(1) Background: We reviewed a stem cell-derived therapeutic strategy for advanced neovascular age-related macular degeneration (nAMD) using a human embryonic stem cell-derived retinal pigment epithelium (hESC-RPE) monolayer delivered on a coated, synthetic basement membrane (BM)—the patch—and assessed the presence and distribution of hESC-RPE over 5 years following transplantation, as well as functional outcomes. (2) Methods: Two subjects with acute vision loss due to sub-macular haemorrhage in advanced nAMD received the hESC-RPE patch. Systematic immunosuppression was used peri-operatively followed by local depot immunosuppression. The subjects were monitored for five years with observation of RPE patch pigmentation, extension beyond the patch boundary into surrounding retina, thickness of hESC-RPE and synthetic BM and review for migration and proliferation of hESC-RPE. Visual function was also assessed. (3) Results: The two study participants showed clear RPE characteristics of the patch, preservation of some retinal ultrastructure with signs of remodelling, fibrosis and thinning on optical coherence tomography over the 5-year period. For both participants, there was evidence of pigment extension beyond the patch continuing until 12 months post-operatively, which stabilised and was preserved until 5 years post-operatively. Measurement of hESC-RPE and BM thickness over time for both cases were consistent with predefined histological measurements of these two layers. There was no evidence of distant RPE migration or proliferation in either case beyond the monolayer. Sustained visual acuity improvement was apparent for 2 years in both subjects, with one subject maintaining the improvement for 5 years. Both subjects demonstrated initial improvement in fixation and microperimetry compared to baseline, at year 1, although only one maintained this at 4 years post-intervention. (4) Conclusions: hESC-RPE patches show evidence of continued pigmentation, with extension, to cover bare host basement membrane for up to 5 years post-implantation. There is evidence that this represents functional RPE on the patch and at the patch border where host RPE is absent. The measurements for thickness of hESC-RPE and BM suggest persistence of both layers at 5 years. No safety concerns were raised for the hypothetical risk of RPE migration, proliferation or tumour formation. Visual function also showed sustained improvement for 2 years in one subject and 5 years in the other subject. Full article

The genus Phlomis is one of the largest genera in the Lamiaceae family and includes species used since ancient times in traditional medicine, as flavoring for food and as fragrance in cosmetics. The secretory structures (represented by glandular trichomes) as well as the essential oils produced by them constitute the subject of this review. While representatives of this genus are not typically regarded as large producers of essential oils compared to other species of the Lamiaceae family, the components identified in their essential oils and their biological properties necessitate more investigation of this genus. A comprehensive analysis of the specialized literature was conducted for each of the 93 currently accepted species to identify all the results obtained by researchers regarding the secretory structures and essential oils of this genus up to the present time. Glandular trichomes, still insufficiently studied, present morphological peculiarities that differentiate this genus within the family: they are of two categories: capitate (with a wide distribution in this genus) and dendroid. The peltate trichomes, characteristic of many species of this family, are absent. The essential oils from the species of the genus Phlomis have been much more widely studied than the secretory structures. They show considerable variability depending on the species and the environmental conditions. Full article

The antibacterial and anti-inflammatory effect of thioglycosides has already been established. This study investigates the effects of thioglycosides extracted from white mustard, specifically the “Bamberka” variety, in the context of oral hygiene. The aim of the study is to clarify an evidence-based link between the documented antibacterial and anti-inflammatory effects attributed to thioglycosides and their practical application in oral care. A randomized, single-blinded (patient-blinded) clinical study was performed on 66 patients using mustard-based toothpaste for oral hygiene. The patients were examined at baseline and after 6 and 12 months. The values of the Approximal Plaque Index (API), the Plaque Index (PI), and Bleeding on probing (BOP) were taken into consideration. The results show a significant reduction in plaque accumulation, especially after 6 months of using mustard-based toothpaste in all examined parameters. This suggests that thioglycosides from mustard contribute to a considerable decrease in dental plaque accumulation, confirming their potential in natural oral care solutions, which is indicated in the main conclusions or interpretations. Full article

