Evaluation Protocol Research Articles

A protocol for a comparative evaluation of the fracture resistance of endodontically treated teeth reinforced with Cention N, resin-modified glass ionomer cement (RMGIC) and short fiber reinforced flowable composite as an intraorifice barrier

Background: Endodontic treatment is the most common method for resolving pulpal and periapical pathology. However, various studies have reported that almost 11%–13% of all teeth that undergo extraction after endodontic treatment show the presence of cracks, craze lines, and vertical root fractures. Teeth with inadequate post endodontic restoration are more prone to fracture and coronal leakage, resulting in the diffusion of oral fluids, bacteria, bacterial products, and possibly root canal treatment failure. Furthermore, studies have advocated the use of endodontically treated teeth with restorative materials that have a similar or higher elastic modulus than the tooth for providing stiffness against forces that cause root fracture. Intraorifice barriers made of restorative materials that can bond to radicular dentin could thus be used to reinforce the radicular dentin while also preventing coronal microleakage. Although the sealing ability of intraorifice barriers has been widely compared in the literature, there have been few studies on the strengthening effect of the materials used in the study as intraorifice barriers when placed into the root canal. As a result, the current in vitro study aims to assess the effect of various materials as intraorifice barriers (Cention N, Resin modified glass ionomer cement, and short fiber reinforced flowable composite) on the force required fracture teeth after root canal treatment. Methods: This in vitro study will be done on extracted human mandibular premolars with single root canal where after doing root canal treatment 2-3 mm obturating material would be replaced by intra orifice barriers (Cention N, resin modified glass ionomer cement [RMGIC], and short fiber reinforced flowable composite). The force required to fracture teeth will be calculated using universal testing machine.

Research hotspots and frontiers of neuromodulation techniques in disorders of consciousness: a bibliometric analysis.

The characteristics of disorders of consciousness (DOC) are changes in arousal and/or awareness caused by severe brain injuries. To date, the management of DOC patients remains a complex and challenging task, and neuromodulation techniques offer a promising solution. However, a bibliometric analysis focusing on neuromodulation techniques in DOC is currently absent. The aim of this study is to provide a bibliometric visualization analysis to investigate the research hotspots and frontiers in the field of neuromodulation techniques in DOC from 2012 to 2022. The publications were collected and retrieved from the Web of Science (WoS) from 2012 to 2022. CiteSpace and Microsoft Excel were utilized perform the first global bibliographic analysis of the literature related to neuromodulation techniques for DOC. The analysis included a total of 338 publications. From 2012 to 2022, a consistent yet irregular increase in the number of articles published on neuromodulation techniques in DOC was observed. Frontiers in Neurology published the highest number of papers (n = 16). Neurosciences represented the main research hotspot category (n = 170). The most prolific country, institution, and author were the USA (n = 105), the University of Liege (n = 41), and Laureys Steven (n = 38), respectively. An analysis of keywords revealed that UWS/VS, MCS, and TMS constituted the primary research trends and focal points within this domain. This bibliometric study sheds light on the current progress and emerging trends of neuromodulation techniques in DOC from 2012 to 2022. The focal topics in this domain encompass the precise diagnosis of consciousness levels in patients suffering from DOC and the pursuit of efficacious neuromodulation-based evaluation and treatment protocols for such patients.

Optimization of the "Perth CT" Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty.

Background and Objectives: Total knee arthroplasty (TKA) has become the treatment of choice for advanced osteoarthritis. The aim of this paper was to show the possibilities of optimizing the Perth CT protocol, which is highly effective for preoperative planning and postoperative assessment of alignment. Materials and Methods: The cross-sectional study comprised 16 patients for preoperative planning or postoperative evaluation of TKA. All patients were examined with the standard and optimized Perth CT protocol using advance techniques, including automatic exposure control (AEC), iterative image reconstruction (IR), as well as a single-energy projection-based metal artifact reduction algorithm for eliminating prosthesis artifacts. The effective radiation dose (E) was determined based on the dose report. Imaging quality is determined according to subjective and objective (values of signal to noise ratio (SdNR) and figure of merit (FOM)) criteria. Results: The effective radiation dose with the optimized protocol was significantly lower compared to the standard protocol (p < 0.001), while in patients with the knee prosthesis, E increased significantly less with the optimized protocol compared to the standard protocol. No significant difference was observed in the subjective evaluation of image quality between protocols (p > 0.05). Analyzing the objective criteria for image quality optimized protocols resulted in lower SdNR values and higher FOM values. No significant difference of image quality was determined using the SdNR and FOM as per the specified protocols and parts of extremities, and for the presence of prothesis. Conclusions: Retrospecting the ALARA ('As Low As Reasonably Achievable') principles, it is possible to optimize the Perth CT protocol by reducing the kV and mAs values and by changing the collimation and increasing the pitch factor. Advanced IR techniques were used in both protocols, and AEC was used in the optimized protocol. The effective dose of radiation can be reduced five times, and the image quality will be satisfactory.

