Melanoma identification and classification model based on fine-tuned convolutional neural network.

Background: Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and Methodology: This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. Results: The proposed CNN model achieves high accuracy demonstrates the model's effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model's efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets. Conclusion: Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.

The characteristics and predictors of mortality in periprosthetic fractures around the knee.

Aims: Periprosthetic fractures (PPFs) around the knee are challenging injuries. This study aims to describe the characteristics of knee PPFs and the impact of patient demographics, fracture types, and management modalities on in-hospital mortality. Methods: Using a multicentre study design, independent of registry data, we included adult patients sustaining a PPF around a knee arthroplasty between 1 January 2010 and 31 December 2019. Univariate, then multivariable, logistic regression analyses were performed to study the impact of patient, fracture, and treatment on mortality. Results: Out of a total of 1,667 patients in the PPF study database, 420 patients were included. The in-hospital mortality rate was 6.4%. Multivariable analyses suggested that American Society of Anesthesiologists (ASA) grade, history of peripheral vascular disease (PVD), history of rheumatic disease, fracture around a loose implant, and cerebrovascular accident (CVA) during hospital stay were each independently associated with mortality. Each point increase in ASA grade independently correlated with a four-fold greater mortality risk (odds ratio (OR) 4.1 (95% confidence interval (CI) 1.19 to 14.06); p = 0.026). Patients with PVD have a nine-fold increase in mortality risk (OR 9.1 (95% CI 1.25 to 66.47); p = 0.030) and patients with rheumatic disease have a 6.8-fold increase in mortality risk (OR 6.8 (95% CI 1.32 to 34.68); p = 0.022). Patients with a fracture around a loose implant (Unified Classification System (UCS) B2) have a 20-fold increase in mortality, compared to UCS A1 (OR 20.9 (95% CI 1.61 to 271.38); p = 0.020). Mode of management was not a significant predictor of mortality. Patients managed with revision arthroplasty had a significantly longer length of stay (median 16 days; p = 0.029) and higher rates of return to theatre, compared to patients treated nonoperatively or with fixation. Conclusion: The mortality rate in PPFs around the knee is similar to that for native distal femur and neck of femur fragility fractures. Patients with certain modifiable risk factors should be optimized. A national PPF database and standardized management guidelines are currently required to understand these complex injuries and to improve patient outcomes.

Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework.

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.

Prostate-specific Antigen Density as a Proxy for Predicting Prostate Cancer Severity: Is There Any Difference between Systematic and Targeted Biopsy?

Background: Prostate cancer screening with prostate-specific antigen (PSA) can result in unnecessary biopsies and overdiagnosis. Alternately, PSA density (PSAD) calculation may help support biopsy decisions; however, evidence of its usefulness is not concrete. Objective: To evaluate the predictive value of PSAD for clinically significant prostate cancer detection by systematic and MRI-targeted biopsies. Methods: This prospective study was conducted at two tertiary hospitals in Riyadh, Saudi Arabia, between December 2018 and November 2021. Patients suspected of prostate cancer were subjected to multi-parametric MRI, and for those with positive findings, systematic and targeted biopsies were performed. Clinically non-significant and significant prostate cancer cases were classified based on histopathology-defined ISUP grade or Gleason score. The PSAD was measured using the prostate volume determined by the MRI and categorized into ≤0.15, 0.16-0.20, and >0.20 ng/ml2 subgroups. Results: Systematic and targeted biopsies were carried out for 284 patients. The discriminant ability of PSAD is higher in MRI-targeted biopsy compared with systematic biopsy (AUC: 0.77 vs. 0.73). The highest sensitivity (97%) and specificity (87%) were detected at 0.07 ng/ml2 in targeted biopsy. More than half of the clinically significant cases were detected in the >0.2 ng/ml2 PSAD category (systematic: 52.4%; targeted: 51.1%). The CHAID methodology found that the probability of having clinically significant cancer (CSC) in patients with PSAD >0.15 ng/ml2 was more than threefold than that in patients with PSAD ≤0.15 ng/ml2 (64% vs. 20.2%). When considered by age, in PSAD ≤0.15 ng/ml2 subgroup, the percentage of CSC detection rate increased from 20.2% to 24.6% in patients aged ≥60 years. Conclusion: PSAD has good discriminant power for predicting clinically significant prostate cancer. A cutoff of 0.07 ng/ml2 should be adopted, but should be interpreted with caution and by considering other parameters such as age.