The aim of this study is to produce a biodegradable food packaging material that reduces environmental pollution and protects food safety. The effects of total solids content, substrate ratio, polyphenol content, and magnetic stirring time on bovine bone gelatin/sodium carboxymethylcellulose nanoemulsion (BBG/SCMC–NE) were investigated using particle size, PDI, turbidity, rheological properties, and zeta potential as evaluation indexes. The micro, structural, antioxidant, encapsulation, and release properties were characterized after deriving its optimal preparation process. The results showed that the nanoemulsion was optimally prepared with a total solids content of 2%, a substrate ratio of 9:1, a polyphenol content of 0.2%, and a magnetic stirring time of 60 min. SEM showed that the nanoemulsion showed a dense and uniform reticulated structure. FTIR and XRD results showed that covalent cross-linking of proteins and polysaccharides altered the structure of gelatin molecular chains to a more compact form but did not change its semi-crystalline structure. DSC showed that the 9:1 BBG/SCMC–NE had a higher thermal denaturation temperature and greater thermal stability, and its DPPH scavenging rate could reach 79.25% and encapsulation rate up to 90.88%, with excellent slow-release performance. The results of the study provide basic guidance for the preparation of stable active food packaging with excellent properties. Full article

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. Full article

Soccer athletic performance varies across a soccer season due to training and fatigue. In addition, it is known that core stability is linked with anterior cruciate ligament (ACL) injury risk but their variations over a season are unknown. The aim of the study was to determine the evolution of core stability, athletic performance, and ACL injury risk among young high-level soccer players at four key moments of a season: pre-season (PRE), start of season (START), mid-season (MID), and the end of the season (END). Core stability scores increased until mid-season, while ACL injury risk scores (measured during sidestep cuttings and single-leg landing) decreased thanks to an injury prevention program between START and MID. These results are in line with the literature, which demonstrates that a high level of core stability is linked to a low injury risk. Evolution of athletic performance was not consistent throughout the season, being dependent on the specific phases of training performed by the athletes. Therefore, assessing core stability, athletic performance, and ACL injury risk multiple times across a soccer season could help coaches to adapt their training programs properly. Full article

Assessing indoor environmental quality (IEQ) is fundamental to ensuring health, well-being, and safety. A particular type of indoor compartment, land transport cabins (LTCs), specifically those of trains and buses, was surveyed. The global rise in commute and in-cabin exposure time gives relevance to the current study. This study discusses indoor climate (IC) in LTCs to emphasize the risk to the well-being and comfort of exposed occupants linked to poor IEQ, using objective assessment and a communication method following recommendations of the CEN-EN16798-1 standard. The measurement campaign was carried out on 36 trips of real-time travel on 15 buses and 21 trains, mainly in the EU region. Although the measured operative temperature, relative humidity, CO₂, and VOC levels followed EN16798-1 requirements in most cabins, compliance gaps were found in the indoor climate of these LTCs as per ventilation requirements. Also, the PMV-PPD index evaluated in two indoor velocity ranges of 0.1 and 0.3 m/s showed that 39% and 56% of the cabins, respectively, were thermally inadequate. Also, ventilation parameters showed that indoor air quality (IAQ) was defective in 83% of the studied LTCs. Therefore, gaps exist concerning the IC of the studied LTCs, suggesting potential risks to well-being and comfort and the need for improved compliance with the IEQ and ventilation criteria of EN16798-1. Full article

The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods. Full article

Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty treatment of information and not considering the weights and correlations between risk factors, we propose a new FMEA method. In this paper, the Fermatean fuzzy Z-number (FFZN) is proposed by fusing the Fermatean fuzzy number and Z-number. Extending it to the Bonferroni mean (BM) operator, the Fermatean fuzzy Z-number-weighted Bonferroni mean (FFZWBM) operator is proposed. A new FMEA method is proposed based on this operator. In order to overcome the factors not considered in the FMEA method, two new risk factors are proposed and added. The ability of experts to express fuzzy information is enhanced by introducing the FFS. The weights and correlations between the influencing factors can be handled by aggregating the evaluation information using the FFZWBM operator. Finally, the proposed method is applied to an arithmetic example and the accuracy of the proposed method is proved by teaming it with other methods. Full article