Data Leakage and Evaluation Issues in Micro-Expression Analysis

Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limited, which make evaluation complex. We show that data leakage and fragmented evaluation protocols are issues among the micro-expression literature. We find that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier. To this end, we go through common pitfalls, propose a new standardized evaluation protocol using facial action units with over 2000 micro-expression samples, and provide an open source library that implements the evaluation protocols in a standardized manner. Code is publicly available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tvaranka/meb</uri> .

Implementing Stay Strong AIMhi for Youth: Protocol for Evaluation

Background: Good mental health and well-being are essential for fulfillment, productivity, and resilience. Mental disorders cause high levels of disability and economic impact worldwide. Vulnerable groups at significantly higher risk include youth and First Nations people. As the demand for mental health support and services increases, there is an urgent need to expand access to culturally appropriate quality mental health services (particularly for young people) and to promote self-care through integration of mobile health technologies. Objectives: This paper describes a protocol to evaluate implementation of the AIMhi for Youth support package into youth wellbeing services in urban, rural, and remote Northern Territory and South Australia. Codesign workshops will tailor the resources to these and other locations. The AIMhi-Y support package will be implemented as an innovative approach to suicide prevention through three years of codesign, training, implementation support and dissemination. Methods: Implementation is guided by the Consolidated Framework for Implementation Research (CFIR) incorporating the recent CFIR Outcomes Addendum and including strategies from the Expert Recommendations for Implementing Change. The protocol incorporates best practice principles in codesign and First Nations research. It builds on a decade of foundational work of the Stay Strong program implementing digital mental health resources in primary care and specialist settings. Implementation and related outcomes will be evaluated from each participant perspective (user, service provider, decision maker, First Nations people with lived experience and cultural consultants) using surveys and small group discussions. Intervention: AIMhi for Youth is a culturally responsive digital mental health solution codesigned with First Nations leaders and young people. Delivered via mobile device it is a gamified app supporting skills development in mental health literacy, emotional regulation, help seeking and goal setting. The app provides a structured intervention which complements existing services and addresses key risk factors for suicide and compromised mental health. The package includes training workshops and supplementary multimedia resources. Support resources will be hosted on a tailored website creating a seamless accessible ecosystem for users. Discussion: At the time of publication, 514 young people and 363 individual service providers have used the app, and 11 services have engaged in implementation planning discussions. Through our implementation evaluation we will identify why the AIMhi-Y package was implemented successfully in some contexts and not in others and will have an evidence-based strategy for successfully engaging First Nations young people and services in digital mental health solutions. This will inform further AIMhi-Y dissemination and provide a guide for implementation of other innovations in similar contexts. Conclusion: This study prospectively plans, monitors, and evaluates implementation through use of the Consolidated Framework for Implementation Research. This threefold approach potentially strengthens the design and enhances likelihood of implementation success. The emphasis on Indigenist research principles, and the addition of measuring outcomes including reach, impact, adoption, implementation, and sustainment, has potential to contribute to conceptual development and clarification of implementation approaches in First Nations communities.

