Hair Fall Count 60-second: Clinic-Based Modified Count Versus Home-Based Count

This study aimed to develop a valid science process skills test both in terms of content and constructs. The selected biodiversity material is integrated into this test. The quizizz application is used so that this test can be accessed online. This study uses the Research and Development (R&D) 4-D model, namely define, design, develop, and disseminate. The final product of this development research is a science process-based test instrument to measure student skills that have been declared valid and feasible by experts in the form of 50 multiple-choice questions. This research trial was conducted on 40 high school students majoring in Mathematics and Natural Sciences. Quantitative data analysis was carried out based on the internal characteristics of the test, such as validity, reliability, level of difficulty, and discriminatory power. The results showed that the validity test contained 28 valid questions and 22 invalid questions. In the difficulty index, there are 33 easy questions, 16 easy questions and 0 difficult questions. There is also a distinguishing power of good criteria consisting of 12 questions, sufficient criteria for 20 questions, and flawed criteria for 18 questions. In the validity test with the material aspect, an average of 74% quite valid, the construction aspect on average 77% quite valid, and the language aspect are 91% quite valid. The students' science process skills are also classified as good, with 76.22 in the high category.

Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns. A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively. Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%). For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

In the past ten years there has been a number of brief textbooks dealing with dermatology. It is my belief that this book may be one of the best, and deserves a serious look by anyone who has not had extensive training in the specialty and who is faced with the day-to-day care of patients with common dermatologic problems. The book contains 207 excellent illustrations, 57 of which are large color plates. The subject of regional diagnoses is presented in a unique format that should be quite helpful to practicing physicians and ancillary health care personnel when they are faced with a difficult differential diagnostic problem, such as diffuse alopecia, nail dystrophies, or mucous membrane lesions. References are current, conveniently located following each major topic, and contain a large number of review articles. The book contains a concise but factual and well-written chapter on embryology, anatomy, and physiology of the

: Research about the development of Science Process Skills (SPS) has been carried out as SPS measuring instrument study materials in Biology subject at 11 th grade of senior high school. The study is aimed to produce a valid and practical test instrument of SPS. The method used in this research is Development Research (DR) method which refers to the instrument development model from Djaali and Mulyono (2008). The steps of the research and development consist of several phases: synthesis theory and requirements analysis, design stage (variable construction, development of indicators, the preparation of the lattice matter, writing of instruments, scoring) and the evaluation phase. Evaluation phase is divided into three: validity test, reliability test and analysis of grain items. The analysis of grain items is done by analyzing the degrees of difficulty, different power and distracters function. The validation of Science Process Skills (SPS) is done in three stages, namely validation theoretical (content experts, constructs experts and linguists) and empirical validation (test participants' answers). Based on the theoretical validation of the results obtained an average value of 4.2 which indicates that the questions of SPS is valid. Practicality of SPS can be detected through the result analysis of the test participants questionnaires in the trial test. The average of the questionnaire practicality while testing is 3.64 which indicate that the question of SPS is a practical category. The results of the analysis from the expert validation test and learner questionnaire showed the SPS that produced is valid and practical. Keywords: Questions development, science process skill, biology learning material

BackgroundProper estimations of resting energy expenditure (REE) are important for developing weight management strategies, but it is unclear which prediction equations best estimate REE for those with severe obesity.ObjectivesThis validation study tested 11 previously validated REE prediction equations to determine which equations estimate REE with the least bias and most precision in participants with severe obesity.MethodsREE was measured by indirect calorimetry in 632 females and 148 males with severe obesity from the Utah Obesity Study. A literature search was conducted to identify prediction equations designed from, validated, or commonly used in samples with severe obesity. All equations were tested on each participant. Equations were considered unbiased if mean predicted REE did not differ significantly (P > 0.05) from measured values. Bland–Altman plots characterized bias across measured REE values for prediction equations consistent with measured values. Precision was the percentage of the sample where an equation estimate was within 10% of the measured REE. Equations were further assessed within sex and body mass index subgroups.ResultsOnly the body weight-based Lazzer equations (Lazzer A) and the Horie–Waitzberg equation generated unbiased predictions across all subgroups, with bias values ranging from −68.1 to 71.6 kcal, yet Bland–Altman plots revealed systematic bias, particularly at extreme values of REE. Equations including body composition did not predict better than body weight-based equations, and no single equation predicted best in every subgroup. Precision measurements never rose above 67.8%.ConclusionsClinicians may benefit from tailoring their choice of REE prediction equation to the specific characteristics of each patient, favoring equations with lower bias and greater precision within relevant subgroups. However, because of the low precision of REE prediction equations and the systematic bias revealed at REE extremes, it is highly recommended to measure REE whenever possible.

