King Abdulaziz University

On a global scale, fire disasters present a serious risk to human well-being and the natural surroundings and infrastructure. Traditional fire detection methods encounter a challenge of data imbalance, with an abundance of non-fire scenario data but limited availability from actual fire incidents, resulting in challenges in building an accurate model capable of detecting various types of fires. Moreover, in real-life situations, sensor data collected from the non-fire scenario may exhibit patterns that fall into more than one class. This specific scenario type, known as nuisance scenarios (e.g., smoking and cooking), shares some characteristics with fire scenarios but is considered non-fire, and neglecting such cases can result in delayed fire detection or trigger nuisance alarms. To address this issue, this paper proposes a novel fire monitoring system based on a multi-class support vector data description with a dynamic time warping kernel, which considers distinct sensor patterns from multiple non-fire scenarios. The developed system is able to effectively detect different fire types without prior knowledge by constructing distinct decision boundaries for different non-fire classes. In addition, the proposed system further considers the temporal dynamic and shape patterns present within sensor signals by incorporating the dynamic time warping kernel function. Experimental results demonstrate the proposed system’s superiority over existing methods in detecting different fire types earlier and more accurately with lower false alarm rates in non-fire scenarios.

Facial palsy (FP) is an acute neuromotor disorder affecting the function of the seventh cranial nerve (i.e., facial nerve) and manifested as hemifacial paralysis. Regular oral functions, including eating, drinking, speaking, and other activities, such as eye blinking and eyebrow raising, are also affected leading to a compromised quality of life. Despite the categorization of FP as an idiopathic disorder, many pathoetiologic factors have been reported. Based on the chronicity and severity of the condition, various treatment modalities have been employed, including systemic corticosteroids, antiviral medications, acupuncture, physiotherapy, and facial muscles exercises. Photobiomodulation (PBM) in combination with other treatment modalities (e.g., physiotherapy) has been tested in few interventional investigations with promising results being reported. Therefore, a multidisciplinary interventional approach that includes a standardized PBM protocol for management of cases of FP is to be developed and followed.

Neuropathic pain (NP) is defined as a pain caused by a lesion or disease of the somatosensory system. Pain information might be sent to central nervous system irrespectively to harmful stimuli. NP can arise from various causes, including but not limited to peripheral nerve traumatic lesions, infections, nerve compression, infarction, metabolic factors, and vitamin deficiencies, or it may also have an idiopathic origin. The physiopathology and biological mechanisms of NP are not fully understood, but it can be classified as central or peripheral based on the location of the nervous lesion. Painkillers stand out as the most employed approach to alleviate NP. However, their efficacy is limited, and prolonged intake may lead to undesirable side effects. Alternative treatments such as neurosurgical procedures and psychotherapy have been explored. Photobiomodulation (PBM) has emerged as a prominent focus on investigations related to tissue regeneration and pain reduction. While certain clinical trials have demonstrated positive outcomes with PBM for NP, it is noteworthy that laser parameters have exhibited significant variability.

Premenopausal women diagnosed with cancer may suffer from ovarian toxicity, which affects their quality of life. Cisplatin (CIS) is an effective chemotherapeutic drug used for different types of tumors. However, CIS has been reported to cause ovarian toxicity. Aberration of the PI3K/AKT signaling has been implicated in CIS-induced ovarian toxic effects. Piceatannol (PIC) is a naturally occurring polyphenolic stilbene that has garnered significant research interest due to its known antioxidant, anti-inflammatory, and anti-apoptotic activities. The aim of this study was to investigate the potential protective role of PIC against CIS-induced ovarian toxicity in female rats. Thirty female rats were divided randomly into five groups, and treated for 17 days. (1) control, (2): PIC (10 mg/kg), (3): CIS (6 mg/kg), (4): low dose PIC (5 mg/kg) + CIS (6 mg/kg), and (5): high dose PIC (10 mg/kg) + CIS (6 mg/kg). Pretreatment with PIC significantly prevented the CIS-induced histopathological alterations, inhibited follicles loss, and inhibited the decrease in serum AMH. PIC exhibited antioxidant activity by significantly preventing MDA production and the depletion of GSH and antioxidant enzymes in ovarian tissues in a dose-dependent manner. PIC markedly decreased the immunostaining expression of iNOS, TNF-α, and NF-κB in ovarian tissues. Furthermore, PIC significantly attenuated immune-expression of PTEN and enhanced those of PI3K and phosphor-AKT in ovarian tissues of CIS-treated rats. Finally, PIC modulated mRNA expression of Bcl-2, BAX and CASP3 in favor of anti-apoptosis. PIC protects against CIS-induced ovarian toxicity by exerting antioxidant, anti-inflammatory, and anti-apoptotic effects, primarily through modulation of the PTEN/PI3K/p-AKT axis.

