Pingchuanning Decotion Alleviates Bronchial Asthma Airway Inflammation Through ROS/HMGB1/Beclin-1 Mediated Cell Autophagy.

Objective: Bronchial asthma is a prevalent respiratory disorder characterized by airway inflammation. This study aimed to investigate the protective effect of Pingchuanning decoction (PCN) on airway inflammation in bronchial asthma, focusing on the role of autophagy and its underlying molecular mechanism. Methods: Using an in vitro lipopolysaccharide (LPS)-induced inflammatory damage model of human airway epithelial cells (16HBE), we assessed the effect of PCN. Various experiments were performed to evaluate the expression of autophagy-related genes, autophagosome and vesicle counts, and reactive oxygen species (ROS) levels. Results: First, PCN reduced LPS-induced cellular inflammation. Second, PCN decreased the number of autophagosomes and autophagic vesicles. And third, PCN significantly reduced reactive oxygen species (ROS) levels. Most importantly, PCN also down-regulated LPS-induced expression of HMGB1, Beclin-1, and autophagy-related gene 5 (ATG5) while enhancing the expression of B-cell lymphoma 2 (Bcl-2), which further reduced the LC3II/I ratio. Conclusion: PCN reduces the 16HBE inflammatory response by inhibiting the overexpression of ROS/HMGB1/Beclin-1 mediated cell autophagy. Therefore, it may serve as a potential drug for treating bronchial asthma.

Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory.

Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.

Research on Comprehensive Evaluation and Early Warning of Transmission Lines' Operation Status Based on Dynamic Cloud Computing.

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability.

An intelligent model for supporting edge migration for virtual function chains in next generation internet of things.

The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations.

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal.

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz-23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

A novel nomogram for decision-making assistance on exemption of axillary lymph node dissection in T1-2 breast cancer with only one sentinel lymph node metastasis.

Background: T1-2 breast cancer patients with only one sentinel lymph node (SLN) metastasis have an extremely low non-SLN (NSLN) metastatic rate and are favorable for axillary lymph node dissection (ALND) exemption. This study aimed to construct a nomogram-based preoperative prediction model of NSLN metastasis for such patients, thereby assisting in preoperatively selecting proper surgical procedures. Methods: A total of 729 T1-2 breast cancer patients with only one SLN metastasis undergoing sentinel lymph node biopsy and ALND were retrospectively selected from Harbin Medical University Cancer Hospital between January 2013 and December 2020, followed by random assignment into training (n=467) and validation cohorts (n=262). A nomogram-based prediction model for NSLN metastasis risk was constructed by incorporating the independent predictors of NSLN metastasis identified from multivariate logistic regression analysis in the training cohort. The performance of the nomogram was evaluated by the calibration curve and the receiver operating characteristic (ROC) curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram. Results: Overall, 160 (21.9%) patients had NSLN metastases. Multivariate analysis in the training cohort revealed that the number of negative SLNs (OR: 0.98), location of primary tumor (OR: 2.34), tumor size (OR: 3.15), and lymph-vascular invasion (OR: 1.61) were independent predictors of NSLN metastasis. The incorporation of four independent predictors into a nomogram-based preoperative estimation of NSLN metastasis demonstrated a satisfactory discriminative capacity, with a C-index and area under the ROC curve of 0.740 and 0.689 in the training and validation cohorts, respectively. The calibration curve showed good agreement between actual and predicted NSLN metastasis risks. Finally, DCA revealed the clinical utility of the nomogram. Conclusion: The nomogram showed a satisfactory discriminative capacity of NSLN metastasis risk in T1-2 breast cancer patients with only one SLN metastasis, and it could be used to preoperatively estimate NSLN metastasis risk, thereby facilitating in precise clinical decision-making on the selective exemption of ALND in such patients.

Multi-Model Running Latency Optimization in an Edge Computing Paradigm.

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study.

Background and Objective: Sentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram. Methods: A total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram. Results: In the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort. Conclusion: The HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

A Novel Necroptosis-Associated lncRNA Signature Can Impact the Immune Status and Predict the Outcome of Breast Cancer.

