of different scoring systems.

Alcohol-associated hepatitis (AAH) is a severe form of liver disease caused by alcohol consumption. In the absence of confounding factors, clinical features and laboratory markers are sufficient to diagnose AAH, rule out alternative causes of liver injury and assess disease severity. Due to the elevated mortality of AAH, assessing the prognosis is a radical step in management. The Maddrey discriminant function (MDF) is the first established clinical prognostic score for AAH and was commonly used in the earliest AAH clinical trials. A MDF > 32 indicates a poor prognosis and a potential benefit of initiating corticosteroids. The model for end stage liver disease (MELD) score has been studied for AAH prognostication and new evidence suggests MELD may predict mortality more accurately than MDF. The Lille score is usually combined to MDF or MELD score after corticosteroid initiation and offers the advantage of assessing response to treatment a 4-7 d into the course. Other commonly used scores include the Glasgow Alcoholic Hepatitis Score and the Age Bilirubin international normalized ratio Creatinine model. Clinical AAH correlate adequately with histologic severity scores and leave little indication for liver biopsy in assessing AAH prognosis. AAH presenting as acute on chronic liver failure (ACLF) is so far prognosticated with ACLF-specific scoring systems. New artificial intelligence-generated prognostic models have emerged and are being studied for use in AAH. Acute kidney injury (AKI) is one possible complication of AAH and is significantly associated with increased AAH mortality. Predicting AKI and alcohol relapse are important steps in the management of AAH. The aim of this review is to discuss the performance and limitations of different scoring models for AAH mortality, emphasize the most useful tools in prognostication and review predictors of recurrence.

Can Shoulder Impairments Be Classified From 3-Dimensional Kinematics Using Inertial Sensors?

Inertial sensors may help clinicians to assess patients' movement and potentially support clinical decision making. Our aim was to determine whether shoulder range of motion during movement tasks measured using inertial sensors is capable of accurately discriminating between patients with different shoulder problems. Inertial sensors were used to measure 3-dimensional shoulder motion during 6 tasks of 37 patients on the waiting list for shoulder surgery. Discriminant function analysis was used to identify whether the range of motion of different tasks could classify patients with different shoulder problems. The discriminant function analysis could correctly classify 91.9% of patients into one of the 3 diagnostic groups based. The tasks that associated a patient with a particular diagnostic group were the following: subacromial decompression: abduction, rotator cuff repair of tears ≤5 cm: flexion and rotator cuff repair of tears >5 cm: combing hair, abduction, and horizontal abduction-adduction. The discriminant function analysis showed that range of motion measured by inertial sensors can correctly classify patients and could be used as a screening tool to support surgery planning.

Using active or functional range of motion as a further indicator to detect hand osteoarthritis. A preliminary study.

PURPOSE: Propose further indicators for helping to detect hand osteoarthritis (HOA) using either active or functional ranges of motion (AROM or FROM). METHOD: Hand kinematics data of 16 hand joint angles from previous studies were used, taken from healthy participants and HOA patients with differently affected joints and distinct levels of compromise. Data consisted of: (i) AROM (extreme values and ranges); (ii) FROM while performing the Sollerman Hand Function Test (mean, extreme percentiles and ranges). Two linear discriminant analyses (stepwise method) were conducted, one per dataset (AROM and FROM), with condition (healthy/patient) as the grouping variable. The potential predictors were the data from the joints with significant differences between samples for each analysis (A-predictors and F-predictors). RESULTS: Good sensitivity-specificity values of 85.2-90.9% and 93.8-93.9% for F-predictors and A-predictors, respectively. Sets of predictors corresponded to the joints more commonly affected by HOA. F-predictors: lower maximal flexion of carpometacarpal and interphalangeal thumb joints, higher maximal flexion of thumb metacarpal joint, lower flexion/extension range of ring proximal interphalangeal joint and higher maximal little finger adduction. A-predictors: narrower flexion/extension range of the thumb carpometacarpal joint, lesser extension of the ring metacarpophalangeal joint; lower flexion of the middle finger proximal interphalangeal joint along with a narrower palmar arch range. CONCLUSION: Both sets of predictors provide discrimination capacity of HOA with good sensitivity-specificity, slightly better for A-predictors. The AROM measurement is technically less demanding and can be clinically applied even with manual goniometry.