Smart grids (SGs) enhance energy management (EM) by integrating distributed energy resources and optimizing energy use across the grid. However, challenges include managing the high costs of integrating diverse technologies and ensuring efficiency in optimizing energy distribution and usage across a distributed network. To prevail over these challenges, this paper proposes a hybrid approach for enhancing SM resilience with distributed energy management. The proposed hybrid method is the joint execution of both the golden eagle optimization (GEO) and dilated residual convolutional neural network (DRCNN), and it is commonly named as GEO-DRCNN technique. The primary goal of the proposed method is to minimize operating costs while simultaneously enhancing the overall efficiency of the system. The proposed GEO is used to optimize energy distribution and load management. DRCNN is utilized to predict the grid performance. By then, the proposed method is executed in the MATLAB platform and contrasted with existing methods. The proposed technique provides optimum outcomes in every existing system such as Grasshopper Optimization Algorithm (GOA), Multi-Objective Improved Slime Mould Algorithm (MOISMA), and Pelican Optimization Algorithm (POA). The existing method shows the total efficiency and total operating cost is 97% and 8365.2$,97.89$, 95.48% and 7723.8$. The proposed method shows the total efficiency and total operating cost is 98.97$. From the result, it concluded that the proposed approach based on total efficiency is high and the total operating cost is low compared to existing methods.

Significance Unrecognized intubation of the esophagus instead of the trachea results in rapid and severe consequences for the patient. Utilizing the spectral properties of the tissues could reduce incidents of these events. Aim We aim to investigate the design and implementation of a smart endotracheal tube (ETT) with integrated optical fiber sensors to distinguish esophageal and tracheal tissues. Approach Computational methods are investigated to characterize and classify nine pairs of ex vivo porcine organs using spectral properties. Two classifiers [K-nearest neighbor and linear discriminant analysis (LDA)] are investigated. Results Of the tissues sampled, 100% are correctly distinguished, with LDA being the preferred choice when considering both performance and applicability. Conclusions In clinical practice, this approach offers a method for confirming correct tracheal intubation using the spectral properties of the tissues, performed in a single step with no other invasive medical device than the ETT required to detect the spectral measurements.

The carboxypeptidase gene from Thermococcus siculi belonging to family metalloprotease was cloned and expressed in Escherichia coli. The gene was expressed utilizing Escherichia coli expression system that showed leaky expression. The recombinant protein was partially soluble and active. The soluble fraction was purified to apparent homogeneity by heat treatment at 85 ℃ for 25 min. 34 mg/L of highly purified enzyme, displaying improved stability and catalytic efficiency was obtained. Recombinant carboxypeptidase exhibited optimal activity at 80 ℃ and pH 7.5. The enzyme specific activity was 1470U/mL and was metal ion dependent. In silico analysis using I-TASSER predicted the carboxypeptidase to be a metalloprotease which requires zinc ions as a co-factor. The predicted active site residues of the Carboxypeptidase by I-TASSER are HIS-269, GLU-299, and TYR-423. This result was further confirmed by Prankweb, Gene ontology analysis suggested its role in catalyzing the C-terminal cleavage of proteins, releasing single amino acids, dipeptides, or tripeptides. Molecular docking showed a high binding affinity of the substrate Hippuryl-L-Arginine with the Carboxypeptidase of Thermococcus siculi, Homo sapiens, and Bubalus bubalis. Activity and stability at high temperatures makes recombinant carboxypeptidase an ideal candidate for various biotechnological applications.

