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Abstract Background Detecting carcinoma prostate (CaP) after intravesical Bacillus Calmette Guerin (BCG) immunotherapy for non‐muscle invasive bladder cancer (NMIBC) poses diagnostic challenges. Granulomatous prostatitis (GP) has an incidence of 0.8%–3.3% in post‐intravesical BCG patients and 6% incidence in a PIRADS 5 lesion on multiparametric MRI (mpMRI). Patients with GP after intravesical BCG may have clinical, biochemical, and radiological features similar to CaP. In our study, we evaluate the reliability of mpMRI in diagnosing CaP after intravesical BCG therapy. Materials and Methods We reviewed the NMIBC patients treated with intravesical BCG therapy between 2017 and 2023 and investigated those who underwent mpMRI and MR fusion biopsy in suspicion of CaP. A total of 120 patients had intravesical BCG immunotherapy, and 10 patients met our selection criteria. We performed a descriptive analysis of these patients and assessed the sensitivity and specificity of mpMRI in diagnosing CaP. Results The sensitivity of mpMRI in detecting CaP was 100%, and the specificity was 28.6%. Similarly, the negative predictive value for detecting CaP was 100%, and the positive predictive value was 37.5%. Among patients evaluated with mpMRI, a PIRADS 4 or 5 lesion was seen in 8 (80%) patients, and there was no lesion in 2 (20%) patients. The mpMRI detected 1 lesion in 6 patients (60%) and 2 (20%) in 2 patients. The lesions had a PIRADS score of 4 and 5 in 6 (60%) and 2 (20%) patients, respectively. Among these lesions, 8 (80%) were in the peripheral zone and 2 (20%) in the transition zone. In the MR fusion biopsy of these 10 patients, 7 (70%) had granulomatous prostatitis, and 3 (30%) had CaP. Conclusion In our study on evaluating the reliability of mpMRI in diagnosing CaP among post‐intravesical BCG patients, we noted that although PIRADS in mpMRI had high sensitivity in identifying prostate lesions, its specificity for detecting CaP is limited.

Abstract Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.

Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

Abstract The quality of images we perceive visually is heavily impacted by the settings used for camera exposure. When these settings are imbalanced, it can result in an undesired prominent phenomenon known as blur effects. To address this problem, an ExposureNet project has been undertaken, which aims to develop an autonomous camera exposure settings control system for blur effects prevention. The proposed ExposureNet model is a CNN/Transformer hybrid neural structure, created and trained in a comprehensive manner to effectively predict the ideal exposure settings based on the semantic features of the scene being captured. This system is designed to learn the necessary steps for processing, such as identifying relevant scene features, using only two camera exposure parameters (shutter speed (SHS) and ISO) as training signals. As a result, this system can associate the semantic features of a scene with the appropriate exposure parameter adjustments, customized to the scene's dynamics and lighting conditions. By simultaneously optimizing all processing steps and bypassing traditional post‐processing stages, the proposed system is designed to achieve faster performance, reduced computational cost, and lower power consumption. Experimental results demonstrate that the proposed system significantly outperforms existing methods and achieves cutting‐edge performance.

Abstract Background Pulsed‐field ablation (PFA) has emerged as an innovative alternative to radiofrequency (RF) and cryoablation because it selectively targets myocardial tissue. Thus, we aim to estimate the efficacy and safety of PFA versus thermal ablation for atrial fibrillation (AF) ablation. Methods A systematic review and meta‐analysis were retrieved from PubMed, WOS, SCOPUS, EMBASE, and CENTRAL through September 2023. We used RevMan V. 5.4 to pool dichotomous data using risk ratio (RR) and continuous data using mean difference (MD) with a 95% confidence interval (CI). PROSPERO ID: CRD42023480321 Results We included 17 studies with a total of 2255 patients. PFA was significantly associated with a decreased incidence of AF recurrence (RR: 0.66 with 95% CI [0.51, 0.87], p = .003). However, there was no significant difference between PFA and thermal ablation in arrhythmia recurrence (RR: 0.92 with 95% CI [0.74, 1.46], p = .42). PFA was significantly associated with decreased total procedure time (MD: −15.15 with 95% CI [−20.23, −10.07], p

Abstract Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.

Objective For degenerative diseases accompanied by cervical malalignment, the starting and ending points of fixation for better cervical sagittal alignment and clinical results are not as clear as the thoracolumbar region. In this study we aimed to compare the effects of posterior subaxial cervical fixation (PSCF), posterior cervical fixation extending to the upper thoracic region and posterior upper cervical fixation extending to the upper thoracic region on cervical sagittal alignment. Methods Sixty‐three patients who underwent posterior cervical and cervical‐up thoracic fixation were retrospectively analyzed in a comparative study. The procedures that we performed from May 2019 to March 2022 on these 63 patients were: (1) C3‐C6 group—posterior subaxial cervical fixation; (2) Subaxial‐T2 group—posterior subaxial cervicothoracic fixation (PSCTF); (3) C2‐T2 upper thoracic posterior fixation group. The C3‐C6 group had 27 patients, Subaxial‐T2 group had 24, and C2‐T2 group had 12. We determined the minimum follow‐up period as 12 months. C0‐2, C2‐7 lordosis angle, sagittal vertical axis (SVA), C2 slope, C7 slope, T1 slope, cervical slope, neck slope, and thoracic inlet angle (TIA) measurements were made in three patient groups. Comparatively, cervical sagittal alignment was evaluated. Result In the C2‐T2 group, a significant increase in C2‐C7 lordosis, decrease in C2 slope, and increase in TS‐CL were observed. Significant C2‐C7 lordosis decrease, C2 slope increase, and TS‐CL decrease were observed in the C3‐C6 group. A significant increase in C2‐C7 lordosis and a decrease in C2 slope were observed in the subaxial‐T2 group. No significant change was observed in the TS‐CL angle. Conclusion In cervical degenerative disorders accompanied by cervical malalignment, we recommend the C2‐T2 fixation method, which provides the desired C2‐C7 lordosis, SVA within the normal range, and the best Neck Disability Index results.

