Chronic wasting disease (CWD) is a fatal prion disease of the family Cervidae that circulates in both wild and captive cervid populations. This disease threatens the health and economic viability of the captive cervid industry, which raises cervids in contained spaces for purposes such as hunting and breeding. Given the high transmissibility and long incubation period of CWD, the introduction and propagation of the infectious prion protein within and between captive cervid farms could be devastating to individual facilities and to the industry as a whole. Despite this risk, there does not yet exist a literature review that summarizes the scientific knowledge, to date, about CWD spread, surveillance, or control measures. Our review, which focused on peer reviewed, primary research conducted in the United States, sought to address this need by searching Google Scholar, Scopus, and Web of Science with a five-term keyword string containing terms related to the (1) location, (2) species affected, (3) disease, (4) captive cervid industry, and (5) topic of focus. Between the three databases, there were 190 articles that were selected for further examination. Those articles were then read to determine if they were about CWD spread, surveillance, and/or control in captive cervid facilities. The 22 articles that met these inclusion criteria were evaluated in detail and discussed, with recommendations for future collaborative work between captive cervid owners, government agencies, and researchers. This work will help to address, inform, and mitigate the rising problem of CWD spread and establishment.

The cerebellum has a large network of neurons that communicate with several brain structures and participate in different functions. Recent studies have demonstrated that the cerebellum is not only associated with motor functions but also participates in several non-motor functions. It is suggested that the cerebellum can modulate behavior through many connections with different nervous system structures in motor, sensory, cognitive, autonomic, and emotional processes. Recently, a growing number of clinical and experimental studies support this theory and provide further evidence. In light of recent findings, a comprehensive review is needed to summarize the knowledge on the influence of the cerebellum on the processing of different functions. Therefore, the aim of this review was to describe the neuroanatomical aspects of the activation of the cerebellum and its connections with other structures of the central nervous system in different behaviors.

Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.

UNASSIGNED: The traditional approach to studying the neurobiological mechanisms of brain disorders and localizing brain function involves identifying brain abnormalities and comparing them to matched controls. This method has been instrumental in clinical neurology, providing insight into the functional roles of different brain regions. However, it becomes challenging when lesions in diverse regions produce similar symptoms. To address this, researchers have begun mapping brain lesions to functional or structural networks, a process known as lesion network mapping (LNM). This approach seeks to identify common brain circuits associated with lesions in various areas. In this review, we focus on recent studies that have utilized LNM to map neurological and psychiatric symptoms, shedding light on how this method enhances our understanding of brain network functions.
UNASSIGNED: We conducted a systematic search of four databases: PubMed, Scopus, and Web of Science, using the term \"Lesion network mapping.\" Our focus was on observational studies that applied lesion network mapping in the context of neurological and psychiatric disorders.
UNASSIGNED: Following our screening process, we included 52 studies, comprising a total of 6,814 subjects, in our systematic review. These studies, which utilized functional connectivity, revealed several regions and network overlaps across various movement and psychiatric disorders. For instance, the cerebellum was found to be part of a common network for conditions such as essential tremor relief, parkinsonism, Holmes tremor, freezing of gait, cervical dystonia, infantile spasms, and tics. Additionally, the thalamus was identified as part of a common network for essential tremor relief, Holmes tremor, and executive function deficits. The dorsal attention network was significantly associated with fall risk in elderly individuals and parkinsonism.
UNASSIGNED: LNM has proven to be a powerful tool in localizing a broad range of neuropsychiatric, behavioral, and movement disorders. It holds promise in identifying new treatment targets through symptom mapping. Nonetheless, the validity of these approaches should be confirmed by more comprehensive prospective studies.

To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission.
a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases.
Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common.
We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.

