Security Analysis for Smart Healthcare Systems.

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems.

This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model's superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.

Association of Chemotherapy Response Score with Multidrug Resistance 1 and CA125 ELIMination Rate Constant K in Patients with Advanced Ovarian Cancer Treated with Neoadjuvant Chemotherapy.

AIM: The relationship between chemotherapy response score (CRS), a widely used response predictor of neoadjuvant chemotherapy-interval debulking surgery (NAC-IDS), and multidrug resistance 1 (MDR1) and CA125 ELIMination rate constant K (KELIM), is undetermined. We evaluated CRS in advanced ovarian cancer patients undergoing NAC and looked for associations between CRS and MDR1 and CA125 KELIM. Our aim was to predict the therapeutic effect of NAC before interval debulking surgery (IDS) by examining its association with CRS. METHODS: This retrospective cohort study included patients who underwent NAC-IDS (first-line treatment) at Kurume University Hospital, Japan, between 2004 and 2017. CRS association with MDR1 and CA125 KELIM was examined using Cox proportional hazard regression analyses. Survival curves used Kaplan-Meier method, and survival differences between groups used log-rank test. RESULTS: Overall, 55 patients were classified into CRS1 (n=22), CRS2 (n=19), and CRS3 (n=14). The CRS3 group had a significantly better prognosis than the CRS1 or CRS2 group. CRS, age, and IDS status were clinical prognostic factors for ovarian cancer. MDR1 positivity for excision repair cross-complementing group 1, ß-tubulin, and Y-box binding protein-1 occurred in 15, 17, and 11 patients, respectively, but these were not associated with CRS. CA125 KELIM was <0.5 (n=8), 0.5-1.0 (n=30), and ≥ 1.0 (n=17) but not associated with CRS. CONCLUSION: CRS is reconfirmed as a treatment response predictor for NAC-IDS, but its association with drug resistance factors remains unconfirmed.

Anhedonia in bipolar depression treated with ketamine.

BACKGROUND: Bipolar depression is the major cause of morbidity in patients with bipolar disorder. It affects psychosocial functioning and markedly impairs occupational productivity. Anhedonia is one of the most debilitating symptoms of depression contributing to treatment resistance. It correlates with suicidality, low quality of life, social withdrawal, and poor treatment response. Currently, there is no approved treatment specifically targeting anhedonia. Emerging evidence suggests that ketamine possesses anti-anhedonic properties in individuals with depression. OBJECTIVES: The aim of this naturalistic open-label study was to investigate the effect of add-on ketamine treatment on anhedonia in treatment resistant bipolar depression. METHODS: Our main interest was the change in patient-reported (Snaith-Hamilton Pleasure Scale) and rater-based anhedonia measure (Montgomery-Åsberg Depression Rating Scale-anhedonia subscale). The secondary aim was to analyze the score change in three Inventory of Depressive Symptomatology-Self Report (IDS-SR) domains: mood/cognition, anxiety/somatic, and sleep. Patients underwent assessments at several time points, including baseline, after the third, fifth, and seventh ketamine infusions. Additionally, a follow-up assessment was conducted 1 week following the final ketamine administration. RESULTS: We found improvement in anhedonia symptoms according to both patient-reported and rater-based measures. The improvement in IDS-SR domains was most prominent in anxiety/somatic factor and mood/cognition factor, improvement in sleep factor was not observed. No serious adverse events occurred. CONCLUSION: Add-on ketamine seems to be a good choice for the treatment of anhedonia in treatment resistant bipolar depression. It also showed a good effect in reducing symptoms of anxiety in this group of patients. Considering unmet needs and the detrimental effect of anhedonia and anxiety, more studies are needed on ketamine treatment in resistant bipolar depression.

Factors influencing operationalization of Integrated Disease Surveillance in Malawi.

