Tag: nevin manimala

J Vis Commun Med. 2022 May 23:1-7. doi: 10.1080/17453054.2022.2071686. Online ahead of print.

ABSTRACT

This study aimed to evaluate the use of medical photography among dermatologists and plastic surgeons in Saudi Arabia. This cross-sectional study was conducted on 63 physicians (43 dermatologists, 20 plastic surgeons) using 36-item multiple choice questionnaire on the use of medical photography. Data were analysed using descriptive statistics, and two-tailed, Chi-square and Exact tests. Medical photography was used by most of dermatologists (90.7%) and plastic surgeons (95%). More than three-fourths of them agreed that medical photography aids in enhancing clinical effectiveness and standard of care. Photography was done mostly to track disease progression for dermatologists (87.2%), and for research and/or future publications for plastic surgeons (89.5%). The primary reason for exchange of photographs via email or text messages was for seeking second opinion and further recommendations from colleagues. Consent from patients before photographing was obtained by majority of both groups. Medical photography is commonly used both in clinical and academic practices for diagnostic, treatment and teaching purposes. Its value in enhancing medical care is agreed upon and the existence of workplace medical photography protocol is important. Consent from patients must be always acquired and stressed upon even with the absence of clear grounding regulations and protocols.

PMID:35603507 | DOI:10.1080/17453054.2022.2071686

Drug Test Anal. 2022 May 23. doi: 10.1002/dta.3326. Online ahead of print.

ABSTRACT

A method using innovative sample preparation was developed for determination of cocaine on banknotes. Aqueous extraction of cocaine from banknotes was performed using a sonication-enhanced technique. Quantitation of cocaine was achieved using high performance liquid chromatography (HPLC) with UV detection at 230 nm, while identification was accomplished utilizing gas chromatography with mass spectrometry (GC-MS). Multiple calibration techniques, including the external calibration method (ECM), internal standard method (ISM), and standard addition method (SAM) were incorporated into the experimental design to simultaneously determine cocaine contents and assess matrix effects. Statistical paired t tests confirmed that matrix effects were not significant with the sample preparation employed. No damage to the features of the banknotes was observed from the extraction procedure. Extraction efficiency, spike recovery, and detection limit were also determined. The unique experimental design allowed for ECM, ISM, and SAM to concurrently determine the contents of cocaine on banknotes collected around Metro-Detroit. The concentration range of cocaine was from 1.58 to 14.7 mg per note, with an average of 6.96 mg per note. The method is simple and suitable for drug analysis and forensic science applications.

PMID:35603522 | DOI:10.1002/dta.3326

Plant J. 2022 May 23. doi: 10.1111/tpj.15840. Online ahead of print.

ABSTRACT

Peroxisomes are universal eukaryotic organelles essential to plants and animals. Most peroxisomal matrix proteins carry Peroxisome Targeting Signal type 1 (PTS1), a C-terminal tripeptide. Studies from various kingdoms have revealed influences from sequence upstream of the tripeptide on peroxisome targeting, supporting the view that positive charges in the upstream region are the major enhancing elements. However, a systematic approach to better define the upstream elements influencing PTS1 targeting capability is needed. Here, we used protein sequences from 177 plant genomes to perform large-scale and in-depth analysis of the PTS1 domain, which includes the PTS1 tripeptide and upstream sequence elements. We identified and verified 12 low-frequency PTS1 tripeptides and revealed upstream enhancing and inhibiting sequence patterns for peroxisome targeting, which were later validated in vivo. Follow-up analysis revealed that nonpolar and acidic residues have relatively strong enhancing and inhibiting effects respectively, on peroxisome targeting. However, in contrast to the previous understanding, positive charges alone do not show the anticipated enhancing effect and that both the position and property of the residues within these patterns are important for peroxisome targeting. We further demonstrated that the three residues immediately upstream of the tripeptide are the core influencers, with a “basic-nonpolar-basic” pattern serving as a strong and universal enhancing pattern for peroxisome targeting. These findings have significantly advanced our knowledge of the PTS1 domain in plants and likely other eukaryotic species as well. The principles and strategies employed in this study may also be applied to deciphering auxiliary targeting signals for other organelles.

