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Introduction: Oral hygiene is the practice of keeping one's mouth clean and free from diseases and other problems (e.g., bad breath). It is one of the most important and necessary aspects to maintain good health. This hygiene is maintained by regular brushing of the teeth (dental hygiene). Toothpaste or gel dentifrice is a paste which is used with the toothbrush to clean the teeth. It helps in removing the food accumulated in the teeth. Hence, brushing with toothpaste suppresses bad breath and maintains oral hygiene. Objective: The aim of this study was to determine the choice of toothpaste among undergraduate (UG) dental students. Materials and Methods: A questionnaire was administered through an online survey. The survey was spread among 100 UG dental students to study and determine the factors which influence them to make a purchase of the toothpaste and use it on a daily basis. Results: The present study revealed that a majority of UG dental students use Colgate toothpaste. Cleansing property and long-lasting freshness are the qualities of the toothpaste which attracts the dental students. Television (advertisement) is considered to be a very strong factor influencing their choice. Conclusion: A dentist is an oral health-care professional who can teach you how to properly take care of your teeth and gums on a daily basis. This study is important because it reveals the choice of toothpaste among the dental students who are very conscious of their dental hygiene, and thus, the study has a motive to help people in making an appropriate choice. [ABSTRACT FROM AUTHOR]

Checkpoint inhibitors targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have revolutionized cancer therapy across many indications including urothelial carcinoma (UC). Because many patients do not benefit, a better understanding of the molecular mechanisms underlying response and resistance is needed to improve outcomes. We profiled tumors from 2,803 UC patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab by RNA sequencing (RNA-seq), a targeted DNA panel, immunohistochemistry, and digital pathology. Machine learning identifies four transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care is observed in immune and basal tumors, through different response mechanisms. A self-supervised digital pathology approach can classify molecular subtypes from H&E slides with high accuracy, which could accelerate tumor molecular profiling. This study represents a large integration of UC molecular and clinical data in randomized trials, paving the way for clinical studies tailoring treatment to specific molecular subtypes in UC and other indications., Competing Interests: Declaration of interests All Genentech-affiliated authors are shareholders in Roche. H.H., S. Mariathasan., and R.B. are listed on a pending patent related to this work. P.G. is consulting for 4D Pharma, Aadi Bioscience, Asieris Pharmaceuticals, Astellas, AstraZeneca, BostonGene, Bristol Myers Squibb, CG Oncology, Dyania Health, Exelixis, Fresenius Kabi, G1 Therapeutics, Genentech, Gilead Sciences, Guardant Health, ImmunityBio, Infinity Pharmaceuticals, Janssen, Lucence, Merck KGaA, Mirati Therapeutics, MSD, Pfizer, PureTech, QED Therapeutics, Regeneron, Roche, Seattle Genetics, Silverback Therapeutics, Strata Oncology, UroGen Pharma, and receives institutional research funding from ALX Oncology, Acrivon Therapeutics, Bavarian Nordic, Bristol Myers Squibb, Clovis Oncology, Debiopharm Group, G1 Therapeutics, Gilead Sciences, GSK, Merck KGaA, Mirati Therapeutics, MSD, Pfizer, QED Therapeutics. E. Sokol. is an employee at Foundation Medicine and a shareholder in Roche. M.D.G. receives research funding from Bristol Myers Squibb, Novartis, Dendreon, Astra Zeneca, Merck, Genentech, and acts as Advisory Board/Consultant for Bristol Myers Squibb, Merck, Genentech, AstraZeneca, Pfizer, EMD Serono, SeaGen, Janssen, Numab, Dragonfly, GlaxoSmithKline, Basilea, UroGen, Rappta Therapeutics, Alligator, Silverback, Fujifilm, Curis, Gilead, Bicycle, Asieris, Abbvie, Analog Devices, Veracyte. T.P. receives honararia and research funding from Roche, Astellas, Seagen, MSD, Merck-Serono, BMS, Pfizer, Ipsen, AZ, Johnson and johnson, Mashup-Media, Exelixis, Eisai, Natera, FMI, Novartis, Bayer. J. Rosenberg is a Consultant for Aadi Bioscience; Alligator Bioscience; Astellas; AstraZeneca Pharmaceuticals LP; Boehringer Ingelheim Pharmaceuticals Inc; Bristol-Myers Squibb Company; Century Therapeutics Inc; EMD Serono Inc; Emergence Therapeutics; Genentech, a member of the Roche Group; Gilead Sciences Inc; Imvax Inc; Jiangsu Hengrui Medicine Co Ltd; Eli Lilly; Merck; Mirati Therapeutics Inc; Pfizer Inc; Seagen Inc; Tyra Biosciences, has Grant/Research support from Seagen Inc, Genentech/Roche, Astellas, Acrivon and receives honoraria from Pfizer Inc, EMD-Serono Inc, MJH Life Sciences, Research to Practice, Clinical Care Options. M.S.v.d.H. receives research funding from Bristol Myers Squibb, AstraZeneca, 4SC and Roche, and consultancy fees from Bristol Myers Squibb, Roche, Merck Sharp & Dohme, Merck, AstraZeneca, Pfizer, Janssen and Seattle Genetics, which were all paid to the Netherlands Cancer Institute., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: This systematic review aimed to assess the effectiveness of Artificial Intelligence (AI)-based Deep Learning (DL) models in the detection of dental caries on intraoral images., Methods: This systematic review adhered to PRISMA 2020 guidelines conducting an electronic search on PubMed, Scopus, and CENTRAL databases for retrospective, prospective, and cross-sectional studies published till 1st June 2024. Methodological and performance metrics of clinical studies utilizing DL models were assessed. A modified QUADAS risk of bias tool was used for quality assessment., Results: Out of 273 studies identified, a total of 23 were included with 19 studies having a low risk and 4 studies having a high risk of bias. Overall accuracy ranged from 56% to 99.1%, sensitivity ranged from 23% to 98% and specificity ranged from 65.7% to 100%. Only 3 studies utilized explainable AI (XAI) techniques for caries detection. A total of 4 studies exhibited a level 4 deployment status by developing mobile or web-based applications., Conclusion: AI-based DL models have demonstrated promising prospects in enhancing the detection of dental caries, especially in terms of low-resource settings. However, there is a need for future deployed studies to enhance the AI models to improve their real-world applications., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to British Dental Association.)