Abstract The utilization of waste plant residues in useful work particularly as the source of green dyes in textiles is now a days welcoming by global community. In this study, brown bollygum chips (Litsea glutinosa) called tallow laurel has been explored for silk dyeing using ecofriendly approaches. Extraction has been done in suitable medium and utilized onto fabric before and after microwave (MW) treatment up to 10 min. Response surface methodology was employed to observe the significant of selected dyeing parameters at which shade development with improved colorfastness was observed. It was found that 2 min radiation to both acidic extract of tallow laureal and silk fabric is the useful condition to get colorfast shade when employed before and after mordanting with iron salt and tannic acid solution as eco‐chemical agent, whereas with walnut bark and pine nut hulls extract as eco bio agents. The highest color strength value of 4.58 was observed using tannic acid as postchemical mordant for silk dyeing. The standard methods for fastness as per ISO to light, washing, and rubbing reveal that using selected shades made under ecofriendly cost time and energy effective conditions, stable colorfast hues were rated good with the value of 4/5. Therefore, it is recommended that MW treatment in addition with statistical approach i.e., central composite design used for exploring new dye yielding plant, then using eco‐mordanting technique will help in getting promising colorfast shades.

Abstract Developing economical and high‐performing sensitizers is crucial in advancing dye‐sensitized solar cells (DSSCs) and optoelectronics. This research paper explores the potential of novel red light‐absorbing organic dyes based on Indolo[3,2‐b]carbazole (ICZ) as the donor applied in co‐sensitizer‐free DSSCs for breakthroughs in photovoltaic (PV) applications. DFT and TD‐DFT based computational methods were employed to calculate the conduction band levels, electron injection capabilities, and power conversion efficiency (PCE) of metal‐free organic dyes (ICZ1–ICZ9) having D‐A‐π‐A architecture. Comprehensive analyses included NBO, DOS, FMO, ICT, MEP, binding energy, and TDM analysis. Quantum chemical calculations of the structural, photochemical, and electrochemical properties, as well as the key parameters, reveals that all the designed dyes could be an excellent candidate for high‐efficiency DSSCs due the small energy gap (2.130–1.947 eV), longer wavelength absorption (759.47–520.63 nm), longer lifetimes (15.65–6.67 ns), a lower ΔGreg (0.29–0.14 eV), a significant dipole moment changes (31.489–16.195D), LHE (0.95‐0.46), the large qCT (0.962–0.689), small DCT (7.657, 4.897 Å), and VOC (1.13–0.86 eV). This quantum simulation showed that, when compared to reference D8, the photovoltaic dyes ICZ8, ICZ2, and ICZ7 are recognized as being eye‐catching. Furthermore, dye@(TiO2)9 cluster model results demonstrate promising prospects for enhancing the photovoltaic (PV) performance of ICZ1–ICZ9 dyes by electron injection and conduction band (CB) engineering. This study will help the experimentalists for developing ICZ‐based PVs as more efficient and sustainable energy solutions.

Abstract The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically‐based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step‐by‐step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands‐on and real‐world examples.

Abstract Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non‐structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM‐RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.

Abstract During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Abstract The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.

Abstract Deep learning has recently become a viable approach for classifying Alzheimer's disease (AD) in medical imaging. However, existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification. To address these issues, a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is proposed. An upgraded primary C3D network is utilised to create rougher low‐level feature maps. It introduces a new convolution block that focuses on the structural aspects of the magnetic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output. Then, several fully connected layers are used to achieve multi‐task learning, generating three outputs, including the primary classification task. The other two outputs employ backpropagation during training to improve the primary classification job. Experimental findings show that the authors’ proposed method outperforms current approaches for classifying AD, achieving enhanced classification accuracy and other indicators on the Alzheimer's disease Neuroimaging Initiative dataset. The authors demonstrate promise for future disease classification studies.

Abstract Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost.

Abstract The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs).

Abstract Solar cells are increasingly being utilised for both energy harvesting and reception in free‐space optical (FSO) communication networks. The authors focus on the implementation of a mid‐band p‐In0.01Ga0.99 N/p‐In0.5Ga0.5 N/n‐In0.5Ga0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS‐1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current‐voltage (IV) and power‐voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.

Abstract Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi‐Fi, Zigbee, and Z‐wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi‐Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model.

Abstract Objective Comorbid anxiety occurs often in MS and is associated with disability progression. Polygenic scores offer a possible means of anxiety risk prediction but often have not been validated outside the original discovery population. We aimed to investigate the association between the Generalized Anxiety Disorder 2‐item scale polygenic score with anxiety in MS. Methods Using a case–control design, participants from Canadian, UK Biobank, and United States cohorts were grouped into cases (MS/comorbid anxiety) or controls (MS/no anxiety, anxiety/no immune disease or healthy). We used multiple anxiety measures: current symptoms, lifetime interview‐diagnosed, and lifetime self‐report physician‐diagnosed. The polygenic score was computed for current anxiety symptoms using summary statistics from a previous genome‐wide association study and was tested using regression. Results A total of 71,343 individuals of European genetic ancestry were used: Canada (n = 334; 212 MS), UK Biobank (n = 70,431; 1,390 MS), and the USA (n = 578 MS). Meta‐analyses identified that in MS, each 1‐SD increase in the polygenic score was associated with ~50% increased odds of comorbid moderate anxious symptoms compared to those with less than moderate anxious symptoms (OR: 1.47, 95% CI: 1.09–1.99). We found a similar direction of effects in the other measures. MS had a similar anxiety genetic burden compared to people with anxiety as the index disease. Interpretation Higher genetic burden for anxiety was associated with significantly increased odds of moderate anxious symptoms in MS of European genetic ancestry which did not differ from those with anxiety and no comorbid immune disease. This study suggests a genetic basis for anxiety in MS.

