Assessment of aortomesenteric distance and mesenteric and retroperitoneal adipose tissue thickness in genetic forms of lipodystrophy.

INTRODUCTION: Lipodystrophy is a rare disease characterized by the loss of adipose tissue. Visceral adipose tissue loss in certain forms of lipodystrophy may affect the amount of mesenteric fat. METHOD: We studied visceral adipose tissue by measuring the thickness of mesenteric and retroperitoneal adipose tissue and the aortomesenteric (AOM) distance in patients with genetic forms of lipodystrophy (n = 48; 7 males; 41 females; mean age 39.1 ± 11.9 years; 19 with congenital generalized lipodystrophy [CGL], and 29 with familial partial lipodystrophy [FPLD]). An age- and gender-matched control group with a ratio of 1:2 was generated. RESULTS: Patients with CGL had severely depleted mesenteric adipose tissue (2.0 [IQR: 1.5-3.5] mm vs. 18.8 [IQR: 4.4-42.2] mm in FPLD, P < .001; 30.3 [IQR: 13.9-46.6] mm in controls, P < .001) and retroperitoneal adipose tissue (1.3 [IQR: 0.0-5.3] mm vs. 33.7 [IQR: 21.6-42.1] mm in FPLD, P < .001; 29.7 [IQR: 23.1-36.7] mm in controls, P < .001). The AOM distance was shorter in patients with CGL (8.1 [IQR: 6.0-10.8] mm) compared to patients with FPLD (vs. 13.0 [IQR: 8.8-18.1] mm; P = .023) and controls (vs. 11.3 [IQR: 8.4-15.5] mm, P = .016). Leptin levels were positively correlated with AOM distance in lipodystrophy (r = .513, P < .001). Multivariate linear regression analysis identified body mass index as a significant predictor of AOM distance (data controlled for age and sex; beta = 0.537, 95% CI: 0.277-0.798, P < .001). Twelve of 19 patients (63%) with CGL had an AOM distance of < 10 mm, a risk factor that may predispose patients to developing superior mesenteric artery syndrome. CONCLUSION: CGL is associated with a severe loss of mesenteric adipose tissue, which leads to a narrowing of the space between the superior mesenteric artery and the aorta.

Navigating truth and disinformation: A comparative analysis of generational responses to the 6 February 2023 earthquake in digital media in Türkiye.

This study aims to examine how different generations perceived and responded to news and disinformation about the February 6, 2023 Earthquake in Türkiye, focusing on their trust in news sources and methods of verifying authenticity. In this study, the data were collected from semi-structured interviews held with 30 participants using qualitative methods and they were analyzed with MAXQDA Analytics Pro 2022, through thematic analysis to uncover generational nuances in digital media engagement and trust. The analysis revealed five primary themes: digital media usage habits, trust and reliability in news sources, fake news verification practices, causes of fake news, and views on media legislation. The findings of the study indicated significant generational differences in digital media consumption habits. Notably, maintaining consistent online presence and integrating digital media into everyday life in Generation Z stood out as decisive factors in their reactions to news and disinformation about the February 6, 2023 Earthquake. The study also highlighted varied approaches among generations toward detecting disinformation. While Generation X preferred to use the methods of verification over broadcast media, Generations Y and Z showed a propensity for utilizing digital tools for identifying and verifying fake news. Attitudes toward media legislation differed among generations, yet there was a general consensus on the necessity of such laws to adapt to the digital age's challenges and play a crucial role in combating disinformation. This study offered a detailed comparative analysis on how different generations use digital media and their attitudes toward accuracy of news, particularly in response to significant events such as the February 6, 2023 Earthquake in Türkiye. This study would contribute to adopt a deeper understanding about the critical role of accurate information access during crises and the varying media consumption habits and attitudes toward disinformation across generations. The study emphasized the importance of tailored approaches in media literacy education and disinformation counter-strategies, as well as the need for media laws to be updated in accordance with the demands of the digital era.

The Evaluation of Clinical Applications for the Detection of the Alzheimer's Disease Biomarker GFAP.

