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Background: Atherosclerosis is a multi-factorial disease, and low-density lipoprotein cholesterol (LDL-C) is a critical risk factor in developing atherosclerotic cardiovascular disease (ASCVD). Cholesteryl-ester transfer-protein (CETP), synthesized by the liver, regulates LDL-C and high-density lipoprotein cholesterol (HDL-C) through the bidirectional transfer of lipids. The novelty of CETP inhibitors (CETPis) has granted new focus towards increasing HDL-C, besides lowering LDL-C strategies. To date, five CETPis that are projected to improve lipid profiles, torcetrapib, dalcetrapib, evacetrapib, anacetrapib, and obicetrapib, have reached late-stage clinical development for ASCVD risk reduction. Early trials failed to reduce atherosclerotic cardiovascular occurrences. Given the advent of some recent large-scale clinical trials (ACCELERATE, HPS3/TIMI55-REVEAL Collaborative Group), conducting a meta-analysis is essential to investigate CETPis’ efficacy. Methods: We conducted a thorough search of randomized controlled trials (RCTs) that commenced between 2003 and 2023; CETPi versus placebo studies with a ≥6-month follow-up and defined outcomes were eligible. Primary outcomes: major adverse cardiovascular events (MACEs), cardiovascular disease (CVD)-related mortality, all-cause mortality. Secondary outcomes: stroke, revascularization, hospitalization due to acute coronary syndrome, myocardial infarction (MI). Results: Nine RCTs revealed that the use of a CETPi significantly reduced CVD-related mortality (RR = 0.89; 95% CI: 0.81–0.98; p = 0.02; I2 = 0%); the same studies also reduced the risk of MI (RR = 0.92; 95% CI: 0.86–0.98; p = 0.01; I2 = 0%), which was primarily attributed to anacetrapib. The use of a CETPi did not reduce the likelihood any other outcomes. Conclusions: Our meta-analysis shows, for the first time, that CETPis are associated with reduced CVD-related mortality and MI.

Background: Skin and soft tissue infections (SSTIs) are common presentations in the emergency department. However, this is less common after contact with contaminated saltwater or freshwater. This review presents the diagnosis and management of water-related soft tissue sepsis in this vulnerable and difficult-to-treat subgroup of necrotizing soft tissue sepsis. Methods: A summative literature overview is presented regarding bacterial and fungal SSTI after contact with contaminated water, with practical diagnostic and management aspects. Results: The literature indicates that these wounds and infections remain difficult to treat. An approach using appropriate diagnostic tools with both medical and surgical management strategies is provided. Conclusions: SSTIs due to water contamination of wounds involve unusual organisms with unusual resistance patterns, and require a nuanced and directed diagnostic approach with an adaptation of the usual antibiotic or antifungal selection to achieve a successful cure, along with aggressive debridement and wound care.

This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously.

Percutaneous Osseointegrated Implants (POI) affix artificial limbs to the body after amputation. Several adverse events challenge mainstream uptake of this technology. This study aims to achieve a consensus regarding “the challenges and adverse events to POI integration and long-term fixation in limb amputation”. We sought a panel of clinical experts divided by profession into surgical, clinical, or clinical academic categories. We used a real time eDelphi method to develop consensus on both the challenges and adverse event items, enabling anonymity, iteration, controlled feedback, and statistical aggregation of group responses. The full panel agreed that the most impactful items are amongst 10 key challenges and eight adverse events. Panellists were in consensus regarding the five most impactful challenges, which were, in decreasing order: patient selection, absence of a multidisciplinary team, design of the implant, soft tissue stability and an experienced surgical team. Panellists considered the five most impactful adverse events, in decreasing order, to be the following: no biological fixation, deep infection, aseptic loosening, no mechanical fixation, and implant breakage. Consensus was obtained on implant breakage and deep infection items. The proportion of consensus from the whole panel across all items was in line with the literature, and we observed an improvement in consensus once the panel was stratified based on job, expertise and implant system.

Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure services. Methods: The proposed framework integrates Microsoft Azure’s cloud services with a permissioned blockchain network. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. Evaluation Metrics: The framework’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the integrated approach in enhancing diagnostic accuracy and data security. Results: The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios (70–30, 80–20, 90–10). The F1-score and k-fold cross-validation accuracy (5-fold and 10-fold) also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management.

Burn injuries, which significantly affect global public health, require effective treatment strategies tailored to varying severity. Fungi are considered a sustainable, easily propagated source for lead therapeutic discovery. In this study, we explored the burn wound healing potential of Aspergillus terreus through a combination of in vitro, in vivo, metabolite profiling, and in silico analysis. The in vitro scratch assays performed with human skin fibroblast cells showed promising wound healing activity. Furthermore, the burn-induced rats model showed a marked improvement in cutaneous wound healing, evidenced by an accelerated rate of wound closure and better skin regeneration after A. terreus extract treatment at 14 days. The results of this study demonstrated significant enhancements in wound closure and tissue regeneration in the treated rat model, surpassing the outcomes of standard treatments. This controlled healing process, evidenced by superior collagen synthesis and angiogenesis and confirmed by histopathological studies, suggests that A. terreus has potential beyond the traditionally studied fungal metabolites. The metabolite profiling of 27 bioactive compounds was further investigated by docking analysis for the potential inhibition of the NF-κB pathway, which has an important function in inflammation and wound repair. The compounds eurobenzophenone A (7), aspernolide D (16), asperphenalenone A (23), aspergilate D (15), kodaistatin A (18), and versicolactone A (14) showed the highest binding affinity to the target protein with a pose score of −16.86, −14.65, −12.65, −12.45, −12.19, and −12.08 kcal/mol, respectively. Drug-likeness properties were also conducted. The findings suggest the potential wound healing properties of A. terreus as a source for lead therapeutic candidate discovery.

Mortality due to suicide is amongst the largest public health concerns across the world today in Bharat (India). There have been concerns that the COVID-19 pandemic has contributed to worsening mental health outcomes across the world, including in Bharat. Furthermore, long COVID has been proposed to be a major consequence of COVID-19, which can also worsen mental health outcomes. Therefore, our objective in this study was to analyse trends in suicide mortality across Bharat and to compare these trends to changes prior to the COVID-19 pandemic; in addition, we aimed to analyse if long COVID had any role in these changes. It was found that, at a national level, the average annual increase in the suicide rate between 2019 and 2022 was 0.7 per 100,000 people (a 6.41% increase). There was also an average annual rise in suicide rates across 27 states/union territories (out of the 33 that were analysed). States/UTs with the highest annual increases since the start of the COVID-19, despite a decrease from 2018 to 2019, were Tamil Nadu (increased by 2.7; a 15.17% increase) and Telangana (increased by 1.9; a 9.22% increase). Multi-linear regression showed that the annual suicide rate changes were not associated with COVID-19 deaths per 10,000 people (standardized beta coefficient = 0.077; p = 0.605) but were associated with COVID-19 cases per 100 people (standardized beta coefficient = 0.578; p < 0.001). It has been shown that suicide mortality has worsened, and long COVID may have a potential role in this in Bharat.