Abstract Multiple myeloma remains an incurable cancer mostly affecting older adults and is characterized by a series of remission inductions and relapses. This study aims to evaluate the outcomes in newly diagnosed transplant‐ineligible patients using bortezomib/lenalidomide‐based regimens in the Canadian real world as well as their outcomes in the second line. The Canadian Myeloma Research Group Database (CMRG‐DB) is a national database with input from multiple Canadian Centres with now up to 8000 patients entered. A total of 1980 transplant ineligible patients were identified in the CMRG‐DB between the years of 2007–2021. The four most commonly used induction regimens are bortezomib/melphalan/prednisone (VMP) (23%), cyclophosphamide/bortezomib/dexamethasone (CyBorD) (47%), lenalidomide/dexamethasone (Rd) (24%), and bortezomib/lenalidomide/dexamethasone (VRd) (6%). After a median follow‐up of 30.46 months (0.89–168.42), the median progression‐free survival (mPFS) and median overall survival (mOS) of each cohort are 23.5, 22.9, 34.0 months, and not reached (NR) and 64.1, 51.1, 61.5 months, and NR respectively. At the time of data cut‐off, 1128 patients had gone on to second‐line therapy. The mPFS2 based on first‐line therapy, VMP, CyBorD, Rd, and VRd is 53.3, 48.4, 62.7 months, and NR respectively. The most common second‐line regimens are Rd (47.4%), DRd (12.9%), CyBorD (10.3%), and RVd (8.9%) with a mPFS and a mOS of 17.0, 31.1, 15.4, and 14.0 months and 34.7, NR, 47.6, 33.4 months, respectively. This study represents the real‐world outcomes in newly diagnosed transplant‐ineligible myeloma patients in Canada. The spectra of therapy presented here reflect the regimens still widely used around the world. While this is sure to change with anti‐CD38 monoclonal antibodies now reflecting a new standard of care in frontline therapy, this cohort is reflective of the type of multiple myeloma patient currently experiencing relapse in the real‐world setting.

Abstract This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

Abstract Hylocereus undatus, occasionally referred to as the dragon fruit or white‐fleshed pitaya, is a rich in nutrients fruit appreciated because of its colorful look and health benefits. It is inexpensive in calories but packed with minerals like iron, magnesium, and calcium in addition to essential nutrients like vitamin C and B vitamins. Similarly rich in dietary fiber, particularly promotes gut wellness and improves digestion, is pitaya. It additionally contains antioxidants, such as phenolic compounds, flavonoids, and betalains, which help with scavenging free radicals and reducing the detrimental effects of oxidative stress on the human body. The substantial amount of betalains in Hylocereus undatus phytochemical composition is notable because it contributes to the plant's anti‐inflammatory and antioxidant properties. There has been an association among these phytochemicals and a decreased likelihood of contracting long‐term conditions such as diabetes, cancer, and cardiovascular disease. Omega‐3 fatty acids and omega‐6 fatty acids, in particular, that reduce cholesterol levels while improving blood circulation, are plentiful in the fruit's seeds and are beneficial to heart wellness. The objective of this review is to highlight the nutritional profile, phytochemical and health benefits of Hylocereus undatus in detail.

ABSTRACT Aim This study aimed to assess the feasibility and effectiveness of the pectoral nerves (PECS) II block in facilitating cardiac implantable electronic device (CIED) insertion in a sample of 120 patients, with a focus on the percentage of cases completed without additional intraoperative local anesthesia. Methods PECS II blocks were performed on the left side using ultrasound guidance in all 120 patients. Feasibility was assessed by the proportion of cases completed without the need for extra intraoperative local anesthetic. Secondary outcomes included the amount of additional local anesthetic used, intraoperative opioid requirements, postoperative pain scores, time to first postoperative analgesia, analgesic consumption, patient satisfaction, and block‐related complications. Results Of the 120 patients, 78 (65%) required additional intraoperative local anesthetic, with a median volume of 8.2 mL (range 3–13 mL). Fifteen patients (12.5%) needed intraoperative opioid supplementation. Nine patients (7.5%) required postoperative tramadol for pain relief. In total, 98 patients (81.7%) reported high satisfaction levels with the procedure. Conclusions The PECS II block, when combined with supplementary local anesthetic, provided effective postoperative analgesia for at least 24 h in 120 patients undergoing CIED insertion. While it did not completely replace surgical anesthesia in most cases, the PECS II block significantly contributed to a smoother intraoperative experience for patients.

Background Flow diversion (FD) has emerged as an effective treatment option for intracranial aneurysms. However, there is limited evidence regarding its safety and efficacy specifically for distal and small‐artery aneurysms of the posterior circulation. This study aimed to investigate the outcomes of FD for aneurysms arising from the posterior inferior cerebellar artery, anterior inferior cerebellar artery, superior cerebellar artery, and the P2 and P3 segments of the posterior cerebral artery. Methods This is a subanalysis of the Post‐FD (Posterior Circulation Aneurysms Treated With Flow Diversion) registry, highlighting distal aneurysms in the posterior inferior cerebellar artery, anterior inferior cerebellar artery, superior cerebellar artery, and the P2 and P3 segments of the posterior cerebral artery treated with FD. Aneurysm characteristics and patient outcomes were described for the total series, and a more focused analysis comparing fusiform/dissecting versus saccular aneurysms was performed. The primary treatment outcome was complete aneurysm occlusion (Raymond–Roy class 1). Primary safety outcome was major ischemic/hemorrhagic stroke following FD. Secondary outcomes included functional outcome, aneurysm retreatment, and in‐stent stenosis. Results Overall, 36 patients with 36 aneurysms were treated with FD, with a median age of 60.0 years (interquartile range [IQR], 52.8—65.3 years). Of those, 13 were fusiform/dissecting, while 23 were saccular aneurysms. Complete occlusion was achieved in 78.1% for all aneurysms at a median follow‐up of 14.0 months (IQR, 9.3–48.6 months). There was a nonsignificant trend in rates of complete occlusion between fusiform/dissecting (91.7%) and saccular aneurysms (70%; P = 0.151). Major stroke was reported in 2 cases (5.6%) and in‐stent stenosis in 4 (11.1%), and retreatment was required for 4 aneurysms (11.4%) There was no difference in rates of major stroke, in‐stent stenosis, or retreatment between fusiform/dissecting and saccular aneurysms. Conclusion This study suggests the safety and feasibility of FD for distal aneurysms of the posterior circulation, particularly fusiform/dissecting aneurysms. Further larger‐scale studies are warranted to confirm these findings.