One of the most prevalent neurodegenerative diseases is Alzheimer's disease (AD). The hallmarks of AD include the accumulation of amyloid plaques and neurofibrillary tangles, which cause related secondary diseases, progressive neurodegeneration, and ultimately death. The most prevalent cell type in the human central nervous system, astrocytes, are crucial for controlling neuronal function. Glial fibrillary acidic protein (GFAP) is released from tissue into the bloodstream due to astrocyte breakdown in neurological diseases. Increased levels of GFAP in the serum can function as blood markers and be an effective prognostic indicator to help diagnose neurological conditions early on, from stroke to neurodegenerative diseases. The human central nervous system (CNS) is greatly affected by diseases associated with blood GFAP levels. These include multiple sclerosis, intracerebral hemorrhage, glioblastoma multiforme, traumatic brain injuries, and neuromyelitis optica. GFAP demonstrates a strong diagnostic capacity for projecting outcomes following an injury. Furthermore, the increased ability to identify GFAP protein fragments helps facilitate treatment, as it allows continuous screening of CNS injuries and early identification of potential recurrences. GFAP has recently gained attention due to data showing that the plasma biomarker is effective in identifying AD pathology. AD accounts for 60-70% of the approximately 50 million people with dementia worldwide. It is critical to develop molecular markers for AD, whose number is expected to increase to about 3 times and affect humans by 2050, and to investigate possible targets to confirm their effectiveness in the early diagnosis of AD. In addition, most diagnostic methods currently used are image-based and do not detect early disease, i.e. before symptoms appear; thus, treatment options and outcomes are limited. Therefore, recently developed methods such as point-of-care (POC), on-site applications, and enzyme-linked immunosorbent assay-polymerase chain reaction (ELISA-PCR) that provide both faster and more accurate results are gaining importance. This systematic review summarizes published studies with different approaches such as immunosensor, lateral flow, POC, ELISA-PCR, and molecularly imprinted polymer using GFAP, a potential blood biomarker to detect neurological disorders. Here, we also provide an overview of current approaches, analysis methods, and different future detection strategies for GFAP, the most popular biosensing field.

A molecularly imprinted polymer-based detection platform confirmed through molecular modeling for the highly sensitive and selective analysis of ipratropium bromide.

This study presented a new method to design a MIP-based electrochemical sensor that could improve the selective and sensitive detection of ipratropium bromide (IPR). The polymeric film was designed using 2-hydroxyethyl methacrylate (HEMA) as the basic monomer, 2-hydroxy-2-methylpropiophenone as the initiator, ethylene glycol dimethacrylate (EGDMA) as the crosslinking agent, and N-methacryloyl-L-aspartic acid (MAAsp) as the functional monomer. The presence of MAAsp results in the functional groups in imprinting binding sites, while the presence of poly(vinyl alcohol) (PVA) allows the generation of porous materials not only for sensitive sensing but also for avoiding electron transport limitations. Electrochemical characterizations of the changes at each stage of the MIP preparation process were confirmed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). In addition, morphological characterizations of the developed sensor were performed using scanning electron microscopy (SEM), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and contact angle measurements. Theoretical calculations were also performed to explain/confirm the experimental results better. It was found that the results of the calculations using the DFT approach agreed with the experimental data. The MAAsp-IPR@MIP/GCE sensor was developed using the photopolymerization method, and the sensor surface was obtained by exposure to UV lamp radiation at 365 nm. The improved MIP-based electrochemical sensor demonstrated the ability to measure IPR for standard solutions in the linear operating range of 1.0 × 10-12-1.0 × 10-11 M under optimized conditions. For standard solutions, the limit of detection (LOD) and limit of quantification (LOQ) were obtained as 2.78 × 10-13 and 9.27 × 10-13 M, respectively. The IPR recovery values for the inhalation form were calculated as 101.70 % and 100.34 %, and the mean relative standard deviations (RSD) were less than 0.76 % in both cases. In addition, the proposed modified sensor demonstrated remarkable sensitivity and selectivity for rapid assessment of IPR in inhalation forms. The sensor's unique selectivity is demonstrated by its successful performance even in the presence of IPR impurities.

Nanomaterial-assisted molecularly imprinted polymer strategies for highly sensitive and selective determination of cefdinir and its validation using computational approach.

