A secure and privacy preserved data aggregation scheme in IoMT.

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

Whole-Exome sequencing identifies GYS2 biallelic variants in individuals with suspected epilepsy.

BACKGROUND: Adequate glucose supply is essential for brain function, therefore hypoglycemic states may lead to seizures. Since blood glucose supply for brain is buffered by liver glycogen, an impairment of liver glycogen synthesis by mutations in the liver glycogen synthase gene (GYS2) might result in a substantial neurological involvement. Here, we describe the phenotypes of affected siblings of two families harboring biallelic mutations in GYS2. METHODS: Two suspected families - a multiplex Pakistani family (family A) with three affected siblings and a family of Moroccan origin (family B) with a single affected child who presented with seizures and reduced fasting blood glucose levels were genetically characterized. Whole exome sequencing (WES) was performed on the index patients, followed by Sanger sequencing-based segregation analyses on all available members of both families. RESULTS: The variant prioritization of WES and later Sanger sequencing confirmed three mutations in the GYS2 gene (12p12.1) consistent with an autosomal recessive pattern of inheritance. A homozygous splice acceptor site variant (NM_021957.3, c. 1646 -2A>G) segregated in family A. Two novel compound heterozygous variants (NM_021957.3: c.343G>A; p.Val115Met and NM_021957.3: c.875A>T; p.Glu292Val) were detected in family B, suggesting glycogen storage disorder. A special diet designed to avoid hypoglycemia, in addition to change of the anti-seizure medication led to reduction in seizure frequency. CONCLUSIONS: This study suggests that the seizures in patients initially diagnosed with epilepsy might be directly caused, or influenced by hypoglycemia due to pathogenic variants in the GYS2 gene.

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Surfactin-Conjugated Silver Nanoparticles as an Antibacterial and Antibiofilm Agent against Pseudomonas aeruginosa.

The emergence of antimicrobial resistance is an alarming global health concern and has stimulated the development of novel functional nanomaterials to combat multi-drug-resistant (MDR) bacteria. In this work, we demonstrate for the first time the synthesis and application of surfactin-coated silver nanoparticles as an efficient antibacterial and antibiofilm agent against the drug-resistant bacteria Pseudomonas aeruginosa for safe dermal applications. Our in vivo studies showed no significant superficial dermal irritation, edema, and erythema, while microscopic analysis revealed that surfactin-coated silver nanoparticles caused no pathological alterations at the applied concentrations. These results support the potential use of surfactin-coated silver nanoparticles against drug-resistant bacterial biofilm infections and in skin wound dressing applications.

Smart Wireless Sensor Technology for Healthcare Monitoring System Using Cognitive Radio Networks.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients' health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles.

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability.

Comparative mutational analysis of the Zika virus genome from different geographical locations and its effect on the efficacy of Zika virus-specific neutralizing antibodies.

The Zika virus (ZIKV), which originated in Africa, has become a significant global health threat. It is an RNA virus that continues to mutate and accumulate multiple mutations in its genome. These genetic changes can impact the virus's ability to infect, cause disease, spread, evade the immune system, and drug resistance. In this study genome-wide analysis of 175 ZIKV isolates deposited at the National Center for Biotechnology Information (NCBI), was carried out. The comprehensive mutational analysis of these isolates was carried out by DNASTAR and Clustal W software, which revealed 257 different substitutions at the proteome level in different proteins when compared to the reference sequence (KX369547.1). The substitutions were capsid (17/257), preM (17/257), envelope (44/257), NS1 (34/257), NS2A (30/257), NS2B (11/257), NS3 (37/257), NS4A (6/257), 2K (1/257), NS4B (15/257), and NS5 (56/257). Based on the coexisting mutational analysis, the MN025403.1 isolate from Guinea was identified as having 111 substitutions in proteins and 6 deletions. The effect of coexisting/reoccurring mutations on the structural stability of each protein was also determined by I-mutant and MUpro online servers. Furthermore, molecular docking and simulation results showed that the coexisting mutations (I317V and E393D) in Domain III (DIII) of the envelope protein enhanced the bonding network with ZIKV-specific neutralizing antibodies. This study, therefore, highlighted the rapid accumulation of different substitutions in various ZIKV proteins circulating in different geographical regions of the world. Surveillance of such mutations in the respective proteins will be helpful in the development of effective ZIKV vaccines and neutralizing antibody engineering.