With the digitization of radiographs, vast amounts of data have become accessible, enabling the curation and development of extensive datasets. Among radiographic modalities, Orthopantomograms (OPGs) are widely utilized in clinical practice. The integration of automated diagnostic processes into routine clinical practice holds great potential as an adjunct for dentists.Various OPG datasets exist, however their limitations affect the robustness of Artificial Intelligence (AI) models trained on them. This paper introduces an OPG dataset specifically designed for training AI algorithms in teeth segmentation and numbering tasks. A key feature of this dataset is its dual annotation, which allows for individual tooth segmentation by class, as well as numbering according to the Fédération Dentaire Internationale system.This dual-annotated dataset enhances the existing pool of OPG datasets and can be leveraged for further training of pre-trained algorithms or the development of new ones. Moreover, it offers researchers to carry out annotations tailored to their respective research objectives, thereby facilitating the development of AI models capable of addressing diverse diagnostic tasks., (© 2024 The Author(s).)

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level., (© 2024. The Author(s).)

Repurposing drugs for neurodegenerative diseases using the nose-to-brain route of administration is an intriguing concept with potential benefits. The nose-to-brain route involves delivering drugs directly to the brain via the olfactory or trigeminal pathways, bypassing the blood-brain barrier, which can improve drug efficacy and reduce systemic side effects. Treatment of numerous neurodegenerative diseases such as Multiple sclerosis, Amyotrophic lateral sclerosis, Huntington's, Alzheimer's, and Parkinson's diseases has been attempted using this route of administration. These drugs may include neuroprotective agents, anti-inflammatory drugs, antioxidants, or diseasemodifying therapies. Nanotheranostics, which integrates therapeutic and diagnostic functions in a nanosystem, improves treatment precision and efficacy. Repurposing nanotherapeutics as nanotheranostics for neurodegenerative diseases through the nose-to-brain route of administration holds great potential for both diagnosis and treatment. This review highlights the various mechanisms engaged in transporting nanocarriers from nose-to-brain and the proposed fate of these nanocarriers using different live imaging techniques. Additionally, the discussion covers the recent combinatorial therapeutic approaches and theranostic applications of various nanocarriers used for neurodegenerative diseases through the nose-to-brain. Toxicity to the CNS and nasal mucosa and regulatory considerations about these delivery systems are also deliberated. Overall, repurposed nanoparticles designed as nanotheranostic agents offer a versatile platform for precise diagnosis, targeted therapy, and personalized management of neurodegenerative diseases, holding great promise for improving patient care and advancing our understanding of these complex disorders., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Skin cancer poses a significant global health concern necessitating innovative treatment approaches. This review explores the potential of vesicle nanoformulation incorporating EA (edge activators) to overcome barriers in skin cancer management. The skin's inherent protective mechanisms, specifically the outermost layer called the stratum corneum and the network of blood arteries, impede the permeation of drugs. Phospholipid-enriched EA based nanoformulation offer a promising solution by enhancing drug penetration through skin barriers. EAs like Span 80, Span 20, Tween 20, and sodium cholate etc., enhance vesicles deformability, influencing drug permeation. This review discusses topical application of drugs treat skin cancer, highlighting challenges connected with the conventional liposome and the significance of using EA-based nanoformulation in overcoming these challenges. Furthermore, it provides insights into various EA characteristics, critical insights, clinical trials, and patents. The review also offers a concise overview of composition, preparation techniques, and the application of EA-based nanoformulation such as transfersomes, transliposomes, transethosomes, and transniosomes for delivering drugs to treat skin cancer. Overall, this review intends to accelerate the development of formulations that incorporate EA, which would further improve topical drug delivery and enhance therapeutic outcomes in skin cancer treatment.

Introduction: The fields of medicine and dentistry are beginning to integrate artificial intelligence (AI) in diagnostics. This may reduce subjectivity and improve the accuracy of diagnoses and treatment planning. Current evidence on pathosis detection on pantomographs (PGs) indicates the presence or absence of disease in the entire radiographic image, with little evidence of the relation of periapical pathosis to the causative tooth., Objective: To develop a deep learning (DL) AI model for the segmentation of periapical pathosis and its relation to teeth on PGs., Method: 250 PGs were manually annotated by subject experts to lay down the ground truth for training AI algorithms on the segmentation of periapical pathosis. Two approaches were used for lesion detection: Multi-models 1 and 2, using U-net and Mask RCNN algorithms, respectively. The resulting segmented lesions generated on the testing data set were superimposed with results of teeth segmentation and numbering algorithms trained separately to relate lesions to causative teeth. Hence, both multi-model approaches related periapical pathosis to the causative teeth on PGs., Results: The performance metrics of lesion segmentation carried out by U-net are as follows: Accuracy = 98.1%, precision = 84.5%, re-call = 80.3%, F-1 score = 82.2%, dice index = 75.2%, and Intersection over Union = 67.6%. Mask RCNN carried out lesion segmentation with an accuracy of 46.7%, precision of 80.6%, recall of 55%, and F-1 score of 63.1%., Conclusion: In this study, the multi-model approach successfully related periapical pathosis to the causative tooth on PGs. However, U-net outperformed Mask RCNN in the tasks performed, suggesting that U-net will remain the standard for medical image segmentation tasks. Further training of the models on other findings and an increased number of images will lead to the automation of the detection of common radiographic findings in the dental diagnostic workflow., Competing Interests: Disclosure None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Approximately 50% to 70% of patients have at least 1 medication discrepancy in their initial medication history. These discrepancies can lead to errors on admission and discharge orders and have the potential to cause patient harm and incur added costs associated with increased length of stay and readmission rates. Several studies have demonstrated improved medication history accuracy with pharmacy-conducted services, but variations in practice exist due to challenges with workflow and resources., Objective: This study aims to assess the impact of implementing a patient risk-scoring tool for the prioritization of medication history review by pharmacy staff., Methods: This quasi-experimental, single-center study was conducted at a 948-bed academic medical center as a pilot study with the medication history team which consists of pharmacists and certified pharmacy technicians in the emergency department. The endpoints assessed included pharmacy completion rate of patients in the high-risk category, overall pharmacy conducted medication history rate, and the proportion of medication discrepancies identified after reconciliation., Results: The number of medication histories completed by pharmacy (n=849) decreased by 5.7% in the postintervention period (P = 0.002). Between the preintervention and postintervention period, there were fewer low-risk patients being captured by pharmacy (89.7% to 59.9%, respectively). There was also an increase in the number of medium-risk (Δ=25.4%) and high-risk patients (Δ=4.4%) being captured by pharmacy staff (P < 0.017, α = 0.017)., Conclusion: Use of a risk-scoring tool allowed pharmacy staff to prioritize workflow and capture more high-risk patients for medication history., Competing Interests: Disclosure The authors declare no relevant conflicts of interest or financial relationships., (Copyright © 2024 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