In this study, the first nanomaterial-supported molecularly imprinted polymer (MIP)-based electrochemical approach was proposed to achieve the successful detection of cefdinir (CFD). Here, p-amino benzoic acid (p-ABA) was used as the monomer and the photopolymerization method was chosen to form MIP on a glassy carbon electrode (GCE). ZnO nanoparticles (ZnO NPs) were added to the MIP sensor to increase sensitivity and create high porosity. Through the use of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), characterization investigations confirmed the alterations at each stage of the MIP production process. Electrochemical (cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS)) and scanning electron microscopy (SEM) methods were used for study the characterization studies of the MIP-based nanocomposite sensor. The measurement of MIP parameters, such as the addition of nanoparticles, the removal procedure, the rebinding period, the monomer ratio, etc., was done using the differential pulse voltammetry (DPV). The findings showed that when ZnO NPs were added, the signal was three times higher than when MIPs were used alone. Under the optimized conditions, CFD/4-ABA@ZnONPs/MIP/GCE showed a linear response in the concentration range between 7.5 pM and 100 pM with LOD and LOQ values of 2.06 pM and 6.86 pM, respectively. Anions, cations, and substances including uric acid, ascorbic acid, paracetamol, and dopamine were all used in the selectivity test. In addition, the imprinting factor (IF) study was carried out using compounds such as cefuroxime, cefazolin, cefixime, ceftazidime, and ceftriaxone, which have structural similarities with CFD, as well as impurities such as thiazolylacetyl glycine oxime (IMP-A), thiazolylacetyl glycine oxime acetal (IMP-B), and cefdinir lactone (IMP-E). The results showed that the proposed sensor was selective for CFD, as evidenced by the relative IF values of these impurities. The recovery studies of CFD were successfully applied to tablet dosage form samples, and the developed sensor demonstrated significant sensitivity and selectivity for rapid detection of CFD in tablet dosage form.

The effect of thoracic trauma scoring system in thoracic trauma patients with rib fracture.

OBJECTIVE: Rib fractures are common in thoracic trauma patients. There are various factors, including flail chest, pulmonary contusion, and accompanying conditions, affecting morbidity and mortality. The study aimed to identify high-risk patients for morbidity and mortality with a scoring system that the authors created. METHODS: Cases over the age of 18 admitted due to trauma and diagnosed with rib fractures between 1 January 2019 and 1 March 2023, were included. Trauma scores were determined by applying the new trauma scoring system. Trauma scores and other variables regarding morbidity and mortality were evaluated. RESULTS: A total of 1023 cases were included in the study. The total trauma scores were higher in bilateral and multiple fractures. In those without respiratory failure, the total score was statistically significantly lower than in the groups with respiratory failure. The total score was significantly higher in those who needed surgery, those who were hospitalized, and those who needed intensive care compared to the non-surgical groups. However, there was no correlation between intensive care unit stay and total score. Trauma mechanism, presence of additional extrathoracic pathology, and thoracic trauma-age score were independent predictors of survival. CONCLUSION: The present study demonstrated that the number of rib fractures and the presence of pulmonary contusion did not have an effect on mortality and morbidity. The presence of extrathoracic pathology and age significantly affect survival.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

Proj2Proj: self-supervised low-dose CT reconstruction.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.

Evaluation of Foot Deformities in Patients With Ingrown Nails.

INTRODUCTION: Ingrown nail is a condition caused by the perforation of the periungual soft tissues on nail folds by the sides of nail plaque, causing inflammation and severe pain. Recently, the role of foot anatomical disorders in ingrown nail development has been emphasized. OBJECTIVES: The main objective of this study aimed to determine whether foot deformities played significant roles in ingrown nail development with objective radiological parameters. METHODS: The study included 64 patients diagnosed with clinical ingrown nail and 71 patients as controls without any ingrown nail history. In both groups, we evaluated the bilateral foot radiographs of patients with ingrown nails for hallux valgus angle (HVA), interphalangeal angle (IPA), and intermetatarsal angle (IMA) associated with hallux valgus, and the calcaneal pitch angle (CPA), talohorizontal angle (THA), and talometatarsal angle (TMA) related to pes planus. RESULTS: No significant difference was found in terms of hallux valgus radiological measurements of HVA, IPA and IMA as well as pes planus radiological measurements of CPA and TMA values, when compared to controls. THA was statistically significantly higher in the control group (P = 0.025). There was a moderate strength positive relationship between ingrown nail stage and measured TMA for pes planus diagnosis (rho = 0.326; P = 0.04), yet there are no significant correlations between ingrown nail stage and other angles. CONCLUSIONS: Therefore, we do not recommend foot anatomy correction in the prevention and treatment of ingrown nails, unless there is an accompanying foot deformity; however, pes planus is a foot deformity that can accompany patients with severely ingrown nails.