The physiological, in-vitro simulation of daily activities in the intervertebral disc using a load Informed kinematic evaluation (LIKE) protocol

Current spinal testing protocols generally adopt pure moments combined with axial compression. However, daily activities involve multi-axis loads, and multi-axis loading has been shown to impact intervertebral disc (IVD) cell viability. Therefore, integrating in-vivo load data with spine simulators is critical to understand how loading affects the IVD, but doing so is challenging due to load coupling and variable load rates. This study addresses these challenges through the Load Informed Kinematic Evaluation (LIKE) protocol, which was evaluated using the root mean squared error (RMSE) between desired and actual loads in each axis. Stage 1 involves obtaining the kinematics from six-axis load control tests replicating 20 Orthoload activities at a reduced test speed. Stage 2 applies these kinematics in five axes, with axial compression applied in load control, at the reduced speed and at the physiological test rate. Stage 3 enables long-term tests through six-axis kinematic control combined with diurnal height correction to account for the natural height fluctuations of the IVD. Stage 1 yielded RMSEs within twice the load cell noise floor. Low RMSEs were maintained during stage 2 at reduced speed (Tx:0.80 ± 0.30 N; Ty:0.77 ± 0.29 N; Tz:1.79 ± 0.50 N; Rx:0.02 ± 0.01Nm; Ry:0.02 ± 0.01Nm; and Rz:0.02 ± 0.01Nm) and at the physiological test rate (Tx:3.45 ± 1.81 N; Ty:3.82 ± 1.99 N; Tz:11.32 ± 8.69 N; Rx:0.13 ± 0.07Nm; Ry:0.16 ± 0.11Nm; and Rz:0.07 ± 0.04Nm). To address unwanted oscillations observed in longer tests (>2h), Stage 3 was introduced to enable the stable and consistent replication of activities at a physiological test rate. Despite higher RMSEs the axial error was 85.5 ± 24.27 N (equivalent to ∼ 0.16 MPa), with shear RMSEs similar to other testing systems conducting pure moment tests at slower rates. The LIKE protocol enables the replication of physiological loads, providing opportunities for enhanced investigations of IVD mechanobiology, and the pre-clinical evaluation of IVD devices and therapies.

Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %–12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.

Pesquisas científicas sobre zumbido no Brasil: Um compilado observacional

ABSTRACT Purpose: to identify the profile of tinnitus research in Brazil and the main procedures adopted by professionals in the field in the national territory. Methods: an observational, descriptive, and cross-sectional study, in which an active search was made on digital platforms to identify researchers in Brazil who have tinnitus as a study focus. After identifying the tinnitus study groups, an online Form was sent to the researchers comprising 21 questions, so as to know the works in development. The collected responses were extracted and tabulated intoMicrosoft Excel Professional Plus2019 through the absolute and relative frequency measurements of the final sample. Results: 117 researchers were identified, of these, 21 participated in the study, being (90.5%) females, specifically from public universities (81%) in the Southeast region of Brazil (47.7%). These professionals develop clinical research (76.1%), focusing on therapies and/or treatments (38.1%), and use sound therapy (52.38%) as the main technological resource of their studies. Conclusion: the research profile is clinical, with a main focus on therapies and/or treatments for tinnitus. As for the procedures, there is a predominant application of the Visual Analogue Scale- VAS and the Tinnitus Handicap Inventory- THI as evaluation protocols and sound therapy as the main technological resource of their studies.

VATr++: Choose Your Words Wisely for Handwritten Text Generation.

Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and, preliminarily, Diffusion Models. Despite this surge in interest, there remains a critical yet understudied aspect - the impact of the input, both visual and textual, on the HTG model training and its subsequent influence on performance. This work extends the VATr[1] Styled-HTG approach by addressing the pre-processing and training issues that it faces, which are common to many HTG models. In particular, we propose generally applicable strategies for input preparation and training regularization that allow the model to achieve better performance and generalization capabilities. Moreover, in this work, we go beyond performance optimization and address a significant hurdle in HTG research - the lack of a standardized evaluation protocol. In particular, we propose a standardization of the evaluation protocol for HTG and conduct a comprehensive benchmarking of existing approaches. By doing so, we aim to establish a foundation for fair and meaningful comparisons between HTG strategies, fostering progress in the field.

Zone Evaluation: Revealing Spatial Bias in Object Detection.