Extra-skeletal Ewing sarcoma (EWS) occurs in about 12% of EWS patients; at the same time, primary involvement of the kidneys remains extremely rare. Since it was first described in 1975, only a small case series have been reported worldwide. About 95% of surgically treated patients with EWS of the kidney described in the literature underwent nephrectomy, and the remaining patients only had a tumor biopsy. Nephron-sparing surgery (NSS) has not been sufficiently investigated as an alternative in the local surgical treatment of localized disease, mostly as a result of technically unfeasible provisions of negative surgical margins. In this report, we present a unique case of primary EWS of the kidney with an asymptomatic course without radiographic signs that suggest a highly aggressive disease, successfully locally treated with robotic-assisted NSS. This report showcases that robotic-assisted NSS could be a feasible alternative in treatment of localized disease yielding equally good oncological results while, at the same time, creating better prerequisites for necessary adjuvant chemotherapy.

Fluorometric method for detecting of five nitrogen-based drugs concentration based on inhibition of emission Eosin Y (EY). The selection of N-drugs comprised indapamide (INDP), clomipramine hydrochloride (CMI), promethazine hydrochloride (PMH), lisinopril (LSP), and trifluoperazine hydrochloride (TFPH). The Stern–Volmer style was plotted between relative emissions of EY vs. N-drugs concentration. The standard curves were linear over the concentration range of 5–50 µg mL−1 with R2 > 0.9, and the LOD for INDP, CMI, PMH, LSP, and TFPH were 2.07, 1.36, 3.02, 3.52, and 2.09 µmol·L−1, respectively. The binding constant Kapp for LSP was greater than other N-drugs. Furthermore, the suggested method was hence applied for the routine detection of the concentration of N-drugs in bulk and tablet or syrup dosage forms. FTIR analysis and the electron-mapping density provided the chemical affinity of N-drugs towards EY.

Diabetes mellitus (DM) is a common non-communicable and chronic metabolic disorder regarded as a global epidemic with high morbidity and mortality. Although, conventional medicines are available for the management of DM, Indigenous knowledge, including the use of botanicals, has contributed to the management of this life-threatening disease in Africa. This is due to the perceived effectiveness and minimal side effects associated with plants and plant-derived compounds as an affordable remedy against DM. This review focuses on the contribution of Indigenous African plants to the management of DM. Relevant literatures were reviewed from online scientific databases, such as PubMed, ScienceDirect, and Google Scholar, using keywords singly and in combination. The review revealed that Indigenous health practitioners use several medicinal plants in the management of DM, with a range of 14–255 botanicals recorded. Approximately 80 compounds with antidiabetic potency have been isolated from different parts of African medicinal plants, with the majority belonging to flavonoids and terpenoids. Particularly, compounds such as apigenin, combretin B, convallatoxin, kaempferol, and quercetin remain the most promising antidiabetic compounds isolated from African medicinal plants. Limited clinical trials have been conducted on these compounds despite these milestones. There is, therefore, the need for further investigations to explain their antidiabetic effects, particularly under clinical conditions. Plants from which these compounds were isolated were selected based on ethnopharmacological knowledge. With these developments, medicinal plants and Indigenous knowledge remain an integral part of the global strategy to combat DM.

Municipal management involves making decisions on various technical issues, and one such crucial aspect is the multicriteria decision-making process. When choosing suitable locations for wastewater treatment plants, it becomes necessary to consider a range of factors such as technical feasibility, economic viability, environmental impact, ecological aspects, and management requirements. However, evaluating these criteria and dealing with uncertainties can be complex. To address this challenge in the Tabuk region, a combination of two powerful analytical methods, the fuzzy analytical hierarchy process (FAHP) and geographical information system (GIS), were employed. The FAHP methodology allows for considering uncertainties and subjective judgements, while GIS provides spatial analysis capabilities. By combining FAHP and GIS, a thorough evaluation of potential wastewater treatment plant locations was conducted by determining the relative weights for each geospatial parameter. These weights were then used to generate a suitability map, visually representing the most favourable areas for site selection. The FAHP analysis resulted in higher importance given to the treatment plant’s distance to urban areas, followed by the distance to roads among the seven investigated parameters. The integrated FAHP-GIS model results show that the western parts of the region are most suitable for constructing wastewater treatment plants. These findings are valuable in facilitating multicriteria decision-making for identifying the optimum site in the area. In summary, integrating FAHP and GIS in the assessment process enables decision-makers to consider various technical, economic, environmental, ecological, and management aspects, thereby providing a comprehensive framework for site selection that can be replicated in other regions with different conditions. This approach enhances the decision-making process in municipal management and promotes more informed and effective planning in the Tabuk region.

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Purpose: The purpose of this study is to assess the effectiveness of the best performing interpretable machine learning models in the diagnoses of leukoplakia and oral squamous cell carcinoma (OSCC). Methods: A total of 237 patient cases were analysed that included information about patient demographics, lesion characteristics, and lifestyle factors, such as age, gender, tobacco use, and lesion size. The dataset was preprocessed and normalised, and then separated into training and testing sets. The following models were tested: K-Nearest Neighbours (KNN), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The overall accuracy, Kappa score, class-specific precision, recall, and F1 score were used to assess performance. SHAP (SHapley Additive ExPlanations) was used to interpret the Random Forest model and determine the contribution of each feature to the predictions. Results: The Random Forest model had the best overall accuracy (93%) and Kappa score (0.90). For OSCC, it had a precision of 0.91, a recall of 1.00, and an F1 score of 0.95. The model had a precision of 1.00, recall of 0.78, and F1 score of 0.88 for leukoplakia without dysplasia. The precision for leukoplakia with dysplasia was 0.91, the recall was 1.00, and the F1 score was 0.95. The top three features influencing the prediction of leukoplakia with dysplasia are buccal mucosa localisation, ages greater than 60 years, and larger lesions. For leukoplakia without dysplasia, the key features are gingival localisation, larger lesions, and tongue localisation. In the case of OSCC, gingival localisation, floor-of-mouth localisation, and buccal mucosa localisation are the most influential features. Conclusions: The Random Forest model outperformed the other machine learning models in diagnosing oral cancer and potentially malignant oral lesions with higher accuracy and interpretability. The machine learning models struggled to identify dysplastic changes. Using SHAP improves the understanding of the importance of features, facilitating early diagnosis and possibly reducing mortality rates. The model notably indicated that lesions on the floor of the mouth were highly unlikely to be dysplastic, instead showing one of the highest probabilities for being OSCC.