ABSTRACT Background Young patients ≤ 50 years old with multiple myeloma (MM) account for about 10% of cases and are underrepresented in the literature. Methods We explored disease characteristics, treatments, and outcomes following modern therapies of young MM patients using the Canadian Myeloma Research Group (CMRG) database. We included 493 patients ≤ 50 years old diagnosed with MM or plasma cell leukemia without concurrent amyloidosis or POEMS syndrome from January 1, 2010, to July 1, 2022. Results The median age was 46 years old (range: 25.6–50). Most patients fell into the R‐ISS II category (72.7%), and 24.1% had high‐risk cytogenetics. The majority of patients (89.9%) received a proteasome inhibitor‐based first‐line treatment, 92.1% received a stem cell transplant, and 65.6% had maintenance therapy post–autologous stem cell transplant (ASCT). Median follow‐up from initial treatment to patients' last follow‐up was 48.5 (range: 0–155) months. Median progression‐free survival (PFS) was 45.0 months (95% CI: 40.2–50.0). Maintenance therapy post‐ASCT improved median PFS to 52.3 months (95% CI: 43.1–68.2), compared to 23.6 months (95% CI: 20.0–34.8) without maintenance [p

Abstract Background The child cancer, neuroblastoma (NB), is characterised by a low incidence of mutations and strong oncogenic embryonal driver signals. Many new targeted epigenetic modifier drugs have failed in human trials as monotherapy. Methods We performed a high‐throughput, combination chromatin‐modifier drug screen against NB cells. We screened 13 drug candidates in 78 unique combinations. Results We found that the combination of two histone methyltransferase (HMT) inhibitors: GSK343, targeting EZH2, and SGC0946, targeting DOT1L, demonstrated the strongest synergy across 8 NB cell lines, with low normal fibroblast toxicity. High mRNA expression of both EZH2 and DOT1L in NB tumour samples correlated with the poorest patient survival. Combination HMT inhibitor treatment caused activation of ATF4‐mediated endoplasmic reticulum (ER) stress responses. In addition, glutathione and several amino acids were depleted by HMT inhibitor combination on mass spectrometry analysis. The combination of SGC0946 and GSK343 reduced tumour growth in comparison to single agents. Conclusion Our results support further investigation of HMT inhibitor combinations as a therapeutic approach in NB.

ABSTRACT Background The inclusion of synthetic amino acids in poultry nutrition plays a crucial role in both enhancing the synthesis of immunoglobulins and elevating the overall comprehensiveness of the amino acid profile. Objectives This research examined the effects of consuming threonine (Thr) in various forms levels with low or high oil on broiler chickens' growth and immunity. Methods We investigate the growth performance, feed efficiency, immune response, intestinal morphology, absorptive capacity, and expression of some genes related to the feed intake (Pro‐opiomelanocortin [POMC]), fatty acid synthesis (Acetyl‐CoA Carboxylase [ACC]), immunity (lipopolysaccharide‐induced tumour necrosis like alpha factor [LITAF]), and heat shock protein 70 (HSP70). Eight groups of chicks were used, including four dietary Thr levels (100%, 115%, 130%, or 145%) with two oil levels (mixture of sunflower 50% and soybean oils 50%): (control) and high. Results The higher dietary Thr level (145%) with high oil inclusion significantly increased ACC and POMC gene expression, resulting in the lowest feed intake, body weight gain (BWG), and liver fat content. Combining high oil with 115% Thr was the optimum for the broilers. The birds have significant (p ≤ .05) growth performance, immune parameters, and intestinal health, as well as the lowest expression of ACC, POMC, HSP70, and LITAF, which was reflected in better feed conversion ratio and lower incidence of fatty liver, thermo‐resistance, and immune status of the birds. Conclusions The combination of high oil and 115% Thr levels optimises broiler health and productivity, enhancing growth, immune function, and gut health. This diet lowers the expression of genes associated with fatty liver and stress, leading to better feed efficiency, thermo‐resistance, and overall well‐being. Adopting these dietary adjustments can improve broiler performance and economic viability in poultry farming by enhancing essential productivity metrics.

ABSTRACT Objective This study aimed to investigate the plasma and milk pharmacokinetics, as well as the withdrawal time (WT) from milk of tolfenamic acid (2 and 4 mg/kg) following intravenous (IV) administration to eight healthy lactating Akkaraman sheep. Methods The trial was conducted in two periods in accordance with a crossover pharmacokinetic design. The concentrations of tolfenamic acid in the plasma and milk were determined using high‐pressure liquid chromatography and evaluated using non‐compartmental analysis. The WT of tolfenamic acid in milk was calculated using the WT 1.4 software. Results Compared to the 2 mg/kg dose, plasma volume of distribution at steady state (from 0.43 to 0.50 L/kg), terminal elimination half‐life (from 2.41 to 4.14 h) and dose‐normalized area under the plasma concentration–time curve (AUC0−∞, from 9.46 to 30.11 h µg/mL) increased, whereas total body clearance (from 0.21 to 0.13 L/h/kg) decreased at the 4 mg/kg dose. The peak milk concentration (Cmax) and AUC0−∞ values in milk were 0.26 µg/mL and 0.28 h µg/mL, respectively, for 2 mg/kg, and 0.43 µg/mL and 0.55 h µg/mL, respectively, for 4 mg/kg. Although the dose‐normalized Cmax of milk decreased depending on the dose, no difference was observed in dose‐normalized AUC0−∞. The AUC0−∞ milk/AUC0−∞ plasma ratio was 0.03 for 2 mg/kg and 0.02 for 4 mg/kg. The WT values calculated for milk at dosages of 2 and 4 mg/kg were 3 and 4 h, respectively. Conclusions A decrease in plasma elimination and an increase in plasma concentration of tolfenamic acid were observed depending on the dose. Tolfenamic acid lowly passed into sheep's milk at 2 and 4 mg/kg doses. This study may provide valuable information for clinicians’ decision‐making processes.