PMID:35603488 | DOI:10.1111/tpj.15840

J Clin Exp Neuropsychol. 2022 May 22:1-11. doi: 10.1080/13803395.2022.2073334. Online ahead of print.

ABSTRACT

OBJECTIVE: The Delis-Kaplan Executive Function System (D-KEFS) Trail Making Test (TMT) is a commonly used measure of processing speed and executive functioning that may also be useful as an embedded performance validity test (PVT). We evaluated the utility of several multi-condition indices on the D-KEFS TMT in three independent samples to determine an optimal multi-condition index and cutoff on the D-KEFS TMT.

METHOD: Classification accuracy statistics for multiple multi-condition indices on the D-KEFS TMT were evaluated in three independent samples, including a sample with history of mild traumatic brain injury (TBI; n = 267) classified into psychometrically defined performance-valid and performance-invalid subgroups, the D-KEFS national normative sample (n = 1713), and a sample of middle- and older-aged adults diagnosed with mild cognitive impairment (MCI; n = 70).

RESULTS: The D-KEFS TMT Conditions 1-5 summation index maximized sensitivity at .31 while maintaining adequate specificity at ≥.9. This index also had acceptable classification accuracy in both the D-KEFS national normative and MCI cross-validation samples, with the exception of the oldest subgroup of the national norming sample (i.e., individuals’ ages 80-89), in which the observed failure rates for all multi-condition indices tested were greater than 10%.

CONCLUSION: Our study provides support for the use the D-KEFS TMT Conditions 1-5 summation index as an embedded PVT among individuals younger than 80 years-old and from a range of conditions spanning from cognitively normal to mildly impaired; however, further validation is necessary.

PMID:35603485 | DOI:10.1080/13803395.2022.2073334

Nucl Med Commun. 2022 May 20. doi: 10.1097/MNM.0000000000001581. Online ahead of print.

ABSTRACT

PURPOSE: Some studies have suggested that cardiac [123I]metaiodobenzylguanidine images obtained 15-20 min after tracer administration are as accurate for dementia with Lewy bodies (DLB) diagnosis as standard images acquired after a delay of 3-4 h; some suggest delayed imaging is preferable. We compare early and delayed heart-to-mediastinum ratios (HMR) in a well-characterised research dataset and make recommendations for clinical practice.

METHODS: Images were acquired using a Siemens gamma camera with medium energy collimators. Early images were obtained at 20 min and delayed at 4 h (± 30) min. In total 167 pairs of images were reviewed: 30 controls, 39 people with dementia and 98 with mild cognitive impairment. HMR normal cutoff values derived from control data were ≥2.10 for early imaging and ≥1.85 for delayed.

RESULTS: HMR tended to drop between early and delayed for abnormal images, but increase for normal images. Histograms of early and delayed HMR showed a slightly better separation of results into two groups for delayed imaging. Accuracy results were slightly higher for delayed imaging than early imaging (73 vs. 77%), sensitivity 63 vs. 65% and specificity 82 vs. 88%. However, this was not statistically significant – in total only 8/167 (5%) of scans changed designation between early and delayed imaging.

CONCLUSION: We suggest that a delayed image could be acquired only if the early result is borderline. This removes the need for delayed imaging in about 70% of patients. Adopting this protocol in clinical practice would reduce the time most patients have to wait and could free up scanner time.

PMID:35603421 | DOI:10.1097/MNM.0000000000001581

Math Biosci Eng. 2022 Apr 18;19(6):6252-6275. doi: 10.3934/mbe.2022292.

