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The present study investigated the microbiological safety of the increasingly popular plant‐based milk alternatives. No (10/27) or only very low microbial counts (17/27) were detected in the tested products. These were mainly identified as spore formers via MALDI‐ToF‐MS. Three products contained Bacillus cereus group isolates, which were able to form considerable amounts of enterotoxins and exhibited cytotoxicity towards CaCo‐2 cells. Preliminary tests showed good growth of B. cereus, Listeria monocytogenes, and Salmonella enterica in all tested products (maximum bacterial counts: 5 × 1012 cfu/mL). These experiments also revealed strain‐, time‐, and temperature‐, but especially product‐specific enterotoxin production of B. cereus. In propagation and persistence tests according to DIN EN ISO 20976‐1:2019‐09, rapid bacterial proliferation was also detected in all products. B. cereus generally showed lower bacterial counts (106–107 cfu/mL) compared to L. monocytogenes and S. enterica (108–109 cfu/mL), but was detectable in a larger number of products over the test period of 6 weeks. pH values decreased (20/27) over time and visual and/or olfactory alterations (24/27) were observed. The present study provides information on the occurrence, growth and persistence of pathogenic bacteria in plant‐based drinking milk alternatives. It also points out that the accompanying changes in pH, odor, and appearance are not necessarily recognizable to the consumer. Practical Application: The present study contributes to the understanding of the microbial risk related to plant‐based drinking milk alternatives. It is crucial that the manufacturer ensures that particularly spore formers have been effectively eliminated from the products. Among them, especially toxin‐producing bacteria can pose a risk to the consumer, as these products promote proliferation and persistence of the bacteria. [ABSTRACT FROM AUTHOR]

Plants are being researched as potential sources of novel drugs, which has led to a recent acceleration in the discovery of new bioactive compounds. Research on tissue culture technology for the synthesis and processing of plant compounds has skyrocketed, surpassing all expectations. These plants can be bought either raw or as extracts, where some of the chemicals are extracted by mashing the plant in water, alcohol, or another solvent. The use of herbal medicine may open new chances for reducing the onset of infections and treating different diseases including cancer. A perennial plant that blooms in the winter, Cyclamen, is one of the most widely used potted flowers in many nations. Alkaloids, flavonoids, phenols, tannins, saponins, sterols, and glycosides are the main active components of Cyclamen. Analgesic, cytotoxic, antioxidant, antimicrobial, and anti-inflammatory properties have all been demonstrated as potential effects of various extracts of Cyclamen tubers. However, the use of this medicinal plant in official medicine will require further research in the areas of pharmacology. Furthermore, it is necessary to create standard operating procedures for a crude herbal medication. In this regard, this review aims to highlight the key characteristics of the Cyclamen plant, such as its various parts, species, stages of development, and geographic range; pinpoint its intriguing bioactivities, its antioxidant, anti-inflammatory, and its anti-cancerous effects; and ascertain its potential medicinal uses and the main future perspectives. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Background/purpose: There is limited literature on the materials of choice and their properties when repairing 3-D printed resin-based restorations. The objective of this in-vitro study is to determine the shear bond strength of various repair materials to 3D printed SLA (stereolithography) resin. Materials and methods: For Group A (control), fifteen cylinders of 3-D printing SLA resin were printed as one unit of a Ø6.8 × 8 mm (diameter and height) cylindrical block with a Ø3 × 5 mm cylindrical block at the center. For the test groups, forty-five specimen cylinders of 3-D printing SLA resin (Ø6.8 × 8 mm) were fabricated and the surfaces were treated with 3 different test materials: Group B: Poly-Methyl Methacrylate (PMMA); Group C: Bis-acrylic composite resin, and Group D: Bis-GMA composite All specimens were tested using an Instron machine at a crosshead speed of 0.5 mm/min. A Shapiro–Wilk test was used to assess normality within the data, then the data was statistically analyzed by a Mann–Whitney test. Results: There were no statistically significant differences between testing groups, except Group A. Group B displayed mixed (87%) and adhesive (13%) failure at the fractured surface. Group C showed both mixed (60%) and adhesive failure at the fractured surface (40%). All Group D showed mixed fracture patterns, partly cohesive fractured surface within the base cylinder area and partly adhesive fractured surface at the bonded interface. Conclusion: No statistically significant differences in the shear bond strength of the different repair materials to 3D printed cylinders were observed. The 3D printed cylinder repaired with Bis-GMA composite demonstrated the most predictability from the fractography analysis.