The aim of this systematic review was to compare the effectiveness of pre-alveolar bonegraft (ABG) orthodontics with no orthodontic treatment for patients with non-syndromic unilateral cleft lip, alveolus and palate. All relevant studies from 1946 to October 30, 2022, were identified using several sources including The Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL), LILACS, Scopus, EMBASE, MEDLINE (Ovid) and EPUB ahead of publications and non-indexed citations. Randomized Controlled Trials (RCT) and Controlled Clinical Trials (CCT) were included., Population: Non-syndromic complete unilateral cleft lip, alveolus and palate patients who have had ABG surgery., Intervention: Orthodontics prior to ABG. Comparison: No orthodontic treatment prior to ABG., Primary Outcome: Successful eruption of permanent canines. All articles were screened for the title, abstract and full text independently and in duplicate by 2 reviewers. The quality assessment of RCT was performed using Cochrane's risk of bias tool and the CCT was assessed using ROBINS-I tool. Of the 904 studies retrieved in the search, one RCT and one CCT were included. Both studies were judged as high risk of bias. The results from one study showed a statistically significant increase in bone volume and decreased bone defect post-ABG in the orthodontic treatment group. However, there was no difference with respect to other variables. Both included studies were of low quality. There is not enough evidence to recommend orthodontic treatment pre-ABG for patients with complete unilateral cleft lip, alveolus and palate. Future high-quality studies are required to inform patients and clinicians about the effectiveness of pre-graft orthodontic treatment., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical., Approach: Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset., Results: Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost., Conclusion: By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

The efficiency of deep aerated vertical flow constructed wetlands (DA-VFCWs) being operated in Hyderabad, India, was evaluated herein using physicochemical analysis and 16S rRNA amplicon sequencing. The results showed 2-4-fold higher removal rate coefficients for Biochemical oxygen demand (1.32---3.53 m/d) and nitrogen (0.88--1.36 m/d) in DA-VFCWs than those of passive VFCWs. Elevated sulfate concentration in the DA-VFCWs effluent (84-113 mg/L) indicated possibility of sulfur-driven autotrophic denitrification (SDAD) as a major pathway operating in these wetlands besides the classical nitrogen removal pathways. The presence of nitrifiers (3.09-10.02 %), heterotrophic and aerobic denitrifiers (0.79-0.83 %), anammox bacteria (1.31-2.22 %) and SDAD bacteria (0.08-0.73 %) in the biofilm samples collected from the DA-VFCWs exemplify an interplay of Carbon-Nitrogen-Sulfur cycles in these systems. If proven, the presence of an operational SDAD pathway in DA-VFCWs can help reduce surface area requirement in VFCWs substantially besides alleviating biological clogging of the wetland substrate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Warfarin is the only oral anticoagulant recommended in women who are breastfeeding. Although warfarin is a compatible and recommended agent in the postpartum period and during lactation, little is known regarding changes to warfarin dose requirements in this patient population. Here, we report the case of a 40-year-old woman who transitioned from enoxaparin monotherapy back to warfarin at 2 months postpartum, while she was breastfeeding. Despite resuming warfarin at her previously therapeutic dose, her international normalized ratio (INR) remained subtherapeutic and required multiple dose increases. She ultimately required a 100% increase in her warfarin dose postpartum, compared to pre-pregnancy, to achieve a therapeutic INR. This case suggests patients may require higher warfarin doses postpartum, compared to pre-pregnancy, especially if breastfeeding. Clinicians should closely monitor these patients and adjust warfarin doses as necessary., (© 2024 The Authors. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy published by Wiley Periodicals LLC on behalf of Pharmacotherapy Publications, Inc.)

Objective: To segment dental implants on PA radiographs using a Deep Learning (DL) algorithm. To compare the performance of the algorithm relative to ground truth determined by the human annotator., Methodology: Three hundred PA radiographs were retrieved from the radiographic database and consequently annotated to label implants as well as teeth on the LabelMe annotation software. The dataset was augmented to increase the number of images in the training data and a total of 1294 images were used to train, validate and test the DL algorithm. An untrained U-net was downloaded and trained on the annotated dataset to allow detection of implants using polygons on PA radiographs., Results: A total of one hundred and thirty unseen images were run through the trained U-net to determine its ability to segment implants on PA radiographs. The performance metrics are as follows: accuracy of 93.8%, precision of 90%, recall of 83%, F-1 score of 86%, Intersection over Union of 86.4% and loss = 21%., Conclusions: The trained DL algorithm segmented implants on PA radiographs with high performance similar to that of the humans who labelled the images forming the ground truth.

Introduction: Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance in the progress of AI is access to diverse datasets which train DL models ensuring optimal performance, comparable to subject experts. However, administration of these traditionally acquired datasets is challenging due to privacy regulations and the extensive manual annotation required by subject experts. Biases such as ethical, socioeconomic and class imbalances are also incorporated during the curation of these datasets, limiting their overall generalizability. These challenges prevent their accrual at a larger scale for training DL models., Methods: Generative AI techniques can be useful in the production of Synthetic Datasets (SDs) that can overcome issues affecting traditionally acquired datasets. Variational autoencoders, generative adversarial networks and diffusion models have been used to generate SDs. The following text is a review of these generative AI techniques and their operations. It discusses the chances of SDs and challenges with potential solutions which will improve the understanding of healthcare professionals working in AI research., Conclusion: Synthetic data customized to the need of researchers can be produced to train robust AI models. These models, having been trained on such a diverse dataset will be applicable for dissemination across countries. However, there is a need for the limitations associated with SDs to be better understood, and attempts made to overcome those concerns prior to their widespread use., (© 2024. The Author(s).)

Natural herbs have garnered significant research recently as their components target multiple disease signaling pathways, making them highly potential for various disease prevention and treatment. Embelin, a naturally occurring benzoquinone isolated from Embelia ribes , has shown promising biological activities such as antitumor, antidiabetic, anti-oxidant, and antimicrobial. Various mechanisms have been reported, including monitoring genes that synchronize the cell cycle, up-regulating multiple anti-oxidant enzymes, suppressing genes that prevent cell death, influencing transcription factors, and preventing inflammatory biomarkers. However, the hydrophobic nature of embelin leads to poor absorption and limits its therapeutic potential. This review highlights a wide range of nanocarriers used as delivery systems for embelin, including polymeric nanoparticles, liposomes, nanostructured lipid carriers, micelles, nanoemulsion, and metallic nanoparticles. These embelin nanomedicine formulations have been developed in preclinical studies as a possible treatment for many disorders and characterized using various in vitro, ex vivo , and in vivo models., Competing Interests: The authors have no conflicts of interest to declare.

