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We present a case of a man immunocompromised due to myelodysplastic syndrome with Candida krusei fungemia who had a rising cell-free DNA (cfDNA) giant magnetoresistance (GMR) signal when tested daily using plasma blood samples. With the rise in GMR signal paralleling the development of skin lesions in this patient, we conclude that cfDNA can be used to indicate uncontrolled infection and thus help monitor response to therapy. This index patient provides evidence that an invasive fungal infection requires both direct antifungal therapy and an intact immune system to control the infection. This biosensing platform has been simplified to potentially serve as a point-of-care test, setting it apart by overcoming the three common barriers of cfDNA testing: complexity, cost, and time. [ABSTRACT FROM AUTHOR]

Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem; still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended Crow Search Algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results. [ABSTRACT FROM AUTHOR]

The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. This research provides useful information for policymakers in the field of logistics. Finally, the results indicate that the velocity of AVs criterion is the most influential criterion in the selection of an intelligent AV. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects. [ABSTRACT FROM AUTHOR]

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal's superiority in terms of the majority of used performance metrics. [ABSTRACT FROM AUTHOR]

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched. [ABSTRACT FROM AUTHOR]

Saving energy and cutting costs without compromising indoor comfort conditions are challenging, especially in hot and humid regions such as Malaysia. This study explores a new approach to reducing energy consumption without compromising staff comfort in office buildings. This study aims to develop a method for lowering Building Energy Index (BEI) and maintaining acceptable indoor conditions while increasing productivity in office buildings. A developed triangulation method using Building Use Studies (BUS) for evaluating occupant satisfaction, physical measurements, and simulation modelling was implemented to measure indoor performance in an office building. The results indicated that enhancing six variables of building conditions managed to improve occupant satisfaction by 44%. Hence, the productivity of staff in the building increased by 16%. The findings demonstrated that a reduction of 3 h in the operating times of chillers while an increase in chillers' temperature by 1.5 °C maintained an acceptable indoor environment and reduced the building's BEI to 89.48 kWh/m2/year, with an energy saving of 21.51%, turning the case study into a low energy building. [ABSTRACT FROM AUTHOR]

This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute the approximate values of the first derivative of the function f. The convergence analysis of the suggested method is established. The comparisons between the performance of the new CG method and the performance of four other CG methods demonstrate that the proposed CG method is promising and competitive for finding a local optimum point. In Part II, three formulas are designed by which a group of solutions are generated. This set of random formulas is hybridized with the globally convergent CG algorithm to obtain a hybrid stochastic conjugate gradient algorithm denoted by HSSZH. The HSSZH algorithm finds the approximate value of the global solution of a global optimization problem. Five combined stochastic conjugate gradient algorithms are constructed. The performance profiles are used to assess and compare the rendition of the family of hybrid stochastic conjugate gradient algorithms. The comparison results between our proposed HSSZH algorithm and four other hybrid stochastic conjugate gradient techniques demonstrate that the suggested HSSZH method is competitive with, and in all cases superior to, the four algorithms in terms of the efficiency, reliability and effectiveness to find the approximate solution of the global optimization problem that contains a non-convex function. [ABSTRACT FROM AUTHOR]

This paper introduces a novel physical-inspired metaheuristic algorithm called "Light Spectrum Optimizer (LSO)" for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm. [ABSTRACT FROM AUTHOR]

Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance. [ABSTRACT FROM AUTHOR]

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems' credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance. [ABSTRACT FROM AUTHOR]

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Water scarcity is a global issue, and its severity is expected to worsen in the near future, prompting further efforts to find new sources of freshwater. Solar-Powered Atmospheric Water Harvesting (SPAWH) is a promising passive approach for atmospheric water generation. This study aims to examine the thermal performance of different glazing materials and water production in SPAWH. The research consists of two phases: a laboratory test of various glazing materials and an experimental study to assess system efficiency in producing water in the tropics. The preliminary results indicated that glass demonstrated better thermal performance than acrylic in the lab, with higher thermal conductivity and less heat loss. The experimental findings showed that the maximum water produced by the proposed SPAWH (60 cm length, 60 cm width and 30 cm height) placed on a 30° tilt angle using glass (3 mm) and acrylic (3 mm) was 0.61 L/m2/day and 0.44 L/m2/day, respectively. The cost analysis revealed that produced water costs $0.18/kg for glass and $0.40/kg for acrylic, respectively. Atmospheric water could be harvested using SPAWH in the tropics, which would help to provide new opportunities for sustainable water supplies and development in these regions. [ABSTRACT FROM AUTHOR]