An innovative method for the detection of alpha synuclein, a potential biomarker of Parkinson's disease: quartz tuning fork-based mass sensitive immunosensor design.

An innovative biosensing fabrication strategy has been demonstrated for the first time using a quartz tuning fork (QTF) to develop a practical immunosensor for sensitive, selective and practical analysis of alpha synuclein protein (SYN alpha), a potential biomarker of Parkinson's disease. Functionalization of gold-coated QTFs was carried out in 2 steps by forming a self-assembled monolayer with 4-aminothiophenol (4-ATP) and conjugation of gold nanoparticles (AuNPs). The selective determination range for SYN alpha of the developed biosensor system is 1-500 ng mL-1 in accordance with the resonance frequency shifts associated with a limit of detection of 0.098 ng mL-1. The changes in surface morphology and elemental composition were evaluated using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and energy-dispersive X-ray spectroscopy (EDX). The remarkable point of the study is that this QTF based mass sensitive biosensor system can capture the SYN alpha target protein in cerebrospinal fluid (CSF) samples with recoveries ranging from 92% to 104%.

Ultrasensitive electrochemical immunosensor system for determination of autologous SOX2 antibody.

Lung cancer is mainly seen as the cancer type in the world. Lung cancer causes the death of many people. It is classified as large-cell neuroendocrine carcinoma (LCNEC), small-cell lung cancer (SCLC), and adenocarcinoma by the World Health Organization (WHO) in 2015. Small cell lung cancer (SCLC) is a highly aggressive type of cancer, accounting for approximately 20% of all cases. By performing the serological analysis of expression cDNA libraries (SEREX), the humoral immune response of SCLC patients is determined. SEREX of SCLC cell lines using pooled sera of SCLC patients led to the isolation of SOX2 genes. The between SOX2 antigen expression intensity and autologous antibody presence has a significant correlation because SOX2 is the main antigen eliciting anti-SOX responses. Electrochemical biosensors take much attention because of their simplicity, selectivity, and sensitivity in clinical analysis. Antibody-based surface recognizes antibody-specific antigens. This work aims to fabricate an immunosensor for determining autologous SOX2 antibodies using a multi-walled carbon nanotube-modified screen-printed electrode (DRP-MWCNT). All immobilization processes were evaluated with cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The critical parameters were optimized, such as EDC/NHS concentration and time, SOX2 protein concentration and incubation time, BSA ratio, BSA blocking time, and anti-SOX2 antibody incubation time. The developed immunosensor, under optimal conditions, shows a linear response of autologous SOX2 antibody between 0.005 ng.mL-1 and 0.1 ng.mL-1. The limit of detection and quantification were 0.001 and 0.004 ng.mL-1, respectively. The electrode morphologies were examined with a scanning electron microscope (SEM). Lastly, the developed immunosensor was applied to a synthetic serum sample, and the linear range was compared with enzyme-linked immunosorbent assay (ELISA).

Small Warriors of Nature: Novel Red Emissive Chlorophyllin Carbon Dots Harnessing Fenton-Fueled Ferroptosis for In Vitro and In Vivo Cancer Treatment.

The appeal of carbon dots (CDs) has grown recently, due to their established biocompatibility, adjustable photoluminescence properties, and excellent water solubility. For the first time in the literature, copper chlorophyllin-based carbon dots (Chl-D CDs) are successfully synthesized. Chl-D CDs exhibit unique spectroscopic traits and are found to induce a Fenton-like reaction, augmenting photodynamic therapy (PDT) efficacies via ferroptotic and apoptotic pathways. To bolster the therapeutic impact of Chl-D CDs, a widely used cancer drug, temozolomide, is linked to their surface, yielding a synergistic effect with PDT and chemotherapy. Chl-D CDs' biocompatibility in immune cells and in vivo models showed great clinical potential.Proteomic analysis was conducted to understand Chl-D CDs' underlying cancer treatment mechanism. The study underscores the role of reactive oxygen species formation and pointed toward various oxidative stress modulators like aldolase A (ALDOA), aldolase C (ALDOC), aldehyde dehydrogenase 1B1 (ALDH1B1), transaldolase 1 (TALDO1), and transketolase (TKT), offering a deeper understanding of the Chl-D CDs' anticancer activity. Notably, the Chl-D CDs' capacity to trigger a Fenton-like reaction leads to enhanced PDT efficiencies through ferroptotic and apoptotic pathways. Hence, it is firmly believed that the inherent attributes of Chl-CDs can lead to a secure and efficient combined cancer therapy.