OBJECTIVE: To perform a meta-analysis of RCTs evaluating donor site morbidity after bone-patellar tendon-bone (BTB), hamstring tendon (HT) and quadriceps tendon (QT) autograft harvest for anterior cruciate ligament reconstruction (ACLR).
METHODS: PubMed, OVID/Medline and Cochrane databases were queried in July 2022. All level one articles reporting the frequency of specific donor-site morbidity were included. Frequentist model network meta-analyses with P-scores were conducted to compare the prevalence of donor-site morbidity, complications, all-cause reoperations and revision ACLR among the three treatment groups.
RESULTS: Twenty-one RCTs comprising the outcomes of 1726 patients were included. The overall pooled rate of donor-site morbidity (defined as anterior knee pain, difficulty/impossibility kneeling, or combination) was 47.3% (range, 3.8-86.7%). A 69% (95% confidence interval [95% CI]: 0.18-0.56) and 88% (95% CI: 0.04-0.33) lower odds of incurring donor-site morbidity was observed with HT and QT autografts, respectively (p < 0.0001, both), when compared to BTB autograft. QT autograft was associated with a non-statistically significant reduction in donor-site morbidity compared with HT autograft (OR: 0.37, 95% CI: 0.14-1.03, n.s.). Treatment rankings (ordered from best-to-worst autograft choice with respect to donor-site morbidity) were as follows: (1) QT (P-score = 0.99), (2) HT (P-score = 0.51) and (3) BTB (P-score = 0.00). No statistically significant associations were observed between autograft and complications (n.s.), reoperations (n.s.) or revision ACLR (n.s.).
CONCLUSIONS: ACLR using HT and QT autograft tissue was associated with a significant reduction in donor-site morbidity compared to BTB autograft. Autograft selection was not associated with complications, all-cause reoperations, or revision ACLR. Based on the current data, there is sufficient evidence to recommend that autograft selection should be personalized through considering differential rates of donor-site morbidity in the context of patient expectations and activity level without concern for a clinically important change in the rate of adverse events.
METHODS: Level I.

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Since 2010, the network of rare malignant tumors of the ovary (TMRG) was developed to optimize the management of patients, also allowing a histological second opinion of rare ovarian tumors. The aim of this work was to study the contribution of second opinion to improve histological diagnostic accuracy on ovarian rare malignant tumors included in the TMRG database.
Histological data of patients diagnosed with a rare ovarian tumor included in TMRG network over a one-year period (2018) were collected. Initial diagnoses were compared with second opinion from national gynecological pathologist experts. The modalities of histological second opinion requests were studied, as well as the histological characteristics of the tumors. The discordances were classified as minor (if the modification of histological diagnosis did not change patient management) and major (if the patient management can be modified).
Of 1185 included patients, 937 matched the inclusion criteria. Full concordance between primary diagnosis and expert second opinion was reached in 611 cases (65,3%), minor discordance was seen in 114 (12,2%) and major discordance in 209 (22,3%) of cases. In systematic review requested by the network, 26% (n = 137) of cases were reported with a change in histological diagnosis, while the change concerned 44% (n = 186) of cases for a second opinion spontaneously requested by the initial pathologist. The discrepancies concerned all categories of ovarian tumors, with a majority of mucinous tumors (43% of major discordances), followed by stromal and sex-cord tumors (13.8% of major discordances) and clear cell tumors (12,4% of major discordances).
This analysis confirms the diagnostic difficulty of ovarian tumors, due to their rarity and morphological heterogeneity. French pathologists are aware of these difficulties and spontaneously refer ovarian tumors with unusual histology for a second opinion and collaborate with rare tumor networks for systematic review.

BACKGROUND: As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.
METHODS: This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.
RESULTS: The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.
CONCLUSIONS: Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.
The clinical effects of new treatments and their costs are often evaluated over a longer time frame than is possible in clinical trials by using computer simulation models. As new treatments are becoming available to treat chronic kidney disease, including in patients with type 2 diabetes, chronic kidney disease models may be used to inform clinical and economic decisions regarding these new treatment options. In the present study, we identified 49 published simulation models of chronic kidney disease used in populations with type 2 diabetes, and reviewed their structures and the data sources they used. The models focused mostly on disease states and outcomes associated with albuminuria (a condition in which the protein albumin is found in the urine) and end-stage kidney disease. Model structures with five disease states, including a kidney disease-free state, three kidney disease states, and death, were the most common. Relatively few models used glomerular filtration rates (a common measure of kidney function) or captured the possibility of an improvement in chronic kidney disease. Important data sources for many models were patient registries, cohort studies, and clinical trials, most conducted several decades ago in high-income countries with a high proportion of White participants. Several models developed in the past 5 years, particularly for Asian countries, instead relied largely or exclusively on country-specific data. In parallel, several individual patient simulations were recently developed from large outcomes trials for new treatments, including from trial subgroups covering specific geographical settings or ethnicities, shortly after trial publication.

Today\'s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.