Plant-assisted synthesis and characterization of MnO2 nanoparticles for removal of crystal violet dye: an environmental remedial approach.

The current study is focused on the use of the Caryota mitis Lour. (Fishtail palm) flower extract as a reducing agent for the preparation of manganese dioxide (MnO2) nanoparticles. Scanning electron microscopy (SEM), four-phase infrared analysis (FT-IR), and x-ray diffraction (XRD) methods were used to characterize the MnO2 nanoparticles. The nature of MnO2 nanoparticles was revealed by an absorption peak at 590 nm in a spectrophotometer (A1000). Then, these MnO2 nanoparticles were applied to decolorize the crystal violet dye. At 0.004% dye concentration, pH 4, and concentration of MnO2 nanoparticles of 0.005 g/L at temperatures of 50 °C, the target dye was decolorized by 91.3%. Percent reductions in COD and TOC were found to be 92.1% and 90.6%, respectively. Finally, the dye decolorization pathway was proposed based on the experimental findings.

Two-Dose Vaccination Significantly Prolongs the Duration from Symptom Onset to Death: A Retrospective Study Based on 173,894 SARS-CoV-2 Cases in Khyber Pakhtunkhwa, Pakistan.

This research was carried out to quantify the duration from symptom onset to recovery/death (SOR/SOD) during the first four waves and the Alpha/Delta period of the epidemic in Khyber Pakhtunkhwa, Pakistan, and identify the associated factors. A total of 173,894 COVID-19 cases were admitted between 16 March 2020 and 30 November 2021, including 458 intensive care unit (ICU) cases. The results showed that the case fatality rate (CFR) increased with age, and females had a higher CFR. The median SOR of ICU cases was longer than that of non-ICU cases (27.6 vs. 17.0 days), while the median SOD was much shorter (6.9 vs. 8.4 days). The SOR and SOD in the Delta period were slightly shortened than the Alpha period. Age, cardiovascular diseases, chronic lung disease, diabetes, fever, breathing issues, and ICU admission were risk factors that were significantly associated with SOD (p < 0.001). A control measure, in-home quarantine, was found to be significantly associated with longer SOD (odds ratio = 9.49, p < 0.001). Infected vaccinated individuals had longer SOD than unvaccinated individuals, especially for cases that had received two vaccine doses (p < 0.001). Finally, an advice on getting full-dose vaccination is given specifically to individuals aged 20-59 years.

Biofunctionalized Nano-antimicrobials - Progress, Prospects and Challenges.

The rapid emergence of multidrug-resistant bacterial strains highlights the need for the development of new antimicrobial compounds/materials to address associated healthcare challenges. Meanwhile, the adverse side effects of conventional antibiotics on human health urge the development of new natural product-based antimicrobials to minimize the side effects. In this respect, we concisely review the recent scientific contributions to develop natural product-based nano-antibiotics. The focus of the review is on the use of flavonoids, peptides, and cationic biopolymer functionalized metal/metal oxide nanoparticles as efficient tools to hit the MDR bacterial strains. It summarizes the most recent aspects of the functionalized nanoparticles against various pathogenic bacterial strains for their minimal inhibitory concentrations and mechanism of action at the cellular and molecular levels. In the end, the future perspectives to materialize the in vivo applications of nano-antimicrobials are suggested based on the available research.

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese.

Premenstrual dysphoric disorder (PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations (CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types, respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.

A survey on healthcare data security in wireless body area networks.