Predictors of mortality in periprosthetic fractures of the hip: Results from the national PPF study.

INTRODUCTION: Periprosthetic fractures (PPFs) around the hip joint are increasing in prevalence. In this collaborative study, we aimed to investigate the impact of patient demographics, fracture characteristics, and modes of management on in-hospital mortality of PPFs involving the hip. METHODS: Using a multi-centre cohort study design, we retrospectively identified adults presenting with a PPF around the hip over a 10-year period. Univariate and multivariable logistic regression analyses were performed to study the independent correlation between patient, fracture, and treatment factors on mortality. RESULTS: A total of 1,109 patients were included. The in-hospital mortality rate was 5.3%. Multivariable analyses suggested that age, male sex, abbreviated mental test score (AMTS), pneumonia, renal failure, history of peripheral vascular disease (PVD) and deep surgical site infection were each independently associated with mortality. Each yearly increase in age independently correlates with a 7% increase in mortality (OR 1.07, p=0.019). The odds of mortality was 2.99 times higher for patients diagnosed with pneumonia during their hospital stay [OR 2.99 (95% CI 1.07-8.37) p=0.037], and 7.25 times higher for patients that developed renal failure during their stay [OR 7.25 (95% CI 1.85-28.47) p=0.005]. Patients with history of PVD have a six-fold greater mortality risk (OR 6.06, p=0.003). Mode of treatment was not a significant predictor of mortality. CONCLUSION: The in-hospital mortality rate of PPFs around the hip exceeds 5%. The fracture subtype and mode of management are not independent predictors of mortality, while patient factors such as age, AMTS, history of PVD, pneumonia, and renal failure can independently predict mortality. Peri-operative optimisation of modifiable risk factors such as lung and kidney function in patients with PPFs around the hip during their hospital stay is of utmost importance.

Haematopoietic Stem Cell Transplant Trends in Pakistan: Activity Survey from Pakistan Bone Marrow Transplant Group.

Pakistan is the fifth most populous country with a population of 225 million and has health expenditure accounting for only 2.8 percent of gross domestic product (GDP). Accordingly, there are a limited number of haematology-oncology and transplant centers in the country. The Pakistan Blood and Marrow Transplant (PBMT) group was established in 2020, and this report is the first activity survey from January 2021 to December 2022 focusing on the trends of matched-related donor, haploidentical, and autologous transplants in a developing country. A total of 12 transplant centers contributed data on the modified PBMT survey form retrospectively and 806 haematopoietic stem cell transplants (HSCTs) were carried out during the study duration. Allogeneic HSCT constituted 595 (73.8%) of all the transplants; this is in stark contrast to Western data, where autologous HSCT accounts for the majority of transplants. ß-thalassemia major and aplastic anemia were the commonest indications for allogeneic HSCT, in contrast to Western data, where acute leukemia is the leading transplant indication. Autologous transplants were more frequently performed for Hodgkin's lymphoma as compared to non-Hodgkin's lymphoma and multiple myeloma. The use of peripheral and bone marrow stem cells was comparable. A myeloablative conditioning regimen was routinely used in patients with acute leukemia. This report provides an insight of HSCT trends in Pakistan which are different from those of Western centers contributing to transplant data from South Asia.

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.

False-positive magnetic resonance imaging prostate cancer correlates and clinical implications.