Lipoprotein(a) (Lp(a)) is one of the strongest causal risk factors of atherosclerotic disease. It is rich in cholesteryl ester and composed of apolipoprotein B and apo(a). Plasma Lp(a) levels are determined by apo(a) transcriptional activity driven by a direct repeat (DR) response element in the apo(a) promoter under the control of (HNF)4α Farnesoid-X receptor (FXR) ligands play a key role in the downregulation of APOA expression. In vitro studies on the catabolism of Lp(a) have revealed that Lp(a) binds to several specific lipoprotein receptors; however, their in vivo role remains elusive. There are more than 1000 publications on the role of diabetes mellitus (DM) in Lp(a) metabolism; however, the data is often inconsistent and confusing. In patients suffering from Type-I diabetes mellitus (T1DM), provided they are metabolically well-controlled, Lp(a) plasma concentrations are directly comparable to healthy individuals. In contrast, there exists a paradox in T2DM patients, as many of these patients have reduced Lp(a) levels; however, they are still at an increased cardiovascular risk. The Lp(a) lowering mechanism observed in T2DM patients is most probably caused by mutations in the mature-onset diabetes of the young (MODY) gene and possibly other polymorphisms in key transcription factors of the apolipoprotein (a) gene (APOA). [ABSTRACT FROM AUTHOR]

This study investigated an indirect evaporative cooling system (IECS) to control latent heat loss on roof ponds by increasing the evaporation rates on wet fabric membranes. The cooling potential of the proposed system was experimentally tested in a real environment and it was compared against a roof pond and a floating fiber (gunny bags) to provide an efficient model for buildings in hot and humid climates. Dry bulb temperatures (DBT) are presented for four experimental models. Solar irradiance, ambient and indoor dry bulb temperatures, and relative humidity (RH) were measured for seven days in each of the following climate conditions: hot sub-humid (mean DBT 27.3 °C and mean RH 72%), hot humid (mean DBT 27.1 °C and mean RH 81%), and warm sub-humid (mean DBT 25.2 °C and mean RH 68%). There were no significant variations in thermal performance between the examined devices under hot humid conditions; however, the wet fabric device had superior thermal performance under sub-humid conditions when compared to the other IECSs. In the three climatic scenarios where the proposed system was tested, the wet fabric managed to reduce the indoor air temperature by 6.6 °C, 5.3 °C, and 5.1 °C, respectively, as compared to the outdoor air temperatures. [ABSTRACT FROM AUTHOR]

Kuala Lumpur, as a major capital city, has undergone a drastic transformation in the past ten years. Many heritage buildings have been sacrificed for urban renewal projects. Those located in the touristic heritage zones within Kuala Lumpur were being converted by their owners into hotels and cafés to meet current demands to sustain their incomes. This approach, however, creates several physical and environmental issues within the new adaptation. The aim of this study is to evaluate the building performance of heritage shophouses that were adapted into budget hotels. The research focuses on two case studies in a strategic and historical location of Jalan Sultan, Kuala Lumpur. At the initial stage, interviews and physical surveys were done to determine the context of this study. The authors of this research then used a triangulation method through indoor environmental condition assessment, measurements of indoor environmental conditions and occupant survey to determine the indoor building performance after the adaption. Results showed that adaptive reuse heritage buildings can perform and meet new indoor environmental requirements, but many sensitive design judgments need to be made before the adaptive reuse renovation. The research found that the use of natural light, natural ventilation, recycled materials and water efficiency have been neglected and thus, they should be prioritized and preserved to ensure a successful change of use. Conserving existing heritage buildings, while incorporating new usages with acceptable comfort, is in line with the principle of sustainability. [ABSTRACT FROM AUTHOR]