Background Body surface area (BSA) is used in clinical practice to prescribe medication, standardize measured parameters etc. Normogram has been the gold standard. However, there are several prediction equations in the published literature. A simple method giving an accurate estimation would help to assess BSA in day to day clinical practice efficiently. Objective To assess the accuracy of different equations for the estimation of BSA in a group of Sri Lankan children. Method Data collected in 3 different school surveys carried out between 2002 and 2005 were used. BSA estimated by 9 prediction equations described in the literature were compared with the BSA assessed by normogram. Results Two thousand three hundred and thirty eight girls and 639 boys, between the ages of 5-16 years, were assessed. The mean ages of the boys and girls were 10.3±1.47 years and 11.5±2.47 years respectively. The highest mean BSA estimation was given by Gehan & George equation (boys 1.14±0.20m 2 and girls 1.2 ± 0.25m 2 ) and lowest by Yu et al (boys 1.08±0.20m 2 and girls 1.13±0.25m 2 ). Estimates correlated highly with BSA assessed by normogram. When the estimates were compared with normogram, equation of Mattar gave the highest bias for both groups (females 0.0152m 2 and males 0.0177m 2 ). Mosteller equation gave relatively low bias (females 0.0077m 2 , and males 0.0072 m 2 ).

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a Pixel Color- and Coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing the Mahalanobis distance metric, aimed at accurately segmenting symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC, combined with manual classification of the segmented regions, was evaluated using Lin’s concordance correlation coefficient (ρc) by comparing its results to visual measurements of ShB lesion height (cm) and to lesion/diseased area (cm2) measured using ImageJ. Low bias (Cb) and high precision (r) were observed for absolute lesion height (Cb = 0.93, r = 0.94) and absolute symptomatic area (Cb = 0.98, r = 0.97) studies. Furthermore, to automatically classify the segmented regions produced by the PCC-KMC algorithm, we employed a convolutional neural network (CNN). Unlike conventional CNNs that require fixed-size image inputs, our CNN is designed to take the RGB histogram of each segmented region (a 1000 by 3 representation) as input and determine whether the region corresponds to ShB infection. This design effectively handles the arbitrary sizes and irregular shapes of segmentation regions generated by PCC-KMC. Our CNN was trained based on an 85%:15% composition for the training and testing dataset from a total of 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (Cb = 0.86, r = 0.90) and absolute diseased area (Cb = 0.99, r = 0.97) studies, indicating that PCC-KMC combined with automatic CNN-based classification performs very effectively. These results demonstrate that the potential of our methodology to serve as an alternative to the traditional visual-based ShB disease severity assessment and can be considered to be utilized for lab-scale, high-throughput phenotyping of rice ShB.

This study aims to evaluate the aerobic fitness of miners and develop a predictive equation to assess their maximum oxygen uptake (V O2max), which is crucial for ensuring their safety and health in harsh working conditions. Measurement of V O2max via indirect calorimetry is costly and requires skilled personnel; developing a predictive equation is a viable alternative. Existing predictive equations, e.g., Hunt, Kline and Dolgener equations, were tested using the Pearson correlation coefficient, constant error, standard error of estimate and total error. The aerobic fitness of shearer operators, bolters miners, jackhammer drillers and road header operators was very low. Bland–Altman analysis revealed that predicted V O2max measurements of all three equations had a total error of >10%, which is unacceptable for usage. Therefore, a new predictive equation was developed with a total error of 9.78%. The developed predictive equation is useful to evaluate miners’ functional capacity with higher precision and assist in allocating work.