A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders. For a long time, there has been a lack of effective ways to measure and identify spatial bias, and little is known about where it comes from and what degree it is. To this end, we present a new zone evaluation protocol, extending from the traditional evaluation to a more generalized one, which measures the detection performance over zones, yielding a series of Zone Precisions (ZPs). For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones. Surprisingly, the detector's performance in the 96% border zone of the image does not reach the AP value (Average Precision, commonly regarded as the average detection performance in the entire image zone). To better understand spatial bias, a series of heuristic experiments are conducted. Our investigation excludes two intuitive conjectures about spatial bias that the object scale and the absolute positions of objects barely influence the spatial bias. We find that the key lies in the human-imperceptible divergence in data patterns between objects in different zones, thus eventually forming a visible performance gap between the zones. With these findings, we finally discuss a future direction for object detection, namely, spatial disequilibrium problem, aiming at pursuing a balanced detection ability over the entire image zone. By broadly evaluating 10 popular object detectors and 5 detection datasets, we shed light on the spatial bias of object detectors. We hope this work could raise a focus on detection robustness. The source codes, evaluation protocols, and tutorials are publicly available at https://github.com/Zzh-tju/ZoneEval.

Polarity Loss: Improving Visual-Semantic Alignment for Zero-Shot Detection.

Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behavior, zero-shot object detection (ZSD) aims to recognize and localize "unseen" object instances by using only their semantic information. The model is first trained to learn the relationships between visual and semantic domains for seen objects, later transferring the acquired knowledge to totally unseen objects. This setting gives rise to the need for correct alignment between visual and semantic concepts so that the unseen objects can be identified using only their semantic attributes. In this article, we propose a novel loss function called "polarity loss" that promotes correct visual-semantic alignment for an improved ZSD. On the one hand, it refines the noisy semantic embeddings via metric learning on a "semantic vocabulary" of related concepts to establish a better synergy between visual and semantic domains. On the other hand, it explicitly maximizes the gap between positive and negative predictions to achieve better discrimination between seen, unseen, and background objects. Our approach is inspired by embodiment theories in cognitive science that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary), and visual perception (seen/unseen object images). We conduct extensive evaluations on the Microsoft Common Objects in Context (MS-COCO) and Pascal Visual Object Classes (VOC) datasets, showing significant improvements over state of the art. Our code and evaluation protocols available at: https://github.com/salman-h-khan/PL-ZSD_Release.

DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition.

In this work, we propose a new Dual Min-Max Games (DMMG) based self-supervised skeleton action recognition method by augmenting unlabeled data in a contrastive learning framework. Our DMMG consists of a viewpoint variation min-max game and an edge perturbation min-max game. These two min-max games adopt an adversarial paradigm to perform data augmentation on the skeleton sequences and graph-structured body joints, respectively. Our viewpoint variation min-max game focuses on constructing various hard contrastive pairs by generating skeleton sequences from various viewpoints. These hard contrastive pairs help our model learn representative action features, thus facilitating model transfer to downstream tasks. Moreover, our edge perturbation min-max game specializes in building diverse hard contrastive samples through perturbing connectivity strength among graph-based body joints. The connectivity-strength varying contrastive pairs enable the model to capture minimal sufficient information of different actions, such as representative gestures for an action while preventing the model from overfitting. By fully exploiting the proposed DMMG, we can generate sufficient challenging contrastive pairs and thus achieve discriminative action feature representations from unlabeled skeleton data in a self-supervised manner. Extensive experiments demonstrate that our method achieves superior results under various evaluation protocols on widely-used NTU-RGB+D, NTU120-RGB+D and PKU-MMD datasets.

Impact of weathering on a cement-treated sand

This study’s main goal was to examine how the wetting/drying solicitations affect the mechanical behavior of a cementtreated sand. A quantitative method based on the stress-dilatancy approach was used to assess the effects of two types of wetting and drying cycles of different intensities. The results showed that bonding is primarily altered by the wetting/drying cycles, leading to lower mechanical performances. It has been demonstrated that the weathering effect not only depends on the cement dosage, but also on the cycles’ intensity. The early cycles appeared to have the greatest impact on changes in mechanical performance on the samples treated with 4% cement. However, the accumulation of numerous cycles caused a more progressive degradation on the samples treated with 1% cement. The quantitative assessment of the treatment effect and the weathering progress with cycles was made possible through the evaluation of the bonding ratio. The importance of the imposed wetting/drying cycle protocol for a proper evaluation of the long-term performance of treated soils is highlighted. Further research is needed to define an appropriate weathering protocol that makes sense in light of the real solicitation faced by engineered structures.