A fiber optic daylighting system is an evolving technology for transporting illumination from sunlight into building interiors. This system is a solution developed by daylighting designers to reduce operational costs and enhance comfort. As an innovative technology, fiber optic daylighting systems can illuminate building interiors efficiently compared with other daylighting strategies. However, as a transmission medium in daylighting systems, optical fibers require uniform light distribution in sunlight concentration, which could generate heat. Therefore, this study aims to investigate the effect of heat buildup produced by end-emitting fiber optic daylighting systems in tropical buildings. The applied method adopts a new fiber optic daylighting system technology from Sweden called Parans SP3, with a 10 m cable to be tested in an actual room size under the Malaysian climatic environment, particularly within the vicinity of the main campus of the Universiti Sains Malaysia. Results show that the system generated a temperature of 1.3 °C under average conditions through fiber optic diffusers and increases indoor temperature by 0.8 °C in a 60 m3 room. According to the results, applying fiber optic daylighting systems, as renewable energy sources, generates extra heat gain in building interiors in the tropics. [ABSTRACT FROM AUTHOR]

Aortopathy is a known complication whose incidence is growing within the population of tetralogy of Fallot (TOF) patients. Its pathology and relationship with other comorbidities remain unclear. This study was designed to determine the prevalence and predictors of proximal aortic dilatation after TOF repair. We retrospectively investigated all patients who underwent follow-up cardiac magnetic resonance imaging (CMR; at least 4 years after TOF repair) between March 2004 and December 2019. The dimensions at the ascending aorta (AAo) and sinus of Valsalva (SoV) levels were measured. Aortic dilatation was defined as an internal aortic diameter that was >2 standard deviation of the previously published normal values. We included 77 patients (mean age 28.9 ± 10.5 years, 41.5% female, mean follow-up of 24.5 ± 8.1 years). AAo and SoV were dilated in 19 (24.6%) and 43 (55.8%) patients, respectively. Patients with dilated AAo and SoV were older during the corrective surgery (p < 0.001 and p = 0.004, respectively) and during CMR (p = 0.002 and 0.024, respectively) than patients without AAo and SoV dilatation. Patients of the dilated AAo group were more likely to have prior palliative shunt (p = 0.008), longer shunt duration (p = 0.005), and a higher degree of aortic valve regurgitation (AR) fraction (p < 0.001) and to undergo pulmonary (PVR) and/or aortic valve replacement (p < 0.001 and p = 0.013, respectively). PVR (p = 0.048, odds ratio = 6.413, and 95% CI = 1.013–40.619) and higher AR fraction (p = 0.031, odds ratio = 1.194, and 95% CI = 1.017–1.403) were independent predictors for AAo dilatation. Aortopathy is a common progressive complication that may require reintervention and lifelong follow-up. Our study shows that proximal aortic dilatation may be attributed to factors that increase the volume overload across the proximal aorta, including late corrective surgery and palliative shunt. We also found that PVR and higher AR fraction are independent predictors of AAo dilatation. [ABSTRACT FROM AUTHOR]

There is now significant evidence to support an independent causal role for lipoprotein(a) (Lp(a)) as a risk factor for atherosclerotic cardiovascular disease. Plasma Lp(a) concentrations are predominantly determined by genetic factors. However, research into Lp(a) has been hampered by incomplete understanding of its metabolism and proatherogeneic properties and by a lack of suitable animal models. Furthermore, a lack of standardized assays to measure Lp(a) and no universal consensus on optimal plasma levels remain significant obstacles. In addition, there are currently no approved specific therapies that target and lower elevated plasma Lp(a), although there are recent but limited clinical outcome data suggesting benefits of such reduction. Despite this, international guidelines now recognize elevated Lp(a) as a risk enhancing factor for risk reclassification. This review summarises the current literature on Lp(a), including its discovery and recognition as an atherosclerotic cardiovascular disease risk factor, attempts to standardise analytical measurement, interpopulation studies, and emerging therapies for lowering elevated Lp(a) levels. [ABSTRACT FROM AUTHOR]

