Exploring Faculty Preparedness for Artificial Intelligence-Driven Dental Education: A Multicentre Study.

Introduction In the modern era, technology, including artificial intelligence (AI), is the centre of digital innovation. AI is revolutionising numerous fields, including the healthcare sector, globally. Incorporating AI in dental education may help in improving the diagnostic accuracy, learners' experiences, and effectiveness of the management of dental education institutions. However, successful implementation of AI requires the faculty's willingness to incorporate it into their practices. Thus, this research aims to explore the readiness of faculty members to integrate AI into dental education. Methodology The study employed a qualitative exploratory design to gather in-depth insights into faculty readiness for AI-driven dental education. Purposive sampling was employed, and 21 faculty members from public and private dental colleges in South Punjab participated in semi-structured interviews. The interviews focused on understanding participants' perceptions, experiences, and challenges related to AI integration in dental education. Thematic analysis was conducted utilising Braun and Clarke's framework to identify key themes and subthemes from the qualitative data using inductive coding. Results Five major themes and 14 subthemes emerged from the data analysis. Faculty members had low AI literacy coupled with diverse perceptions; some participants perceived AI as a solution for revolutionising teaching and learning, while others criticised its misuse as academic misconduct by students, an effect on students' critical thinking, and a threat to conventional jobs. However, most of the respondents also considered AI beneficial for students with remote access or from marginalised populations in terms of accessing and learning from limited resources. Concerns that participants highlighted included a lack of training opportunities, limited facilities, ethical concerns pertaining to data privacy, and assessment bias. Some of the recommendations provided by the respondents include the provision of training opportunities, the allocation of resources and infrastructure, and continuous effective support from institutions for the integration of AI in dental education. Conclusions This study emphasised the readiness of the faculty when it comes to the integration of AI in dental education. The faculty considered AI favourable for digitization and innovative education, although there is a lack of awareness of its application. Regarding the benefits of utilising AI, respondents highlighted its quick response, prediction of students' performance, and flexibility in learning. The challenges included lack of awareness regarding its implementation, inadequate training, lack of availability of resources, lack of institutional support, the problem of data confidentiality, and resistance to change. Suggestions included the provision of technical support, skills training, and the provision of required infrastructure. Participants recommended that AI tools must incorporate cultural and contextually specific content, use technical support for problems, and incorporate constant response systems to improve the AI tools for novice users, especially within developing regions such as Pakistan.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

The interplay between vitamin D status, subclinical inflammation, and prediabetes.

Vitamin D's role extends beyond classical calcium and phosphate homeostasis to encompass a pivotal influence on immune modulation and metabolic health. The mechanisms by which vitamin D exerts these effects involve its conversion to hormonally active calcitriol, which binds intracellular vitamin D receptors, initiating various downstream cascades. In this review, we tease out the evidence showing the relationship between vitamin D deficiency and prediabetes within the context of subclinical inflammation, with a special focus on the novel monocyte-to-HDL ratio (MHR), a novel inflammatory marker reflecting subclinical inflammation. This was based on a thorough literature review using reputable databases covering the period from 1980 to 2024. In light of this, we discuss calcitriol's anti-inflammatory effects and consequently link vitamin D deficiency to both overt and subclinical inflammation. Additionally, the utility of several biomarkers, notably MHR, in investigating this association is also discussed. We further reviewed the role of vitamin D deficiency in precipitating prediabetes and type 2 diabetes mellitus (T2DM) via insulin resistance, decreased insulin synthesis and secretion, and subclinical inflammation. Taken together, this mini review highlights that vitamin D deficiency is significantly associated with subclinical inflammation, playing a critical role in the development of prediabetes and the progression to T2DM. Addressing vitamin D deficiency through appropriate interventions may serve as a preventative measure against the development of prediabetes and T2DM.

Evaluation of Sleep Quality Among People Living With Type 2 Diabetes Mellitus in Taif, Saudi Arabia: A Cross-Sectional Study.