Chronic inflammatory diseases are leading causes of morbidity and mortality, necessitating the development of targeted therapeutics with improved safety. Many drugs have been withdrawn from the market because of their off-target effects, particularly hERG inhibition, which leads to severe cardiotoxicity. The NF-κB pathway plays a critical role in inflammation and immune response, making IKKβ an attractive therapeutic target. Thioridazine, a known inhibitor of IKKβ, has demonstrated potential anti-inflammatory effects. However, its clinical utility is severely limited by the strong inhibition of the hERG potassium channel, which increases the risk of cardiac arrhythmias. Therefore, it is necessary to develop safer IKKβ inhibitors using rational drug design approaches. By leveraging a similar compound library and in silico techniques, we aimed to retain the original therapeutic potential of thioridazine, while minimising its drawbacks. A library of thioridazine derivatives was computationally designed and screened by molecular docking and simulations. The selected compounds were subjected to patch-clamp analysis, confocal microscopy, western blotting, and qRT-PCR to evaluate their anti-inflammatory potential and hERG affinity, respectively. TDZ-D2{10-(2-oxo-2-pyrrolidin-1-ylethyl)acridin-9-one}, a thioridazine derivative, displayed significantly lower hERG binding while maintaining strong IKKβ inhibition, preserving IκBα stability, reducing NF-κB p65 translocation, and suppressing pro-inflammatory cytokine expression. This study highlights the potential of ligand-based lead optimisation techniques for mitigating off-target effects, thereby offering a safer anti-inflammatory therapeutic candidate. By overcoming the cardiotoxicity associated with thioridazine, TDZ-D2 presents a promising avenue for drug development for inflammatory diseases.

This study addresses the critical challenge of mitigating vortex-induced vibrations (VIV), a phenomenon that compromises the structural stability of chimneys, towers, cables, high-rise supports, bridges, and offshore installations. The objective was to experimentally evaluate the performance of helical strakes with varying pitch and diameter ratios as passive flow control devices for suppressing VIV. Using a 64-channel simultaneous pressure scanner, pressure data were collected across the surface of a cylindrical model equipped with helical strakes featuring circular cross-sections. The strakes were tested with pitch ratios of 0.5, 1, and 2 and diameter ratios of 0.03, 0.04, 0.1, and 0.13. The results demonstrate that helical strakes effectively disrupt unsteady vortex shedding, leading to significant reductions in aerodynamic drag and VIV. These findings highlight the efficacy of helical strakes in enhancing the stability and durability of structures exposed to unsteady fluid flows. The study offers practical design insights for engineers and researchers seeking to optimize structural resilience in dynamic environments.

Alternaria species pose a considerable risk to agricultural production, resulting in severe crop losses, with documented losses for tomatoes between 50% and 86%. The application of traditional fungicides to manage Alternaria impacts the environment and public health. In this study, twenty-five plant growth-promoting rhizobacteria (PGPR) were isolated from the soil rhizosphere, selecting those with a reduction rate exceeding 60% against Alternaria terricola (AT) for identification via MALDI-TOF mass spectroscopy. The isolates A9, A15, and A22 were designated as Bacillus megaterium A9, Bacillus wiedmannii A15, and Bacillus oceanisediminis A22, respectively, and employed as prospective alternative fungicides. Bacillus megaterium A9 demonstrated significant antifungal efficacy against the examined tomato pathogenic fungi. The widths of the inhibition zones varied from 24 to 35 mm. A9 isolate showed considerable potential as PGPR, which produced considerable IAA content, which enhanced the tomato seed germination by 31% compared to the other isolates. The three isolates were administered to healthy and AT-infected tomato plants using drench and foliar application. The findings indicated that A9 was the most effective in reducing A. terricola and improving the plant’s physicochemical characteristics and output. The results indicated that A9 markedly reduced disease severity index (DSI) by 92.2%, augmenting fruit weight by 46.37% and increasing tomato output by 77.6% in A19-treated plants. Drenching soil with the A9 isolate was more successful in downregulating polyphenols, malondialdehyde (MDA), and proline content to their control levels. Compared to the infected control, chlorophyll content enhanced by 210% and 190% in drench and foliar treatments, respectively, while carotenoid content increased by 29% and 18%. The Bacillus megaterium A9 isolate is proposed as a substitute biocontrol agent for phytopathogenic fungi.