Abstract Carbon nanomaterials (CNMs), comprising carbon dots, graphene‐related materials, and carbon nanotubes, have significant potential for enhancing agricultural productivity. Their compositional compatibility and exceptional properties intrigue a great deal of explorations in agricultural applications, such as fertilizers, pesticides, and regulators of plant growth. However, the evaluation of their agricultural applicability often lacks quantitative sustainability metrics, with insufficient scrutiny on the carbon footprint and scalability of the manufacturing. This review attempts to provide a quantitative ranking system for evaluating the manufacturing processes of the CNMs by applying the twelve principles of Green Chemistry, particularly in the context of agriculture applications. The review also offers a systematically organized account of CNMs' effects on plant systems, encompassing nutrient enhancement, photosynthesis, soil amelioration, disease resistance, and phytotoxicity, which can provide design rationales for the further development of CNMs.

Abstract Here, a deep learning‐based zero trust network access (DL‐ZTNA) system to enhance the security of the Message Queuing Telemetry Transport (MQTT) protocol within Internet of Things (IoT) applications was proposed. Combining multi‐head convolutional neural networks and attention‐based bi‐directional long short‐term memory networks with ZTNA provides real‐time security analysis of device behaviour. This behaviour‐based approach ensures that only authorized devices can access network resources and continuously monitors for potential threats like distributed denial of service (DDoS) attacks. The proposed DL‐ZTNA system revokes device access when a threat is detected and prevents further malicious activities. Evaluation in a testbed environment showed improvements in CPU usage efficiency, throughput, and attack detection probability compared to traditional methods. This highlights the system's effectiveness in securing MQTT‐based IoT networks against DDoS attacks while maintaining high performance, showcasing the potential of integrating deep learning techniques into ZTNA system for addressing security challenges in IoT environments.

Abstract Temporomandibular joint osteoarthritis (TMJOA) is a commonly encountered degenerative joint disease in oral and maxillofacial surgery. Recent studies have shown that the excessive unbalanced activation of Wnt/β‐catenin signaling is connected with the pathogenesis of TMJOA and due to the inability to inhibit the over‐activated Wnt pathway, while Wnt16‐deficient mice has a more severe Knee OA. However, the efficacy of direct intra‐TMJ injection of Wnt16 for the relief of TMJOA is still not directly confirmed. Moreover, small‐molecule drugs such as Wnt16 usually exhibit short‐lived efficacy and poor treatment adherence. Therefore, in order to obtain a stable release of Wnt16 both in the short and long term, this study fabricates a double‐layer slow‐release Wnt16 carrier based on mesoporous silica nanospheres (MSNs) encased within hyaluronic acid (HA) hydrogels. The biofunctional hydrogel HA/Wnt16@MSN is analyzed both in vitro and in vivo to evaluate the treatment of TMJOA. As a result, it shows superior pro‐cartilage matrix restoration and inhibition of osteoclastogenesis ability, and effectively inhibits the over‐activation of the Wnt/β‐catenin pathway. Taken together, biofunctional hydrogel HA/Wnt16@MSN is a promising candidate for the treatment of TMJOA.

Abstract Extracting lithium resources from seawater and brine can promote the development of the new energy materials industry. The electrochemical method is green and efficient. Iron phosphate (FePO4) crystal, with its 1D ion channel, holds significant potential as a primary lithium extraction electrode material. Li+ encounters a substantial concentration disadvantage in brines, and the co‐intercalation of Na+ diminishes Li+ selectivity. To address this issue, this work enhances the energy barrier for Na+ insertion through prelithiation strategies applied to the 1D channels of FePO4 crystal, thereby improving Li+ selectivity, and further investigating the prelithiation effect with particle size and morphology control. The results indicate that the Li(4C‐40%)FePO4// Activated carbon(AC) system enhances selectivity of lithium. The Li(4C‐40%)FePO4 with size diameter of 2500 nm demonstrates an energy consumption of 0.79 Wh mol−1 and a purity of 97.94% for lithium extraction at a unit lithium extraction of 5.93 mmol g−1 in simulated brine. Li(4C‐40%)FePO4‐nanoplates demonstrate the most optimal lithium extraction performance among the three morphologies due to their lamellar structure's short ion diffusion path in the [010] channel, favoring Li+ diffusion. The diffusion energy barriers of Li+ and Na+ are calculated using Density Functional Theory (DFT) before and after prelithiation, showing good agreement with experimental results.

