Colorectal Cancers from Distinct Ancestral Populations Show Variations in BRAF Mutation Frequency

It has been demonstrated for some cancers that the frequency of somatic oncogenic mutations may vary in ancestral populations. To determine whether key driver alterations might occur at different frequencies in colorectal cancer, we applied a high-throughput genotyping platform (OncoMap) to query 385 mutations across 33 known cancer genes in colorectal cancer DNA from 83 Asian, 149 Black and 195 White patients. We found that Asian patients had fewer canonical oncogenic mutations in the genes tested (60% vs Black 79% (P = 0.011) and White 77% (P = 0.015)), and that BRAF mutations occurred at a higher frequency in White patients (17% vs Asian 4% (P = 0.004) and Black 7% (P = 0.014)). These results suggest that the use of genomic approaches to elucidate the different ancestral determinants harbored by patient populations may help to more precisely and effectively treat colorectal cancer

National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.Peer reviewe

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Collaborative ambient intelligence-based demand variation prediction model

Inventory control problem is faced by companies on a daily basis to optimise the supply chain process and for predicting the optimal pricing for the item sales or for providing services. The problem is heavily dependent on a key factor, i.e., demand variations. Inventories must be aligned according to demand variations to avoid overheads or shortages. This work focuses on exploring various machine learning algorithms to solve demand variation problem in real-time. Prediction of demand variations is a complex and non-trivial problem, particularly in the presence of open order. In this work, prediction of demand variation is addressed with the use-cases which are characterised with open orders. This work also presents a novel prediction model which is a hybrid of learning domains as well as domain specific parameters. It exploits the use of Internet of Things (IoT) to extract domain specific knowledge while a reinforcement learning technique is used for predicting the variations in these domain specific parameters which depend on demand variations. The new model is explored and compared with state-of-the-art machine learning algorithms using Grupo Bimbo case study. The results show that new model predicts the demand variations with significantly higher accuracy as compared to other models

Performance Evaluation of Error Correcting Techniques for OFDM Systems

Orthogonal frequency-division multiplexing (OFDM) systems provide efficient spectral usage by allowing overlapping in the frequency domain. Additionally, they are highly immune to multipath delay spread. In these systems, modulation and demodulation can be done using Inverse Fast Fourier Transform (IFFT) and Fast Fourier Transform (FFT) operations, which are computationally efficient. OFDM allows suppression of inter-symbol interference (ISI), provides flexible bandwidth allocation and may increase the capacity in terms of number of users.In this work, we have investigated the performance of different error correcting techniques for OFDM systems. These techniques are based on Convolutional codes, Linear Block codes and Reed-Solomon codes. Simulations are performed to evaluate the considered techniques for different channel conditions.By comparing the three techniques, the results show that Reed-Solomon codes performs the best for all error rates due to its consistency in performance at both low and high code rates which we verified by results

Low Cost Road Health Monitoring System: A Case of Flexible Pavements

A healthy road network plays a significant role in the socio-economic development of any country. Road management authorities struggle with pavement repair approaches and the finances to keep the existing road network to its best functionality. It has been observed that real-time road condition monitoring can drastically reduce road and vehicle maintenance expenses. There are various methods to analyze road health, but most are either expensive, costly, time-consuming, labor-intensive, or imprecise. This study aims to design a low-cost smart road health monitoring system to identify the road section for maintenance. An automized sensor-based system is developed to assist the road sections for repair and rehabilitation. The proposed system is mounted in a vehicle and the data have been collected for a more than 1000 km road network. The data have been processed using SPSS, and it shows that the proposed system is adequate for detecting the road quality. It is concluded that the proposed system can identify the vulnerable sections to add to the pavement maintenance plan. In the future, the created application can be launched as a smart citizen app where each car driver can install this application and can monitor the road quality automatically

Simulative Survey of Flooding Attacks in Intermittently Connected Vehicular Delay Tolerant Networks