Previous reports showed that prolonged operative time increases the risk of surgical site infection rates, prolonged hospital stays, and potentially higher rates of revision surgeries. In the context of rhinoplasty, the type of nasal deformity may complicate the surgical procedure and increase the operative time. We aimed to investigate the association between the type of nasal deformity and operative time in rhinoplasty patients. This retrospective chart review studies 349 patients who underwent primary and secondary rhinoplasty procedures due to various nasal deformities in King Saud University-Medical City. The primary outcome of the present study was the association between operative time, defined as the time from the initial incision to the completion of skin closure, and the type of nasal deformity. The association between the type of deformity and operative time was assessed using one-way ANOVA and Bonferroni post hoc analysis. There was a statistically significant association between the type of nasal deformity and operative time (p < 0.001). Patients with dorsal, alar base, and tip deformities had significantly longer operative times than patients with isolated dorsal deformities (208.01 ± 57.73 min) (p < 0.001). The analysis also showed that the presence of crooked nose deformities (p < 0.001), an inverted V deformity (p = 0.01), internal nasal valve collapse (p = 0.025), axis deviation (p = 0.003), over-projection, and under-projection significantly increased surgical duration. The complexity of nasal deformities significantly impacts the operative time in rhinoplasty surgeries; more complex deformities that require extensive surgical procedures are associated with a longer operative time. Further research is warranted to corroborate these findings and investigate other potential influencing factors.

Human action recognition (HAR) based on skeleton data is a challenging yet crucial task due to its wide-ranging applications, including patient monitoring, security surveillance, and human- machine interaction. Although numerous algorithms have been proposed to distinguish between various activities, most practical applications require highly accurate detection of specific actions. In this study, we propose a novel, highly accurate spatiotemporal graph autoencoder network for HAR, designated as GA-GCN. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from skeleton data. The proposed method achieved accuracies of 92.3% and 96.8% on the NTU RGB+D dataset for cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8% and 90.4% for cross-subject and cross-set evaluations. Overall, our model outperforms the majority of the existing state-of-the-art methods on these common benchmark datasets.

Background/Objectives: This study explores an optimization-based strategy for muscle force estimation by employing simplified cost functions integrated with physiologically relevant muscle models. Methods: Considering elbow flexion as a case study, we employ an inverse-dynamics approach to estimate muscle forces for the biceps brachii, brachialis, and brachioradialis, utilizing different combinations of cost functions and muscle constitutive models. Muscle force generation is modeled by accounting for active and passive contractile behavior to varying degrees using Hill-type models. In total, three separate cost functions (minimization of total muscle force, mechanical work, and muscle stress) are evaluated with each muscle force model to represent potential neuromuscular control strategies without relying on electromyography (EMG) data, thereby characterizing the interplay between muscle models and cost functions. Results: Among the evaluated models, the Hill-type muscle model that incorporates both active and passive properties, combined with the stress minimization cost function, provided the most accurate predictions of muscle activation and force production for all three arm flexor muscles. Our results, validated against existing biomechanical data, demonstrate that even simplified cost functions, when paired with detailed muscle models, can achieve high accuracy in predicting muscle forces. Conclusions: This approach offers a versatile, EMG-free alternative for estimating muscle recruitment and force production, providing a more accessible and adaptable tool for muscle force analysis. It has profound implications for enhancing rehabilitation protocols and athletic training, not only broadening the applicability of muscle force estimation in clinical and sports settings but also paving the way for future innovations in biomechanical research.

A treasure trove of naturally occurring biomolecules can be obtained from sea living organisms to be used as potential antioxidant and anti-inflammatory agents. These bioactive molecules can target signaling molecules involved in the severity of chronic autoimmune diseases such as rheumatoid arthritis (RA). The intracellular tyrosine kinases family, Janus kinases (JAKs, includes JAK1, JAK2, and JAK3), is implicated in the pathogenesis of RA through regulating several cytokines and inflammatory processes. In the present study, we conducted molecular docking and structural analysis investigations to explore the role of a set of bioactive molecules from marine sources that can be used as JAKs’ specific inhibitors. Around 200 antioxidants and anti-inflammatory molecules out of thousands of marine molecules found at the Comprehensive Marine Natural Products Database (CMNPD) website, were used in that analysis. The details of the interacting residues were compared to the recent FDA approved inhibitors tofacitinib and baricitinib for data validation. The shortlisted critical amino acids residues of our pharmacophore-based virtual screening were LYS905, GLU957, LEU959, and ASP1003 at JAK1, GLU930 and LEU932 at JAK2, and GLU905 and CYS909 of JAK3. Interestingly, marine biomolecules such as Sargachromanol G, Isopseudopterosin E, Seco-Pseudopterosin, and CID 10071610 showed specific binding and significantly higher binding energy to JAK1 active/potential sites when being compared with the approved inhibitors. In addition, Zoanthoxanthin and Fuscoside E bind to JAK2′s critical residues, GLU930 and LEU932. Moreover, Phorbaketal and Fuscoside E appear to be potential candidates that can inhibit JAK3 activity. These results were validated using molecular dynamics simulation for the docked complexes, JAK1(6sm8)/SG, JAK2 (3jy9)/ZAX, and JAK3 (6pjc)/Fuscoside E, where stable and lower binding energy were found based on analyzing set of parameters, discussed below (videos are attached). A promising role of these marine bioactive molecules can be confirmed in prospective preclinical/clinical investigations using rheumatoid arthritis models.

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy.

Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. We introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. We benchmarked our multi-agent model solutions against the Google OR tools, which served as the baseline for comparison. The results demonstrated that both multi-agent models—Multi-Agent 1, which includes the initializer, critic, and scorer agents, and Multi-Agent 2, which comprises only the initializer and critic agents—significantly improved the solution quality for TSP and mTSP problems. Multi-Agent 1 excelled in environments requiring detailed route refinement and evaluation, providing a robust framework for sophisticated optimizations. In contrast, Multi-Agent 2, focusing on iterative refinements by the initializer and critic, proved effective for rapid decision-making scenarios. These experiments yield promising outcomes, showcasing the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems. The findings underscore the potential of MLLMs as powerful tools in computational optimization, offering insights that could inspire further advancements in this promising field.

Background/Objectives: Deep hypothermic circulatory arrest (DHCA) is safe, but subtle neurodevelopmental deficits may persist far beyond the perioperative period. We aimed to investigate the relationship between DHCA duration and neurodevelopmental outcomes in young children undergoing cardiac surgery with DHCA. Methods: Children aged < 42 months, including neonates who underwent cardiac surgery using DHCA without regional perfusion techniques, were included as the DHCA group. Children in the same age range who underwent cardiac surgery without DHCA were included as the control group. All enrolled patients underwent neurodevelopmental assessment using the Bayley Scales of Infant and Toddler Development (BSTID) by a trained pediatrician, and 17 DHCA patients and 6 control patients completed the BSTID assessment. Results: Both groups showed no significant preoperative, operative, or postoperative differences. Adjusted multivariable analysis revealed that prematurity and age at assessment were significant changing predictors of each of the BSTID components (p < 0.001), except for the gross motor component, where only age at assessment was a significant adjusting predictor. Longer DHCA was associated with lower fine and gross motor BSTID components; however, the association was not statistically significant (p = 0.06). Conclusions: Long-duration DHCA without regional perfusion techniques may be associated with less optimal neurodevelopmental outcomes.