This study investigates the optimal conditions for producing Al–Si cast alloys by reacting sodium fluorosilicate (Na2SiF6) with molten aluminium, employing artificial neural network via the Levenberg–Marquardt algorithm (LMA-ANN). The goal is to identify the lowest reaction times that yield the highest silicon recovery percentages at minimal stirring speeds and reaction temperatures, optimizing processing parameters and economic outcomes. Characterization techniques such as XRD and LOM confirmed the presence of α Al, and a uniform fine fibrous eutectic silicon. Differential thermal analysis (DTA) showed an exothermic peak at approximately 871 °C, indicating a multi-step reaction involving Al and Na2SiF6. The efficiency of silicon recovery was directly proportional to the stirring speed and temperature within the reaction time range of 15 to 30 minutes. The cascade-forward back-propagation ANN was trained to optimize silicon recovery, considering reaction time, temperature, and stirring speed. Analysis revealed that higher temperatures led to increased silicon recoveries, with significant gains at higher temperatures for all stirring speeds. The maximum silicon recovery efficiency of 92.14% was obtained with a reaction time of 25.86 minutes, a temperature of 950 °C, and a stirring speed of 600 rpm. This study highlights the effectiveness of the ANN-LMA approach in deriving optimal processing conditions for high-efficiency silicon recovery in Al–Si alloys.

Objective Medication adherence (MA) is crucial to patient treatment and vital for therapeutic outcomes. Due to its ability to continuously monitor a patient's MA behavior, the recent focus on sensor technology for MA monitoring is a promising development. The primary objective of this research is to implement sensor devices/smart wearables powered by advanced deep learning (DL) techniques to evaluate complex data patterns effectively and make accurate predictions. This study introduces a novel smart wearable sensors-based hand gesture recognition system to predict medication behaviors. Methods A device equipped with accelerometer and gyroscope sensors acquires and analyzes data from hand motions. A mobile app records the data from the smart device, subsequently storing it in a database in .csv file. The data is gathered, preprocessed, and classified to identify MA behavior utilizing the developed DL model known as the sheep flock optimization algorithm-attention-based bidirectional long short-term memory network (SFOA-Bi-LSTM). The data was initially gathered and preprocessed via the Z-score normalization method. The data samples are classified using the attention-based Bi-LSTM model after undergoing preprocessing. The SFOA method was utilized to optimize the hyperparameters of the attention-based Bi-LSTM model. Results The model's performance was examined using a five-fold cross-validation based on recall, accuracy, F1 score, and precision. The SFOA-Bi-LSTM model achieved 98.90% accuracy, 97.80% recall, 98.80% precision, and 98.62% F1 score, demonstrating its novelty and potential to inspire and motivate healthcare professionals to adopt this promising method for monitoring MA in healthcare applications. Conclusion The results indicate that the SFOA-Bi-LSTM model performs well in predicting MA. The SFOA-Bi-LSTM model offers several unique advantages, including efficient hyperparameter tuning via the SFOA, enhanced feature representation through an attention mechanism, and comprehensive temporal analysis using Bi-LSTM. It demonstrates superior performance compared to conventional models while being robust to noisy data due to effective preprocessing.