Advances in remote interchanges, the internet of nano things have empowered the wireless body area networks (WBAN) to end up a promising systems of networking standard. It involves interconnected tiny sensors to gather ongoing biomedical data and transmit over the network for further analysis. Due to possibility of active and passive number of attacks, the healthcare data security is quite essential and challenging. This paper presents the systematic literature review (SLR) of the multiple security schemes for WBAN. We have identified a research question to analyses the possibility of several attacks while preserving the memory constraints. We have performed quality valuation to ensure the relevance of schemes with the research question. Moreover, the schemes are considered from 2016 to 2020 to focus on recent work. In literature, several existing schemes are explored to identify how the security is enhanced for exchanging patients' healthcare data. The data security schemes using AES, ECC, SHA-1 and hybrid encryption are analyzed based on influential traits. Several methodologies for data security in WBAN are considered and the most appropriate methodologies are appraised. We also analyses the security for different attack scenarios.

Application of region-based video surveillance in smart cities using deep learning.

Smart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.

Highly Recurrent Copy Number Variations in GABRB2 Associated With Schizophrenia and Premenstrual Dysphoric Disorder.

OBJECTIVE: Although single-nucleotide polymorphisms in GABRB2, the gene encoding for GABAA receptors ß2 subunit, have been associated with schizophrenia (SCZ), it is unknown whether there is any association of copy number variations (CNVs) in this gene with either SCZ or premenstrual dysphoric disorder (PMDD). METHODS: In this study, the occurrences of the recurrent CNVs esv2730987 in Intron 6 and nsv1177513 in Exon 11 of GABRB2 in Chinese and German SCZ, and Chinese PMDD patients were compared to controls of same ethnicity and gender by quantitative PCR (qPCR). RESULTS: The results demonstrated that copy-number-gains were enriched in both SCZ and PMDD patients with significant odds ratios (OR). For combined-gender SCZ patients versus controls, about two-fold increases were observed in both ethnic groups at both esv2730987 (OR = 2.15, p = 5.32E-4 in Chinese group; OR = 2.79, p = 8.84E-3 in German group) and nsv1177513 (OR = 3.29, p = 1.28E-11 in Chinese group; OR = 2.44, p = 6.17E-5 in German group). The most significant copy-number-gains were observed in Chinese females at nsv1177513 (OR = 3.41), and German females at esv2730987 (OR=3.96). Copy-number-gains were also enriched in Chinese PMDD patients versus controls at esv2730987 (OR = 10.53, p = 4.34E-26) and nsv1177513 (OR = 2.39, p = 3.19E-5). CONCLUSION: These findings established for the first time the association of highly recurrent CNVs with SCZ and PMDD, suggesting the presence of an overlapping genetic basis with shared biomarkers for these two common psychiatric disorders.

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks.

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.

Nanoparticles-assisted delivery of antiviral-siRNA as inhalable treatment for human respiratory viruses: A candidate approach against SARS-COV-2.

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has challenged healthcare structures across the globe. Although a few therapies are approved by FDA, the search for better treatment options is continuously on rise. Clinical management includes infection prevention and supportive care such as supplemental oxygen and mechanical ventilatory support. Given the urgent nature of the pandemic and the number of companies and researchers developing COVID-19 related therapies, FDA has created an emergency program to move potential treatments with already approved drugs to patients as quickly as possible in parallel to the development of new drugs that must first pass the clinical trials. In this manuscript, we have reviewed the available literature on the use of sequence-specific degradation of viral genome using short-interfering RNA (siRNA) suggesting it as a possible treatment against SARS-CoV-2. Delivery of siRNA can be promoted by the use of FDA approved lipids, polymers or lipid-polymer hybrids. These nanoparticulate systems can be engineered to exhibit increased targetability and formulated as inhalable aerosols.

AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data.

The usage of next-generation sequencing (NGS) to detect copy number variation (CNV) is widely accepted in cancer research. Based on an AluScanCNV software developed by us previously, an AluScanCNV2 software has been developed in the present study as an R package that performs CNV detection from NGS data obtained through AluScan, whole-genome sequencing or other targeted NGS platforms. Its applications would include the expedited usage of somatic CNVs for cancer subtyping, and usage of recurrent germline CNVs to perform machine learning-assisted prediction of a test subject's susceptibility to cancer.

Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT.

The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs' sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.