Exploring positive welfare measures: preliminary findings from a prototype protocol in loose housing dairy cattle farms.

Following the increasing interest about the development of indicators of positive welfare and affective state in farm animals, the aim of this research is to present some preliminary results on the application of a prototype protocol based exclusively on positive welfare measures and to suggest potential benefits that can promote positive welfare. The protocol was applied in 20 loose housing dairy cattle farms (6 on deep litter with straw, 14 in cubicles) and included only indicators of positive welfare and emotional states: feeding and resting synchronization, rumination during resting, comfortable lying postures, no visible eye white, relaxed ear postures, percentage of cow contacts with humans in the Avoidance Distance test. Potential benefits in terms of housing, feeding and management were then related to these variables (Mann-Whitney U test). Qualitative Behavior Assessment (QBA) was also carried out and analyzed by Principal Component Analysis to explore the effect of factors that were not evenly distributed in our sample (number of feed distributions, access to pasture, presence of paddock or environmental enrichments, automatic milking systems). When hay was included in the diet, higher feeding synchronization (93.7 ± 1.6 vs. 52.2 ± 4.7%; p < 0.01), percentage of cows with relaxed ear postures (35.8 ± 5.4 vs. 15.5 ± 2.1%; p < 0.01) and percentage of cows with no visible eye white (55.9 ± 17.0 vs. 36.6 ± 4.1%; n.s.) were recorded. A higher level of feeding synchronization was observed also when the feeding places/cow ratio was > 1 (72.1 ± 9.9 vs. 53.8 ± 5.8%), although differences were not significant (p = 0.14). Deep litter had a more positive effect than cubicles on comfort at resting, with a significantly higher percentage of ruminating cows (65.8 ± 10.2 vs. 34.2 ± 3.7%; p < 0.01), a higher percentage of cows with no visible eye white (55.6 ± 9.9 vs. 33.1 ± 3.7%; p < 0.05) and a higher percentage of cows in a more comfortable posture, with stretched legs (14.3 ± 5.1 vs. 5.6 ± 1.6%; p = 0.09). QBA highlighted the most positive emotional state in the only farm that allowed access to pasture. This study represents a first attempt to apply a protocol for on-farm welfare evaluation based exclusively on the use of positive welfare indicators and provides suggestions on possible benefits (e.g., deep litter, feeding places/cow ratio > 1, hay in the diet and access to pasture) to enhance dairy cattle welfare.

Impact of black soldier fly (Hermetia illucens) larvae biomass on the immune status of rats

The improvement of the novel foods' safety assessment algorithms is currently one of the food hygiene significant areas. Within the studying of Hermetia illucens insects' effect, the standard in vivo allergological research integrated in the protocol of medical and biological evaluation of genetically modified food has been used. The protocol was supplemented with cytokine profile indicators and pathomorphologic characteristics of immunocompetent organs' lymphoid tissue. The purpose of the research was to study the effect of black soldier fly (Hermetia illucens) larvae biomass on the rats' immune status in the experiment on the induced anaphylactic shock model. Material and methods. The effect of black soldier fly (Hermetia illucens) larvae biomass was studied in a 29-day experiment on growing (43-72 days of life) male Wistar rats fed with Hermetia illucens biomass - main group (n=29) and semi-synthetic casein diet - control group (n=29). The complex assessment of allergenic potential of Hermetia illucens biomass was carried out in the experiment on the induced anaphylactic shock model in Wistar rats. An expanded pool of immune status indicators was studied including active anaphylactic shock severity (lethality, number of severe anaphylaxis reactions, anaphylactic index); cytokine profile (content of proinflammatory and anti-inflammatory cytokines, as well as regulators of cellular and humoral immune response); IgG1 and IgG4 level before and after administration of ovalbumin permissive dose (4 mg/kg b.w.). In addition to this pathomorphologic characteristics of lymphoid tissue of the main immunocompetent organs (thymus, spleen, Payer's patches) have been obtained. Results. The significant systemic anaphylaxis reaction decrease in the main group has been shown. Comparative assessment of the serum cytokines (GM-CSF, IFN-γ, IL-10, IL-12(p70), IL-13, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, TNF-α) as well as the level of immunoglobulins of the IgG1, IgG4 class before and after administration of ovalbumin permissive dose did not reveal significant differences in rats of the control and main groups. In the main group, there was a decrease in blood serum proallergic cytokines: the level of IL-4 reduced by 1.3 fold, IL-10 - 1.1 and IL-13 - 1.2 fold (p>0.05), and in animals with mild anaphylactic reaction - by 1.8, 1.4 and 1.4 times, respectively (p>0.05). The morphologic studies of the immune system organs showed no intergroup differences. Conclusion. Thus, allergological studies of black soldier fly (Hermetia illucens) larvae in the experiment with the use of systemic anaphylaxis rat model and determination of immune status indicators (anaphylactic shock severity, cytokine profile, IgG1 and IgG4 level, morphologic structure of immunocompetent organs) did not reveal any allergenic effect of the studied product.