The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively.

Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies.

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers.

Background: Nanocomposite resins have been widely used in modern denture manufacturing. However, their long-term color stability is a concern for both dental professionals and patients. Purpose: to evaluate the effect of different beverages on the color stability of 3D-printed denture base resins modified with zirconium dioxide nanoparticles (ZrO2NPs). Methods: A total of 440 specimens were fabricated and distributed into 11 groups (n = 40/group). The control group of heat polymerized (PMMA) and five groups of two different 3D-printed resins (NextDent and ASIGA) as experimental groups with various concentrations of ZrO2NPs added to the 3D-printed resins (0.5 wt%, 1 wt%, 3 wt%, 5 wt%) in addition to one unmodified group per resin. Specimens per group are sorted into four subgroups (n = 10) according to tested beverages, as follows: coffee, tea, cola, and mineral water. Before immersion, all the specimens were exposed to 5000 thermal cycles. Color changes (ΔE00) were assessed prior (T0) and following immersion for 6 days (T1) and 12 days (T2) using a spectrophotometer. Color difference values were calculated by using CIEDE2000 color difference. Data was analyzed by ANOVA and post hoc Tukey test with a significant level of less than 0.05. Results: Tea produced the highest color change for both NextDent and ASIGA materials, whereas water caused the least color change on PMMA at T2. Increasing the immersion time resulted in more color changes, with tea and coffee showing significant differences. PMMA had considerably less color change than 3D-printed resins. The color change of 3D-printed increased after adding ZrO2NPs. Conclusions: Beverage type and immersion time have an impact on the color stability of unmodified and ZrO2NP-modified denture base resins with significant change after immersion in tea and coffee.

Background and Objectives: Accelerated orthodontics represents a significant shift in dental practice aimed at reducing treatment times while maintaining optimal patient outcomes. This bibliometric analysis evaluated the research landscape of accelerated orthodontics from 2012 to 2023, focusing on publication trends, citation patterns, influential journals, leading institutions, and key contributors. Materials and Methods: A comprehensive search in Scopus identified 600 relevant articles, with the top 50 most-cited papers encompassing systematic reviews, randomized controlled trials, and experimental studies. Key techniques, such as corticotomy and piezocision, have been frequently highlighted for their effectiveness in expediting tooth movement. Results: The analysis revealed fluctuating annual scientific outputs, with notable peaks driven by technological advancements and increased patient demand for quicker orthodontic solutions. However, the production of high-impact papers was hindered by delays in citation accumulation and disruptions caused by the COVID-19 pandemic. Keyword analysis identified critical themes, such as orthodontic tooth movement, malocclusion, and demographic factors, while a global collaboration map underscored extensive international research partnerships. Leading journals included the American Journal of Orthodontics and Dentofacial Orthopedics, and prominent institutions such as the University of California at Los Angeles played significant roles in advancing the field. Conclusions: This study provides a comprehensive overview of the current state of accelerated orthodontics, emphasizing the need for continued research, particularly RCTs, to further refine and validate accelerated orthodontic techniques and improve clinical outcomes.

Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of the existing methods. To understand how these methods have developed and been applied and what evidence has been accumulated through model training and analysis, we carried out a tertiary literature review that takes as input systematic literature reviews published between 1992 and 2023. We evaluated 40 systematic literature review papers and presented binary tabular overviews of researched XAI methods and their respective characteristics, such as the scope, scale, input data, explanation data, and machine learning models researched. We identified seven distinct characteristics and organized them into twelve specific categories, culminating in the creation of comprehensive research grids. Within these research grids, we systematically documented the presence or absence of research mentions for each pairing of characteristic and category. We identified 14 combinations that are open to research. Our findings reveal a significant gap, particularly in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.

Zinc oxide nanoparticles (ZnO NPs) have gathered interest because of their unique characteristics and potential applications. In the current work, ZnO NPs underwent an eco-friendly biosynthesis process using garlic peel extract. The biosynthesized ZnO NPs were characterized using different analyses including Ultraviolet-visible (UV-vis) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), dynamic light scattering (DLS), and Fourier transform infrared spectroscopy (FTIR). The produced ZnO NPs exhibited a UV–vis spectrum absorption peak at 365 nm, thus indicating the formation of ZnO NPs. The SEM showed that the biosynthesized ZnO NPs had an irregular surface morphological shape with an average size of 17 nm, according to the DLS analysis. Based on the FTIR findings, the bioactive functional groups responsible for stabilizing and capping the ZnO-NPs were confirmed. The biosynthesized ZnO NPs exhibited 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging activity and antimicrobial activities against Gram-positive (Bacillus cereus) and Gram-negative bacteria (Klebsiella pneumonia). Therefore, the plant-mediated biosynthesized ZnNPs can be considered a promising candidate as an antioxidant and antimicrobial agent against pathogenic microbes found in different areas such as food safety and agriculture. Through the utilization of bioinformatics, we identified six potential targets for drug development in K. pneumonia and B. cereus, along with their corresponding interacting residues with zinc oxide nanoparticles. Additionally, our research revealed that the zinc oxide nanoparticles exhibited binding capabilities with the sulfiredoxin domain located at the specific targets of K. pneumonia, a crucial mechanism responsible for the repair of bacterial cells under oxidative stress.

Determining nutrient-release patterns of organic manures can give an estimate of the potential amount of nutrients that a given material can contribute to crops along with chemical fertiliser. Nutrients released from organic manure depend on several factors, and temperature is one of them. To evaluate how different types of conventional organic manures release nitrogen (N) under varying temperature conditions, an incubation study was conducted at the Bangladesh Agricultural Research Institute. Six organic manures—poultry manure (PM), vermicompost (VC), bio-slurry (BS), cowdung (CD), water-hyacinth compost (WHC), and rice straw compost (RSC)—were evaluated at three temperature regimes (15, 25, and 35 °C) to study the dynamics of N incubated for 330 days. The N release was significantly influenced by the interaction of organic manures and temperature regimes. Poultry manure-treated soil incubated at 35 °C had the highest mineralisation of all parameters than other manures. The mineralisation of N followed the order: PM > VC > BS > CD > WHC > RSC > control and 35 °C > 25 °C > 15 °C. Across different temperatures, the mineralisation rate of PM was 15–55% higher than that of other manures. At 35 °C, the mineralisation rate was 10% and 20% higher compared to 25 °C and 15 °C, respectively. The first-order kinetic models predicted the organic N release from manures satisfactorily. The findings of the present study enrich the understanding of N-release patterns under different temperature regimes that prevail in different crop growing seasons in Indo-Gangetic Plains, providing valuable data for researchers and policymakers interested in sustainable integrated nutrient management practices.