BACKGROUND:  Type 2 diabetes mellitus (T2DM) is characterized by high blood glucose levels, which are highly associated with poor sleep quality, cardiovascular disease, and pathological changes. This research examines the relationship between sleep quality and T2DM and compares it with nondiabetics within the Taif community. The findings of this study will provide valuable insights and recommendations to enhance the overall health quality in Taif, Saudi Arabia. METHODOLOGY:  A cross-sectional study was conducted on 547 patients with T2DM between December 1, 2023, and April 1, 2024, in Taif. The sleep quality was assessed using the Sleep Quality Questionnaire (SQQ). Data were collected using an online questionnaire with two parts: primary demographic data and an assessment of sleep quality using the SQQ. RESULTS:  Our study enrolled 814 participants, including 547 with T2DM and 267 nondiabetics. Participants with T2DM had poorer sleep quality, with a median score of 21 vs. 25 (P < 0.001). Significant factors affecting sleep quality included gender (P = 0.002), marital status (P = 0.023), and job status (P = 0.023). Nondiabetics had better sleep quality (76%) than participants with T2DM (61.1%). Males, married, and employed individuals reported higher sleep quality scores. CONCLUSIONS:  Research indicates that individuals with T2DM experience lower sleep quality than the general population, particularly among female, unmarried, and unemployed individuals. To enhance sleep quality in patients with T2DM, it is essential to increase awareness, provide education on proper sleep habits, and highlight the importance of effective diabetes management, screening for sleep disorders, and consistent monitoring.

Association between Serum Vitamin D Status and Circadian Syndrome: A Cross-Sectional Study.

BACKGROUND: Circadian Syndrome (CircS) encompasses cardiometabolic risk factors and comorbidities, indicating an elevated susceptibility to cardiovascular disease and type 2 diabetes. METHODS: This cross-sectional study aimed to investigate the association between vitamin D levels and each of the following: CircS, metabolic syndrome (MetS), and the individual components of CircS. Data from 14,907 adults who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 were utilized. CircS was defined based on MetS components, alongside depression, short sleep, and non-alcoholic fatty liver disease (NAFLD). RESULTS: Our results indicated that low vitamin D levels exhibited meaningful associations with CircS, with vitamin D deficiency and inadequacy demonstrating 2.21-fold (95% CI 1.78-2.74, p < 0.001) and 1.33-fold (95% CI 1.14-1.54, p < 0.001) increases in CircS odds, respectively. The association between vitamin D deficiency and CircS was stronger than that with MetS. Additionally, a dose-response gradient in odds of CircS components, particularly with short sleep duration, was noted as serum vitamin D levels decreased. CONCLUSIONS: our findings highlight a significant association between low serum vitamin D levels and CircS and its components, particularly with short sleep. This suggests a potentially pivotal role of vitamin D in the pathogenesis of Circadian syndrome.

Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

OBJECTIVES: To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for stroke prediction. METHODS: This cross-sectional study analyzed data on 438,693 adults from the 2021 Behavioral Risk Factor Surveillance System. Features encompassed demographics and clinical factors. Descriptive analysis profiled the dataset. Logistic regression quantified risk relationships. Adjusted mutual information evaluated feature importance. Multiple machine learning models were built and evaluated on metrics like accuracy, AUC ROC, and F1 score. RESULTS: Key factors significantly associated with higher stroke odds included older age, diabetes, hypertension, high cholesterol, and history of myocardial infarction or angina. Random forest model achieved the best performance with accuracy of 72.46%, AUC ROC of 0.72, and F1 score of 0.74. Cross-validation confirmed its reliability. Top features were hypertension, myocardial infarction history, angina, age, diabetes status, and cholesterol. CONCLUSION: The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.

Electrochemical detection of nalbuphine drug using oval-like ZnO nanostructure-based sensor.

Monitoring pharmaceutical drugs in various mediums is crucial to mitigate adverse effects. This study presents a chemical sensor using an oval-like zinc oxide (ZnO) nanostructure for electrochemical detection of nalbuphine. The ZnO nanostructure, produced via an efficient sol-gel technique, was extensively characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), UV-visible spectrophotometry, and fourier transform infrared spectroscopy (FTIR). A slurry of the ZnO nanostructure in a binder was applied to a glassy carbon electrode (GCE). The sensor's responsiveness to nalbuphine was assessed using linear sweep voltammetry (LSV), achieving optimal performance by fine-tuning the pH. The sensor demonstrated a proportional response to nalbuphine concentrations up to 150.0 nM with a good regression coefficient (R2) and a detection limit of 6.20 nM (S/N ratio of 3). Selectivity was validated against various interfering substances, and efficacy was confirmed through real sample analysis, highlighting the sensor's successful application for nalbuphine detection.