OBJECTIVES: Malawi's disease surveillance system is built on several different data sources and systems and is informed by the Integrated Diseases Surveillance and Response (IDSR) strategy. This study was carried out as part of a larger multicountry study to identify context-specific factors, which influence the operationalization of integrated disease surveillance. STUDY DESIGN AND METHODS: A total of six focus group discussions were conducted with 43 relevant personnel at the primary and secondary healthcare levels in two districts (Lilongwe and Dowa) and at the national level. The discussions were analyzed and sorted into predefined categories based on the domains of the International Association of Public Health conceptual framework. RESULTS: We found ongoing efforts to enhance integrated disease surveillance operationalization, including the establishment of the Public Health Institute of Malawi for coordination, digitalizing the surveillance system through One Health Surveillance Platform, and improving communication among rapid response teams using WhatsApp. The adoption of World Health Organization's third edition IDSR technical guidelines was also underway. Nonetheless, there were major implementation barriers such as parallel and uncoordinated surveillance systems, priority conditions that cannot be diagnosed at the point of reporting, lack of case definitions and diagnostic codes for priority conditions, reporting forms with unexplained acronyms, illegible data sources, unstable electronic data transfers, inadequate supervision and training, poor enforcement of reporting from private health facilities, high reporting burden, and lack of and feedback to those reporting. CONCLUSIONS: The results fit well into the predefined categories used. The study reveals basic problems with the operationalization, tools, and reporting forms used for IDSR. These findings may have implications for practice and policy in Malawi and other countries where IDSR is the national strategy for surveillance.

Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards.

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.

Screening Non-neutralizing Anti-idiotype Antibodies Against a Drug Candidate for Total Pharmacokinetic and Target Engagement Assay.

Non-neutralizing anti-idiotype antibodies against a therapeutic monoclonal antibody (mAb) play a crucial role in the creation of total pharmacokinetic (PK) assays and total target engagement (TE) assays during both pre-clinical and clinical development. The development of these anti-idiotype antibodies is challenging. In this study, we utilized a hybridoma platform to produce a variety of anti-idiotype antibodies against GSK2857914, a humanized IgG1 anti-BCMA monoclonal antibody. The candidate clones were evaluated using surface plasmon resonance (SPR) and bio-layer interferometry (BLI) for binding affinity, binding profiling, matrix interference, and antibody pairing determination. We discovered that three anti-idiotype antibodies did not prevent BCMA from binding to GSK2857914. All three candidates demonstrated high binding affinities. One of the three exhibited minimal matrix inference and could pair with the other two candidates. Additionally, one of the three clones was biotinylated as a capture reagent for the total PK assay, and another was labeled with ruthenium as a detection reagent for both the total PK assay and total TE assay. The assay results clearly show that these reagents are genuine non-neutralizing anti-idiotypic antibodies and are suitable for total PK and TE assay development. Based on this and similar studies, we conclude that the hybridoma platform has a high success rate for generating non-neutralizing anti-idiotype antibodies. Our methodology for developing and characterizing non-neutralizing anti-idiotype antibodies to therapeutic antibodies can be generally applied to any antibody-based drug candidate's total PK and total TE assay development.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

A close-up view of the Hunter syndrome.

The Lysosomal Storage disease known as Mucopolysaccharidosis type II, is caused by mutations affecting the iduronate-2-sulfatase required for heparan and dermatan sulfate catabolism. The central nervous system (CNS) is mostly and severely affected by the accumulation of both substrates. The complexity of the CNS damage observed in MPS II patients has been limitedly explored. The use of mass spectrometry (MS)-based proteomics tools to identify protein profiles may yield valuable information about the pathological mechanisms of Hunter syndrome. In this further study, we provide a new comparative proteomic analysis of MPS II models by using a pipeline consisting of the identification of native protein complexes positioned selectively by using a specific antibody, coupled with mass spectrometry analysis, allowing us to identify changes involving in a significant number of new biological functions, including a specific brain antioxidant response, a down-regulated autophagic, the suppression of sulfur catabolic process, a prominent liver immune response and the stimulation of phagocytosis among others.

Lentiviral Gene Therapy for Mucopolysaccharidosis II with Tagged Iduronate 2-Sulfatase Prevents Life-Threatening Pathology in Peripheral Tissues But Fails to Correct Cartilage.