Several studies have correlate improved patient outcomes with increased physician–patient contacts, particularly in chronic diseases. Extending this approach to inflammatory bowel disease (IBD) care presents a promising means of improving outcomes. At LSU Health Shreveport (LSUHS), a new approach called “STABILITY” (Symptomatic Review during Biologic Therapy) was implemented during infusion therapy visits for IBD patients. These brief 15 min physician–patient interviews aimed to discuss the patients’ current IBD-related symptoms and evaluate the need for any changes in their treatment plan. Our goal was to remove a care gap and prevent intensifying symptoms created by missed appointments and loss of contact. To analyze the effectiveness of the STABILITY approach, a retrospective chart review was conducted on 111 IBD patients (18 with ulcerative colitis, 93 with Crohn’s disease) seen at LSUHS between 2011 and 2022. Since March 2019, STABILITY has been mandatory for all infusion therapy visits. The data collected included patients’ demographics, lab levels for biomarkers (fecal calprotectin, C-reactive protein, and erythrocyte sedimentation rates), hospitalizations, medication changes, and diagnosis dates before and after the implementation of STABILITY. Additionally, voluntary, anonymous infusion patient satisfaction surveys post-STABILITY were used to gather patient responses. In males with IBD, disease severity and hospitalizations were reduced significantly (p = 0.004 and 0.0234, respectively). In females with IBD, disease severity and hospitalizations were also reduced significantly (p = 0.0001 and 0.0072, respectively). In patients with UC and CD, there were significant improvements in disease severity (p = 0.043 and p = 0.0001, respectively), and CD hospitalizations were also improved (p = 0.0013). In males and females with UC, disease severity was marginally and significantly reduced (p = 0.0781 and p = 0.0379, respectively). In males and females with CD, disease severity was significantly reduced (p = 0.0161 and 0.0003, respectively), and CD male and female hospitalizations were also reduced significantly (p = 0.0436 and 0.013). Analyzing of survey responses, we found that the most patients reported improved IBD symptoms (56%), gained understanding of their condition (84%) and were in favor of continuing STABILITY consultations during infusion therapy (93%). To further investigate the impact of STABILITY, we conducted a comparative analysis between IBD patients undergoing STABILITY infusion therapy and LSUHS patients solely on self-injectable biologics. Our paired data analysis showed significant improvements in disease severity in female IBD patients (1.69 ± 0.13 vs. 1.41 ± 0.12, p = 0.0001) and male IBD patients (1.58 ± 0.16 vs. 1.2 ± 0.135, p = 0.004), in UC patients (1.833 ± 0.4.2 vs. 1.444, p = 0.043), in all CD patients (1.59 ± 0.11 vs. 1.29 ± 0.01, p = 0.0001), in male CD patients (1.52 ± 0.167 vs. 1.15 ± 0.15, p = 0.016), in female CD patients (1.66 ± 0.15 vs. 1.4 ± 0.13, p = 0.0003), in female UC patients (1.82 ± 0.32 vs. 1.45 ± 0.31, p = 0.0379), and marginally in male UC patients (p = 0.0781). Similarly, hospitalizations were significantly reduced in CD patients considered in aggregate (0.21 ± 0.04 vs. 0.11 ± 0.03, p = 0.0013), in male IBD patients (0.175 ± 0.06 vs. 0.05 ± 0.035, p = 0.024), in female IBD patients (0.21 ± 0.05 vs. 0.11 ± 0.04, p = 0.0072), in male CD patients (0.18 ± 0.07 vs. 0.06 ± 0.042, p = 0.0436), and in females with CD (0.23 ± 0.06 vs. 0.13 ± 0.04, p = 0.013). Although average values for fecal calprotectin, CRP, and sedimentation rate were frequently reduced after STABILITY interviews, these data did not reach statistical significance. These preliminary findings suggest that STABILITY may be effective in maintaining low disease activity or remission in IBD patients.

The rapid growth of the cannabis industry necessitates accurate and efficient methods for detecting and classifying cannabis seed varieties, which is crucial for quality control, regulatory compliance, and genetic research. This study presents a deep learning approach to automate the detection and classification of 17 different cannabis seed varieties, addressing the limitations of manual inspection processes. Leveraging a unique dataset of 3319 high-resolution seed images, we employ self-supervised bounding box annotation using the Grounding DINO model. Our research evaluates two prominent object detection models, Faster R-CNN and RetinaNet, with different backbone architectures (ResNet50, ResNet101, and ResNeXt101). Extensive experiments reveal that RetinaNet with a ResNet101 backbone achieves the highest strict mean average precision (mAP) of 0.9458 at IoU 0.5–0.95. At the same time, Faster R-CNN with ResNet50 excels at the relaxed 0.5 IoU threshold (0.9428 mAP) and maintains superior recall. Notably, the ResNeXt101 backbone, despite its complexity, shows slightly lower performance across most metrics than ResNet architectures. In terms of inference speed, the Faster R-CNN with a ResNeXt101 backbone demonstrates the fastest processing at 17.5 frames per second. This comprehensive evaluation, including performance-speed trade-offs and per-class detection analysis, highlights the potential of deep learning for automating cannabis seed analysis. Our findings address challenges in seed purity, consistency, and regulatory adherence within the cannabis agricultural domain, paving the way for improved productivity and quality control in the industry.

This study presents a compact educational photovoltaic/thermal (PV/T) system designed for thorough performance assessment under simulated weather conditions. As an affordable educational tool, the system offers significant pedagogical value. The PV/T system features two photovoltaic modules: a thermally enhanced module and a standard one. The thermally enhanced module uses water as a coolant, which transfers heat from the PV cells to a fan-operated heat exchanger, with the coolant being recirculated to maintain optimal conditions. A halogen lamp, placed between the modules, simulates solar radiation to ensure effective electrical current generation. The system’s remote-control capabilities, managed via the Message Queuing Telemetry Transport (MQTT) protocol, enable real-time adjustments to the coolant flow rate, heat exchanger efficiency, and lamp brightness, as well as monitoring of electrical parameters. Experimental findings indicate that the PV/T module achieves a 7.71% increase in power output compared to the standard PV module and offers a 17.41% improvement in cooling efficiency over scenarios without cooling. Additionally, the numerical methods used in the study show a maximum deviation of 4.29% from the experimental results, which is considered acceptable. This study showcases a best practice model for solar training, applicable from elementary to university levels, and suggests innovative approaches to enhancing solar energy education.