The Role of Epithelial Cell Adhesion Molecule Cancer Stem Cell Marker in Evaluation of Hepatocellular Carcinoma.

Background and Objectives: Hepatocellular carcinoma (HCC) is a prevalent form of malignancy that is characterized by high mortality rates and prognosis that remain suboptimal, largely due to treatment resistance mechanisms. Recent studies have implicated cancer stem cells (CSCs), particularly those expressing epithelial cell adhesion molecule (EpCAM), in HCC progression and resistance. In the present study, we sought to assess EpCAM expression in HCC patients and its correlation with various clinicopathological parameters. Materials and Methods: Tissue samples from 42 HCC patients were subjected to immunohistochemical staining to evaluate EpCAM expression. Clinicopathological data were obtained including the size, grade and stage of tumors, vascular invasion status, alpha-fetoprotein levels, and cirrhosis status. The Chi square and Fisher's exact tests were employed to assess the association between categorical groups. Independent Student-t test or Mann-Whitney U test was used to investigate the association between continuous patient characteristics and survival. Results: Immunohistochemical analysis revealed EpCAM expression in 52.5% of HCC cases. EpCAM-positive tumors exhibited characteristics indicative of aggressive disease, including larger tumor sizes (p = 0.006), greater tumor multiplicity (p = 0.004), higher grades (p = 0.002), more advanced stages (p = 0.003), vascular invasion (p = 0.023), elevated alpha-fetoprotein levels (p = 0.013), and cirrhosis (p = 0.052). Survival analysis demonstrated that EpCAM expression was significantly associated with lower overall rates of survival and higher rates of recurrence in HCC patients. Conclusions: Our findings suggest that EpCAM expression may serve as a prognostic biomarker for HCC with a potential role in patient management. Targeting EpCAM-positive CSCs may represent a promising approach to overcome treatment resistance and improve clinical outcomes in HCC. However, further investigation into the molecular mechanisms underlying EpCAM's role in HCC progression is warranted to facilitate the development of personalized therapeutic interventions.

Whole-Genome Sequence Analysis of Antibiotic Resistance, Virulence, and Plasmid Dynamics in Multidrug-Resistant E. coli Isolates from Imported Shrimp.

We analyzed antimicrobial resistance and virulence traits in multidrug-resistant (MDR) E. coli isolates obtained from imported shrimp using whole-genome sequences (WGSs). Antibiotic resistance profiles were determined phenotypically. WGSs identified key characteristics, including their multilocus sequence type (MLST), serotype, virulence factors, antibiotic resistance genes, and mobile elements. Most of the isolates exhibited resistance to gentamicin, streptomycin, ampicillin, chloramphenicol, nalidixic acid, ciprofloxacin, tetracycline, and trimethoprim/sulfamethoxazole. Multilocus sequence type (MLST), serotype, average nucleotide identity (ANI), and pangenome analysis showed high genomic similarity among isolates, except for EC15 and ECV01. The EC119 plasmid contained a variety of efflux pump genes, including those encoding the acid resistance transcriptional activators (gadE, gadW, and gadX), resistance-nodulation-division-type efflux pumps (mdtE and mdtF), and a metabolite, H1 symporter (MHS) family major facilitator superfamily transporter (MNZ41_23075). Virulence genes displayed diversity, particularly EC15, whose plasmids carried genes for adherence (faeA and faeC-I), invasion (ipaH and virB), and capsule (caf1A and caf1M). This comprehensive analysis illuminates antimicrobial resistance, virulence, and plasmid dynamics in E. coli from imported shrimp and has profound implications for public health, emphasizing the need for continued surveillance and research into the evolution of these important bacterial pathogens.