The skin is the largest organ of the human body and provides protective protection from the external environment and pathogens that risk injury. Hydrogels are closely biomimic to the inherent extracellular matrix and hold promising applications for wound healing. Herein, we have fabricated composite hydrogels from carrageenan (CG), polyvinyl alcohol (PVA), and carboxymethyl chitosan (CMCs) by incorporating zeolitic imidazolate frameworks (ZiF-8) via a simple blending method using tetraethyl orthosilicate (TEOS) as crosslinker. We have studied the structural, thermal, surface morphology, and elemental composition by advanced characterization techniques using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and energy-dispersive x-ray (EDX). The swelling, degradation, water contact angle, and gel fraction were performed to determine physicochemical properties. It was found that increasing ZiF-8 decreases the swelling in different media and increases degradation in PBS media. With increased gel fraction, the increasing ZiF-8 shifted wettability from more hydrophilicity to less hydrophilicity. The biocompatibility of the composite hydrogels has been evaluated using fibroblast cell lines (3T3) after different time intervals (24–72 h), and it was found that increasing ZiF-8 caused the mature and spread cell morphology with increasing cell viability and proliferation under controlled in vitro conditions. Similarly, increasing ZiF-8 improved the antibacterial activities against Gram-positive (Staphylococcus aureus (S. aureus)) and Gram-negative (Escherichia coli (E. coli)), which will provide an extra protective antibacterial shield to support wound healing. We have found that CPC-ZiF-10% was the best dressing material with a complete scarless wound of full-thickness skin wound model using an albino SD rat model. Thus, all the results confirmed the successful fabrication of composite hydrogels with a potential candidate for wound healing applications. Graphical abstract The graphical abstract illustrates the fabrication of composite hydrogels incorporated with ZiF-8 into the polymeric matrix of carrageenan, carboxymethyl chitosan, and polyvinyl alcohol and their potential antibacterial and wound healing applications

This study introduces a novel implicit Jungck-type iterative scheme for solving nonlinear functional integral equations with double delays. By incorporating weak contractive conditions, the proposed scheme enhances computational efficiency and stability while achieving convergence equivalent to its explicit counterpart. Compared to existing Jungck-type explicit schemes, the implicit iteration exhibits superior stability and faster convergence. This work addresses key limitations in previous studies (Alam, Rohen in J. Appl. Math. Comput., https://doi.org/10.1007/s12190-024-02134-z, 2024; Copur et al. in Math. Comput. Simul. 215:476–497, 2024; Sharma et al. in Contemp. Math. 5:743–760, 2024) by: 1. Establishing fixed-point results for a broader class of mappings with guaranteed existence and uniqueness of fixed point, 2. Refining the stability framework for more reliable theoretical outcomes, 3. Removing restrictive conditions on control sequences to improve convergence, and 4. Deriving sharper estimates for data dependence, ensuring greater accuracy. Theoretical analysis within the weak w²-stability framework is supported by numerical experiments, including boundary value problems, confirming the scheme’s enhanced stability and efficiency. By refining and extending recent developments in fixed-point and coincidence-point theory, this study provides a robust framework for solving complex nonlinear problems.

Subungual epidermoid inclusions (SEI) are benign cystic lesions of the nail bed. To our knowledge, there has been only one case series describing SEI. We report eight cases of SEI. The patients had a median age of 72 years (range 3–84) with a female: male ratio of 1.6. Five occurred in toenails and three in fingernails. Histologically, SEI is characterized by bulbous proliferation of rete ridges and unilocular cysts lined by thin squamous epithelium with hypogranulosis, filled with orthokeratin. The connection to the nail bed epithelium may be disrupted and calcified. SEI are tumors that should be kept in the differential diagnosis of the subungual nail bed lesions.

Plant responses to salt stress involve regulatory networks integrating ion transport, hormonal signaling, and root system architecture remodeling. A key adaptive mechanism is the regulation of sodium (Na⁺) transport by Class 1 HKT1 transporters, which compertamentalize Na⁺ in non-photosynthetic tissues. High HKT1 expression reduces Na ⁺ accumulation in shoots, leading to increased salt tolerance, but simultaneously results in reduced lateral root development. In this study, we explored transcriptional responses that are altered by high HKT1 expression in root stelle in two Arabidopsis backgrounds, Col-0 and C24. We identified TMAC2 , a negative ABA regulator, and TIP2:2 , a tonoplast aquaporin, as key modulators of root development under salt stress. While TIP2:2 function was conserved, TMAC2 exhibited genotype-specific effects on ABA accumulation and HKT1 -mediated salt sensitivity. Co-expression of TMAC2 and HKT1 in Col-0 upregulated ABI4 and ABI5 , linking Na⁺ transport to ABA signaling. Our findings underscore genetic context in shaping salt responses and provide molecular targets for enhancing root plasticity under stress.