Klebsiella pneumoniae is an opportunistic Gram-negative bacterium in the Enterobacteriaceae family associated with a wide range of diseases, such as pneumonia, bloodstream infections, meningitis and urinary tract infections. Infections caused by drug-resistant strains of Klebsiella pneumoniae pose a significant threat to the effectiveness of conventional antibiotics. Hence, this has led to the need to explore alternative antimicrobial therapies, especially natural products derived from plant sources. This study assessed the phytochemical composition and antibacterial properties and performed a molecular docking analysis of Henna leaves (Lawsonia inermis L.) extracts on strains of Klebsiella pneumoniae. Crude ethanol and methanol extracts of L. inermis L. were prepared at different concentrations (25, 50, 75 and 100 mg/mL) and tested on extended spectrum beta-lactamases (ESBLs)-producing strains of Klebsiella pneumoniae. Phytocompounds were identified using gas chromatography–mass spectrometry (GC-MS) and further subjected to virtual ligands screening with DataWarrior (v05.02.01) and a molecular docking analysis using AutoDock4.2 (v4.2.6). The active compounds of L. inermis L. were determined by the docking analysis, including phytochemical, physicochemical, pharmacokinetics and docking score. The GC-MS analysis identified 27 phytoconstituents, including ethyl acetate, sclareol, 2-[1,2-dihydroxyethyl]-9-[β-d-ribofuranosyl] hypoxanthine, α-bisabolol and 2-Isopropyl-5-methylcyclohexyl 3-(1-(4-chlorophenyl)-3-oxobutyl)-coumarin-4-yl carbonate. The 27 compounds were then screened for their physicochemical and pharmacokinetic properties. The results revealed that the methanol extracts at 100 mg/mL showed significantly higher (p < 0.05) zones of inhibition (13.7 ± 1.2 mm), while the ethanol extracts at 50 mg/mL were significantly lower (6.3 ± 0.6 mm) compared to all the other treatments. The docking analysis revealed that out of the 27 compounds identified, only twelve (12) compounds have a drug-likeness activity. The 12 compounds were further subjected to docking analysis to determine the binding energies with the CTX-M protein of Klebsiella pneumoniae. Only one compound [CID_440869; (2-[1,2-dihydroxyethyl]-9-[β-d-ribofuranosyl] hypoxanthine)] had the best binding energy of −9.76 kcal/mol; hence, it can be considered a potentially suitable treatment for infections caused by ESBLs-producing strains of Klebsiella pneumoniae. This study has demonstrated that L. inermis L. extracts have antibacterial effects. Further research could explore the potential antimicrobial applications of L. inermis L. extracts to many bacterial strains.

This paper presents experimental investigations on buried Glass Reinforced Plastic (GRP) pipes with a diameter of 1400 mm. The tested pipes were buried in dense, gravelly sand and subjected to traffic loads to study the effects of backfill cover on pipe deflection. The experimental program included tests on three GRP pipes with backfill covers of 100 cm, 75 cm, and 50 cm. The maximum traffic loads applied to the pipe–soil system corresponded to Iraqi Truck Type 3 (AASHTO H type). Vertical deflections of the pipes were monitored during the application of these loads. The experimental results showed that, as the backfill cover increased, the maximum vertical deflection of the pipe decreased. Deflection reductions were 38.0% and 33.3% when the backfill increased from 50 cm to 100 cm and from 50 cm to 75 cm, respectively. A 500 mm compacted backfill cover was found to be sufficient to resist traffic loads, with the vertical deflection percentage remaining below the allowable limit. Additionally, the behavior of the GRP pipes under different traffic load configurations was analyzed using finite element (FE) analysis with Plaxis 3D. The model was validated using field data. The study investigated numerous variables impacting the behavior of embedded pipes, including pipe material, pipe thickness, backfill properties, backfill depth, and the properties of the soil beneath the GRP pipe. The deflections of the steel pipe were lower than those of the GRP pipe when using different thicknesses.

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems.

Theoretical modeling and numerical simulation of an integrated micro-thermoelectric generator system for thermal power generation are carried out. The system measures 4.2 × 4.2 × 5 mm and consists of a micro-thermoelectric module (bismuth telluride) and two finned heat sinks (aluminum). The system can be used to convert thermal energy to electricity in Seebeck effect-based micro-applications. This work aims to improve an advanced model to effectively predict the thermal performance of the system and to develop thermal and flow simulations to accurately evaluate real micro-thermoelectric generator systems. The advanced model solves the thermoelectric module’s energy equations, incorporating heat balance in the heat transfer calculations. The thermal and flow simulations take into account the dynamic calculations under the thermal loads occurring in the system. This innovative aspect can considered separately for the different materials (ceramics, semiconductors and copper strips) of the micro-thermoelectric module for heat transfer enhancement. The results predicted that when the temperature difference of the thermoelectric module was increased from 18 K to 58 K, the power output and the conversion efficiency of the system increased by about 0.5 W and 50%, respectively. Also, the transfer of useful heat to electrical power was achieved at 83%, with 11% saved heat and thermal losses of 6% W at maximum temperature difference of the module. In terms of overall energy consumption, the integrated micro-thermoelectric generator system has a little environmental impacts. Validation of the model with particular experimental works was accomplished for dependability. Comparisons with different modeling strategies demonstrate that the accuracy and performance of the advanced model can be used to reliably study the thermal performance of real micro-thermoelectric generator systems.

(1) Background: The SARS-CoV-2 papain-like protease (PLpro) remains an underexplored antiviral target so far. The reduced efficacy of approved treatments against novel variants highlights the importance of developing new agents. This review aims to provide a comprehensive understanding of phytochemicals as inhibitors of PLpro, identify gaps, and propose novel insights for future reference. (2) Methods: A thorough literature search was conducted using Google Scholar, ScienceDirect, and PubMed. Out of 150 articles reviewed, 57 met inclusion criteria, focusing on SARS-CoV-2 PLpro inhibitors, excluding studies on other coronaviruses or solely herbal extracts. Data were presented class-wise, and phytochemicals were grouped into virtual, weak, modest, and potential inhibitors. (3) Results: Approximately 100 phytochemicals are reported in the literature as PLpro inhibitors. We classified them as virtual inhibitors (70), weak inhibitors (13), modest inhibitors (11), and potential inhibitors (6). Flavonoids, terpenoids, and their glycosides predominated. Notably, six phytochemicals, including schaftoside, tanshinones, hypericin, and methyl 3,4-dihydroxybenzoate, emerged as potent PLpro inhibitors with favorable selectivity indices and disease-mitigation potential; (4) Conclusions: PLpro stands as a promising therapeutic target against SARS-CoV-2. The phytochemicals reported in the literature possess valuable drug potential; however, certain experimental and clinical gaps need to be filled to meet the therapeutic needs.