Aim: To develop a deep learning (DL) artificial intelligence (AI) model for instance segmentation and tooth numbering on orthopantomograms (OPGs)., Materials and Methods: Forty OPGs were manually annotated to lay down the ground truth for training two convolutional neural networks (CNNs): U-net and Faster RCNN. These algorithms were concurrently trained and validated on a dataset of 1280 teeth (40 OPGs) each. The U-net algorithm was trained on OPGs specifically annotated with polygons to label all 32 teeth via instance segmentation, allowing each tooth to be denoted as a separate entity from the surrounding structures. Simultaneously, teeth were also numbered according to the Fédération Dentaire Internationale (FDI) numbering system, using bounding boxes to train Faster RCNN. Consequently, both trained CNNs were combined to develop an AI model capable of segmenting and numbering all teeth on an OPG., Results: The performance of the U-net algorithm was determined using various performance metrics including precision = 88.8%, accuracy = 88.2%, recall = 87.3%, F-1 score = 88%, dice index = 92.3%, and Intersection over Union (IoU) = 86.3%. The performance metrics of the Faster RCNN algorithm were determined using overlap accuracy = 30.2 bounding boxes (out of a possible of 32 boxes) and classifier accuracy of labels = 93.8%., Conclusions: The instance segmentation and tooth numbering results of our trained AI model were close to the ground truth, indicating a promising future for their incorporation into clinical dental practice. The ability of an AI model to automatically identify teeth on OPGs will aid dentists with diagnosis and treatment planning, thus increasing efficiency.

The primary goal of orthodontic therapy in pseudo-class III is to restore the proper dental connection by rectifying the canine and molar relationship to Class I through lower molar and premolar visualization, as well as providing normal anterior overjet. The purpose of this systematic study was to determine the efficacy of clear aligners in treating class III malocclusion with mandibular molar distalization. A wide range of searches were done on various search engines like Cochrane, Web of Science, Embase, PubMed, Scopus, and Google Scholar to collect relevant articles related to our study. This review's article selection was guided by the PRISMA flowchart. The electronic findings provided numerous articles with nearly 78 articles regarding clear aligners in class III malocclusion with molar distalization. From this, seven full-text papers were evaluated for eligibility criteria, with two articles being rejected with justification and five articles being elaborated in the current systematic review. The current evidence of this review suggested that the clear aligners were effective in correcting class III malocclusion with molar distalization. The amount of molar distalization is about 2 to 3 mm, which helps in achieving molar and canine relationship in class I, with a high compliance level and also improvement of the facial profile., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Padmanabhan et al.)

Periapical diseases ranges from mild granulomatous lesions to large cystic ones, with the treatments corresponding to their respective pre-operative diagnoses. However, the determination of cause of periapical radiolucency is impossible on pre-operative clinical and radiographic examinations. We present a case highlighting the difficulties encountered in treating a periapical cyst using the current evidence in literature. It demonstrates the uncertainty involved in treating such lesions, owing to the impossible nature of determining the histopathological nature of the cyst, i.e., being either true cysts or pocket cysts. This case includes orthograde re-treatment; decompression of the cystic lesion, followed by peri-apical surgery of two teeth over a course of three years; and the uncertain outcomes encountered after each phase of the treatment.

Osteoporosis is a fatal bone-wearing malady and a substantial reason behind the impermanence of human life and economic burden. Risedronate Sodium along with Ursolic acid has been studied to ameliorate osteoporosis. To bypass problems associated with bioavailability, we have developed a microneedle transdermal patch loaded with optimized formulation nanotransfersomes. It was optimized using three factor, three-level Central composite design with independent variables namely, the concentration of phospholipid, surfactant, and sonication time on dependent variables (vesicle size, entrapment efficiency and Polydispersity index). Vesicles of size 271.9 ± 8.45 nm with PDI 0.184 ± 0.01, having entrapment efficiency of 86.12 ± 5.20% and 85.65 ± 4.88% for RIS and UA respectively were observed. In vitro release study showed the sustained release pattern with 78.16 ± 1.12% and 75.72 ± 1.01% release of RIS and UA respectively. Dissolving MN patch prepared from gelatin was found to have good strength and folding endurance with uniform drug content (98.68 ± 0.004%). Ex vivo permeation study revealed that up to 80% of the drug can be permeated within 24 h. CLSM analysis was also performed to show penetration of RU-NTRs. From the results obtained, we can conclude that dissolving MN patch loaded with RU-NTRs has great potential than its conventional counterpart., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Sunburn is caused by prolonged exposure to ultraviolet (UV) rays from the sun, resulting in redness of the skin as well as tenderness, swelling, and blistering issues. During the healing process, it can cause peeling, irritation, and some long-term effects, including premature aging, pigmentation, and a high risk of skin cancer. Rutin has antioxidant and anti-inflammatory effects, which could potentially reduce inflammation and soothe sunburned skin. The objective of the current proposal is to develop and create carbopol gel-encased glycerosomes for the treatment of sunburn. The Design of Expert (DoE) technique was used to optimize the proposed formulation and was subjected to various characterization parameters such as nanovesicles size, polydispersity index (PDI), surface charge, entrapment efficiency (EE), and surface morphology. The optimized rutin-loaded glycerosomes (opt-RUT-loaded-GMs) were further characterised for drug release, 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assay, and confocal laser scanning microscopy (CLSM). The formulation showed sustained release, greater permeation into the skin, and good antioxidant activity. The dermatokinetic study of opt-RUT-loaded-GMs confirms that the Rutin hydrate had better retention in the epidermis as compared to the dermis, owing to its potential for long lasting protection after topical application. It was observed that the prepared formulation was stable, highly safe, and had good sun protection factor (SPF) values that could be used as a suitable option for topical drug administration to maximize the therapeutic efficacy of the drugs.