Deficiency of iduronate 2-sulfatase (IDS) causes Mucopolysaccharidosis type II (MPS II), a lysosomal storage disorder characterized by systemic accumulation of glycosaminoglycans (GAGs), leading to a devastating cognitive decline and life-threatening respiratory and cardiac complications. We previously found that hematopoietic stem and progenitor cell-mediated lentiviral gene therapy (HSPC-LVGT) employing tagged IDS with insulin-like growth factor 2 (IGF2) or ApoE2, but not receptor-associated protein minimal peptide (RAP12x2), efficiently prevented brain pathology in a murine model of MPS II. In this study, we report on the effects of HSPC-LVGT on peripheral pathology and we analyzed IDS biodistribution. We found that HSPC-LVGT with all vectors completely corrected GAG accumulation and lysosomal pathology in liver, spleen, kidney, tracheal mucosa, and heart valves. Full correction of tunica media of the great heart vessels was achieved only with IDS.IGF2co gene therapy, while the other vectors provided near complete (IDS.ApoE2co) or no (IDSco and IDS.RAP12x2co) correction. In contrast, tracheal, epiphyseal, and articular cartilage remained largely uncorrected by all vectors tested. These efficacies were closely matched by IDS protein levels following HSPC-LVGT. Our results demonstrate the capability of HSPC-LVGT to correct pathology in tissues of high clinical relevance, including those of the heart and respiratory system, while challenges remain for the correction of cartilage pathology.

Dominant mechanism in spinal cord injury-induced immunodeficiency syndrome (SCI-IDS): sympathetic hyperreflexia.

Clinical studies have shown that individuals with spinal cord injury (SCI) are particularly susceptible to infectious diseases, resulting in a syndrome called SCI-induced immunodeficiency syndrome (SCI-IDS), which is the leading cause of death after SCI. It is believed that SCI-IDS is associated with exaggerated activation of sympathetic preganglionic neurons (SPNs). After SCI, disruption of bulbospinal projections from the medulla oblongata C1 neurons to the SPNs results in the loss of sympathetic inhibitory modulation from the brain and brainstem and the occurrence of abnormally high levels of spinal sympathetic reflexes (SSR), named sympathetic hyperreflexia. As the post-injury survival time lengthens, mass recruitment and anomalous sprouting of excitatory interneurons within the spinal cord result in increased SSR excitability, resulting in an excess sympathetic output that disrupts the immune response. Therefore, we first analyze the structural underpinnings of the spinal cord-sympathetic nervous system-immune system after SCI, then demonstrate the progress in highlighting mechanisms of SCI-IDS focusing on norepinephrine (NE)/Beta 2-adrenergic receptor (ß2-AR) signal pathways, and summarize recent preclinical studies examining potential means such as regulating SSR and inhibiting ß2-AR signal pathways to improve immune function after SCI. Finally, we present research perspectives such as to promote the effective regeneration of C1 neurons to rebuild the connection of C1 neurons with SPNs, to regulate excitable or inhibitory interneurons, and specifically to target ß2-AR signal pathways to re-establish neuroimmune balance. These will help us design effective strategies to reverse post-SCI sympathetic hyperreflexia and improve the overall quality of life for individuals with SCI.

The Putative Effects of Neoadjuvant Chemotherapy on the Immune System of Advanced Epithelial Ovarian Carcinoma.

The epithelial ovarian carcinoma (EOC) is one of leading causes of cancer-related mortality in females. For some patients, complete resection cannot be achieved, thus neoadjuvant chemotherapy (NACT) following interval debulking surgery (IDS) could be an alternative choice. In general-held belief, cytotoxic chemotherapy is assumed to be immunosuppressive, because of its toxicity to dividing cells in the bone marrow and peripheral lymphoid tissues. However, increasing evidence highlighted that the anticancer activity of chemotherapy may also be related to its ability to act as an immune modulator. NACT not only changed the morphology of cancer cells, but also changed the transcriptomic and genomic profile of EOC, induced proliferation of cancer stem-like cells, gene mutation, and tumor-related adaptive immune response. This review will provide a comprehensive overview of recent studies evaluating the impact of NACT on cancer cells and immune system of advanced EOC and their relationship to clinical outcome. This information could help us understand the change of immune system during NACT, which might provide new strategies in future investigation of immuno-therapy for maintenance treatment of EOC.

Clinical performance of bonded partial lithium disilicate restorations: The influence of preparation characteristics on survival and success.