Despite the large number of existing varieties of Vitis vinifera L., only few occupy a large niche in today’s highly globalized wine market. The increasing consumer demand for diversified products, as well as the changing climatic conditions, make establishing a process of varietal diversification essential to achieve both challenges. It is for this reason that the study of minority varieties, which have a higher level of adaptation to each area of origin, is of particular interest. With the main objective of achieving an in-depth knowledge of minority varieties in Spain, the national research project ‘Valorization of Minority Grapevine Varieties for their Potential for Wine Diversification and Resilience to Climate Change’ (MINORVIN), has been proposed. Within this extensive project, the present study describes the aroma profiles of 60 single-variety wines, corresponding with 44 different varieties, with 12 of these varieties being studied at the same time in several Spanish regions. Volatile compounds were determined through three consecutive vintages using gas chromatography-mass spectrometry (GC-MS) and gas chromatography–flame ionization detector (GC-FID). Compounds were grouped into major compounds, including alcohols, C₆ compounds, esters, acetates, acids, carbonyl compounds, and other type of compounds, and minor compounds, including lactones, terpenes, and C₁₃-norisoprenoids, according to their concentration in the wines being analyzed. Among this last group of compounds, lactones were quantitatively the most abundant, followed by terpenes. This study reflects that minority variety wines show distinctive aromatic profiles, supporting the importance of valuing and promoting the autochthonous minority grapevine varieties for the Spanish winemaking industry. Full article

Underwater gliders have emerged as effective tools for long-term ocean exploration. Employing aircraft for launching underwater gliders could significantly expand their application. Compared to slender underwater vehicles, the distinctive wing structure of underwater gliders may endure huge impact forces when entering water, leading to more intricate impact load characteristics and potential wing damage. This paper employs a computational fluid dynamics approach to analyze the water entry event of an airdropped underwater glider and its impact load behavior. The results indicate that the glider impact load is enhanced prominently by the wing, and that the extent of enhancement is influenced by the entry attitude. At an entry angle of 80°, the glider exhibits the maximum impact load during different water entry angles. In addition, a larger attack angle indicates a higher glider impact load. Our present study holds significant importance for both the hydrodynamic shape design and water entry strategy control of airdropped underwater gliders. Full article

To investigate the dissolution mechanism of Ti metal, ab initio calculations were conducted to observe the impact of Ti vacancy defects on the O-adsorbed Ti(0001) surface, focusing on the formation energies of Ti vacancy, geometric structures, and electronic structures. The surface structures subsequent to Ti dissolution were simulated by introducing a Ti cavity on both clean and O-adsorbed Ti(0001) surfaces. Our findings indicated that Ti vacancy formation energies and electrochemical dissolution potential on the O-adsorbed Ti(0001) surface surpassed those on the clean surface, and they increased with increasing O coverage. This suggested that O adsorption inhibited Ti dissolution and enhanced O atom interaction with the Ti surface as O coverage increased. Furthermore, at higher O coverage, Ti vacancies contributed to the strengthening of Ti-O bonds on the O-adsorbed Ti(0001) surface, indicating that Ti dissolution aided in stabilizing the Ti surface. The formation of Ti vacancies brought the atomic ratio of Ti to O on the Ti surface closer to that of TiO₂, potentially explaining the increased stability of the structure with Ti vacancies. Full article

It is a common view that Popper’s defense of the open society has been a defense of Western, liberal democracies. This seems to imply that by fostering democratic institutions we are ipso facto fostering open societies. I criticize this view by arguing that in-built incentives in democratic mechanisms move us away from (or hamper) the open society. Democracy promotes voters’ ignorance, indulges voters’ irrationality, and allows voters to externalize costs. This is contrary to well-informed, rational decisions and personal responsibility that lie at the fundamentals of the open society. I suggest that it has been free-market capitalism, or free-market societies, which has moved us closer to the ideal of the open society and which best realizes open society’s values. Full article