Introduction: Stem cell therapy has been gaining interest in the regeneration rather than repair of lost human tissues. However, the manual analysis of stem cells prior to implantation is a cumbersome task that can be automated to improve the efficiency and accuracy of this process., Objective: To develop a Deep Learning (DL) algorithm for segmentation of human mesenchymal stem cells (MSCs) on micrographic images and to validate its performance relative to the ground truth laid down via annotation., Methodology: Pre-trained DeepLab algorithms were trained on annotated images of human MSCs obtained from the open-source EVICAN dataset. This dataset comprises of partially annotated images; a limitation that is overcome by blurring backgrounds of these images which consequently blurs the unannotated cells. Two algorithms were trained on the two different kinds of images from this dataset; with blurred and normal backgrounds, respectively. Algorithm 1 was trained on 139 images with blurred backgrounds and algorithm 2 was trained on 37 images from the same dataset with normal backgrounds to replicate real-life scenarios., Results: The performance metrics of algorithm 1 included accuracy of 99.22%, dice co-efficient of 99.66% and Intersection over Union (IoU) score of 0.84. Algorithm 2 was 96.34% accurate with dice co-efficient and IoU scores of 98.39% and 0.48, respectively., Conclusion: Both algorithms showed adequate performance in the segmentation of human MSCs with performance metrics close to the ground truth. However, algorithm 2 has better clinical applicability, even with smaller dataset and relatively lower performance metrics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Selinexor (KPT-330) is a small molecule inhibitor of XPO1, which mediates the transport of tumor suppressor proteins, oncogene messenger RNAs, and other proteins involved in governing cell growthfrom the cell nucleus to the cytoplasm. It is overexpressed in many cancer types. Because eukaryotic translation initiator factor 4E (eIF4E) plays a critical role in protein translation in cancer cells in multiple myeloma (MM), we evaluated the effectiveness of combined inhibition of protein translation and nuclear export in MM. Selinexor, an inhibitor of nuclear protein export, dose-dependently decreased eIF4E, IKZF1, and c-MYC protein levels. Using a doxycycline-inducible-pLKO-Tet-On vector, knockdown of eIF4E significantly enhanced the antiproliferative effects of selinexor, sensitized resistant MM cells to selinexor, and increased apoptosis in MM cells. Immunofluorescent analysis of MM cells showed that the combined treatment increased the localization of residual eIF4E to the nucleus compared with selinexor-only treatment. The overexpression of eIF4E at least partially rescued the effects of selinexor in MM cells by reducing G1 cell cycle arrest and increasing the selinexor-IC50 10-fold. Moreover, the combination of selinexor with pharmacologic inhibitors of protein translation showed synergistic anti-MM effects. These results suggest a synergistic anti-MM effect of selinexor combined with eIF4E inhibitors in vitro. Our work provides a better understanding of the potential mechanism of resistance to selinexor and a rationale for combining selinexor with eIF4E inhibitors for the treatment of MM., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Introduction: Transdermal drug delivery is limited by the stratum corneum, inhibiting the therapeutic potential of the permeants. Microneedles (MNs) have opened new frontiers in transdermal drug delivery systems. These micro-sized needles offer painless and accentuated delivery of drugs even with high molecular weights., Areas Covered: The review embodies drug delivery strategies with MNs with a description of MN types and fabrication techniques using various materials. The application of MN is not limited to drug delivery, but it also encompasses in vaccine delivery, diagnosis, phlebotomy, and even in the cosmetic industry. The review also tabulates MN-based marketed formulations. In a nutshell, we aim to present a panoramic view of MNs, including the design, applications, and regulatory aspects of MN., Expert Opinion: With the availability of numerous materials at the disposal of pharmaceutical scientists; the MN-based drug delivery technology has offered significant interventions toward the management of chronic maladies, including cardiovascular disorders, diabetes, asthma, mental depression, etc. As happens with any new technology, there are concerns with MN also such as biocompatibility issues with the material used for the fabrication. Nevertheless, the pharmaceutical industry must strive for preparing harmless, efficient, and cost-effective MN-based delivery systems for wider acceptance and patient compliance.

Secondary datasets of 42 low organic loading Vertical flow constructed wetlands (LOLVFCWs) were assessed to optimize their area requirements for N and P (nutrients) removal. Significant variations in removal rate coefficients (k 20 ) (0.002-0.464 md -1 ) indicated scope for optimization. Data classification based on nitrogen loading rate, temperature and depth could reduce the relative standard deviations of the k 20 values only in some cases. As an alternative method of deriving k 20 values, the effluent concentrations of the targeted pollutants were predicted using two machine learning approaches, MLR and SVR. The latter was found to perform better (R 2  = 0.87-0.9; RMSE = 0.08-3.64) as validated using primary data of a lab-scale VFCW. The generated model equations for predicting effluent parameters and computing corresponding k 20 values can assist in a customized design for nutrient removal employing minimal surface area for such systems for attaining the desired standards., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Purpose: This study was done to find the hidden balance problem in individuals across age groups using perceptual measure (Dizziness Index of Impairment in Activities of Daily Living Scale questionnaire, DII-ADL) and vestibulospinal-cerebellar function tests (Sharpened Romberg test, Fukuda stepping test, Tandem gait test, and Finger-to-nose test)., Methods: A total of 150 individuals in three age groups, young adults (20-40 years), middle-aged adults (40-60 years), and older adults (> 60 years) were considered. All the individuals had normal hearing sensitivity with no reported perceptual balance issues. DII-ADL questionnaire, Sharpened Romberg test, Fukuda stepping test, Tandem gait test, and Finger-to-nose test were administered to all the participants., Results: Balance disturbances were observed in all three age groups. The symptoms and test findings showed increased abnormality with the increase in age. DII-ADL questionnaire suggests older adults have more difficulty performing activities of daily living than young and middle-aged adults. The sharpened Romberg test result showed a moderate negative correlation and the Fukuda stepping test showed a moderate positive correlation with the sections of the DII-ADL questionnaire., Conclusion: Individuals of any age can have difficulty performing activities of daily living even though they do not have evident balance disorder perceptually. Thus there is a need to spread awareness among professionals and emphasize the need for screening individuals across age groups for balance disturbances., Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-022-03459-6., Competing Interests: Conflict of interest/Competing interestsThe authors have no relevant financial or non-financial interests to disclose., (© Association of Otolaryngologists of India 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Apigenin is a natural polyphenolic compound widely distributed as a glycoside in fruits and vegetables. Apigenin belongs to BCS class II with low solubility, which leads to poor absorption and bioavailability. It is mostly absorbed from the small intestine and extensively metabolized through glucuronidation and sulfation processes. Apigenin is known for its antioxidant and anti-inflammatory properties. It is also used as a chemopreventive drug in the management of various cancers. Pharmacological effects of apigenin have a wide range, from neuroprotective to treating renal disorders. Apigenin is non-toxic in nature and acts through various pathways (JAK/STAT, Wnt/β-catenin, MAPK/ERK, PI3K/Akt, and NF-κB) to exert its therapeutic efficacy. Numerous formulations have been researched to enhance the bioavailability and pharmacological effects of apigenin. Combinatorial therapies are also researched to minimize the side-effects of chemotherapeutic drugs. The review presents pharmacokinetic and pharmacodynamic aspects of apigenin. Apigenin is safe for the treatment and management of numerous diseases. It can be easily incorporated into nanoformulation alone or in combination with other active ingredients to widen the therapeutic window. This review intends to help in drug optimization and therapeutic efficacy maximization for future studies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

This study aimed to analyze the existing literature on how artificial intelligence is being used to support the identification of cephalometric landmarks. The systematic analysis of literature was carried out by performing an extensive search in PubMed/MEDLINE, Google Scholar, Cochrane, Scopus, and Science Direct databases. Articles published in the last ten years were selected after applying the inclusion and exclusion criteria. A total of 17 full-text articles were systematically appraised. The Cochrane Handbook for Systematic Reviews of Interventions (CHSRI) and Newcastle-Ottawa quality assessment scale (NOS) were adopted for quality analysis of the included studies. The artificial intelligence systems were mainly based on deep learning-based convolutional neural networks (CNNs) in the included studies. The majority of the studies proposed that AI-based automatic cephalometric analyses provide clinically acceptable diagnostic performance. They have worked remarkably well, with accuracy and precision similar to the trained orthodontist. Moreover, they can simplify cephalometric analysis and provide a quick outcome in practice. Therefore, they are of great benefit to orthodontists, as with these systems they can perform tasks more efficiently.