Background Acute ischemic stroke arising from medium vessel occlusions (MeVO) poses substantial challenges in treatment and management. This study aims to elucidate the outcomes and factors contributing to achieving excellent recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] 2c–3) versus successful recanalization (mTICI 2b) in patients with MeVO stroke undergoing mechanical thrombectomy (MT). Methods We conducted a multinational study analyzing data from the MAD‐MT (Multicenter Analysis of Distal Medium Vessel Occlusions: Effect of Mechanical Thrombectomy) registry, encompassing 37 centers across North America, Asia, and Europe, collected between September 2017 and July 2023. The study included acute ischemic stroke patients with MeVO treated with MT, with or without intravenous thrombolysis, who achieved mTICI 2b–3 post‐MT. Results Among 1463 patients with successful recanalization (mTICI 2b–3), 523 achieved mTICI 2b recanalization, and 940 achieved mTICI 2c–3. Distal occlusions exhibited higher odds of excellent recanalization compared with proximal MeVO vessel occlusions (odds ratio, 1.58; 95% CI, 1.17–2.15; P = 0.003). Cardioembolic stroke pathogenesis was associated with a higher likelihood of excellent recanalization (1.67; 95% CI, 1.07–2.59; P = 0.018). Patients achieving mTICI 2c–3 recanalization demonstrated lower initial National Institutes of Health Stroke Scale scores, significant improvements in postprocedural National Institutes of Health Stroke Scale shift, and a higher percentage of favorable 90‐day outcomes compared with those with mTICI 2b. However, no significant difference in 90‐day mortality rates was observed. Conclusion This study underscores that among patients with MeVO stroke with successful recanalization (mTICI 2b–3) there is higher likelihood of achieving excellent recanalization (mTICI 2c–3) in distal occlusions and cardioembolic pathogenesis. mTICI 2c–3 scores post‐MT correlate with improved clinical outcomes compared with mTICI 2b, affirming the superiority of excellent recanalization over successful recanalization in patients with MeVO stroke. Further prospective studies and randomized controlled trials are warranted for validation.

Abstract Selecting the right rootstock is crucial for successful fruit production and managing both biotic and abiotic stresses in commercial fruit orchards. To enhance the resilience of peach orchards, this study evaluated the physiological and biochemical responses of Prunus spp. rootstocks , ‘Flordaguard’ and ‘MP‐29,’ under normoxia (sufficient oxygen content) or short‐term hypoxia (low‐oxygen content) and ambient or high temperature (40°C) in the root zone. Physiological responses measured were net photosynthesis, stomatal conductance, transpiration, intercellular CO2 concentration, water use efficiency, the leaf chlorophyll index, and the maximum potential quantum efficiency of photosystem II. The leaf concentrations of nitrogen, phosphorus, potassium, magnesium, calcium, sulfur, boron, zinc, manganese, iron, and copper were also analyzed. Reactive oxygen species (ROS) and antioxidants analyzed were superoxide dismutase (SOD) activity, peroxidase (POD) activity, catalase (CAT) activity, ascorbate peroxidase (APX) activity, glutathione peroxidase (GPX) activity, proline content, glycine betaine content (GB), lipid peroxidation (LPO), superoxide (O2−) concentration, and hydrogen peroxide (H2O2) concentration. When subjected to root zone hypoxia or high temperature individually, ‘MP‐29’ performed better physiologically than ‘Flordaguard’. However, when root zone hypoxia and high temperature were combined, ‘MP‐29’ performed better biochemically with enhanced antioxidant activity, osmolyte content, and nutrient absorption. Nutrient analysis of leaves revealed that ‘MP‐29’ had higher N, P, K, Ca, and B concentrations than ‘Flordaguard’. Consequently, ‘MP‐29’ demonstrated greater tolerance to short‐term exposure to the combined effects of high root zone temperature and hypoxia. This research contributes to identifying a suitable rootstock within the Prunus genus able to withstand root zone conditions that often result from severe weather events commonly experienced in Florida and other parts of the world.

Abstract This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods.

Abstract Introduction The prevalence of abdominal injuries in war and conflict zones, particularly in low‐ and middle‐income countries (LMICs), presents a significant healthcare challenge. These injuries, often resulting from explosive devices, firearms, and shrapnel, lead to high morbidity and mortality rates due to delayed diagnoses, inadequate medical infrastructure, and limited access to specialised care. This review aims to summarise the literature on conflict‐related abdominal injuries in LMICs, highlighting the impact of such trauma on healthcare systems and patient outcomes, and suggesting strategies for improvement. Methods A comprehensive narrative review was conducted, focusing on studies from contemporary and historical conflict‐ridden nations. Databases such as PubMed, EMBASE, Google Scholar, the Cochrane Library, and Scopus were searched using specific keywords. Inclusion criteria encompassed various study designs and both paediatric and adult populations, with studies providing raw data prioritised. Exclusions included non‐English articles, non‐peer‐reviewed studies, and those not reporting outcomes or involving high‐income countries. Results The review identified significant challenges in managing war‐related abdominal trauma in LMICs, including a shortage of healthcare personnel and infrastructure, socio‐political barriers, and research gaps. Clinical implications of such injuries include elevated mortality rates, with surgical and nonsurgical management outcomes varying significantly. Positive advancements in diagnostics and surgical techniques have improved survival rates, yet the need for further infrastructural and workforce enhancements remains critical. Conclusion Abdominal trauma in conflict‐affected LMICs necessitates focused efforts to improve healthcare delivery, including targeted funding for infrastructure and equipment, development of training programs for trauma specialists, and increased humanitarian aid. Bridging research gaps and fostering collaborative efforts are essential for advancing the management of abdominal trauma and enhancing patient outcomes in these challenging environments.

Abstract Background Liver cancer (LC) is among the deadliest cancers worldwide, with existing treatments showing limited efficacy. This study aimed to elucidate the role and underlying mechanisms of pyrroline‐5‐carboxylate reductase 1 (PYCR1) as a potential therapeutic target in LC. Methods Immunohistochemistry and Western blot were used to analyse the expression of PYCR1 in LC cells and tissues. EdU assays, colony‐forming assays, scratch wound healing assays, Transwell assays, nude mouse xenograft models and nude mouse lung metastasis models were used to detect the growth and metastasis abilities of LC cells. Transcriptome sequencing was used to search for downstream target genes regulated by PYCR1, and metabolomics was used to identify the downstream metabolites regulated by PYCR1. ChIP assays were used to analyse the enrichment of H3K18 lactylation in the IRS1 promoter region. Results We found that the expression of PYCR1 was significantly increased in HCC and that this high expression was associated with poor prognosis in HCC patients. Knockout or inhibition of PYCR1 inhibited HCC cell proliferation, migration and invasion both in vivo and in vitro. In addition, we revealed that knocking out or inhibiting PYCR1 could inhibit glycolysis in HCC cells and reduce H3K18 lactylation of the IRS1 histone, thereby inhibiting IRS1 expression. Conclusions Our findings identify PYCR1 as a pivotal regulator of LC progression that influences tumour cell metabolism and gene expression. By demonstrating the potential of targeting PYCR1 to inhibit LC cell proliferation and metastasis, this study identified PYCR1 as a promising therapeutic target for LC. Highlights Pyrroline‐5‐carboxylate reductase 1 (PYCR1) promotes the proliferation and metastasis of liver cancer (LC) cells. The expression of PYCR1 in LC is regulated by DNA methylation. Knocking down or inhibiting PYCR1 inhibits glycolysis as well as the PI3K/AKT/mTOR and MAPK/ERK pathways in LC cells. PYCR1 regulates the transcriptional activity of IRS1 by affecting H3K18 lactylation in its promoter region.