This paper proposes a smart real-time health monitoring structured for hospitals' distributor based on wearable health data sensors. Health data were received from multiple heterogeneous wearable sensors, such as electrocardiogram (ECG), oxygen saturation sensor (SpO2), blood pressure monitor, and non-sensory measurement (text frame), from 500 patients with different symptoms. Triage level and healthcare services were identified based on the new four-level remote triage and package localization (4LRTPL). The numbers of healthcare services that represent hospital status were collected from 12 hospitals located in Baghdad city. This study constructed a decision matrix based on the crossover of “multi-healthcare services” and “hospital list” within Tier 4. The hospitals were then ranked using multi-criteria decision-making (MCDM) techniques, namely, integrated analytic hierarchy process (AHP) and vlsekriterijumskaoptimizacija i kompromisnoresenje (VIKOR). Mean ± standard deviation was computed to ensure that the hospital ranking undergoes systematic ranking for objective validation. This research provided scenarios and checklist benchmarking to evaluate the proposed and existing health recommender frameworks. Results corroborated that: 1) the integration of AHP and VIKOR effectively solved hospital selection problems; 2) in the objective validation, significant differences were recognized between the scores of groups, indicating that the ranking results were identical; 3) in evaluation, the proposed framework exhibited an advantage over the benchmark framework with a percentage of 56.25%; and 4) hospitals with multiple healthcare services received the highest ranks, whereas hospitals with fewer healthcare services received low ranks.

The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient's emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the “healthcare services package/TAH” of each hospital and the “hospital list”. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.

Steganography is a form of technology utilised to safeguard secret data during communication in addition to data repository. Numerous researchers have endeavoured to enhance the performance of steganography techniques through the development of an effective algorithm for the selection of the optimal pixel location within the host image for the concealment of secret bits, for the enhancement of the embedding capacity of the secret data, and for maintaining the visual quality of the host image (stego image) in an accepted rate after the concealment of the secret data. Therefore, steganography is perceived as a challenging task. Thus, the current study proposes a new technique for image steganography based on particle swarm optimisation (PSO) algorithm by using pixel selection for the concealment of a secret image in spatial domain, for the purpose of high embedment capacity. The stego possesses a high level of resistance against a steganalytic attack due to the security provided via image steganography. The function of PSO algorithm is to choose an optimal pixel in grey scale host image for the concealment of secret bits, as the PSO has the ability to achieve an efficient fitness calculation that depends on the cost matrix by dividing the host and secret images into four parts. First of all, the secret bits are modified, which are then embedded within the host image. Several locations in the host image are determined through the order of scanning the host pixels and starting point of the scanning for better least significant bits LSBs of each pixel. The PSO algorithm was utilised to ascertain the ideal initiating point and scanning order. Experimental results show that (1) the average peak signal to noise ratio PSNR value in the benchmark technique based on genetic algorithm for five standard stego images is 45.13%, whereas the result obtained from the recommended technique is 56.60%. (2) The proposed technique has an advantage over the benchmark with a percentage of 33.34%, which encompasses all associated issues within the checklist scenario. Therefore, the performance of the recommended technique is superior over existing techniques.

Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (n=39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (n=1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

This case report describes a new digital workflow for computer-assisted implant surgery in an edentulous patient using transitional implants to support a fixed surgical template and interim prosthesis. The accuracy of the final implant position using the described protocol was evaluated and compared to the outcomes obtained using other types of surgical templates. This novel digital approach appears to enhance the accuracy of implant positioning for edentulous patients and seems to be comparable to a tooth-supported surgical template. [ABSTRACT FROM AUTHOR]

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients' data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients' score and dose availability without sharing the patients' data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death. [ABSTRACT FROM AUTHOR]