Background: The Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp) is a widely used measure of spiritual wellbeing. However, consensus on the best factor structure for this measure has not been reached. Both a 2-factor (Meaning/Peace, Faith) and a 3-factor (Meaning, Peace, Faith) structure are reported in the literature. In this study, we examined the factorial structure of the FACIT-Sp in a population of patients with severe and/or life-limiting medical illnesses., Methods: The present study is a part of a larger study that validated the National Institute of Health-Healing Experiences of All Life Stressors (NIH-HEALS), a measure of psycho-social-spiritual healing developed by the Pain and Palliative Care Service at the National Institutes of Health Clinical Center (NIH-CC). The sample included 200 subjects who were recruited from the NIH Clinical Center inpatient units and outpatient clinics with severe and/or life limiting illnesses (cancer, non-genetic conditions, genetic conditions, blood dyscrasias). FACIT-Sp is a 12-item questionnaire scored on a 5-point Likert scale (0 = not at all; 4 = very much). Exploratory factor analysis (EFA) and principal component analysis (PCA) were used to analyze results and to identify the number of latent constructs and underlying factor structure., Results: The results supported the 3-factor (Meaning, Peace, and Faith) model of the FACIT-Sp and accounted for the most variability (74.20%), followed by the 2-factor solution (64.95%). The identified factors related to Faith, Peace, and Meaning and were consistent with previously reported 3-factor model., Conclusions: This study confirmed the 3-factor structure of FACIT-Sp. This information can inform interventions aimed at improving quality of life and spiritual wellbeing in clinical and palliative care settings.

Treatment of skin cancer demands targeted delivery without minimal systemic circulation for maximum therapeutic window. Dermal delivery with nano-formulation offers such advantages. Therefore, present study aims to formulate Lyotropic liquid crystalline nanoparticles (LLC NPs) loaded with Apigenin (API) for dermal delivery using quality by design (QbD) approach for effective permeation resulting in improved bioavailability. Apigenin loaded LLC NPs (API-LLC NPs) were formulated and optimized by applying risk assessment and design of experiments (Box-Behnken Design). The optimized API-LLC NPs showed particle size, PdI and entrapment efficiency of 287.7 ± 9.53 nm, 0.152 ± 0.051 and 80 ± 2.2 % respectively. The optimized API-LLC NPs were characterized for morphology and crystallinity using optical microscopy, TEM, DSC and PXRD. In vitro and ex vivo studies showed sustained release and better permeation profile. CLSM study presented better penetration of API-LLC NPs which were quantitatively confirmed with dermatokinetics. Cytotoxic efficacy assessed on B16F10 cell lines showed a dose-dependent efficacy of API-LLC NPs with an IC 50 of 45.74 ± 0.05. In a nutshell, the developed API-LLC NPs exhibit excellent potential for targeting deeper skin layers thereby can be considered a promising topical drug delivery nanocarrier in the treatment and management of skin cancer., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Purpose: The purpose of this study was to evaluate the impact of implementing an electronic health record (EHR)-integrated mobile dispense tracking solution., Methods: This quasi-experimental study was conducted at Houston Methodist Hospital. The study timeframe consisted of 1-year pre- and postimplementation phases, with a 1-month washout period. The medication tracking function was implemented in a multiphase approach. The primary endpoint was the weekly redispense rate due to missing medication or delay in delivery. The secondary endpoints were total redispenses due to missing medication or delay in delivery per 1,000 medication messages and per 1,000 inpatient discharges and dispense tracking scanning compliance post implementation., Results: Analyses demonstrated a sustained decrease in redispenses from 3.24% to 2.70% (95% CI, 0.10-0.60; P = 0.006). The study also demonstrated a statistically significant decrease in redispenses per 1,000 medication messages from 190 to 127 (95% CI, -27.21 to -4.23, P = 0.008). Analysis of redispenses per 1,000 patient discharges showed a nonsignificant reduction after implementation from 216 to 194 (95% CI, 54.63 to -77.71; P = 0.730). The department achieved 90.7% dispense tracking compliance., Conclusion: EHR-integrated mobile dispense tracking technology effectively reduced the redispense rate and total redispenses normalized to medication messages for missing medication and delay in delivery. Interprofessional collaboration and effective change management strategies are essential to the successful implementation of medication dispense tracking technology., (© American Society of Health-System Pharmacists 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Sedaghatian type spondylometaphyseal dysplasia (SSMD) is a rare skeletal dysplasia with only 24 reported cases to date. Despite the limited literature available, evidence suggests this is a multi-system disorder, with neurological and cardiovascular abnormalities reported in addition to the skeletal features. We report a new family with two affected siblings and detailed phenotypic description of the affected proband. Diagnosis in the neonatal period led to retrospective genetic diagnosis of a previous affected pregnancy that was terminated due to severe ventriculomegaly. We suggest that a diagnosis of SSMD should be considered when shortened long bones are found in combination with significant brain abnormalities., (Copyright © 2022 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Objectives: To compare the prevalence of adverse events related to vasoactive drug infusions administered via a peripheral venous catheter versus a central venous or intraosseous catheter., Design: Retrospective observational study., Setting: A pediatric critical care transport team, and the PICUs and regional hospitals within the North Thames and East Anglia regions of the United Kingdom., Patients: Children (up to 18 yr old) transported by the Children's Acute Transport Service receiving an infusion of a vasoactive drug (epinephrine, dobutamine, dopamine, norepinephrine, and vasopressin)., Interventions: None., Measurements and Main Results: The medical records of all children transported between April 2017 and May 2020 receiving a vasoactive drug infusion were reviewed and cross-referenced with the service critical incident database. The outcome measure was anatomic catheter-related adverse events (including extravasation) reported during transport or in the first 24 hours on the PICU. During the study period, the service undertook 3,836 transports. Vasoactive drugs were administered during 558 patient transports (14.5%). During 198 of 558 transports (35.5%), vasoactive drugs were administered via a peripheral venous catheter, with seven of 198 (3.5%) adverse events. One extravasation event resulted in tissue necrosis. The median time to injury after the infusion was commenced was 60 minutes (interquartile range, 30-60 min). During 360 of 558 transports (64.5%), vasoactive infusions were administered by central venous or intraosseous catheter, with nine of 360 (2.5%) adverse events., Conclusions: During pediatric critical care transport, we did not find a difference in prevalence of adverse events following the administration of vasoactive drugs via peripheral venous catheters or via central venous and intraosseous catheters., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.)