Abstract In the past decade, molybdenum ditelluride (MoTe2) has received significant attention from the scientific community due to its structural features and unique properties originate from them. In the current review, the properties, various preparation approaches, and versatile applications of MoTe2 are presented. The review provides a brief update on the state of our fundamental understanding of MoTe2 material and also discusses the issues that need to be resolved. To introduce MoTe2, we briefly summarize its structural, optoelectronic, magnetic, and mechanical properties in the beginning. Then, different preparation methods of MoTe2, such as exfoliation, laser treatment, deposition, hydrothermal, microwave, and molecular beam epitaxy, are included. The excellent electrical conductivity, strong optical activity, tunable bandgap, high sensitivity, and impressive stability make it an ideal contender for different applications, including energy storage, catalysis, sensors, solar cells, photodetectors, and transistors. The performance of MoTe2 in these applications is systematically introduced along with mechanistic insights. At the end of the article, the challenges and possible future directions are highlighted to further modify MoTe2 material for the numerous functionalities. Therefore, the availability of different phases and layer structures implies a potential for MoTe2 to lead an era of two‐dimensional materials that began from the exfoliation of graphene.

Abstract Dewdrops, the droplets of water naturally occurring on leaves and carapaces of insects, are a fascinating phenomenon in nature. Here, a man‐made array of dewdrops with arbitrary shapes and arrangements, which can function as an electromagnetic metasurface, is demonstrated. The realization of the dewdrop array is enabled by a surface covered by a tailored pattern of hydrophilic and hydrophobic coatings, where tiny droplets of water can aggregate and form dewdrops on the former. Interestingly, this metasurface made of dewdrops can be modulated by the condensation and evaporation process. By increasing relative humidity and decreasing temperature, the dewdrop metasurface is gradually formed with increasing amounts of water. While the reverse operation can make it completely disappear. This idea is demonstrated through two examples with different functions of dynamically controllable microwave absorption and scattering. The work shows a principle to construct functional electromagnetic devices with dewdrops, as well as a mechanism of dynamic control based on condensation and evaporation, promising unprecedented applications.

Abstract In the development of lithium‐ion batteries (LIBs), cheaper and safer solid polymer electrolytes are expected to replace combustible organic liquid electrolytes to meet the larger market demand. However, low ionic conductivity and inadequate cycling stability impede their commercial viability. Herein, a novel flexible conducting solid polymer electrolytes (CSPEs) based on polyvinyl alcohol (PVA) and ion‐polarized diethylenetriaminepentaacetic acid (P‐DETP) is developed for the first time and applied in LIBs. PVA and P‐DETP form a compact polymer network through hydrogen bonding, enhancing the thermomechanical stability of CSPE while restricting the migration of larger anions. Furthermore, density functional theory calculations confirm that P‐DETP can facilitate the dissociation of Li+‐TFSI‐ via electrostatic attraction, resulting in increased mobility of lithium ions. Additionally, P‐DETP contributes to the formation of a stable electrode‐electrolyte interface layer, effectively suppressing the growth of lithium dendrites and improving antioxidant capacity. These synergistic effects enable CSPE to exhibit remarkable properties including high ionic conductivity (2.8 × 10−4 S cm−1), elevated electrochemical potential (5.1 V), and excellent lithium transference number (0.869). Notably, the P‐DETP/LiTFSI CSPE demonstrates stable performance not only in LiFePO4 batteries but also adapts to high‐nickel ternary LiNi0.88Co0.06Mn0.06O2 cathode, highlighting its immense potential for application in high energy density LIBs.

Background Diagnostic variables from insertable cardiac monitors may be useful in identifying patients at increased risk of heart failure (HF) events. High‐risk alerts must be coupled with interventions to improve outcomes. We aim to assess the safety of a predefined protocolized intervention pathway activated by insertable cardiac monitor high‐risk alerts. Methods and Results ALLEVIATE‐HF (Algorithm Using LINQ Sensors for Evaluation and Treatment of Heart Failure) Phase 1 was a randomized interventional study enrolling patients with New York Heart Association class II/III and a recent HF event. A HF risk score based on insertable cardiac monitor diagnostics, including impedance, respiration rate, atrial fibrillation burden, heart rate during atrial fibrillation, heart rate variability, and activity duration, was calculated. A protocolized intervention pathway was activated when high‐risk scores were detected that involved physician‐prescribed nurse‐implemented uptitration of diuretic for 4 days, unless safety rule‐out conditions were met. Interventions could be repeated if high‐risk scores persisted and did not require worsening symptoms. In total, 59 patients were randomized (mean age 68.2±11.8 years; 59.3% male); 67.8% with ejection fraction ≥50%. The mean follow‐up was 11.8±8.1 months. Overall, 146 high‐risk scores were recorded in 33 patients and 118 interventions occurred in 75 (51.4%) high‐risk alerts that did not meet safety rule‐out criteria. There were no serious adverse events and 13 adverse events related to interventions. In patients with symptoms at intervention initiation, symptoms resolved in 37 interventions (80%) and worsened in 8 (17%). In asymptomatic patients, symptoms developed in 3 interventions (7%). Conclusions A personalized medication intervention based on insertable cardiac monitor risk score can be safely instituted in patients with HF, irrespective of symptoms. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04452149.