Background: False-positive (FP) multiparametric magnetic resonance imaging (MPMRI) obscures and swift needless biopsies in men with a high prostate-specific antigen. Materials and Methods: This was a retrospective study, in which all patients who had been exposed to consecutive MP-MRI of the prostate combined with transrectal ultrasound-guided-magnetic resonance imaging fusion-guided prostate biopsy between 2017 and 2020 were involved in the study. The FP was measured as the number of biopsies that did not encompass prostate cancer divided by the whole number of biopsies. Results: The percentage of FP cases was 51.1%, the highest percentage was found in Prostate Imaging-Reporting and Data System (PI-RADs) 3 (37.7%) and the lowest was detected in PI-RAD 5 (14.5%). Those with FP biopsies are younger, and their total prostate antigen (PSA) and PSA density (PSAD) are significantly lesser. The area under the curve PSAD, age, and total PSA are 0.76, 0.74, and 0.69, respectively. An optimum PSAD value of 0.135 was chosen as a cutoff because it showed the highest sum of sensitivity and specificity, 68% and 69%, respectively. Conclusion: FP results of mpMRI were detected in more than half of our sample, more than one-third were presented in Pi-RAD3, improved imaging techniques to decrease FP rates are highly needed.

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.

Hand-based multibiometric systems: state-of-the-art and future challenges.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

Transperineal versus transrectal multi-parametric magnetic resonance imaging fusion targeted prostate biopsy.

OBJECTIVES: To compare transperineal biopsies (TPBx) with transrectal ultrasound-guided biopsy (TRUSBx) in order to provide evidence, making clinicians able to select the appropriate biopsy approach under different conditions. METHODS: A comparative prospective study, conducted in King Khalid University Hospital (KKUH) and King Faisal Specialist Hospital and Research Centre (KFSH&RC), Riyadh, Kingdom of Saudi Arabia, between March 2019 and February 2020. All patients with raised prostate-specific antigen or atypical digital rectal examination findings were subjected to multi-parametric magnetic resonance imaging (MRI). Those with positive findings were referred to targeted fusion- guided biopsy either TPBx or TRUSBx, randomly. Complication rate, cancer detection rate, and procedure time were recorded. RESULTS: Transperineal biopsies and TRUSBx had an equivalent complication rate. However, both case detection rate and clinically significant cancer detection rate were significantly higher in TPBx versus TRUSBx (45.1% versus 29.1%, p=0.003; and 71.8% versus 43.7%, p=0.002; respectively). Transperineal biopsies was a longer procedure than TRUSBx (41.2±0.7 min versus 13±2.3 min, p=0.0001). CONCLUSION: No difference in complication rate was detected between the 2 procedures; however, TPBx was more effective for cancer detection in general and clinically significant cancer detection in particular.

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things.

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks.

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches.

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security.

The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

An Efficient Routing Protocol Based on Stretched Holding Time Difference for Underwater Wireless Sensor Networks.

Underwater Wireless Sensors Networks (UWSNs) use acoustic waves as a communication medium because of the high attenuation to radio and optical waves underwater. However, acoustic signals lack propagation speed as compared to radio or optical waves. In addition, the UWSNs also pose various intrinsic challenges, i.e., frequent node mobility with water currents, high error rate, low bandwidth, long delays, and energy scarcity. Various UWSN routing protocols have been proposed to overcome the above-mentioned challenges. Vector-based routing protocols confine the communication within a virtual pipeline for the sake of directionality and define a fixed pipeline radius between the source node and the centerline station. Energy-Scaled and Expanded Vector-Based Forwarding (ESEVBF) protocol limits the number of duplicate packets by expanding the holding time according to the propagation delay, and thus reduces the energy consumption via the remaining energy of Potential Forwarding Nodes (PFNs) at the first hop. The holding time mechanism of ESEVBF is restricted only to the first-hop PFNs of the source node. The protocol fails when there is a void or energy hole at the second hop, affecting the reliability of the system. Our proposed protocol, Extended Energy-Scaled and Expanded Vector-Based Forwarding Protocol (EESEVBF), exploits the holding time mechanism to suppress duplicate packets. Moreover, the proposed protocol tackles the hidden terminal problem due to which a reasonable reduction in duplicate packets initiated by the reproducing nodes occurs. The holding time is calculated based on the following four parameters: (i) the distance from the boundary of the transmission area relative to the PFNs' inverse energy at the 1st and 2nd hop, (ii) distance from the virtual pipeline, (iii) distance from the source to the PFN at the second hop, and (iv) distance from the first-hop PFN to its destination. Therefore, the proposed protocol stretches the holding time difference based on two hops, resulting in lower energy consumption, decreased end-to-end delay, and increased packet delivery ratio. The simulation results demonstrate that compared to ESEVBF, our proposed protocol EESEVBF experiences 20.2 % lesser delay, approximately 6.66 % more energy efficiency, and a further 11.26 % reduction in generating redundant packets.