Multiple immunoassay interference in a patient with falsely elevated calcitonin

SUMMARY Calcitonin (CT) is a diagnostic and follow-up marker of medullary thyroid carcinoma. Heterophile antibodies (HAbs) may interfere during immunometric assay measurements and result in falsely high CT levels and different markers. A 50-year-old female patient was referred to our institution for elevated CT levels (3,199 pg/mL [0-11,5]). Physical examination and thyroid ultrasonography show no thyroid nodules. Because of the discrepancy between the clinical picture and the laboratory results, various markers and hormones were examined to determine whether there was any interference in the immunometric assay. Thyroglobulin (Tg) and Adrenocorticotropic hormone (ACTH) levels were also found inaccurately elevated. After precipitation with polyethylene glycol, CT, Tg, and ACTH levels markedly decreased, showing macro-aggregates. Also, serial dilutions showed non-linearity in plasma concentrations. Additionally, CT samples were pretreated with a heterophilic blocking tube before measuring, and the CT level decreased to < 0.1 pg/mL, suggesting a HAb presence. Immunoassay interference should be considered when conflicting laboratory data are observed. This may help reduce the amount of unnecessary laboratory and imaging studies and prevent patients from complex diagnostic procedures.

Synergized photothermal therapy and magnetic field induced hyperthermia via bismuthene for lung cancer combinatorial treatment.

Thanks to its intrinsic properties, two-dimensional (2D) bismuth (bismuthene) can serve as a multimodal nanotherapeutic agent for lung cancer acting through multiple mechanisms, including photothermal therapy (PTT), magnetic field-induced hyperthermia (MH), immunogenic cell death (ICD), and ferroptosis. To investigate this possibility, we synthesized bismuthene from the exfoliation of 3D layered bismuth, prepared through a facile method that we developed involving surfactant-assisted chemical reduction, with a specific focus on improving its magnetic properties. The bismuthene nanosheets showed high in vitro and in vivo anti-cancer activity after simultaneous light and magnetic field exposure in lung adenocarcinoma cells. Only when light and magnetic field are applied together, we can achieve the highest anti-cancer activity compared to the single treatment groups. We have further shown that ICD-dependent mechanisms were involved during this combinatorial treatment strategy. Beyond ICD, bismuthene-based PTT and MH also resulted in an increase in ferroptosis mechanisms both in vitro and in vivo, in addition to apoptotic pathways. Finally, hemolysis in human whole blood and a wide variety of assays in human peripheral blood mononuclear cells indicated that the bismuthene nanosheets were biocompatible and did not alter immune function. These results showed that bismuthene has the potential to serve as a biocompatible platform that can arm multiple therapeutic approaches against lung cancer.

Multiple immunoassay interference in a patient with falsely elevated calcitonin.

Calcitonin (CT) is a diagnostic and follow-up marker of medullary thyroid carcinoma. Heterophile antibodies (HAbs) may interfere during immunometric assay measurements and result in falsely high CT levels and different markers. A 50-year-old female patient was referred to our institution for elevated CT levels (3,199 pg/mL [0-11,5]). Physical examination and thyroid ultrasonography show no thyroid nodules. Because of the discrepancy between the clinical picture and the laboratory results, various markers and hormones were examined to determine whether there was any interference in the immunometric assay. Thyroglobulin (Tg) and Adrenocorticotropic hormone (ACTH) levels were also found inaccurately elevated. After precipitation with polyethylene glycol, CT, Tg, and ACTH levels markedly decreased, showing macro-aggregates. Also, serial dilutions showed non-linearity in plasma concentrations. Additionally, CT samples were pretreated with a heterophilic blocking tube before measuring, and the CT level decreased to < 0.1 pg/mL, suggesting a HAb presence. Immunoassay interference should be considered when conflicting laboratory data are observed. This may help reduce the amount of unnecessary laboratory and imaging studies and prevent patients from complex diagnostic procedures.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things.