Abstract A depth investigation into the impact of high temperatures on the load‐bearing capacity of reinforced concrete beams in the case of probabilistic design is presented in this paper, employing advanced finite element analysis techniques. This study addresses a critical knowledge gap in the design of fire‐resistant concrete structures, with specific emphasis on the function of concrete cover. The research aims to enhance the overall safety and reliability of concrete buildings under high temperature conditions by providing valuable insights into the behavior of reinforced concrete beams under thermal loading. The analysis incorporates reliability‐based modeling to account for uncertainties in temperature distribution within the beams. A validated finite element model is employed to simulate the performance of reinforced concrete beams at elevated temperatures. By considering various concrete cover thicknesses and heat distribution scenarios, the influence of these factors on the load‐bearing capacity is thoroughly examined. The results underscore the importance of augmenting the concrete cover to enhance the load‐carrying capacity of the beams. Furthermore, the study examines the impact of temperature distribution uncertainties, unveiling diverse load capacities associated with different configurations of concrete cover.

Abstract Genetic and epigenetic alterations occur in many physiological and pathological processes. The existing knowledge regarding the association of PIWI‐interacting RNAs (piRNAs) and their genetic variants on risk and progression of prostate cancer (PCa) is limited. In this study, three genome‐wide association study datasets are combined, including 85,707 PCa cases and 166,247 controls, to uncover genetic variants in piRNAs. Functional investigations involved manipulating piRNA expression in cellular and mouse models to study its oncogenetic role in PCa. A specific genetic variant, rs17201241 is identified, associated with increased expression of PROPER (piRNA overexpressed in prostate cancer) in tumors and are located within the gene, conferring an increased risk and malignant progression of PCa. Mechanistically, PROPER coupled with YTHDF2 to recognize N6‐methyladenosine (m6A) and facilitated RNA‐binding protein interactions between EIF2S3 at 5′‐untranslated region (UTR) and YTHDF2/YBX3 at 3′‐UTR to promote DUSP1 circularization. This m6A‐dependent mRNA‐looping pattern enhanced DUSP1 degradation and inhibited DUSP1 translation, ultimately reducing DUSP1 expression and promoting PCa metastasis via the p38 mitogen‐activated protein kinase (MAPK) signaling pathway. Inhibition of PROPER expression using antagoPROPER effectively suppressed xenograft growth, suggesting its potential as a therapeutic target. Thus, targeting piRNA PROPER‐mediated genetic and epigenetic fine control is a promising strategy for the concurrent prevention and treatment of PCa.

Abstract Modifying the coordination or local environments of single‐, di‐, tri‐, and multi‐metal atom (SMA/DMA/TMA/MMA)‐based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long‐term durability of these materials. Advanced sheet materials supported by metal atom‐based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom‐based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal–metal and metal–carbon with metal–heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure‐activity relationship of metal atom‐based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA‐based materials are presented.

Abstract Gene‐editing technology shows great potential in glioblastoma (GBM) therapy. Due to the complexity of GBM pathogenesis, a single gene‐editing‐based therapy is unlikely to be successful; therefore, a multi‐gene knockout strategy is preferred for effective GBM inhibition. Here, a non‐invasive, biodegradable brain‐targeted CRISPR/Cas12a nanocapsule is used that simultaneously targeted dual oncogenes, EGFR and PLK1, for effective GBM therapy. This cargo nanoencapsulation technology enables the CRISPR/Cas12a system to achieve extended blood half‐life, efficient blood‐brain barrier (BBB) penetration, active tumor targeting, and selective release. In U87MG cells, the combinatorial gene editing system resulted in 61% and 33% knockout of EGFR and PLK1, respectively. Following systemic administration, the CRISPR/Cas12a system demonstrated promising brain tumor accumulation that led to extensive EGFR and PLK1 gene editing in both U87MG and patient‐derived GSC xenograft mouse models with negligible off‐target gene editing detected through NGS. Additionally, CRISPR/Cas12a nanocapsules that concurrently targeted the EGFR and PLK1 oncogenes showed superior tumor growth suppression and significantly improved the median survival time relative to nanocapsules containing single oncogene knockouts, signifying the potency of the multi‐oncogene targeting strategy. The findings indicate that utilization of the CRISPR/Cas12a combinatorial gene editing technique presents a practical option for gene therapy in GBM.

Abstract Aim To translate the Empowerment Scale for Pregnant Women (ESPW) into Chinese and to assess its linguistic validity. Methods The integrative method of the translation process, the Delphi technique, and cognitive interviews were used to implement cross‐cultural adaptation and enhance comprehensibility and linguistic validation. This study recruited 14 experts in the expert review and cognitively reviewed 15 pregnant women. Results The two‐round Delphi method created agreement on cultural applicability. The results of content validity achieved good levels: The item‐level content validity index (CVI) ranged from 0.78 to 1.00, and the scale‐level content validity index, calculated using two different formulas, were 0.97 and 0.81, respectively. Kappa values ranged from 0.74 to 1.00. Pregnant women could understand most of the items and response options in the cognitive interview. The revisions to the wording were made based on suggestions from experts and pregnant women. Conclusion The prefinal simplified Chinese ESPW was semantically and conceptually equivalent to the English version, which was well prepared for further psychometric tests in the next stage of cross‐cultural adaptation. Patient or Public Contribution This comprehensive method successfully developed a Chinese tool to measure the empowerment of pregnant women, indicating the international applicability of this tool and the methodological scientific nature. The simplified Chinese ESPW has the potential to support the identification of empowerment levels of pregnant women and the evaluation of the effectiveness of health education and promotion programmes.