Cultivated land plays a crucial role as the basis of grain production, and it is essential to effectively manage the unregulated expansion of non-grain production (NGP) on cultivated land in order to safeguard food security. The study of NGP has garnered significant attention from scholars, but the prediction of NGP trends is relatively uncommon. Therefore, we focused on Jiangsu Province, a significant grain production region in China, as the study area. We extracted data on cultivated land for non-grain production (NGPCL) in 2000, 2005, 2010, 2015, and 2019, and calculated the ratio of non-grain production (NGPR) for each county unit in the province. On this basis, Kernel Density Estimation (KDE) and spatial autocorrelation analysis tools were utilized to uncover the spatio-temporal evolution of NGP in Jiangsu Province. Finally, the Patch-Generating Land Use Simulation (PLUS) model was utilized to predict the trend of NGP in Jiangsu Province in 2038 under the three development scenarios of natural development (NDS), cultivated land protection (CPS), and food security (FSS). After analyzing the results, we came to the following conclusions:(1) During the period of 2000–2019, the NGPCL area and NGPR in Jiangsu Province exhibited a general decreasing trend. (2) The level of NGP displayed a spatial distribution pattern of being “higher in the south and central and lower in the north”. (3) The results of multi-scenario simulation show that under the NDS, the area of NGPCL and cultivated land for grain production (GPCL) decreases significantly; under the CPS, the decrease in NGPCL and GPCL is smaller than that of the NDS. Under the FSS, NGPCL decreases, while GPCL increases. These results can provide reference for the implementation of land use planning, the delineation of the cultivated land protection bottom line, and the implementation of thee cultivated land use control system in the study area. Full article

Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations. Full article

Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches. Full article

In this work, Ce-doped Ti₆Cr₁₄V₈₀ BCC hydrogen-storage alloys have been synthesized as catalysts to enhance the hydrogen-storage performance of MgH₂ based on its room-temperature activation features and excellent durability. The Ti₆Cr₁₄V₈₀Ce₁ alloy was pre-ball milled under a hydrogen atmosphere into a Ti₆Cr₁₄V₈₀Ce₁H_x hydride. Different amounts of the Ti₆Cr₁₄V₈₀Ce₁H_x hydride were incorporated into MgH₂ by ball milling to obtain the MgH₂ + y wt%Ti₆Cr₁₄V₈₀Ce₁H_x (y = 0, 3, 5, 10, 15) nano-composites. With an optimization doping of 10 wt%Ti₆Cr₁₄V₈₀Ce₁H_x, the initial dehydrogenated temperature was decreased to 160 °C. Moreover, the composite can rapidly release 6.73 wt% H₂ within 8 min at 230 °C. Also, it can absorb 2.0 wt% H₂ within 1 h even at room temperature and uptake 4.86 wt% H₂ within 10 s at 125 °C. In addition, the apparent dehydrogenated activation energy of the MgH₂ + 10 wt%Ti₆Cr₁₄V₈₀Ce₁H_x composite was calculated to be 62.62 kJ mol⁻¹ fitted by the JMAK model. The capacity retention was kept as 84% after 100 cycles at 300 °C. The ball milled Ti₆Cr₁₄V₈₀Ce₁H_x transformed from the initial FCC phase structure into a BCC phase after complete dehydrogenation and back into an FCC phase when fullly hydrogenated. A catalyst mechanism analysis revealed that the ‘autocatalytic effect’ originating in Ti₆Cr₁₄V₈₀Ce₁H_x plays a crucial role in boosting the de-/hydrogenation properties of MgH₂. This work provides meaningful insights into rational designs of nano-compositing with different hydrogen-storage alloy catalyzed MgH₂. Full article

Agricultural activities are the second largest source of greenhouse gas emissions, and carbon emissions from agricultural land use (CEALU) have become a hot issue across the world. Although there are some studies on the impact of high-standard farmland construction policies on carbon emissions, they focus on quantitative analysis and do not give sufficient consideration to the relationship between HSFC and CEALU. Therefore, in this study, by relying on provincial panel data of China for the period 2005–2017, the effect of the high-standard basic farmland construction policy on carbon emissions from agricultural land use per unit area and its regional differences were quantitatively analyzed using the difference-in-difference (DID) model. The results showed that: (1) China’s CEALU per unit area presented a fluctuating upward change, but the growth rate slowed down during the period 2005–2017, from 392.58 kg/ha to 457.72 kg/ha, with an average annual growth rate of 1.31%; (2) the high-standard farmland construction (HSFC) policy led a significant carbon emission reduction effect in agricultural land use and reduced the CEALU per unit area by 10.80% on average. With the promotion of this policy, its carbon emission reduction effect in agricultural land use presented an overall increasing change; (3) the carbon emission reduction effect of the high-standard farmland construction policy in agricultural land use was significant in central China, but non-significant in eastern China and western China. Full article