ABSTRACT

In real-life experiments, collecting complete data is time-, finance-, and resources-consuming as stated by statisticians and analysts. Their goal was to compromise between the total time of testing, the number of units under scrutiny, and the expenditures paid through a censoring scheme. Comparing failure-censored schemes (Type-Ⅱ and Progressive Type-Ⅱ) to Time-censored schemes (Type-Ⅰ), it’s worth noting that the former is time-consuming and is no more suitable to be applied in real-life situations. This is the reason why the Type-Ⅰ adaptive progressive hybrid censoring scheme has exceeded other failure-censored types; Time-censored types enable analysts to accomplish their trials and experiments in a shorter time and with higher efficiency. In this paper, the parameters of the inverse Weibull distribution are estimated under the Type-Ⅰ adaptive progressive hybrid censoring scheme (Type-Ⅰ APHCS) based on competing risks data. The model parameters are estimated using maximum likelihood estimation and Bayesian estimation methods. Further, we examine the asymptotic confidence intervals and bootstrap confidence intervals for the unknown model parameters. Monte Carlo simulations are carried out to compare the performance of the suggested estimation methods under Type-Ⅰ APHCS. Moreover, Markov Chain Monte Carlo by applying Metropolis-Hasting algorithm under the square error of loss function is used to compute Bayes estimates and related to the highest posterior density. Finally, two data sets are studied to illustrate the introduced methods of inference. Based on our results, we can conclude that the Bayesian estimation outperforms the maximum likelihood estimation for estimating the inverse Weibull parameters under Type-Ⅰ APHCS.

PMID:35603400 | DOI:10.3934/mbe.2022292

Math Biosci Eng. 2022 Apr 6;19(6):5813-5831. doi: 10.3934/mbe.2022272.

ABSTRACT

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

PMID:35603380 | DOI:10.3934/mbe.2022272

Math Biosci Eng. 2022 Apr 2;19(6):5738-5753. doi: 10.3934/mbe.2022268.

ABSTRACT

With the development of science and technology, digital economy has penetrated into traditional manufacturing industry extensively and deeply, which has motivated the progress of the digital transformation for traditional manufacturing industry. Through comparative analysis of theoretical mechanism of synergetic development, this paper researches the order degree model and the synergy degree model based on the social shaping of technology theory to evaluate the synergetic degree of the digital transformation for China’s traditional manufacturing industry. An empirical study is presented based on the proposed models and the spatial analysis method by using the statistical data of Yangtze River Delta in China from 2014 to 2019. The results were consistent with the actual situation and indicated that the model fully reflect the dynamic interaction between systems, which is a sound basis for scientific judgment and effective decision-making when seeking to the digital transformation of traditional manufacturing industry.

PMID:35603376 | DOI:10.3934/mbe.2022268

Math Biosci Eng. 2022 Mar 28;19(6):5446-5481. doi: 10.3934/mbe.2022256.

ABSTRACT

We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.

PMID:35603364 | DOI:10.3934/mbe.2022256

Educ Inf Technol (Dordr). 2022 May 17:1-26. doi: 10.1007/s10639-022-11106-4. Online ahead of print.

ABSTRACT

Educational institutions abruptly implemented online higher education to cope with sanitary distance restrictions in 2020, causing an increment in student failure. This negative impact attracts the analyses of online higher education as a critical issue for educational systems. The early identification of students at risk is a strategy to cope with this issue by predicting their performance. Computational techniques are projected helpful in performing this task. However, the accurateness of predictions and the best model selection are goals in progress. This work objective is to describe two experiments using student grades of an online higher education program to build and apply three classifiers to predict student performance. In the literature, the three classifiers, a Probabilistic Neural Network, a Support Vector Machine, and a Discriminant Analysis, have proved efficient. I applied the leave-one-out cross-validation method, tested their performances by five criteria, and compared their results through statistical analysis. The analyses of the five performance criteria support the decision on which model applies given particular prediction goals. The results allow timely identification of students at risk of failure for early intervention and predict which students will succeed.

PMID:35603317 | PMC:PMC9110636 | DOI:10.1007/s10639-022-11106-4