TRL-PROTAC: A pre-trained generator of PROTACs targeting specific proteins optimized by reinforcement learning

Proteolysis-targeting chimeras (PROTACs) introduce a novel paradigm in drug development, incorporating three essential components: the warhead, the E3 ligand, and the linker. The complexity of the ternary structure, particularly the intricate design of the linker, presents a significant challenge in PROTACs drug design. Here an integrated protocol for design and evaluation of PROTACs targeting specific proteins, called TRL-PROTAC is proposed. TRL-PROTAC is focused on the de novo design of complete PROTACs by effectively joining the designed ligands targeting the proteins of interest (POI) with linkers. The ligands for POIs and E3 ligases are generated by a molecular generation model for targeting proteins, and the linker design is generated by a sequence-to-sequence model consisting of a transformer variant and the policy-based reinforcement learning method which is employed to optimize the reward values for generating PROTACs. The three components are then integrated and optimized based on their pharmacokinetic (PK) and degradation (DEG) properties. The experimental results have strongly confirmed that TRL-PROTAC is superior in optimizing properties. For existing PROTACs, TRL-PROTAC improves DEG scores by 0.45 and lowers PK scores by 1.20. Moreover, TRL-PROTAC enhances binding affinity by 2.15 in PROTACs generated from scratch.

Fluorescent Techniques for RNA Detection in Nanoparticles.

The utilization of drug delivery systems, such as lipid nanoparticles and polyplexes/micelleplexes, has shown promise in intracellularly delivering nucleic acids for addressing various diseases. Accurate quantification of the nucleic acid cargo within nanoparticles is essential for the development of safe and effective nanomedicines. Currently, the RiboGreen and SYBR Gold methods are regarded as standard techniques for the precise quantification of RNA in lipid nanoparticles and polyplexes/micelleplexes, respectively. In this chapter, we present a comprehensive protocol for the precise evaluation of the encapsulation efficiency in such formulations using these methods. Additionally, we offer detailed instructions for nanoparticle preparation, characterization, and a comparative analysis of the sensitivity of both methods in quantifying unencapsulated siRNA.

Efficacy of Salivary Urea and Creatinine Compared to Serum Levels in Chronic Kidney Disease Patients: A Cross-sectional Study