Range of motion is affected by hand osteoarthritis even before joint deformity is noticeable.Active (AROM) or functional (FROM) ranges of motion can help to identify potential hand osteoarthritis and opening up the possibility of applying rehabilitation treatments to improve quality of life.The AROM measurement is technically less demanding and can be clinically applied even with manual goniometry.

Sex estimation of Malaysian sub-adults using craniometrics: A computed tomography study.

Sex estimation is crucial in biological profiling of skeletal human remains. Methods used for sex estimation in adults are less effective for sub-adults due to varied cranium patterns during the growth period. Hence, this study aimed to develop a sex estimation model for Malaysian sub-adults using craniometric measurements obtained through multi-slice computed tomography (MSCT). A total of 521 cranial MSCT dataset of sub-adult Malaysians (279 males, 242 females; 0-20 years old) were collected. Mimics software version 21.0 (Materialise, Leuven, Belgium) was used to construct three-dimensional (3D) models. A plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA) and binary logistic regression (BLR) were used to statistically analyze the data. In this study, low level of sexual dimorphism was observed in cranium below 6 years old. The level was then increased with age. For sample validation data, the accuracy of DFA and BLR in estimating sex improved with age from 61.6% to 90.3%. All age groups except 0-2 and 3-6 showed high accuracy percentage (≥75%) when tested using DFA and BLR. DFA and BLR can be utilised to estimate sex for Malaysian sub-adult using MSCT craniometric measurements. However, BLR showed higher accuracy than DFA in sex estimation of sub-adults.

The Impact of Metabolic Syndrome on the Prognosis of High-Risk Alcoholic Hepatitis Patients: Redefining Alcoholic Hepatitis.

Background: Alcoholic hepatitis (AH) is characterized by acute symptomatic hepatitis associated with heavy alcohol use. This study was designed to assess the impact of metabolic syndrome on high-risk patients with AH with discriminant function (DF) score ≥ 32 and its effect on mortality. Methods: We searched the hospital database for ICD-9 diagnosis codes of acute AH, alcoholic liver cirrhosis, and alcoholic liver damage. The entire cohort was categorized into two groups: AH and AH with metabolic syndrome. The effect of metabolic syndrome on mortality was evaluated. Also, an exploratory analysis was used to create a novel risk measure score to assess mortality. Results: A large proportion (75.5%) of the patients identified in the database who had been treated as AH had other etiologies and did not meet the American College of Gastroenterology (ACG)-defined diagnosis of acute AH, thus had been misdiagnosed as AH. Such patients were excluded from analysis. The mean body mass index (BMI), hemoglobin (Hb), hematocrit (HCT), and alcoholic liver disease/non-alcoholic fatty liver disease index (ANI) were significantly different between two groups (P < 0.05). The results of a univariate Cox regression model showed that age, BMI, white blood cells (WBCs), creatinine (Cr), international normalized ratio (INR), prothrombin time (PT), albumin levels, albumin < 3.5, total bilirubin, Na, Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD ≥ 21, MELD ≥ 18, DF score, and DF ≥ 32 had a significant effect on mortality. Patients with a MELD greater than 21 had a hazard ratio (HR) (95% confidence interval (CI) of 5.81 (2.74 - 12.30) (P < 0.001). The adjusted Cox regression model results showed that age, Hb, Cr, INR, Na, MELD score, DF score, and metabolic syndrome were independently associated with high patient mortality. However, the increase in BMI and mean corpuscular volume (MCV) and sodium significantly reduced the risk of death. We found that a model including age, MELD ≥ 21, and albumin < 3.5 was the best model in identifying patient mortality. Our study showed that patients admitted with a diagnosis of alcoholic liver disease with metabolic syndrome had an increased mortality risk compared to patients without metabolic syndrome, in high-risk patients with DF ≥ 32 and MELD ≥ 21. A bivariate correlation analysis revealed that patients with AH with metabolic syndrome were more likely to have infection (43%) compared to AH (26%) with correlation coefficient of 0.176 (P = 0.03, CI: 0.018 - 1.0). Conclusion: In clinical practice, the diagnosis of AH is inaccurately applied. Metabolic syndrome significantly increases the mortality risk in high-risk AH. It signifies that the presence of features of metabolic syndrome modifies the behavior of AH in acute settings, warranting different therapeutic strategies. We propose that in defining AH, patients overlapping with metabolic syndrome may need to be excluded as their outcome is different with regard to risk of renal dysfunctions, infections and death.