Phenotypic, genotypic and proteomic variations between poor and robust colonizing Campylobacter jejuni strains.

Campylobacter jejuni is one of the major causes of bacterial gastrointestinal disease in humans worldwide. This foodborne pathogen colonizes the intestinal tracts of chickens, and consumption of chicken and poultry products is identified as a common route of transmission. We analyzed two C. jejuni strains after oral challenge with 105 CFU/ml of C. jejuni per chick; one strain was a robust colonizer (A74/C) and the other a poor colonizer (A74/O). We also found extensive phenotypic differences in growth rate, biofilm production, and in vitro adherence, invasion, intracellular survival, and transcytosis. Strains A74/C and A74/O were genotypically similar with respect to their whole genome alignment, core genome, and ribosomal MLST, MLST, flaA, porA, and PFGE typing. The global proteomes of the two congenic strains were quantitatively analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and 618 and 453 proteins were identified from A74/C and A74/O isolates, respectively. Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that carbon metabolism and motility proteins were distinctively overexpressed in strain A74/C. The robust colonizer also exhibited a unique proteome profile characterized by significantly increased expression of proteins linked to adhesion, invasion, chemotaxis, energy, protein synthesis, heat shock proteins, iron regulation, two-component regulatory systems, and multidrug efflux pump. Our study underlines phenotypic, genotypic, and proteomic variations of the poor and robust colonizing C. jejuni strains, suggesting that several factors may contribute to mediating the different colonization potentials of the isogenic isolates.

Unrevealing the multitargeted potency of 3-1-BCMIYPPA against lung cancer structural maintenance and suppression proteins through pharmacokinetics, QM-DFT, and multiscale MD simulation studies.

Lung cancer, a relentless and challenging disease, demands unwavering attention in drug design research. Single-target drugs have yielded limited success, unable to effectively address this malignancy's profound heterogeneity and often developed resistance. Consequently, the clarion call for lung cancer drug design echoes louder than ever, and multitargeted drug design emerges as an imperative approach in this landscape, which is done by concurrently targeting multiple proteins and pathways and offering a beacon of hope. This study is focused on the multitargeted drug designing approach by identifying drug candidates against human cyclin-dependent kinase-2, SRC-2 domains of C-ABL, epidermal growth factor and receptor extracellular domains, and insulin-like growth factor-1 receptor kinase. We performed the multitargeted molecular docking studies of Drug Bank compounds using HTVS, SP and XP algorithms and poses filter with MM\GBSA against all proteins and identified DB02504, namely [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BCMIYPPA) as multitargeted lead with docking and MM\GBSA score range from -8.242 to -6.274 and -28.2 and -44.29 Kcal/mol, respectively. Further, the QikProp-based pharmacokinetic computations and QM-based DFT showed acceptance results against standard values, and interaction fingerprinting reveals that THR, MET, GLY, VAL, LEU, GLU and ASP were among the most interacting residues. The NPT ensemble-based 100ns MD simulation in a neutralised state with an SPC water model has also shown a stable performance and produced deviation and fluctuations <2Å with huge interactions, making it a promising multitargeted drug candidate-however, experimental studies are suggested.

Chicken Juice Enhances C. jejuni NCTC 11168 Biofilm Formation with Distinct Morphological Features and Altered Protein Expression.

Campylobacter jejuni is the foodborne pathogen causing most gastrointestinal infections. Understanding its ability to form biofilms is crucial for devising effective control strategies in food processing environments. In this study, we investigated the growth dynamics and biofilm formation of C. jejuni NCTC 11168 in various culture media, including chicken juice (CJ), brain heart infusion (BHI), and Mueller Hinton (MH) broth. Our results demonstrated that C. jejuni exhibited a higher growth rate and enhanced biofilm formation in CJ and in 1:1 mixtures of CJ with BHI or MH broth compared to these measures in BHI or MH broth alone. Electron microscopy unveiled distinct morphological attributes of late-stage biofilm cells in CJ, including the presence of elongated spiral-shaped cells, thinner stretched structures compared to regular cells, and extended thread-like structures within the biofilms. Proteomic analysis identified significant alterations in protein expression profiles in C. jejuni biofilms, with a predominance of downregulated proteins associated with vital functions like metabolism, energy production, and amino acid and protein biosynthesis. Additionally, a significant proportion of proteins linked to biofilm formation, virulence, and iron uptake were suppressed. This shift toward a predominantly coccoid morphology echoed the reduced energy demands of these biofilm communities. Our study unlocks valuable insights into C. jejuni's biofilm in CJ, demonstrating its adaptation and survival.