Vehicular Adhoc Networks (VANETs) are an emerging and promising technology that enables vehicles to communicate with roadside units (RSUs) and other vehicles. VANETs contribute to improved traffic efficiency, accident safety, and entertainment services for passengers and drivers. However, VANETs face limitations in areas with intermittent connectivity. To address this scenario, researchers have proposed a specialized use-case known as intermittently-connected-vehicular delay-tolerant-networks (ICV-DTNs), which are a subset of delay-tolerant-networks (DTNs). Security is less explored area compare to routing. Malicious nodes pose significant threats by launching selective packet drops, fake/bogus packets, and flood attacks, depleting limited resources such as bandwidth and node buffer space. Consequently, these attacks result in low message delivery ratios and high message loss ratios. Among these attacks, flood attacks are particularly challenging in ICV-DTNs/Flying Adhoc Networks/Internet of Drones. Various algorithms have been proposed to mitigate flood attacks, but previous approaches have exhibited shortcomings. Firstly, previously proposed algorithms lack efficiency in terms of detection time and accuracy. Secondly, the extent of resource waste or savings after implementing these schemes has not been adequately demonstrated, with no simulation results quantifying the amount of buffer consumption. Additionally, prior algorithms lack a comprehensive definition of flood attacks, which represents a critical research question in this field. To address these gaps, this article not only proposed a unique taxonomy of the flooding attacks but also evaluate various algorithms on diverse parameters. The article also contribute open research areas for the community to investigate the nitty gritty of flooding attacks in ICV-DTNs

Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

With increased interest in 6G (6th Generation) cellular networks that can support intelligently small-cell communication will result in effective device-to-device (D2D) communication. High throughput requirement in 5G/6G cellular technology requires each device to act as intelligent transmission relays. Inclusion of such intelligence relays and support of quantum computing at D2D may compromise existing security mechanisms and may lead towards primitive attacks such as impersonation attack, rouge device attack, replay attack, MITM attack, and DoS attack. Thus, an effective yet lightweight security scheme is required that can support existing low computation devices and can address the challenges that 5G/6G poses. This paper proposes a Lightweight ECC (elliptic curve cryptography)-based Multifactor Authentication Protocol (LEMAP) for miniaturized mobile devices. LEMAP is the extension of our previous published work TLwS (trust-based lightweight security scheme) which utilizes ECC with Elgamal for achieving lightweight security protocol, confidentiality, integrity, and non-repudiation. Multi-factor Authentication is based on OTP (Biometrics, random number), timestamp, challenge, and password. This scheme has mitigated the above-mentioned attacks with significantly lower computation cost, communication cost, and authentication overhead. We have proven the correctness of the scheme using widely accepted Burrows-Abadi-Needham (BAN) logic and analyzed the performance of the scheme by using a simulator. The security analysis of the scheme has been conducted using the Discrete Logarithm Problem to verify any quantum attack possibility. The proposed scheme works well for 5G/6G cellular networks for single and multihop scenario

Lightweight multifactor authentication scheme for NextGen cellular networks.

With increased interest in 6G (6th Generation) cellular networks that can support intelligently small-cell communication will result in effective device-to-device (D2D) communication. High throughput requirement in 5G/6G cellular technology requires each device to act as intelligent transmission relays. Inclusion of such intelligence relays and support of quantum computing at D2D may compromise existing security mechanisms and may lead towards primitive attacks such as impersonation attack, rouge device attack, replay attack, MITM attack, and DoS attack. Thus, an effective yet lightweight security scheme is required that can support existing low computation devices and can address the challenges that 5G/6G poses. This paper proposes a Lightweight ECC (elliptic curve cryptography)-based Multifactor Authentication Protocol (LEMAP) for miniaturized mobile devices. LEMAP is the extension of our previous published work TLwS (trust-based lightweight security scheme) which utilizes ECC with Elgamal for achieving lightweight security protocol, confidentiality, integrity, and non-repudiation. Multi-factor Authentication is based on OTP (Biometrics, random number), timestamp, challenge, and password. This scheme has mitigated the above-mentioned attacks with significantly lower computation cost, communication cost, and authentication overhead. We have proven the correctness of the scheme using widely accepted Burrows-Abadi-Needham (BAN) logic and analyzed the performance of the scheme by using a simulator. The security analysis of the scheme has been conducted using the Discrete Logarithm Problem to verify any quantum attack possibility. The proposed scheme works well for 5G/6G cellular networks for single and multihop scenarios