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Neurological disorders pose a huge worldwide challenge to the healthcare system, necessitating innovative strategies for targeted drug delivery to the central nervous system. Alzheimer's disease (AD) is an untreatable neurodegenerative condition characterized by dementia and alterations in a patient's physiological and mental states. Since ancient times, medicinal plants have been an important source of bioactive phytochemicals with immense therapeutic potential. This review investigates new and safer alternatives for prevention and treatment of disease related to inevitable side effects associated with synthetic compounds. This review examines how nanotechnology can help in enhancing the delivery of neuroprotective phytochemicals in AD. Nevertheless, despite their remarkable neuroprotective properties, these natural products often have poor therapeutic efficacy due to low bioavailability, limited solubility and imperfect blood brain barrier (BBB) penetration. Nanotechnology produces personalized drug delivery systems which are necessary for solving such problems. In overcoming these challenges, nanotechnology might be employed as a way forward whereby customized medication delivery systems would be established as a result. The use of nanocarriers in the design and application of important phytochemicals is highlighted by this review, which indicate potential for revolutionizing neuroprotective drug delivery. We also explore the complications and possibilities of using nanocarriers to supply nutraceuticals and improve patients' standard of living, and preclinical as well as clinical investigations displaying that these techniques are effective in mitigating neurodegenerative diseases. In order to fight brain diseases and improve patient's health, scientists and doctors can employ nanotechnology with its possible therapeutic interventions., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study aimed to investigate the effects of prenatal docosahexaenoic acid (DHA) supplementation on pregnancy outcomes through a systematic review and meta-analysis. We hypothesized that prenatal DHA intake through supplements will improve pregnancy outcomes. Detailed literature search was performed using online databases such as PubMed, EMBASE, and Google Scholar till November 2022, to identify the randomized controlled trials (RCT) with maternal intake of DHA supplementation during the latter half of pregnancy compared to the placebo/control. Based on the inclusion and exclusion criteria, nine RCTs involving 5710 subjects were included. This meta-analysis showed that DHA supplementation (450-800 mg/day) was associated with a significantly higher birth weight of infants (Inverse variance [IV]: 101.71 [57.36-146.06] at 95% CI, P = .00001, I 2 = 0%), and fewer low birth weights (LBWs) (Mantel-Haenszel [M-H]: 0.53 [0.33-0.86] at 95% CI, P = .01, I 2 = 72%), with lesser but statistically insignificant pre-term births (PTB) (M-H: -0.02 [-0.04 to 0.00] at 95% CI, P = .07, I 2 = 0%) compared to the placebo. However, the DHA supplementation has no effect on gestational length (IV: -2.26 [-9.64 to 5.12] at 95% CI, P = .55, I 2 = 100%) compared to the placebo. In conclusion, the outcomes of this meta-analysis showed that prenatal DHA supplementation (450-800 mg/day) may reduce the risk of preterm births and increase infant birth weight., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals. Approach. In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels. Main results. The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier. Significance. Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals., (© 2023 Institute of Physics and Engineering in Medicine.)

Background and Objective: Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP)., Methods: Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images., Results: For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC., Conclusions: To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers., Competing Interests: Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2022. Published by Elsevier B.V.)

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet , and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Background: Ideal pulpectomy for primary dentition demands fast, simple procedures, with short treatment times and minimal appointments. Recently, exclusive pediatric rotary files are available for use in primary teeth. There is a paucity of literature on the clinical efficacy of pediatric rotary files. Hence, this study was planned to evaluate and compare pediatric rotary files and K-files., Aim: The aim of this study was to evaluate and compare instrumentation time, obturation time, and radiographic quality of obturation using rotary systems (Kedo-S; Pro-AF Baby GOLD files) and manual (K-files) technique in primary molar pulpectomies., Materials and Methods: Forty-five primary mandibular molars from 42 children aged 5-9 were selected. Single-visit pulpectomy was performed after dividing selected teeth into three equal groups: (i) K-files, (ii) Kedo-S files, (iii) Pro-AF Baby GOLD files. Instrumentation and obturation times were recorded during the procedure. Immediate postoperative radiographs were taken and evaluated later for obturation quality by two independent evaluators blinded to the instrumentation technique. The results were then statistically analyzed., Results: Kedo-S (Group II) files required the least instrumentation time followed by Pro-AF (Group III) and K-files (Group I). The superior quality of obturation in lesser time was achieved using Pro-AF files (Group III) followed by Kedo-S (Group II) and K-files (Group I)., Conclusion: Pediatric rotary files are efficient alternatives to hand instrumentation and can be considered as the standard of care in pulpectomies of primary teeth., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Contemporary Clinical Dentistry.)

Context: Autism prevalence is increasing, with current estimates at 1/68-1/50 individuals diagnosed with autism. Diagnosis is based on behavioral assessments. Early diagnosis and intervention are known to greatly improve functional outcomes in people with autism. Diagnosis, treatment monitoring, and prognosis of autistic children's symptoms could be facilitated with biomarkers to complement behavioral assessments., Aims: The aim of this study is to compare and evaluate salivary zinc concentration in saliva samples of autistic and healthy children in mixed dentition age group., Settings and Design: Cross-sectional comparative study carried out in dental college and special child school. Unstimulated whole saliva collected for finding a biomarker., Subjects and Methods: Unstimulated whole saliva sample was collected from 10 autistic and 10 healthy children in mixed dentition age group. Diluted saliva sample was then subjected to inductively coupled plasma emission spectroscopy for the estimation of salivary zinc concentration., Statistical Analysis Used: Mann-Whitney U-test., Results: In children with autism salivary zinc concentration showed a linear equation when compared to healthy children., Conclusions: The low salivary zinc concentration in autistic children can reveal the pathogenesis of autism., Competing Interests: None

As part of our ongoing structure-activity studies of the vesicular acetylcholine transporter ligand 2-(4-phenylpiperidino)cyclohexanol (vesamicol, 1), 22 N-hydroxy(phenyl)alkyl derivatives of 3 beta-phenyltropane, 6, and 1-methylspiro[1H-indoline-3,4'-piperidine], 7, were synthesized and tested for binding in vitro. Although a few compounds displayed moderately high affinity for the vesicular acetylcholine transporter, no compound was more potent than the prototypical vesicular acetylcholine transporter ligand vesamicol. However, a few derivatives of 6 displayed higher affinity for the dopamine transporter than cocaine. We conclude that modification of the piperidyl fragment of 1 will not lead to more potent vesicular acetylcholine transporter ligands.