The Depression Anxiety Stress Scale 8: investigating its cutoff scores in relevance to loneliness and burnout among dementia family caregivers.

The global trend of advanced aging comes at the cost of amplified onset of age-related diseases. Dementia is a common multifactorial age-related neurodegenerative disorder, which manifests with progressive declines in cognitive functioning and ability to perform activities of daily living. As polices discourage institutionalized care, family members act as primary caregivers and endure increased vulnerability to physical and mental health problems secondary to care-related changes in life routine and relationships. Targeting clinically significant distress at earlier stages through valid brief measures may promote caregivers' wellbeing and dementia care continuity/quality. This study aimed to determine the optimal cutoff score of the Depression Anxiety Stress Scale 8-items (DASS-8) in a convenience sample of 571 European caregivers (Mean age = 53 ± 12 years, Italian = 74.4%, Swiss = 25.6%) through three methods. K-means clustering classified the sample into high- and low-distress clusters based on DASS-8 score of 19. Receiver operator curve (ROC) analysis using 48 and 7 cutoffs of the Zarit Burden Interview (ZBI) and the Three-Item University of California, Los Angeles, Loneliness Scale-version 3 (UCLALS3), revealed two DASS-8 cutoffs (12.5 and 14.5, area under the curve (AUC) = 0.85 and 0.92, p values < .001, 95% CI 0.82-0.88 and 0.89 to 0.94, sensitivity = 0.81 and 0.78, specificity = 0.76 and 0.89, Youden index = 0.57 and 0.67, respectively). Decision modeling produced two DASS-8 cutoffs (9.5 and 14.5) for predicting low and high caregiving burden and loneliness, respectively. According to the median of all DASS-8 cutoffs (14.5) the prevalence of mental distress was 50.8%. Distress correlated with key mental problems such as burnout and loneliness-in path analysis, DASS-8 scores were predicted by the ZBI, UCLALS3, care dependency, and receiving help with care, especially among older, female, and spouse caregivers. Further diagnostic workup should follow to confirm psycho-pathogenicity among caregivers with DASS-8 scores above 14.5. Investigations of the DASS-8 in other countries/populations may confirm the validity of this cutoff score.

Vagal Nerve Stimulation in the Pediatric Population and Correlation Between Family and Treatment Team Perspectives: Single-center Experience.

BACKGROUND: Vagal nerve stimulation (VNS) is an adjunctive therapy to pharmacological treatment in patients with drug-resistant epilepsy. This study aimed to assess the efficacy of VNS therapy for seizure frequency reduction and improving quality of life (QOL) measures in children with refractory epilepsy and to evaluate the correlation between the perspectives of families and those of the treating team. METHODS: This was a prospective cohort study conducted at Abha Maternity and Children's Hospital, Saudi Arabia, from 2018 to 2022. A total of 21 pediatric patients who completed one year of follow-up after VNS implantation were included. Patients were aged between 2 and 14 years, with a mean age of 8.14 ± 3.92; 11 (52.4%) patients were female. Family and physician assessments were collected blinded to each other using Clinical Global Impression of Improvement (CGI-I) scores and QOL assessments to evaluate the correlation between the families' and treating team's perspectives on VNS outcomes. RESULTS: In this study involving 21 patients with intractable epilepsy, VNS showed significant efficacy in reducing the frequency of seizures. VNS significantly reduced the number of seizures per week from a baseline median of 35 to a median of 0.25 at the end of the follow-up period, representing a dramatic reduction of 99.3% (p < 0.001). The number of emergency department visits per year decreased from a baseline median of 12 to a median of 2, a reduction of 83.3% (p < 0.001), whereas the number of hospital admissions per year decreased from a baseline median of 3 to a median of 1, a 66.7% decrease (p < 0.001). The number of antiepileptic medications taken decreased from a median of 4 to 3 (p < 0.001). Notably, 28.57% of the patients achieved complete seizure freedom, and 38% exhibited significant improvement, with at least 50% reduction in seizure frequency. Importantly, none of the patients experienced an escalation in seizure frequency following VNS treatment. The family and physician assessments showed varying degrees of alignment in perceptions, with "concentration" exhibiting a significant positive correlation (r = 0.498, p = 0.022), indicating noteworthy agreement, whereas verbal communication did not show a substantial correlation (r = -0.062, p = 0.791), indicating a divergence of views. CONCLUSION: VNS is a promising and well-tolerated therapy for individuals with intractable seizures, offering clinical benefits and potential enhancements in various aspects of QOL. The varying perceptions between family and physician assessments highlight the importance of considering multiple perspectives when evaluating treatment outcomes.