Introduction: Chronic Kidney Disease (CKD) has become an impending health concern due to the massive rise in the number of patients with diabetes mellitus and hypertension. Monitoring CKD patients typically requires regular invasive testing, and a simple diagnostic test that does not involve venipuncture would greatly benefit patients and healthcare professionals. Extensive research is being conducted to explore the use of saliva as a Non invasive tool for evaluation of systemic diseases like CKD. However, most of these studies have focused on End Stage Renal Disease (ESRD) patients. Aim: To investigate the correlation between salivary urea and creatinine levels and their serum counterparts in CKD patients and healthy controls. Additionally, the study aimed to assess the efficacy of salivary urea and creatinine compared to serum urea and creatinine in predicting CKD. Materials and Methods: The present cross-sectional study was conducted between January 2021 and July 2022 in the Department of Biochemistry, in collaboration with the Department of Medicine, at Heritage Institute of Medical Sciences in Varanasi, Uttar Pradesh, India. The study included a total of 60 participants: 30 CKD patients (stage 1-3) and 30 age-matched healthy controls. Serum and salivary urea were analysed using the Urease-Glutamate Dehydrogenase (GLDH) method, and creatinine was measured using the Modified Jaffe’s method on the Dirui-300B autoanalyser. Data were statistically analysed using Pearson’s correlation coefficient. The sensitivity, specificity, and Area Under the Curve (AUC) of salivary urea and creatinine were evaluated in comparison to their serum counterparts. Results: The participants consisted of 30 CKD patients with a mean age of 54.8±8.8 years and 30 age-matched healthy controls with a mean age of 52.42±8.4 years. A significant difference in salivary urea and creatinine levels was observed between the control and CKD groups. There was a strong and significant correlation (p-value &lt;0.01) between salivary creatinine and serum creatinine in both the control group (r-value=0.76) and the CKD group (r-value=0.82). Additionally, a strong and significant correlation (p-value &lt;0.01) was found between salivary urea and serum urea in the CKD group (r-value=0.63). However, the correlation between salivary and serum urea was not significant in the control group, with an r-value of 0.58 and a p-value of 0.24. Both salivary urea and creatinine demonstrated high sensitivity (90% and 89%, respectively), specificity (80% and 80%, respectively), and AUC (0.78 and 0.86, respectively) compared to their serum counterparts, validating their practical clinical utility. Conclusion: The concentration of urea and creatinine in saliva can reflect kidney damage and help monitor kidney function in CKD patients. Standardising the protocol for evaluation of salivary urea and creatinine and establishing a reference range will make it useful for screening for CKD.

Experimental protocol for evaluation of biomaterials in an in-vivo silicone implant coverage.

To describe an experimental surgical model in rats using a dual-plane technique for evaluation of biomaterials in an in-vivo silicone implant coverage. This study was developed following the ISO 10993-6 standard. In this study, 40 male Wistar rats weighing between 250 and 350 g were used, distributed into two groups: experimental, biomaterial superimposed on the minimammary prosthesis (MP); and control, MP without implantation of the biomaterial, with eight animals at each biological point: 1, 2, 4, 12, and 26 weeks. Thus, at the end of biological points (1, 2, 4, 12, and 26 weeks; n = 8 animals per week), the tissue specimens achieved were fixed in buffered formalin and stained with hematoxylin-eosin. Macroscopically, throughout the study, no postoperative complications were apparent. In the histological analysis, it was possible to observe the evolution of the inflammatory response, tissue repair, and fibrous capsule during the biological points. The experimental model described in this study proved to be suitable for evaluating the biomaterial used in the coverage of breast silicone implants.

A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach.

Through meticulous examination of lymph nodes, the stage and severity of cancer can be determined. This information is invaluable for doctors to select the most appropriate treatment plan and predict patient prognosis; however, any oversight in the examination of lymph nodes may lead to cancer metastasis and poor prognosis. In this review, we summarize a significant number of articles supported by statistical data and clinical experience, proposing a standardized evaluation protocol for lymph nodes. This protocol begins with preoperative imaging to assess the presence of lymph node metastasis. Radiomics has replaced the single-modality approach, and deep learning models have been constructed to assist in image analysis with superior performance to that of the human eye. The focus of this review lies in intraoperative lymphadenectomy. Multiple international authorities have recommended specific numbers for lymphadenectomy in various cancers, providing surgeons with clear guidelines. These numbers are calculated by applying various statistical methods and real-world data. In the third chapter, we mention the growing concern about immune impairment caused by lymph node dissection, as the lack of CD8 memory T cells may have a negative impact on postoperative immunotherapy. Both excessive and less lymph node dissection have led to conflicting findings on postoperative immunotherapy. In conclusion, we propose a protocol that can be referenced by surgeons. With the systematic management of lymph nodes, we can control tumor progression with the greatest possible likelihood, optimize the preoperative examination process, reduce intraoperative risks, and improve postoperative quality of life.