As one of the emerging nanomaterials, boron nitride nanotubes (BNNTs) provide promising opportunities for diverse applications due to their unique properties, such as high thermal conductivity, immense inertness, and high-temperature durability, while the instability of BNNTs due to their high surface induces agglomerates susceptible to the loss of their advantages. Therefore, the proper functionalization of BNNTs is crucial to highlight their fundamental characteristics. Herein, a simplistic low-cost approach of BNNT surface modification through catechol-polyamine (CAPA) interfacial polymerization is postulated to improve its dispersibility on the polymeric matrix. The modified BNNT was assimilated as a filler additive with AlN/Al₂O₃ filling materials in a PDMS polymeric matrix to prepare a thermal interface material (TIM). The resulting composite exhibits a heightened isotropic thermal conductivity of 8.10 W/mK, which is a ~47.27% increase compared to pristine composite 5.50 W/mK, and this can be ascribed to the improved BNNT dispersion forming interconnected phonon pathways and the thermal interface resistance reduction due to its augmented compatibility with the polymeric matrix. Moreover, the fabricated composite manifests a fire resistance improvement of ~10% in LOI relative to the neat composite sample, which can be correlated to the thermal stability shift in the TGA and DTA data. An enhancement in thermal permanence is stipulated due to a melting point (Tm) shift of ∼38.5 °C upon the integration of BNNT-CAPA. This improvement can be associated with the good distribution and adhesion of BNNT-CAPA in the polymeric matrix, integrated with its inherent thermal stability, good charring capability, and free radical scavenging effect due to the presence of CAPA on its surface. This study offers new insights into BNNT utilization and its corresponding incorporation into the polymeric matrix, which provides a prospective direction in the preparation of multifunctional materials for electric devices. Full article

Access to mental healthcare is undoubtedly of major importance for LGBT+ people worldwide, given the high prevalence of mental health difficulties due to minority stress exposures. This study drew mixed-method survey data from the community-based KAMI Survey (n = 696) to examine the enablers, barriers, and unmet needs experiences of LGBT+ individuals in accessing mental healthcare services in Malaysia. First, we present findings from a series of descriptive analyses for sociodemographic differences in unmet needs for mental healthcare, barriers, and satisfaction levels with different types of mental healthcare. Next, we conducted an inductive thematic analysis of open-text comments (n = 273), with relevance drawn to Andersen’s Behavioural Model of Healthcare. More than a quarter (29.5%) reported an unmet need for mental healthcare, and some groups (younger, asexual or queer, or participants living in non-major cities) reported higher unmet needs. More than three-fifths (60.5%) reported not knowing where to find culturally safe mental health professionals. The thematic analysis uncovered key contextual (e.g., mental health practitioners’ stance, stigma, collaborative client-care) and individual (e.g., positive expectation of mental health services and anticipated stigma) attributes that influence healthcare experiences. Participants also identified resources that facilitate healthcare utilisation, such as affordability, availability of suitable professionals, and geographical considerations. The implications of our findings for the mental healthcare practices in Malaysia were outlined. Full article

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine. Full article

Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer classification at an early stage. Accurate cancer classification is a complex and challenging task due to the high-dimensional nature of the gene expression data relative to the small sample size. This research proposes using a dimensionality-reduction technique to address this limitation. Specifically, the mutual information (MI) technique is first utilized to select influential biomarker genes. Next, an ensemble learning model is applied to the reduced dataset using only the most influential features (genes) to develop an effective cancer classification model. The bagging method, where the base classifiers are Multilayer Perceptrons (MLPs), is chosen as an ensemble technique. The proposed cancer classification model, the MI-Bagging method, is applied to several benchmark gene expression datasets containing distinctive cancer classes. The cancer classification accuracy of the proposed model is compared with the relevant existing methods. The experimental results indicate that the proposed model outperforms the existing methods, and it is effective and competent for cancer classification despite the limited size of gene expression data with high dimensionality. The highest accuracy achieved by the proposed method demonstrates that the proposed emerging gene-expression-based cancer classifier has the potential to help in cancer treatment and lead to a higher cancer survival rate in the future. Full article