Purpose: Dry eye disease (DED) is a common age-related ocular surface disease. However, it is unknown how aging influences the ocular surface microenvironment. This systematic review aims to investigate how the aging process changes the ocular surface microenvironment and impacts the development of DED., Methods: An article search was performed in PubMed, EMBASE, and Web of Science. 44 studies reporting on age-related ocular changes and 14 large epidemiological studies involving the prevalence of DED were identified. 8 out of 14 epidemiological studies were further analyzed with meta-analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were followed. Study-specific estimates (impact of aging on the prevalence of DED) were combined using one-group meta-analysis in a random-effects model., Results: Meta-analysis revealed the prevalence of DED in the elderly aged 60 years old or older was 5519 of 60107 (9.2%) and the odds ratio of aging compared to younger age was 1.313 (95% confidence interval [CI]; 1.107, 1.557). With increasing age, the integrity of the ocular surface and tear film stability decreased. Various inflammatory cells, including senescent-associated T-cells, infiltrated the ocular surface epithelium, lacrimal gland, and meibomian gland, accompanied by senescence-related changes, including accumulation of 8-OHdG and lipofuscin-like inclusions, increased expression of p53 and apoptosis-related genes, and decreased Ki67 positive cells., Conclusions: The aging process greatly impacts the ocular surface microenvironment, consequently leading to DED., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: To investigate the current developments of Artificial Intelligence (AI) in teeth identification on Panoramic Radiographs (PR). Our aim was to evaluate and compare the performances of Deep Learning (DL) models that have been employed in the execution of this task., Methods: The systematic review was registered on PROSPERO. All recent studies that utilized DL models for identifying teeth on PRs were included in this review. An extensive search of the medical electronic databases including PubMed NLM, EBSCO Dentistry & Oral Sciences Source, and Wiley Cochrane Library was conducted. This was followed by a hand search of the IEEE Xplore database. The diagnostic performance of DL models in teeth identification tasks on PR was the primary outcome assessed in this review. The risk of bias assessment of the included studies was evaluated via the modified QUADAS-2 tool. Owing to the heterogeneity of the reported performance metrics, a meta-analysis was not possible.., Results: The search yielded a total of 282 articles, out of which 13 relevant ones were included in this review. These studies utilized a diverse range of DL models for teeth identification tasks on PRs and reported their performances using a variety of metrics., Conclusion: The results of teeth identification tasks carried out by DL models are encouraging; however, there is a need for the shortcomings that have been identified in our preliminary review, to be addressed by future researchers.

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.

Objective: The aim of this study was to evaluate the Social Vulnerability Index (SVI) as a predictor of long-term outcomes after injury., Background: The SVI is a measure used in emergency preparedness to identify need for resources in the event of a disaster or hazardous event, ranking each census tract on 15 demographic/social factors., Methods: Moderate-severely injured adult patients treated at 1 of 3 level-1 trauma centers were prospectively followed 6 to 14 months post-injury. These data were matched at the census tract level with overall SVI percentile rankings. Patients were stratified based on SVI quartiles, with the lowest quartile designated as low SVI, the middle 2 quartiles as average SVI, and the highest quartile as high SVI. Multivariable adjusted regression models were used to assess whether SVI was associated with long-term outcomes after injury., Results: A total of 3153 patients were included [54% male, mean age 61.6 (SD = 21.6)]. The median overall SVI percentile rank was 35th (IQR: 16th-65th). compared to low SVI patients, high SVI patients were more likely to have new functional limitations [odds ratio (OR), 1.51; 95% confidence interval (CI), 1.19-1.92), to not have returned to work (OR, 2.01; 95% CI, 1.40-2.89), and to screen positive for post-traumatic stress disorder (OR, 1.56; 95% CI, 1.12-2.17). Similar results were obtained when comparing average with low SVI patients, with average SVI patients having significantly worse outcomes., Conclusions: The SVI has potential utility in predicting individuals at higher risk for adverse long-term outcomes after injury. This measure may be a useful needs assessment tool for clinicians and researchers in identifying communities that may benefit most from targeted prevention and intervention efforts., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4 + T and CD8 + T cells, whereas in LUSC, the immune patterns were comprised of CD4 + T, CD8 + T, and CD20 + B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC., (© 2022. The Author(s).)

Synaptic plasticity in the hippocampal Cornu Ammonis (CA) subfield, CA2, is tightly regulated. However, CA2 receives projections from several extra-hippocampal modulatory nuclei that release modulators that could serve to fine-tune plasticity at CA2 synapses. Considering that there are afferent projections from the serotonergic median raphe to hippocampal CA2, we hypothesized that the neuromodulator serotonin (5-hydroxytryptamine; 5-HT) could modulate CA2 synaptic plasticity. Here, we show that bath-application of serotonin facilitates the persistence of long-term depression (LTD) at the CA3 Schaffer collateral inputs to CA2 neurons (SC-CA2) when coupled to a weak low frequency electrical stimulation, in acute rat hippocampal slices. The observed late-LTD at SC-CA2 synapses was protein synthesis- and N-methyl-D-aspartate receptor (NMDAR)-dependent. Moreover, this late-LTD at SC-CA2 synapses paves way for the associative persistence of transient forms of LTD as well as long-term potentiation to long-lasting late forms of plasticity through synaptic tagging and cross-tagging respectively, at the entorhinal cortical synapses of CA2. We further observe that the 5-HT-mediated persistence of activity-dependent LTD at SC-CA2 synapses is blocked in the presence of the brain-derived neurotrophic factor scavenger, TrkB/Fc., Competing Interests: None declared., (© The Author(s) 2022. Published by Oxford University Press.)

