Intravitreal tolerance of a new perfluorocarbon vitreous replacement, Multifluor APF-144

OBJECTIVE: To evaluate the intravitreal tolerance of a new perfluorocarbon vitreous replacement, Multifluor APF-144 (perfluorotetramethylcyclohexane). DESIGN: Ten New Zealand albino rabbits (one eye from each) underwent vitrectomy. The vitreous was replaced in five eyes with Multifluor APF-144 and in five eyes with saline (control group). OUTCOME MEASURES: Appearance on indirect ophthalmoscopy, electroretinography recordings before and 2, 4 and 8 weeks after vitrectomy, findings on electron and light microscopy at 8 weeks. RESULTS: Endophthalmitis did not develop in any of the eyes. There was no significant change in electroretinography values for the experimental eyes after vitrectomy. No evidence of retinal toxicity was found on light or electron microscopic examination. CONCLUSIONS: Multifluor APF-144 shows promise as a short-term postoperative retinal tamponading agent.     

Dual recognition of lipid A and DNA by human antibodies encoded by the VH4-21 gene: a possible link between infection and lupus

The VH4-21 (V4-34) gene segment, a member of the VH4 family, is expressed early in B-cell maturation and is utilized by approximately 6% of normal adult B lymphocytes. This prevalence indicates an importance of VH4-21 in the B-cell repertoire. The gene also encodes certain autoantibodies being mandatory for pathological IgM anti-red cell antibodies directed against the I/i antigen, and also capable of encoding anti-DNA antibodies. Recognition of I/i antigen or DNA appears to be via two distinct sites on VH, with I/i binding mediated by sequences in the framework region, and DNA binding correlating with the presence of positively charged amino acids in complementarity-determining region 3. However, these positively charged residues appear to suppress the ability of the framework region to interact with I/i, rendering a single sequence monospecific for I/i or DNA. The IgM anti-DNA antibodies also recognize bacterial lipid A, whereas the anti-I/i antibodies do not, indicating that CDR3 may be involved in binding the negatively charged lipid A. Structural similarities between the DNA backbone and lipid A provide a possible explanation for this cross-reactivity. This dual recognition of bacterial antigen and autoantigen provides a potential link between infection and autoimmunity.     

The significance of gastric helicobacteriosis in the genesis hemorrhage from duodenal peptic ulcer

Based on a comprehensive analysis of findings from examination and surgical treatment of 196 patients with bleeding pyloroduodenal ulcers, it was found out that patients with concurrent helicobacteriosis of the stomach are prone to more unfavourable course of their illness, which fact is evidenced by a greater loss of blood and higher risk for bleeding recurrences. A classification is proposed by the authors, taking advantage of the data from endoscopic, microscopic and immunoenzymatic techniques, with the purpose of providing an assessment of degree of severity of helicobacteriosis of the stomach.     

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.     

Mutation analysis of the MEN1 gene in multiple endocrine neoplasia type 1, familial acromegaly and familial isolated hyperparathyroidism

Multiple endocrine neoplasia type 1 (MEN 1) is an autosomal dominant disease characterized by neoplasia of the parathyroid glands, the endocrine pancreas, and the anterior pituitary gland. In addition, families with isolated endocrine neoplasia, notably familial isolated hyperparathyroidism (FIHP) and familial acromegaly, have also been reported. However, whether these families constitute MEN 1 variants or separate entities remains speculative as the genetic bases for these diseases are unclear. The gene for MEN 1 has recently been cloned and characterized. Using single strand conformation analysis (SSCA) and sequencing, we performed mutation analysis in: a) a total of 55 MEN 1 families from 7 countries, b) 13 isolated MEN 1 cases without family history of the disease, c) 8 acromegaly families, and d) 4 FIHP families. Mutations were identified in 27 MEN 1 families and 9 isolated cases. The 22 different mutations spread across most of the 9 translated exons and included frameshift (11), nonsense (6), splice (2), missense mutations (2), and in-frame deletions (1). Among the 19 Finnish MEN 1 probands, a 1466del12 mutation was identified in 6 families with identical 11q13 haplotypes and in 2 isolated cases indicating a common founder. One frameshift mutation caused by 359del4 (GTCT) was found in 1 isolated case and 4 kindreds of different origin and haplotypes; this mutation therefore represents a common "warm" spot in the MEN1 gene. By analyzing the DNA of the parents of an isolated case one mutation was confirmed to be de novo. No mutation was found in any of the acromegaly and small FIHP families, suggesting that genetic defects other than the MEN1 gene might be involved and that additional such families need to be analyzed.     

Effect of brain edema on the recurrence pattern of malignant gliomas

PURPOSE: To assess the influence of initial preoperative brain edema in malignant gliomas on regrowth patterns. SUBJECTS AND METHODS: 79 patients with histologically verified supratentorial malignant glioma were prospectively studied by magnetic resonance imaging (MRI) before and every 2-3 months after surgery. The median follow-up time was 11 months. We correlated the configuration of the initial vasogenic edema on T2-weighted images with tumor regrowth patterns on contrast-enhanced T1-weighted images. RESULTS: 35/47 tumor regrowths (75%) imitated the initial edema configuration, while 11/47 occurred within the initial tumor bed; in one case tumor recurrence was multilocal. CONCLUSION: In glioblastoma, tumor regrowth patterns correlate positively with the configuration of the initial vasogenic brain edema. The initial, "presurgical" peritumoral edema should thus be considered when planning further treatment.