Endophthalmitis is a rare but vision-threatening infection characterized by marked inflammation of intraocular fluids and tissues, uncommonly seen following surgery and intravitreal injection. Antimicrobials are used worldwide in the prophylaxis and treatment of bacterial and fungal infections of the eye and are standard treatment in the preoperative and postoperative care of surgical patients. However, antimicrobials are reported to be overprescribed in many parts of the world, which contributes to antimicrobial resistance (AMR). AMR complicates the prophylaxis and treatment of endophthalmitis. This article examines the prevalence and mechanisms of AMR in ocular microorganisms, emphasizing the importance of understanding AMR patterns for tailored treatments. It also explores prophylaxis and management strategies for endophthalmitis, with a discussion on the use of intracameral antibiotic administration. The use of prophylactic intracameral antibiotics during cataract surgery is common in many parts of the world but is still controversial in some locations, especially in the US. Finally, it highlights the role of stewardship in ophthalmology and its benefits in the treatment of endophthalmitis.

Benzodiazepines, psychotropic drugs, are ubiquitous in the aquatic environment due to over-consumption and inefficient removal by sewage treatment plants. Bioaccumulation with consequent behavioral and physiological effects has been reported in many aquatic species. However, the responses are species-specific and still poorly understood. To improve the knowledge, we exposed the freshwater snail Planorbarius corneus to 1, 5, or 10 µg/L of delorazepam, the most widely consumed benzodiazepine in Italy. Conventional behavioral tests were used to assess the effects on locomotor and feeding behavior. Histological and biochemical analyses were also performed to detect possible changes in the structure and composition of the foot mucus and glands. The results show a paradoxical response with reduced feeding activity and locomotor hyperactivity. Pedal mucus was altered in texture but not in composition, becoming particularly rich in fibrous collagen-like material, and a significant change in the protein composition was highlighted in the foot. In conclusion, exposure to delorazepam induces disinhibited behavior in Planorbarius corneus , potentially increasing the risk of predation, and an increase in mucus protein production, which, together with reduced feeding activity, would severely compromise energy resources.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause severe respiratory illness with high mortality. SARS-CoV-2 infection results in a massive inflammatory cell infiltration into the infected lungs accompanied by excessive pro-inflammatory cytokine production. The lung histology of dead patients shows that some areas are severely emphysematous, with enormously dilated blood vessels and micro-thromboses. The inappropriate inflammatory response damaging the pulmonary interstitial arteriolar walls suggests that the respiratory distress may come in a large part from lung vasculature injuries. It has been recently observed that low plasmatic sphingosine-1-phosphate (S1P) is a marker of a worse prognosis of clinical outcome in severe coronavirus disease (COVID) patients. S1P is an angiogenic molecule displaying anti-inflammatory and anti-apoptotic properties, that promote intercellular interactions between endothelial cells and pericytes resulting in the stabilization of arteries and capillaries. In this context, it can be hypothesized that the benefit of a normal S1P level is due to its protective effect on lung vasculature functionality. This paper provides evidence supporting this concept, opening the way for the design of a pharmacological approach involving the use of an S1P lyase inhibitor to increase the S1P level that in turn will rescue the lung vasculature functionality.

Gap-junction-forming connexins are exquisitely regulated by post-translational modifications (PTMs). In particular, the PTM of connexin 43 (Cx43), a tumor suppressor protein, regulates its turnover and activity. Here, we investigated the interaction of Cx43 with the ubiquitin-related modifier 1 (URM-1) protein and its impact on tumor progression in two breast cancer cell lines, highly metastatic triple-negative MDA-MB-231 and luminal breast cancer MCF-7 cell lines. To evaluate the subsequent modulation of Cx43 levels, URM-1 was downregulated in these cells. The transcriptional levels of epithelial-to-mesenchymal transition (EMT) markers and the metastatic phenotype were assessed. We demonstrated that Cx43 co-localizes and interacts with URM-1, and URMylated Cx43 was accentuated upon cellular stress. The significant upregulation of small ubiquitin-like modifier-1 (SUMO-1) was also observed. In cells with downregulated URM-1, Cx43 expression significantly decreased, and SUMOylation by SUMO-1 was affected. The concomitant expression of EMT markers increased, leading to increased proliferation, migration, and invasion potential. Inversely, the upregulation of URM-1 increased Cx43 expression and reversed EMT-induced processes, underpinning a role for this PTM in the observed phenotypes. This study proposes that the URMylation of Cx43 in breast cancer cells regulates its tumor suppression properties and contributes to breast cancer cell malignancy.

