Rapid genotyping of inversion variants in Mucopolysaccharidosis type II using long-range PCR: A case report.

Mucopolysaccharidosis II (MPS II) is a lysosomal storage disease caused by a deficiency in iduronate-2-sulfatase (IDS), leading to the accumulation of dermatan sulfate and heparan sulfate in lysosomes. Traditionally, genotyping of the IDS gene has been conducted through exome sequencing, which fails to detect inversion variants. Consequently, when no pathogenic variants are detected in exons, additional PCR-based analysis is required. Herein, we introduce a rapid genotyping technique method using long-range PCR for MPS II patients. We successfully identified an inversion variant and confirmed the sequences of the inversion regions. We also confirmed that the pathogenic variant in the patient originated de novo. These findings suggest that long-range PCR genotyping can identify inversion variants more rapidly compared to the previous PCR-based methods, making it a valuable tool for newborn screening (NBS) and genetic diagnosis.

Genotype-phenotype findings in patients with mucopolysaccharidosis II from the Hunter Outcome Survey.

PURPOSE: This study investigated the relationship between mucopolysaccharidosis II (MPS II) iduronate-2-sulfatase gene (IDS) variants and phenotypic characteristics, particularly cognitive impairment, using data from the Hunter Outcome Survey (HOS) registry. METHODS: HOS data for male patients (n = 650) aged ≥5 years at latest cognitive assessment with available genetic data were analyzed. Predefined genotype categories were used to classify IDS variants and report phenotypic characteristics by genotype. RESULTS: At their latest cognitive assessment, 411 (63.2%) of 650 patients had cognitive impairment. Missense variants were the most common MPS II genotype, with about equal frequency for patients with and patients without cognitive impairment. Complete deletions/large rearrangements were associated with cognitive impairment. Cognitive impairment and behavioral issues were most common, and height and weight abnormalities most apparent, in patients with large IDS structural changes. Broadly, missense variants NM-000202.8:c.998C>T p.(Ser333Leu), NM-000202.8:c.1402C>T p.(Arg468Trp), NM-000202.8:c.1403G>A p.(Arg468Gln) and NM-000202.8:c.262C>T p.(Arg88Cys), and splice site variant NM-000202.8:c.257C>T p.(Pro86Leu), were associated with cognitive impairment, and variants NM-000202.8:c.253G>A p.(Ala85Thr), NM-000202.8:c.187 A>G p.(Asn63Asp), NM-000202.8:c.1037C>T p.(Ala346Val), NM-000202.8:c.182C>T p.(Ser61Phe) and NM-000202.8:c.1122C>T were not. CONCLUSION: This analysis contributes toward the understanding of MPS II genotype-phenotype relationships, confirming and expanding on existing findings in a large, geographically diverse population.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks.

Smart cities have developed advanced technology that improves people's lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems.

The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection.

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN).

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network's performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks' resilience in different scenarios.

Impact of an Intervention Program on Clostridioides difficile Infections: Comparison of 2 Hospital Cohorts.

Background: Clostridioides difficile infection (CDI) occurs in various contexts and care settings and is managed by multiple specialists who are not experts in its management. While there are many initiatives to improve the diagnosis and avoid overdiagnosis, there is less focus on the overall management of the infection. Methods: We studied a cohort of patients with a positive test result for toxigenic C difficile in 2 hospitals. Hospital A has a program that provides advice from an infectious disease specialist (IDS) and promotes continuity of care by providing a phone number to contact the IDS. Hospital B does not have any specific CDI program. The evaluation assessed the proportion of patients not treated (carriers or self-limited disease), adherence to Infectious Diseases Society of America guidelines, access to novel therapies, recurrence and mortality rates, and readmission and emergency department visits due to CDI. We assessed the program's effectiveness through a logistic regression model adjusted for covariates chosen by clinical criteria. Results: Hospital A avoided more unnecessary treatments (19.3% vs 11.5%), provided access to novel therapies more frequently (35.3% vs 13%), and adhered more closely to current guidelines (95.8% vs 71.3%). Although the mortality and recurrence rates did not differ, the absence of an intervention program was associated with greater odds of admission due to recurrence (odds ratio, 4.19; P = .037) and more visits to the emergency department due to CDI (odds ratio, 8.74; P = .001). Conclusions: Implementation of a CDI intervention program based on recommendations from IDSs and improved access to specialized care during the follow-up is associated with enhanced quality of CDI management and potential reductions in hospital resource utilization.

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.

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.

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.

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.