OBJECTIVES: The aim of this study was to investigate the influence of preparation characteristics on the survival, success, and clinical performance of partial indirect lithium disilicate restorations with immediate dentin sealing. METHODS: This retrospective clinical study evaluated partial indirect lithium disilicate restorations placed in conjunction with Immediate Dentin Sealing (IDS) in (pre)molar teeth between March 2018 and May 2021. The restorations were luted using pre-heated composite. The study focused on survival, success, and clinical performance, which was evaluated using the modified United States Public Health Service (USPHS) criteria. Results were analyzed using the Kaplan-Meier estimates, log-rank tests, and Fisher exact tests. RESULTS: Partial indirect lithium disilicate restorations (N = 454) were evaluated in 214 patients. The mean evaluation time was 37 months, with a cumulative survival rate of 99.2 % and a cumulative success rate of 97.6 %. Fourteen failures occurred, with endodontic pathology as the predominant failure mode, followed by secondary caries, debonding, and tooth fracture. No statistically significant influence of the preparation variables on survival and success was observed (p > .05). The short-term clinical performance was clinically acceptable in > 90 % of the evaluations. CONCLUSIONS: This retrospective study on partial indirect lithium disilicate restorations in conjunction with IDS demonstrates survival and success rates of 99.2 and 96.7 % over a mean evaluation period of 37 months. A marked influence of the studied preparation characteristics on the survival, success and clinical performance of lithium disilicate partial restorations could not be demonstrated. Partial lithium disilicate restorations exhibit good clinical performance in >90 % of the cases. CLINICAL SIGNIFICANCE: The results of this study suggest that preparation characteristics had no significant impact on the survival, success, and clinical performance of partial lithium disilicate restorations in conjunction with IDS. Results show good clinical performance and high survival and success rates, regardless of preparation characteristics.

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments.

The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge-cloud deployment. A new edge-cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge-cloud deployment.

An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning.

The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios.

Tagged IDS causes efficient and engraftment-independent prevention of brain pathology during lentiviral gene therapy for Mucopolysaccharidosis type II.

Mucopolysaccharidosis type II (OMIM 309900) is a lysosomal storage disorder caused by iduronate 2-sulfatase (IDS) deficiency and accumulation of glycosaminoglycans, leading to progressive neurodegeneration. As intravenously infused enzyme replacement therapy cannot cross the blood-brain barrier (BBB), it fails to treat brain pathology, highlighting the unmet medical need to develop alternative therapies. Here, we test modified versions of hematopoietic stem and progenitor cell (HSPC)-mediated lentiviral gene therapy (LVGT) using IDS tagging in combination with the ubiquitous MND promoter to optimize efficacy in brain and to investigate its mechanism of action. We find that IDS tagging with IGF2 or ApoE2, but not RAP12x2, improves correction of brain heparan sulfate and neuroinflammation at clinically relevant vector copy numbers. HSPC-derived cells engrafted in brain show efficiencies highest in perivascular areas, lower in choroid plexus and meninges, and lowest in parenchyma. Importantly, the efficacy of correction was independent of the number of brain-engrafted cells. These results indicate that tagged versions of IDS can outperform untagged IDS in HSPC-LVGT for the correction of brain pathology in MPS II, and they imply both cell-mediated and tag-mediated correction mechanisms, including passage across the BBB and increased uptake, highlighting their potential for clinical translation.

Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection.

With the rapid growth of social media networks and internet accessibility, most businesses are becoming vulnerable to a wide range of threats and attacks. Thus, intrusion detection systems (IDSs) are considered one of the most essential components for securing organizational networks. They are the first line of defense against online threats and are responsible for quickly identifying potential network intrusions. Mainly, IDSs analyze the network traffic to detect any malicious activities in the network. Today, networks are expanding tremendously as the demand for network services is expanding. This expansion leads to diverse data types and complexities in the network, which may limit the applicability of the developed algorithms. Moreover, viruses and malicious attacks are changing in their quantity and quality. Therefore, recently, several security researchers have developed IDSs using several innovative techniques, including artificial intelligence methods. This work aims to propose a support vector machine (SVM)-based deep learning system that will classify the data extracted from servers to determine the intrusion incidents on social media. To implement deep learning-based IDSs for multiclass classification, the CSE-CIC-IDS 2018 dataset has been used for system evaluation. The CSE-CIC-IDS 2018 dataset was subjected to several preprocessing techniques to prepare it for the training phase. The proposed model has been implemented in 100,000 instances of a sample dataset. This study demonstrated that the accuracy, true-positive recall, precision, specificity, false-positive recall, and F-score of the proposed model were 100%, 100%, 100%, 100%, 0%, and 100%, respectively.