Increasing evidence indicates that tumor vasculature normalization could be an appropriate strategy to increase therapies' efficacy in solid tumors by decreasing hypoxia and improving drug delivery. We searched for a novel approach that reduces hypoxia and enhances chemotherapy efficacy in pancreatic adenocarcinoma which is characterized by disrupted blood vasculature associated with poor patient survival. Clinical significance of plasma levels of the angiogenic lipid sphingosine-1-phosphate (S1P) was assessed at baseline in 175 patients. High plasma S1P concentration was found to be a favorable prognostic/predictive marker in advanced/metastatic pancreatic adenocarcinoma patients treated by gemcitabine alone but not in patients receiving a combination gemcitabine and PDGFR-inhibitor. In pancreatic adenocarcinoma PDX models, oral administration of an S1P lyase inhibitor (LX2931) significantly increased plasma S1P levels, decreased tumor expression of the hypoxia marker (CA IX), and enhanced chemotherapy efficacy when combined with gemcitabine treatment. The direct effect of S1P on tumor oxygenation was assessed by administration of S1P onto tumor-grafted CAM model and measuring intra-tumoral pO2 using a tissue oxygen monitor. S1P increased pO2 in a tumor-CAM model. Thus, increasing plasma S1P is a promising strategy to decrease tumor hypoxia and enhance therapy efficacy in solid tumors. S1P may act as a tumor vasculature normalizer.

Primary effusion lymphoma (PEL) is a rare aggressive subset of non-Hodgkin B cell lymphoma. PEL is secondary to Kaposi sarcoma herpes virus (KSHV) and predominantly develops in serous cavities. Conventional chemotherapy remains the treatment of choice for PEL and yields high response rates with no significant comorbidities. Yet, chemotherapy often fails in achieving or maintaining long-term remission. Lenalidomide (Lena), an immunomodulatory drug, displayed some efficacy in the treatment of PEL. On the other hand, arsenic trioxide (ATO) in combination with other agents effectively treated a number of blood malignancies, including PEL. In this study, we present evidence that the combination of ATO/Lena significantly enhanced survival of PEL mice, decreased the volume of exacerbated ascites in the peritoneum, and reduced tumor infiltration in organs of treated animals. In ex vivo treated PEL cells, ATO/Lena decreased the proliferation and downregulated the expression of KSHV latent viral proteins. This was associated with decreased NF-κB activation, resulting in reactivation of viral replication, downregulation of interleukin-6 (IL-6) and IL-10, inhibition of vascular endothelial growth factor, and apoptosis. Our results elucidate the mechanism of action of ATO/Lena and present it as a promising targeted therapeutic modality in PEL management, which warrants further clinical investigation.

The inappropriate or excessive use of antimicrobial agents caused an emerging public health problem due to the resulting resistance developed by microbes. Therefore, there is an urgent need to develop effective antimicrobial strategies relying on natural agents with different mechanisms of action. Nature has been known to offer many bioactive compounds, in the form of animal venoms, algae, and plant extracts that were used for decades in traditional medicine. Animal venoms and secretions have been deeply studied for their wealth in pharmaceutically promising molecules. As such, they were reported to exhibit many biological activities of interest, such as antibacterial, antiviral, anticancer, and anti-inflammatory activities. In this review, we summarize recent findings on the antimicrobial activities of crude animal venoms/secretions, and describe the peptides that are responsible of these activities.