Complicated small bowel diverticular disease: a case series.

Small bowel diverticulosis of the jejunum and ileum is an uncommon finding with a prevalence rate of 0.2% to 1.3% at autopsy and 0.3% to 1.9% on small bowel studies. Diagnosis can be difficult because there are no pathognomonic features or clinical symptoms that are specific for small bowel diverticulosis. Though rare, it is critical to keep the possibility of small bowel diverticulosis in mind when evaluating cases of malabsorption, chronic abdominal pain, haemorrhage, perforation and intestinal obstruction, especially in patients with connective tissue disorders, a family history of diverticula and a personal history of colonic diverticulosis. Guidelines for the treatment of complicated small bowel diverticulosis are not clearly defined. However, the consensus in treatment is to do a small bowel resection with primary anastomosis. We report three interesting cases of jejunoileal diverticula that presented in an occult manner and later progressed to more emergent manifestations.

Replaced right and left hepatic arteries: a variation in origin and course.

In ∼60% of cases, hepatic artery anatomy is of the normal variant. However, in 40% of cases, anomalies can exist. Preserving the hepatic blood supply is paramount in hepatobiliary procedures. We report an aberrant right hepatic artery coursing retroportally, with an aberrant left hepatic artery originating directly from the coeliac artery in a patient who underwent an elective pancreaticoduodenectomy (Whipple procedure).

A de-escalation protocol for febrile neutropenia cases and its impact on carbapenem resistance: A retrospective, quasi-experimental single-center study.

Our objective was to evaluate the impact of using an imipenem de-escalation protocol for empiric febrile neutropenia on the development of carbapenem resistance. A pre-post intervention design was used. The intervention was adopting the imipenem de-escalation approach, which began on January 1, 2012. A retrospective chart review of cases of febrile neutropenia bacteremia was performed one year before and one year after the intervention. We compared the development of carbapenem resistance between the two study periods. Seventy-five episodes of febrile neutropenia bacteremia were included in the study. They had similar demographics, clinical features and outcomes. There were 78 and 12 pathogens in the primary and follow-up blood cultures, respectively. Approximately 61% and 66% of the primary and follow-up blood cultures, respectively, were gram-negative bacteria with similar carbapenem resistance profiles in the two study periods. In our study population, 57% of the gram-negative bacteria were ESBL pathogens. The resistance of the gram-negative bacteria to piperacillin/tazobactam (72% versus 53%, p=0.161), imipenem (16% versus 11%, p=0.684), and meropenem (8% versus 16%, p=0.638) did not significantly change after our policy change. In conclusion, the use of the carbapenem de-escalation approach for febrile neutropenia in our institution was not associated with an increase in carbepenem resistance. Future prospective multi-center studies are recommended to further confirm the current findings.

Key Agreement Schemes in Wireless Body Area Networks: Taxonomy and State-of-the-Art.

Advances in wearable and implantable biosensors have enabled the applicability and usability of wireless body area networks (WBANs). A WBAN allows biosensors to collect and communicate human physiological data using wireless communication. The communication security of the collected data in WBAN is a major concern. Because of the dependability of cryptographic schemes for key management, these have become an important aspect of this security. However, the extremely constrained nature of biosensors has made designing key management schemes a challenging task. For this reason, many lightweight key management schemes have been proposed to overcome these constraints. In this article, we present a review of the state of the art of these solutions. We classify the WBAN schemes into three classes and evaluate them based on adequate metrics for key management in WBAN.