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

Evaluating the conversion degree of interim restorative materials produced by different 3-dimensional printer technologies.

STATEMENT OF PROBLEM: Three-dimensional (3D) printers are a relatively new technology, but the degree of conversion (DC) of the resin specimens produced by using this method is currently unknown. However, the DC of resin interim restorative materials is critical for their biocompatibility and physical properties. PURPOSE: The purpose of this in vitro study was to evaluate the DC of interim restorative materials produced by using different 3D printer technologies and compare them with conventionally manufactured polymethyl methacrylate. MATERIAL AND METHODS: Stereolithography, digital light processing, and liquid crystal display 3D printers were used as experimental groups, and a conventional (C) method was used as the control. Five different 3D printers (DWS Systems, Formlabs [FL], Asiga, Mega, and Vega) were included. The 3D printed specimens were designed in a rectangular prism geometry (10×4×2.5 mm) by using a computer-aided design software program (Materialise 3-matic) and printed with a layer thickness of 50 µm in the horizontal direction (n=15). Fourier transform infrared spectroscopy (FT-IR) spectra were measured in 3 steps: the liquid state of the resins, after washing with 99% isopropanol, and after final polymerization. For the C method, FT-IR spectra were assessed in 2 steps: immediately after mixing the liquid and powder and after polymerization. Statistical analysis of the data was performed with 1-way ANOVA followed by the post hoc Tukey honestly significant difference (HSD) test (α=.05). RESULTS: There was no statistically significant difference in DC values between the 3D printed groups (P>.05). There was a statistically significant difference only between FL and the C in terms of DC (P=.042). CONCLUSIONS: Three-dimensionally printed interim resin materials found comparable results with those of the C group. The DC was not affected by different 3D printing technologies.

The Diversity in Grapes of Vitis labrusca Grown in Bolu (Türkiye) Assessed by Multivariate Approaches.

The grape is one of the most produced and processed horticultural crops. This study evaluated the grape genetic resource belonging to the Vitis labrusca species. The diversity was assessed according to morphometric, antioxidant, physicochemical, and colorimetric characteristics. The diversity was evaluated using a variation index and multivariate analyses. The bunch weight of the vines exhibited a range from 21.05 g to 162.46 g, with a coefficient of variation (CV) of 38.97%. The average bunch weight was 64.74 g. In terms of the berry properties, the highest CV was observed for the berry weight (21.95%). The peel thickness displayed a CV of 36.40%, and an average of 0.23 mm. The CVs for the juice characteristics in the berries of the studied vines were 7.11%, 16.61%, 19.41%, and 28.10% for the pH, TSS, must yield, and TA, respectively. The TPC of the accessions exhibited a notably low variation (CV = 4.63%). The color properties of the accessions displayed an immense variation, except for the L* values. The hierarchical clustering analysis divided the accessions into two main clusters, which both had two subclusters. The multivariate approaches separated individuals into different groups, and they were considered useful tools for utilization in the genetic diversity assessments. Further studies on the cultivation technique and crossbreeding with Vitis vinifera will provide more insights into the population, and this study will be a source for upcoming studies on V. labrusca in the region.

A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems.

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

Effect of Disulfide Bonds on the Thermal Stability of Pediocin: In-silico Screening Using Molecular Dynamics Simulation.

The thermal stability properties of pediocin at 310, 313, 323, 333, 343, and 348 K (37, 40, 50, 60, 70, and 75°C, respectively) are reported in this study. A theoretical approach, such as the molecular dynamics method, was used to analyze the structure. Molecular dynamics simulation confirms the stability of molecules with Cys. Furthermore, this study reveals that Cys residues play an essential role in structure stability at high temperatures. To understand the structural basis for the stability of pediocin, a detailed in-silico analysis using molecular dynamics simulations to explore the thermal stability profiles of the compounds was conducted. This study shows that thermal effects fundamentally alter the functionally crucial secondary structure of pediocin. However, as previously reported, pediocin's activity was strictly conserved due to the disulfide bond between Cys residues. These findings reveal, for the first time, the dominant factor behind the thermodynamic stability of pediocin.