Purpose: To compare the efficacy and safety of percutaneous mechanical debulking (PMD) using mechanical rotational atherectomy combined with paclitaxel drug-coated balloon (DCB) versus using paclitaxel DCB alone in the treatment of in-stent restenosis. Material and Methods: This is a multicentric retrospective observational study conducted over a period of 2 years from 2020 to 2022. The study included 49 patients presented with chronic limb-threatening ischemia (CLTI) associated with in-stent restenosis, either acute (3 months). The enrolled patients underwent endovascular revascularization using either PMD combined with paclitaxel DCB or paclitaxel DCB only. They were followed up for 6 months after the intervention clinically and by duplex evaluation. Results: The lesion length was about 14.2 mm in the group treated by PMD+ DCB and 9.3 mm in the group treated by DCB alone. The technical success rate was the same between the two groups. However, the follow-up after 6 months showed that patencies for PMD + DCB and DCB alone were 15 (68.2%) patients and 15 (55.6%) patients, respectively (significant p value = 0.028). Procedural-related complications for PMD + DCB are distal embolization (9%) of cases and no vessel perforation. Regarding the candidates who were treated by DCB alone, there were minor groin hematomas (11.1%), distal arterial thrombosis (11.1%) and contrast-induced nephropathy (CIN) (11.1%) cases. Conclusion: The endovascular management of in-stent restenosis using percutaneous mechanical debulking (PMD) in conjunction with paclitaxel drug-coated balloon (DCB) showed that PMD combined with DCB is a safe and effective modality for achieving recanalization. It gives a satisfactory outcome in terms of technical success, freedom from clinically driven target lesion revascularization rate (CD-TLR) and mortality. Despite these promising results, further research with a large enrolled population may be required to determine the cost/benefit.

Computed tomography (CT) multi-detector array has been heavily utilized over the past decade. While transforming an individual’s diagnosis, the risk of developing pathogenesis as a result remains a concern. The main aim of this institutional cumulative effective dose (CED) review is to highlight the number of adult individuals with a record of CED ≥ 100 mSv over a time span of 5 years. Further, we aim to roughly estimate both incidence and mortality life-attributable risks (LARs) for the shortlisted individuals. CT studies performed over one year, in one dedicated trauma and emergency facility, were retrospectively retrieved and analyzed. Individuals with historical radiological CED ≥ 100 mSv were short-listed. LARs were defined and established based on organ, age and gender. Out of the 4406 CT studies reviewed, 22 individuals were found with CED ≥ 100 mSv. CED varied amongst the short-listed individuals, with the highest CED registered being 223.0 mSv, for a 57-year-old male, cumulated over an average study interval of 46.3 days. The highest median mortality risk was for females, 214 per 100,000 registered for the age group 51–60 years. While certain clinical indications and diseases require close follow-up using radiological examinations, the benefit-to-risk ratio should be carefully considered, particularly when CT is requested.

Freshwater is becoming increasingly vulnerable to pollution due to both climate change and an escalation in water consumption. The management of water resource systems relies heavily on accurately predicting fluctuations in lake water levels. In this study, an artificial neural network (ANN), a deep learning (DL) neural network model, and an autoregressive integrated moving average (ARIMA) model were employed for the water level forecasting of the St. Clair and Ontario Lakes from 1981 to 2021. To develop the models, we utilized the average mutual information and incorporated lag periods of up to 6 months to identify the optimal inputs for the water level assessment in the lakes. The results were compared in terms of the root mean square error (RMSE), coefficient of correlation (r), and mean absolute percentage error (MAPE) and graphical criteria. Upon evaluating the results, it was observed that the error values for the deep learning models were insignificant at the designated stations: Lake St. Clair—0.16606 m < RMSE < 1.0467 m and Lake Ontario—0.0211 m < RMSE < 0.7436 m. The developed deep learning model increased the accuracy of the models by 5% and 3.5% for Lake St. Clair and Lake Ontario, respectively. Moreover, the violin plot of the deep learning model for each lake was most similar to the violin plot of the observed data. Hence, the deep learning model outperformed the ANN and ARIMA model in each lake.

Virtual experiments (VEs) and digital twins (DTs), pivotal for realizing European strategic policies on sustainability and digitalization within Industry 4.0 and the European Green Deal, simulate physical systems and characteristics in a virtual environment, with DTs incorporating dynamic inputs from and outputs to the real-world counterpart. To ensure confidence in their use and outcomes, traceability and methods to evaluate measurement uncertainty are needed, topics that are hardly covered by the literature so far. This paper provides a harmonized definition of VEs and DTs and introduces a framework for evaluating measurement uncertainty. Furthermore, it discusses how to propagate the uncertainty of the contributions coming from the different parts of the DT. For the core part of the DT, the framework derived for VEs can be used. For the physical-to-virtual (P2V) connection and the virtual-to-physical (V2P) connection, additional sources of uncertainty need to be considered. This paper provides a metrological framework for taking all these uncertainty contributions into account while describing a framework to establish traceability for DTs. Two case studies are presented to demonstrate the proposed methodology considering industrially relevant measuring instruments and devices, namely, a coordinate measuring machine (CMM) and a collaborative robot arm (cobot).

Comprehending the anaerobic nitrogen transformations, including denitrification, anaerobic ammonium oxidation (anammox), and anaerobic ammonium oxidation linked with iron reduction (Feammox) in soil, is essential for improving soil fertility and minimizing the environmental impacts of nitrogen loss. Despite this, research on anaerobic nitrogen transformations, particularly Feammox in paddy soil, is sparse. This study examined soil denitrification, anammox, and Feammox, along with their respective contributions to nitrogen loss in paddy soil at various depths, under different fertilization and irrigation treatments. It utilized 15N isotope labeling to investigate the limiting factors of these anaerobic nitrogen transformations and their interactions. The findings showed that denitrification rates ranged from 0.41 to 2.12 mg N kg−1 d−1, while anammox rates ranged from 0.062 to 0.394 mg N kg−1 d−1, contributing 84.3% to 88.1% and 11.8% to 15.7% of total soil nitrogen loss, respectively. Denitrification was identified as the predominant pathway for nitrogen loss across different soil depths. Fertilization and irrigation had more pronounced impacts on anaerobic nitrogen transformations than did soil depth, potentially affecting these processes through both abiotic and biotic pathways. This study identified significant correlations among the three types of anaerobic nitrogen transformations. These findings offer a theoretical foundation for optimizing nitrogen management strategies to mitigate losses in agricultural systems.

Background: As the most widespread eating disorder in the world now, binge eating disorder (BED) is a mental condition characterized by recurrent periods of excessive food consumption and an inability to regulate one’s portion sizes. The utilization of a bupropion–naltrexone (NB) combination has been suggested as a potential therapeutic approach for BED. Assessing the effectiveness of NB in the treatment of BED and its accompanying obesity is the purpose of this research. Methods: A comprehensive search was conducted in order to obtain any pertinent articles. PubMed, Scopus, Web of Science, and Cochrane Clinical Trials were consulted against in the databases that were searched. In our final meta-analysis, we incorporated interventional or observational studies that documented the effects of NB therapy for binge eating in adults. We also examined the difference in the mean change between the NB and placebo groups, as well as the disparity in outcomes before and after treatment. Results: This study shows that the use of an NB combination is associated with a statistically significant reduction in the weight, BMI, and Binge Eating Scale (BES) of the patients compared to their weight before treatment with MD: −8.52 (95% CI: −10.01–−6.94, p < 0.00001), MD: −4.95 (95%CI: −9.72–−0.17, p = 0.04), and MD: −7.66 (95%CI: −14.36–0.96, p = 0.02), respectively. The absolute mean change was statistically significantly higher in the drug combination group compared to the placebo group. Conclusions: NB showed efficacy in the improvement of the weight and psychiatric symptoms associated with BED and this provides a promising treatment option.