Numerous studies have shown that alteration of actin remodeling plays a pivotal role in the regulation of morphologic and phenotypic changes leading to malignancy. In the present study, we searched for drugs that can regulate actin polymerization and reverse the malignant phenotype in cancer cells. We developed a cell-free high-throughput screening assay for the identification of compounds that induce the actin polymerization in vitro, by fluorescence anisotropy. Then, the potential of the hit compound to restore the actin cytoskeleton and reverse the malignant phenotype was checked in EWS-Fli1-transformed fibroblasts and in B16-F10 melanoma cells. A β-carboline extracted from Peganum harmala (i.e., harmine) is identified as a stimulator of actin polymerization through a mechanism independent of actin binding and requiring intracellular factors involved in a process that regulates actin kinetics. Treatment of malignant cells with non-cytotoxic concentrations of harmine induces the recovery of a non-malignant cell morphology accompanied by reorganization of the actin cytoskeleton, rescued cell-cell adhesion, inhibition of cell motility and loss of anchorage-independent growth. In conclusion, harmine induces the reversion of the malignant phenotype by a process involving the modulation of actin dynamics and is a potential anti-tumor agent acting principally through a non-cytotoxic process.

Human pluripotent stem cells (hPSCs) can provide unlimited supply for mesenchymal stem cells (MSCs) and adipocytes that can be used for therapeutic applications. Here we developed a simple and highly efficient all- trans -retinoic acid (RA)-based method for generating an off-the-shelf and scalable number of human pluripotent stem cell (hPSC)-derived MSCs with enhanced adipogenic potential. We showed that short exposure of multiple hPSC lines (hESCs/hiPSCs) to 10 μM RA dramatically enhances embryoid body (EB) formation through regulation of genes activating signaling pathways associated with cell proliferation, survival and adhesion, among others. Disruption of cell adhesion induced the subsequent differentiation of the highly expanded RA-derived EB-forming cells into a pure population of multipotent MSCs (up to 1542-fold increase in comparison to RA-untreated counterparts). Interestingly, the RA-derived MSCs displayed enhanced differentiation potential into adipocytes. Thus, these findings present a novel RA-based approach for providing an unlimited source of MSCs and adipocytes that can be used for regenerative medicine, drug screening and disease modeling applications.

Toxins have been proposed to facilitate fungal root infection by creating regions of readily-penetrated necrotic tissue when applied externally to intact roots. Isolates of the charcoal rot disease fungus, Macrophomina phaseolina , from soybean plants in Mississippi produced a phytotoxic toxin, (-)-botryodiplodin, but no detectable phaseolinone, a toxin previously proposed to play a role in the root infection mechanism. This study was undertaken to determine if (-)-botryodiplodin induces toxic responses of the types that could facilitate root infection. (±)-Botryodiplodin prepared by chemical synthesis caused phytotoxic effects identical to those observed with (-)-botryodiplodin preparations from M. phaseolina culture filtrates, consistent with fungus-induced phytotoxicity being due to (-)-botryodiplodin, not phaseolinone or other unknown impurities. Soybean leaf disc cultures of Saline cultivar were more susceptible to (±)-botryodiplodin phytotoxicity than were cultures of two charcoal rot-resistant genotypes, DS97-84-1 and DT97-4290. (±)-Botryodiplodin caused similar phytotoxicity in actively growing duckweed ( Lemna pausicostata ) plantlet cultures, but at much lower concentrations. In soybean seedlings growing in hydroponic culture, (±)-botryodiplodin added to culture medium inhibited lateral and tap root growth, and caused loss of root caps and normal root tip cellular structure. Thus, botryodiplodin applied externally to undisturbed soybean roots induced phytotoxic responses of types expected to facilitate fungal root infection.

Snake venom serves as a tool of defense against threat and helps in prey digestion. It consists of a mixture of enzymes, such as phospholipase A2, metalloproteases, and l-amino acid oxidase, and toxins, including neurotoxins and cytotoxins. Beside their toxicity, venom components possess many pharmacological effects and have been used to design drugs and as biomarkers of diseases. Viperidae is one family of venomous snakes that is found nearly worldwide. However, three main vipers exist in the Middle Eastern region: Montivipera bornmuelleri , Macrovipera lebetina , and Vipera (Daboia) palaestinae . The venoms of these vipers have been the subject of many studies and are considered as a promising source of bioactive molecules. In this review, we present an overview of these three vipers, with a special focus on their venom composition as well as their biological activities, and we discuss further frameworks for the exploration of each venom.