CYP2D6 inhibition by diphenhydramine leading to fatal hydrocodone overdose.

OBJECTIVES: Fatal drug overdoses often involve multiple co-intoxicants, including opioids. Hydrocodone, the most prescribed opioid for pain management, is metabolized to the active metabolite hydromorphone by hepatic CYP2D6. Inhibition of CYP2D6 by other compounds can disrupt the analgesic properties of hydrocodone and extend its half-life. Diphenhydramine is an over-the-counter cold medication and is known to inhibit CYP2D6 activity. CASE PRESENTATION: A woman in her late 50s was prescribed hydrocodone/acetaminophen (Norco® 10/325). Days before her death, she began taking diphenhydramine for cold symptoms. A post-mortem toxicology report detected the following compounds by High Performance Liquid Chromatography/Time of Flight-Mass Spectrometry (LC/TOF-MS) analysis: acetaminophen (14 µg/mL), hydrocodone (410 ng/mL), dihydrocodeine (24 ng/mL), and diphenhydramine (150 ng/mL). Hydromorphone was not detected (<2 ng/mL). All compounds were detected in therapeutic concentrations, except for hydrocodone, which was present at lethal concentrations. CONCLUSIONS: This case highlights a fatal drug-drug interaction between hydrocodone and diphenhydramine. The estimated total body burden of hydrocodone was 6- to 12-fold higher than acetaminophen, which is unexpected, as these two drugs were administered as a single formulation and have similar half-lives. Furthermore, hydromorphone was undetectable. Taken together, these findings are highly suggestive of a fatal opioid overdose precipitated by diphenhydramine.

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.

Exploring the association between microscopic colitis and celiac disease: A comprehensive analysis using the national in-patient data (2016-2019).

BACKGROUND: Several investigations suggested correlation between microscopic colitis (MC) and celiac disease (CD). This study aimed to examine this relationship using large-sized, population-based data with adequate control for confounding factors. METHODS: This study employed the National Inpatient Sample (NIS) database over 4 years (2016-2019). Patients with/without MC in the presence/absence of CD were identified through ICD-10 codes. Univariate and multi-variate analyses involving odds ratios (OR) and 95% confidence intervals (CI) were performed. RESULTS: Overall, 26,836,118 patients were analyzed. Of whom, 6,836 patients had MC (n = 179 with CD and n = 6,657 without CD). The mean hospital stay was not significantly different between both groups (5.42 ± 5.44 days vs. 4.95 ± 4.66 days, P = 0.202). The univariate analysis revealed a significant association between MC and CD (OR = 22.69, 95% [19.55, 26.33], P < 0.0001). In the multi-variate analysis, which adjusted for potential confounders including age, race, hospital region, hospital teaching status, ZIP income, smoking status, alcohol overuse, hypertension, diabetes mellitus, lipidemia-related disorders, non-steroidal anti-inflammatory drug use, and selected auto-immune diseases, the association remained significant (OR = 15.71, 95% CI [13.52, 18.25], P < 0.0001). Moreover, in patients with MC, the presence of CD emerged as a significant, independent variable of in-hospital mortality in univariate (OR = 2.87, 95% [1.14, 7.21], P = 0.025) and multi-variate (OR = 3.37, 95% CI [1.32, 8.60], P = 0.011) analyses. CONCLUSION: This study establishes a probable link between MC and CD, backed by both univariate and multi-variate analyses, while also identifying CD as an independent risk factor for increased mortality among MC patients. These findings need to be validated in real-world clinical studies.