In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R2 value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R2 of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management.

This research investigates the extrusion-based fabrication and characterization of nanocomposites derived from bio-sourced polypropylene (PP) and poly(butylene succinate) (PBS: a biodegradable polymer derived from renewable biomass sources such as corn or sugarcane), incorporating Cloisite 20 (C20) clay nanofillers, with a specific focus on their suitability for electrical insulation applications. The research includes biodegradation tests employing the fungus Phanerochaete chrysosporium to evaluate the impact of composition and extrusion conditions. These tests yield satisfactory results, revealing a progressive disappearance of the PBS phase, as corroborated by scanning electron microscopy (SEM) observations and a reduction in the intensity of Fourier transform infrared spectroscopy (FTIR) peaks associated with C-OH and C-O-C bonds in PBS. Despite positive effects on various properties (i.e., barrier, thermal, electrical, and mechanical properties, etc.), a high clay content (5 wt%) does not seem to enhance biodegradability significantly, highlighting the specific sensitivity of the PBS phase to the addition of clay during this process. This study provides valuable insights into the complex interplay of factors conditioning nanocomposite biodegradation processes and highlights the need for an integrated approach to understanding these processes. This is the first time that research has focused on studying the degradation of nanocomposites for electrical insulation, utilizing partially bio-sourced materials that contain PBS.

COVID-19 has caused a deep economic impact on the lives of small and marginal farmers due to travel restrictions, market closures, and social distancing requirements. Due to COVID-induced labor scarcity and water shortage in India, direct-seeded rice (DSR) has emerged as a viable alternative to puddled transplanted rice (PTR). However, there was plenty of labor available in Pakistan and Bangladesh for rice cultivation during COVID-19 times. Therefore, both countries did not observe the shift from PTR to DSR. The cost of inputs, such as seed, fertilizer, pesticide, and fuel, was high due to a supply–demand conflict during the COVID-19 pandemic in three countries. Farmers faced weed problems and physical and/or economical non-availability of suitable machinery for DSR cultivation during the COVID-19 pandemic. In the later years of 2022 and 2023 (post-COVID), the area under DSR decreased by 88% in India, while it remained stagnant in Pakistan and Bangladesh.

The widespread adoption of automatic milking systems (AMS) in the United States has afforded dairy cows the flexibility to establish personalized milking, feeding, and resting schedules. Our study focused on investigating the short-term effects of transitioning milking permissions from every 4 (MP4) to 6 (MP6) hours on the 100th day of lactation on milking frequency, milk yields, and cow behavior. Twenty-four Holstein dairy cows were divided into control (maintaining a 4 h milking interval) and test groups (transitioning to a 6 h milking interval) and observed for 6 days. The analysis revealed that parity and treatment had no significant impact on milking frequency, milk/visit, or daily milk yield. However, multiparous cows spent more time inside the commitment pen, while test group cows exhibited more tail-swishing and displacement behavior, approached the AMS more frequently, and spent longer idle times. The interaction between parity and treatment influenced heart rate variability parameters, indicating increased stress in the test group cows. Additionally, the test group cows had greater total and daytime lying frequencies, suggesting short-term behavioral modifications. Despite no immediate impact on milk production, further research is recommended to assess the potential long-term effects on milk yield in AMS farms, considering the identified stress indicators short-term.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) ranges from hepatic steatosis to nonalcoholic steatohepatitis and may lead to liver cirrhosis. This study aimed to assess the feasibility of numerical grading MASLD using noncontrast computed tomography (NCCT). Methods: In a retrospective study of 166 patients diagnosed with MASLD between June 2020 and January 2024, MASLD was graded by ultrasonography, and liver density was measured on NCCT. The MASLD grades and NCCT densities were compared. Results: The MASLD grades were distributed as follows: grade 0 (n = 79, 47.6%), grade 2 (n = 48, 28.9%), grade 1 (n = 25, 15.1%), and grade 3 (n = 14, 8.4%). The mean liver density was 57.75 Hounsfield units (HU) ± 6.18 (range: 48.9–78.2), 51.1 HU ± 4.7 (range: 41.4–59.7), 39.3 ± 6.4 (range: 21.4–48.9), and 22.87 ± 7.5 (range: 12–36.4) in the grade 0, grade 1, grade 2, and grade 3 patients, respectively. An analysis of variance test showed significant variance in the distribution of mean liver density in the different MASLD grades (p < 0.001). Conclusions: After ultrasonography diagnosis of MASLD, NCCT offers an objective, numerical, and calculable method for MASLD grading that is available for radiologists, radiologic technologists, and interested physicians away from experience dependence. NCCT determined that grade 2 had a specific density from 36.4 to 41.4 HU that significantly overlapped with grade 1 (41.4–48.9) HU and with grade 3 (21.4–36.4 HU). Grade 1 showed a significant overlap with the normal liver (48.9–59.7 HU).

This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.

Rice (Oryza sativa L.), an important food crop, necessitates more water to complete its life cycle than other crops. Therefore, there is a serious risk to rice output due to water-related stress. Drought stress results in morphological changes, including the inhibition of seed germination, reduced seeding growth, leaf area index, flag leaf area, increased leaf rolling, as well as the decrement of yield traits, such as plant height, plant biomass, number of tillers, and 1000-grain yield. Stress also causes the formation of reactive oxygen species (ROS) such as O2−, H2O2, and OH−, which promote oxidative stress in plants and cause oxidative damage. The process of oxidative degradation owing to water stress produces cell damage and a reduction in nutrient intake, photosynthetic rate, leaf area, RWC, WUE, and stomatal closure, which may be responsible for the decrement of the transpiration rate and plant dry matter under decreasing soil moisture. Plants have the ability to produce antioxidant species that can either be enzymatic (SOD, POD, CAT, GPX, APX) or non-enzymatic (AsA, GSH) in nature to overcome oxidative stress. During drought, several biochemical osmoprotectants, like proline, polyamines, and sugars, can be accumulated, which can enhance drought tolerance in rice. To meet the demands of an ever-growing population with diminishing water resources, it is necessary to have crop varieties that are highly adapted to dry environments, and it may also involve adopting some mitigation strategies. This study aims to assess the varying morphological, physiological, and biochemical responses of the rice plant to drought, and the various methods for alleviating drought stress.