Previously, we reported the anti-diabetic effect of Morus alba root bark and the compounds therein. In our continuous study of other parts of this plant, the ability of the branch of Morus alba to inhibit α-glucosidase, protein tyrosine phosphatase 1B (PTP1B), and advanced glycation end products (AGEs) formation was evaluated. Moreover, there are no previous studies that have performed enzyme kinetics and molecular docking analyses, along with assessments of peroxynitrite (ONOO - ) inhibitory activities. Since the Morus alba branch exhibited favorable inhibitory effects, repeated column chromatography was performed to obtain eight compounds, including four flavonoids ( 1, 3 , 6 , 8 ), one arylbenzofuran ( 2 ), one stilbene ( 5 ), one Diels-Alder-type adduct ( 7 ), and one sterol ( 4 ). Among them, compounds 1 - 3 and 5 - 7 were mixed-type inhibitors of α-glucosidase, sharing the same catalytic residues with acarbose and the same allosteric sites with (Z)-3-bytylidenephthalide. On the other hand, kuwanon C ( 1 ) and oxyresveratrol ( 5 ) interacted with residues of the allosteric site ( α 3 and α 6 helices) of PTP1B, indicating their use as non-competitive inhibitors. Interestingly, kuwanon G ( 7 ) directly bound the catalytic site, or interrupted the binding between the substrate and the active site, as a mixed-type inhibitor. Moreover, most of the compounds exhibited greater activity against AGE formation and ONOO - than positive controls. The IC 50 values required to inhibit ONOO - using compounds 1 , 3 , 5 , 6 , and 7 were reported for the first time, and range from 1.08 to 12.92 μM. Based on the structure-activity relationship, the presence of hydroxyl, resorcinol, and prenyl moieties was important in the prevention of diabetes' pathological mechanisms, and these findings have been further supported by molecular docking analysis. These computational and experimental results will be useful in the development of therapeutic candidates to prevent/treat diabetes and its complications.

Introduction: Metastatic breast cancer (MBC) patients have several unmet needs. The needs and quality of life of MBC women living in sub-Saharan Africa (SSA) are understudied. Facilitating the interaction of various caregivers is beneficial in addressing the needs. Internet-based resources play an important role in reaching out to these patients. We aimed to bring the various stakeholders into a joint network force, create a web-based portal, understand the needs of MBC patients, and assess the utilization of web-based resources for women from Kenya., Methods: A network of various stakeholders considered crucial in the care of Kenyan women with MBC was created. We conducted educational camps and assessed their needs, quality of life (QoL), and knowledge. We assessed the impact of utilizing web-based resources by MBC patients from here., Results: We formed a network involving partners and launched the first dedicated website for MBC from Kenya. The website has received 13,944 visits and 310,379 hits in 2 years. One hundred fourteen women living with MBC were interviewed, and our findings show that psychological needs (63%), physical support needs (60%), and health care system needs (55%) are leading areas of needs that increase with rural residence (p = 0.001), less education (p = 0.003), and aggressive treatments (p = 0.008). Quality of life (QoL) confirmed better scores with urban residence (p = 0.002), internet access (p = 0.010), and stable disease (p = 0.042)., Conclusions: Creating a network of caregivers provides opportunities for cohesive efforts in understanding the psychosocial and medical needs of patients with MBC. Internet-based resources are an effective way of reaching out to them. Kenyan patients show extremely good uptake of internet-based resources., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. The narrative review may facilitate the interpretation of seemingly perplexing research published widely in dental journals.

Senna occidentalis is an annual leguminous herb that is rich in anthraquinones, which have various pharmacological activities. However, little is known about the genetics of S. occidentalis , particularly its anthraquinone biosynthesis pathway. To broaden our understanding of the key genes and regulatory mechanisms involved in the anthraquinone biosynthesis pathway, we used short RNA sequencing (RNA-Seq) and long-read isoform sequencing (Iso-Seq) to perform a spatial and temporal transcriptomic analysis of S. occidentalis . This generated 121,592 RNA-Seq unigenes and 38,440 Iso-Seq unigenes. Comprehensive functional annotation and classification of these datasets using public databases identified unigene sequences related to major secondary metabolite biosynthesis pathways and critical transcription factor families (bHLH, WRKY, MYB, and bZIP). A tissue-specific differential expression analysis of S. occidentalis and measurement of the amount of anthraquinones revealed that anthraquinone accumulation was related to the gene expression levels in the different tissues. In addition, the amounts and types of anthraquinones produced differ between S. occidentalis and S. tora . In conclusion, these results provide a broader understanding of the anthraquinone metabolic pathway in S. occidentalis ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kang, Lee, Sim, Thaku, Chang, Hong and Oh.)

Sepsis is a common, complex condition that requires early recognition and aggressive management to improve outcomes. There has been significant improvement in the management of sepsis and septic shock in the last decade; however, it continues to be a leading cause of mortality, morbidity and burden on healthcare services globally. Several guidelines with evidence-based recommendations for the management of children with septic shock and associated organ dysfunction have been produced with the objective of helping clinicians in various settings to provide standardised high-quality care. This article aims to increase awareness among all clinicians, including those working in emergency departments, general paediatric wards and primary care physicians, about the management of sepsis in children., Competing Interests: Conflict of interest statement: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s), 2021.)

Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

Synthetic auxins such as 1-naphthalene acetic acid (NAA) and 2,4-dichlorophenoxyacetic acid (2,4-D) have been extensively used in plant tissue cultures and as herbicides because they are chemically more stable and potent than most endogenous auxins. A tool for rapid in planta detection of these compounds will enhance our knowledge about hormone distribution and signaling and facilitate more efficient usage of synthetic auxins in agriculture. In this work, we show the development of real-time and nondestructive in planta NAA and 2,4-D nanosensors based on the concept of corona phase molecular recognition (CoPhMoRe), to replace the current state-of-the-art sensing methods that are destructive and laborious. By designing a library of cationic polymers wrapped around single-walled carbon nanotubes with general affinity for chemical moieties displayed on auxins and its derivatives, we developed selective sensors for these synthetic auxins, with a particularly large quenching response to NAA (46%) and a turn-on response to 2,4-D (51%). The NAA and 2,4-D nanosensors are demonstrated in planta across several plant species including spinach, Arabidopsis thaliana ( A. thaliana ), Brassica rapa subsp. chinensis (pak choi), and Oryza sativa (rice) grown in various media, including soil, hydroponic, and plant tissue culture media. After 5 h of 2,4-D supplementation to the hydroponic medium, 2,4-D is seen to accumulate in susceptible dicotyledon pak choi leaves, while no uptake is observed in tolerant monocotyledon rice leaves. As such, the 2,4-D nanosensor had demonstrated its capability for rapid testing of herbicide susceptibility and could help elucidate the mechanisms of 2,4-D transport and the basis for herbicide resistance in crops. The success of the CoPhMoRe technique for measuring these challenging plant hormones holds tremendous potential to advance the plant biology study.

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management., (© 2021. The Author(s).)