Anthropometry of the nose pre- and post-photogrammetric adjustments in a sample of Syrian medical students. A cross-sectional study.

Background and Aims: The individual's perceived beauty, sense of identity, and general well-being are all influenced by the social implications of the esthetics of their facial appearance. One of the major indicators of one's facial beauty is anthropometric proportions. This study was conducted to measure and evaluate the anthropometric proportions in a Syrian population, and to determine their desired nasal shape. Methods: A cross-sectional study was conducted on Syrian university students. Lateral and frontal pictures of the participants were taken using Canon EOS 250D from a standard distance of 100 cm. These pictures were analyzed, then nasal measurements were adjusted according to the participants' desire using Adobe Photoshop 2020. All anthropometric measurements were conducted on AutoCAD® 2019 software before and after the adjustments. Data was analyzed using the SPSS-25 using paired samples T test to compare the means. Results: The study included 53 females and 47 males. The mean age was 22.25. The mean unadjusted nasofrontal angle, nasolabial angle, nasomental angle, nasofacial angle, nasal index, Byrd and Hobar ratio, and Powell-modified Baum ratio were 141.80, 102.18, 129.21, 34.60, 71.71, 0.62, and 3.08 in the rhinoplasty-negative participants, and 145.89, 102.03, 130.20, 32.44, 67.96, 0.64, and 3.06 in the rhinoplasty-positive participants, respectively. While the mean of the adjusted previously mentioned angles were 144.59, 106.32, 131.19, 31.15, 62.91, 0.60, and 3.19 in the rhinoplasty-negative participants, and 146.31, 102, 130.20, 31, 62.55, 0.62, and 3.08 in the rhinoplasty-positive participants, respectively. Conclusion: In contrast to other populations, our study concluded that Syrians had unique anthropometric measurements and facial esthetics preferences. Moreover, the local plastic surgeons were able to identify most of the preferred nasal measurements for rhinoplasty in this ethnic group. It is advisable to use photogrammetry before rhinoplasty to identify the preferred nasal measurements of each individual to ensure the patient's satisfaction with the outcome of the surgery. Level of Evidence: Level IV, cross-sectional study.

Epidemiology and phylogeny of Haemonchus contortus through internal transcribed spacer 2 gene in small ruminants.

Introduction: Haemonchus contortus (H. contortus) is a blood-feeding nematode causing infectious disease haemonchosis in small ruminants of tropical and subtropical regions around the world. This study aimed to explore the prevalence and phylogeny of H. contortus in small ruminants using the internal transcribed spacer-2 (ITS-2) gene. In addition, a comprehensive review of the available literature on the status of H. contortus in Pakistan was conducted. Methods: Fecal samples were collected from sheep and goats (n = 180). Microscopically positive samples were subjected to DNA extraction followed by PCR using species-specific primers. Results: The overall prevalence of H. contortus was 25.55% in small ruminants. The prevalence of H. contortus was significantly associated with months and area. The highest occurrence of haemonchosis was documented in July (38.70%), whereas the lowest occurred in December (11.11%), with significant difference. The prevalence was highest in the Ghamkol camp (29.4%) and lowest in the arid zone of the Small Ruminant Research Institute (17.5%) (p = 0.01). The results of the systematic review revealed the highest prevalence of haemonchosis (34.4%) in Khyber Pakhtunkhwa (p = 0.001). Discussion: Phylogenetic analysis revealed a close relationship between H. contortus and isolates from Asia (China, India, Iran, Bangladesh, Malaysia, and Mongolia) and European countries (Italy and the United Kingdom). It has been concluded that H. contortus is prevalent in small ruminants of Kohat district and all over Pakistan, which could be a potential threat to food-producing animals, farmers, dairy, and the meat industry. Phylogenetic analysis indicates that H. contortus isolates share close phylogenetic relationships with species from Asia and Europe.