A comparative study of the C-MAC D-blade videolaryngoscope and McCoy laryngoscope for oro-tracheal intubation with manual in-line stabilization of neck in patients undergoing cervical spine surgery.

Background and Aims: Airway management in cervical spine injury patients requires manual in-line stabilization (MILS) of the neck to avoid exacerbation of cord injury, which impedes visualization of glottis during laryngoscopy. Specially designed blades such as McCoy and C-MAC D-blades can improve laryngoscopic view in such patients. This study was performed to compare the efficacy of C-MAC D-blades and the McCoy laryngoscope for oro-tracheal intubation using MILS in patients undergoing cervical spine surgery. Material and Methods: This randomized, prospective study was performed in 60 adult patients of American Society of Anesthesiologists grade I-III, either sex, 18 to 60 years of age undergoing elective cervical spine surgery. Patients were randomly categorized into two groups, group D and group M. Intubation was performed using a C-MAC D-blade videolaryngoscope in group D and a McCoy laryngoscope in group M using MILS. The intubation difficulty scale (IDS) score, laryngoscopy and intubation times, percentage of glottic opening (POGO) score, Cormack Lehane (CL) grading with and without external laryngeal pressure (ELP), need for bougie or change of blade or operator, and change in hemodynamics following intubation were recorded. Results: Group D showed lower mean IDS scores than group M (P value < 0.0001). There were statistically significant differences found in duration of laryngoscopy (group D < group M), CL grading without ELP (group D: CL-1,2a >CL-2b, 3; group M: CL-1,2a group M), need for ELP and lifting force (group D < group M), and hemodynamic responses after intubation (group D < group M). Conclusion: A C-MAC D-blade videolaryngoscope provides better and rapid visualization of glottis with less intubation difficulties than a McCoy laryngoscope during intubation using MILS in patients with cervical spine injury.

Novel Biodegradable Chelating Agents for Micronutrient Fertilization.

Serious concerns about the negative impact of ethylenediaminetetraacetic acid (EDTA) on the environment resulted in severe restrictions imposed on this compound in many countries. One of the main concerns is related to the use of EDTA in agriculture as a chelator in microelement fertilizers: being introduced directly into the sawing fields, it penetrates into groundwater, with no chance to be captured/recycled. Respectively, there is an active search for environmentally friendly, biodegradable alternatives for this chelator. In this study, we proposed a biodegradable chelating agent, 2-((1,2-dicarboxyethyl)amino)pentanedioic acid (IGSA). It was synthesized in accordance with the principles of "green chemistry" from readily available nonhazardous precursors using water as a solvent; in addition, the method yields literally no waste. The synthesized chelator in the form of the crude reaction mixture was further used for preparing a multicomponent micronutrient fertilizer (B, Zn, Fe, Cu, Mn, and Mo). The fertilizer was shown to be highly biodegradable (72% in 28 days), while the EDTA-based product degraded only by 13%. The plant growing efficiency was tested on lettuce in the greenhouse experiments. The results were compared against the known commercial fertilizers based on EDTA and iminodisuccinic acid (IDS). The newly developed IGSA-based fertilizer significantly outperformed the EDTA-based fertilizer in lettuce biomass (1.4 and 1.6 times for root and foliar application, respectively). The total mineral uptake was almost two times higher (1.9 and 1.8 times for root and foliar treatments, respectively) compared to the EDTA-based complex and even slightly higher (1.2 and 1.1 times, respectively) compared to the IDS-based complex. Our work opens the doors for the industrial scale production and application of this fully "green", inexpensive microelement fertilizer that has the potential to replace the EDTA-based products.

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks.

The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.