Candida infections represent a major component of invasive and non‐invasive mycoses globally, including the countries in the Arabian Peninsula. In this review, we present epidemiological features and trends, clinical manifestations, species distribution, antifungal susceptibility, and outcomes available for candidemia and candiduria in six countries of the Gulf Cooperation Council (GCC) and Yemen, all located in the Arabian Peninsula. We discuss gaps in knowledge and provide recommendations for improving various aspects for better management of infections by these fungal pathogens. Candida species prevail, with Candida albicans being the most isolated organism, though its prevalence varies over time. The second most frequently isolated species varies from country to country within the region. Generally, invasive infections by non‐albicans Candida species are increasing. Candidozyma auris, formerly known as Candida auris, is causing serious health risks in all GCC countries, including those with appropriate diagnostic capacity and awareness.

Cancer is one of the most devastating diseases all over the globe, and it is the second worldwide cause of death, exceeded only by cardiovascular diseases. The therapeutic approach to human cancer has evolved significantly and has varied depending on the type and stage of cancer, as well as the general health status of the patient. Despite the advancements in cancer treatment, various challenges persist in the treatment of cancer, including side effects, drug resistance, and incomplete eradication of tumors. The use of oncolytic bacteria (cancer targeting and destroying bacteria) has been identified to have several advantages over the traditional methods of cancer treatment. Several bacterial species have been identified to be used in the treatment of different types of cancers. Oncolytic therapy can be achieved through the use of a naturally occurring and/or genetically modified bacterial species, including Clostridium, Salmonella, Escherichia coli, and Listeria spp. with their toxins, enzymes, biofilms, and secondary metabolites as well as their spores that leads to direct or indirect killing of cancer cells. This review provides some highlights about the biology and therapeutic potential of oncolytic bacteria individually or in combination with other therapeutic approaches against different types of cancers. Besides, the current challenges and future perspectives will be explored.

Cataract surgery is often associated with postoperative inflammation and pain, which, if inadequately managed, can lead to complications such as macular edema, increased intraocular pressure, and delayed recovery. Topical corticosteroids are the standard treatment but are limited by poor patient adherence and administration challenges. The intracanalicular dexamethasone insert (DEXTENZA) offers a sustained, automated drug delivery platform that addresses these limitations. This systematic review and meta-analysis evaluated the efficacy and safety of DEXTENZA for managing postoperative pain and inflammation following cataract surgery. A comprehensive search of PubMed, Embase, Cochrane Library, Directory of Open Access Journals (DOAJ), and ClinicalTrials.gov—without date restrictions—identified four randomized controlled trials (RCTs) involving 985 participants The protocol was registered with PROSPERO (CRD42023457981). This systematic review and meta-analysis was conducted in accordance with the PRISMA guidelines. Meta-analysis demonstrated that DEXTENZA significantly reduced anterior chamber cell clearance (RR = 1.63, 95% CI = 1.27–2.08, P = 0.0001) by day 14 and improved pain resolution (RR = 1.59, 95% CI = 1.24–2.03, P = 0.0002) by day 8 compared to placebo. Adverse events, including non-serious and serious ocular incidents, were lower in the DEXTENZA group (RR = 0.57, 95% CI = 0.42–0.78, P = 0.0005). The sustained-release mechanism of DEXTENZA improves adherence and may offer practical advantages in postoperative management, particularly in patients with challenges using topical drops these findings support the use of intracanalicular corticosteroids as a promising alternative for managing postoperative pain and inflammation in cataract surgery patients.