Performance validity tests (PVTs) are frequently used to detect invalid performance on cognitive testing. The inclusion of PVTs in cognitive test batteries is commonplace irrespective of the condition of interest. However, base rates of invalid performance vary across clinical populations. Research accounting for base rates of invalid performance in varying clinical populations and PVT classification accuracy rates are not commonly synthesized. To address this gap, the present study examined the clinical utility of select PVTs used with older adults presenting for dementia evaluations. We computed posterior probabilities of invalid performance for the select PVTs using an estimated 5% base rate of invalid performance based on prior published studies. Posterior probabilities of invalid performance based on a PVT failure (i.e., invalid performance identified as invalid) ranged from 7.3% to 60.3% across PVTs; posterior probabilities of a false positive (i.e., valid performance identified as invalid) ranged from 39.7% to 92.7%. Conversely, posterior probabilities of a true negative (i.e., valid performance identified as valid) ranged from 95.7% to 99.3%; posterior probabilities of a false negative (i.e., invalid performance identified as valid) ranged from 0.7% to 4.3%. Results call into question the utility of PVTs in dementia evaluations. Consequently, the use of PVTs in dementia evaluations is likely to erroneously identify valid test data as invalid (i.e., false-positive error) at a frequency that exceeds the estimated 5% base rate of invalid performance. Further research examining correlates of invalid performance among older adults will clarify base rate estimates and potentially enhance the utility of PVTs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Background: The glomerular filtration rate (GFR) is considered the gold standard for assessing renal function. According to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, GFR estimation is often carried out using predictive equations that incorporate serum creatinine levels, along with demographic factors such as age, gender, race, and body size. However, these equations exhibit varying levels of accuracy across different populations, necessitating the evaluation of their performance and clinical relevance in diverse patient groups.Objectives: This study aimed to evaluate the performance of three commonly used GFR estimation equations, the Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, along with their race-modified versions, by comparing them with measured 24-hour creatinine clearance (CrCl) in adult patients with chronic kidney disease (CKD).Methods: A cross-sectional descriptive study was conducted at Jos University Teaching Hospital (JUTH) between November 2019 and July 2020, involving 111 consecutively recruited CKD patients. Data collection included medical history, physical examination, laboratory investigations, calculation of CrCl, estimated glomerular filtration rate (eGFR) using different equations, and albumin-creatinine ratio (ACR). Statistical analysis was performed using SPSS version 20 (IBM Inc., Armonk, New York), with p-values <0.05 considered statistically significant.Results: The mean age of participants was 51.1±15.5 years. Diabetes mellitus was the most common cause of CKD, affecting 38% of the cohort. Among participants, 31% and 22% were classified as having stage 5 and stage 4 CKD, respectively. The median CrCl was 26 (9-56) mL/min. The median eGFR values across the equations were as follows: CG, 26 (11-60) mL/min/1.73m²; MDRD, 26 (11-60) mL/min/1.73m²; MDRD1, 22 (9-50) mL/min/1.73m²; CKD-EPI, 26 (10-62) mL/min/1.73m²; and CKD-EPI1, 22 (9-53) mL/min/1.73m². Strong positive correlations were observed between measured CrCl and the estimated GFR from each equation: CG (r=0.948, p=0.001), MDRD (r=0.940, p=0.001), MDRD1 (r=0.939, p=0.001), CKD-EPI (r=0.943, p=0.001), and CKD-EPI1 (r=0.942, p=0.001). Furthermore, significant correlations were found between the different GFR equations themselves, with the highest correlation observed between MDRD and CKD-EPI (r=0.999, p=0.001). The median ACR was 395.5 (45.3-2887.0) mg/g, and albuminuria was present in 82% of participants. All three GFR equations closely approximated the measured CrCl of 26 mL/min/1.73m². The equations performed optimally in patients with GFR values below 45 mL/min, with the CG equation exhibiting the least bias and the highest precision. Regression analysis revealed a significant association between albuminuria and all GFR equations and a notable association between age and CrCl, CG, and CKD-EPI estimates.Conclusion: The CG, MDRD, and CKD-EPI equations demonstrated comparable accuracy to measured 24-hour CrCl in estimating GFR in Nigerian patients with CKD. These findings support the clinical use of these predictive equations for renal function assessment in CKD, though local validation in diverse populations is recommended for optimal clinical application.

Total body water (TBW), fat-free mass (FFM), and fat as a percentage of body weight (fat%) were estimated by the doubly labeled water (DLW) technique in 23 healthy rural Bolivian subjects of a typical herding community of the high Andes. Using these values as the frame of reference, validation tests of several prediction equations for TBW and body density were made. Estimated values and errors for TBW, FFM, and fat% varied greatly with prediction equation. Among the prediction equations tested, however, three, Mellits and Cheek (1970; M&C), Durnin and Womersley (1974; D&W) and Conlisk et al. (1992; CNL), had a smaller bias and higher precision than others. Compared to the DLW method, the prediction errors of these equations for FFM and fat% as evaluated by the mean bias and the 95% limits of agreement were −0.7 ± 4.5 kg and 1.5 ± 10% (M&C), 0.9 ± 3.6 kg and −2.1 ± 8.5% (D&W), and 1.1 ± 3.6 kg and −2.7 ± 9.3% (CNL). For most practical purposes, these prediction errors for FFM as percentage of body weight may not be very important, but the errors in fat% may be serious. The interpretation of predicted data thus needs caution. In spite of differences in ethnic background and age range between the subjects and the samples on which the equations were developed, it is interesting that regression equations based on U.S., British and Guatemalan subjects also predicted the body composition of native Bolivian subjects with unexpectedly high precision. Am. J. Hum. Biol. 10:371–384, 1998. © 1998 Wiley-Liss, Inc.

BackgroundWe investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m2>BMI<40 kg/m2) women.MethodsWe measured REE (indirect calorimetry), body weight, height, and fat mass (FM) and fat free mass (FFM, dual X-ray absorptiometry) in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women) the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM.ResultsAt baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241–247) when using weight (bias:−0.2%, P = 0.982), 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction). After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1–19) equation when using weight (bias:−1.7%, P = 0.044), 81% accurate prediction, yet it provided 53% accurate predictions at baseline.ConclusionsThere is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.

Partial or total unilateral vestibular loss is the third most common cause of peripheral vestibular dysfunction. Dysfunction of one or both of the vestibular mechanisms can manifest physically as abnormalities of posture, balance, and/or visual acuity. This case report describes physical therapy examination and individualized intervention with vestibular rehabilitation for a patient with unilateral vestibular hypofunction. The patient was an 80-year-old male with electronystamographically confirmed unilateral vestibular loss of 98.3%. He demonstrated altered balance and gaze stability classifying him as having an increased risk for falling. After 5 weeks of individualized vestibular rehabilitation, the patient significantly decreased his fall risk from 11 to 20 of 24 on the Dynamic Gait Index. His gaze stability also improved from a 4 to 1 line disparity with dynamic visual acuity testing. The patient also had a decrease in perceived disability on the Dizziness Handicap Inventory from 30/100 at evaluation to 12/100 at discharge. Individualized vestibular rehabilitation decreased fall risk and improved gaze stability for a patient with significant unilateral vestibular hypofunction.

The aim of this study was to evaluate the concordance between Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectrometry and traditional biochemical methods for identifying urease-producing Enterobacteria in wastewater samples. This research was a comparative study assessing the efficacy of MALDI-TOF mass spectrometry versus traditional biochemical methods for bacterial identification. The study was conducted in Abidjan over a period of five weeks. A total of 50 wastewater samples were collected for analysis. The samples underwent a two-step enrichment process: initial pre-enrichment in EPT broth at 37°C for 18-24 hours, followed by enrichment in RV10 broth at 42°C for 24 hours. Subsequently, isolates were cultured on Hektoen agar, observed for specific colony characteristics, and subjected to the urease test. Final identification was performed using MALDI-TOF mass spectrometry. Of the 50 samples, 46 produced identifiable colonies. The distribution of identified species was as follows: Proteus mirabilis in 22 samples (47.82%), Providencia stuartii in 16 samples (34.78%), and Klebsiella pneumoniae in 8 samples (17.39%). MALDI-TOF mass spectrometry demonstrated over 90% concordance with traditional biochemical methods, accurately identifying bacterial species with high precision. MALDI-TOF mass spectrometry proved to be a rapid, precise, and efficient method for identifying urease-producing Enterobacteria in wastewater. Its high concordance with traditional methods suggests its potential to replace conventional techniques in microbiological laboratories, enhancing the speed and accuracy of bacterial identification in water quality management.

IntroductionTranspulmonary thermodilution is used to measure cardiac output (CO), global end-diastolic volume (GEDV) and extravascular lung water (EVLW). A system has been introduced (VolumeView/EV1000™ system, Edwards Lifesciences, Irvine CA, USA) that employs a novel algorithm for the mathematical analysis of the thermodilution curve. Our aim was to evaluate the agreement of this method with the established PiCCO™ method (Pulsion Medical Systems SE, Munich, Germany, clinicaltrials.gov identifier: NCT01405040)MethodsSeventy-two critically ill patients with clinical indication for advanced hemodynamic monitoring were included in this prospective, multicenter, observational study. During a 72-hour observation period, 443 sets of thermodilution measurements were performed with the new system. These measurements were electronically recorded, converted into an analog resistance signal and then re-analyzed by a PiCCO2™ device (Pulsion Medical Systems SE).ResultsFor CO, GEDV, and EVLW, the systems showed a high correlation (r2 = 0.981, 0.926 and 0.971, respectively), minimal bias (0.2 L/minute, 29.4 ml and 36.8 ml), and a low percentage error (9.7%, 11.5% and 12.2%). Changes in CO, GEDV and EVLW were tracked with a high concordance between the two systems, with a traditional concordance for CO, GEDV, and EVLW of 98.5%, 95.1%, and 97.7% and a polar plot concordance of 100%, 99.8% and 99.8% for CO, GEDV, and EVLW, respectively. Radial limits of agreement for CO, GEDV and EVLW were 0.31 ml/minute, 81 ml and 40 ml, respectively. The precision of GEDV measurements was significantly better using the VolumeView™ algorithm compared to the PiCCO™ algorithm (0.033 (0.03) versus 0.040 (0.03; median (interquartile range), P = 0.000049).ConclusionsFor CO, GEDV, and EVLW, the agreement of both the individual measurements as well as measurements of change showed the interchangeability of the two methods. For the VolumeView method, the higher precision may indicate a more robust GEDV algorithm.Trial registrationclinicaltrials.gov NCT01405040.