Breast cancer (BRCA) is one of the leading causes of death among women worldwide, and drug resistance often leads to a poor prognosis. Necroptosis is a type of programmed cell death (PCD) and exhibits regulatory effects on tumor progression, but few studies have focused on the relationships between necroptosis-associated lncRNAs and BRCA. In this study, we established a signature basis of 7 necroptosis-related lncRNAs associated with prognosis and divided BRCA patients into high- and low-risk groups. Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. Cox assays confirmed that risk score was an independent prognostic factor for BRCA patients. The receiver operating characteristic (ROC) curve proved the predictive accuracy of the signature and the area under the curve (AUC) values of the risk score reached 0.722. The nomogram relatively accurately predicted the prognosis of the patients. GSEA analysis suggested that the related signaling pathways and biological processes enriched in the high- and low-risk groups may influence the tumor microenvironment (TME) of BRCA. ssGSEA showed the difference in immune cell infiltration, immune pathway activation, and immune checkpoint expression between the two risk groups, with the low-risk group more suitable for immunotherapy. According to the significant difference in IC50 between risk groups, patients can be guided for an individualized treatment plan. Overall, the authors established a prognostic signature consisting of 7 necroptosis-associated lncRNAs that can independently predict the clinical outcome of BRCA patients. The difference in the tumor immune microenvironment between the low- and high-risk populations may be the reason for the resistance to immunotherapy in some patients.

Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones.

Scientific evidence shows that acoustic analysis could be an indicator for diagnosing COVID-19. From analyzing recorded breath sounds on smartphones, it is discovered that patients with COVID-19 have different patterns in both the time domain and frequency domain. These patterns are used in this paper to diagnose the infection of COVID-19. Statistics of the sound signals, analysis in the frequency domain, and Mel-Frequency Cepstral Coefficients (MFCCs) are then calculated and applied in two classifiers, k-Nearest Neighbors (kNN) and Convolutional Neural Network (CNN), to diagnose whether a user is contracted with COVID-19 or not. Test results show that, amazingly, an accuracy of over 97% could be achieved with a CNN classifier and more than 85% on kNN with optimized features. Optimization methods for selecting the best features and using various metrics to evaluate the performance are also demonstrated in this paper. Owing to the high accuracy of the CNN model, the CNN model was implemented in an Android app to diagnose COVID-19 with a probability to indicate the confidence level. The initial medical test shows a similar test result between the method proposed in this paper and the lateral flow method, which indicates that the proposed method is feasible and effective. Because of the use of breath sound and tested on the smartphone, this method could be used by everybody regardless of the availability of other medical resources, which could be a powerful tool for society to diagnose COVID-19.

Asymmetric Synthesis of Axially Chiral Naphthyl-C3-indoles via a Palladium-Catalyzed Cacchi Reaction.

Atropoisomeric biaryl motifs are widely found in natural products and bioactive compounds as well as chiral catalysts and ligands. Various efficient approaches have been disclosed for the construction of chiral six-six biaryl skeletons. In contrast, the enantioselective synthesis of axially chiral arylindoles through the strategy of de novo construction, other than the asymmetric functionalization of indoles, remain a challenging task. Herein we report an efficient Pd(0)/(S)-Segphos-catalyzed atroposelective Cacchi reaction of 2-alkynylanilines with sterically congested naphthyl halides, which afforded an array of naphthyl-C3-indoles in high yields with good to excellent atroposelectivities. The addition of water and the modulation of the manipulation procedure by premixing the palladium complex and the naphthyl halide were the keys to success. The conformational stability of the obtained axially chiral naphthyl-C3-indole containing a synthetically more-valuable free NH moiety is revealed through kinetic experiments.

Qiyusanlong Formula Induces Autophagy in Non-Small-Cell Lung Cancer Cells and Xenografts through the mTOR Signaling Pathway.

OBJECTIVE: Qiyusanlong (QYSL) formula has been used in the clinic for more than 20 years and has been proved to have pronounced efficacy in the treatment of non-small-cell lung cancer (NSCLC). This work aims to evaluate the molecular mechanism of QYSL formula action on NSCLC, specifically in relation to autophagy induction. METHODS: In vitro, CCK-8 was used to detect the effect of QYSL serum on cell viability in A549 cells. In vivo, A549 cells were implanted subcutaneously in nude mice to establish a xenograft model. TUNEL staining was used to measure cell apoptosis and TEM to observe the autophagy-related morphological changes in vitro and in vivo. Western blotting, RT-qPCR, and immunofluorescence were used to measure autophagy-related proteins. In addition, rapamycin (an inhibitor of mTOR and inducer of autophagy) and MHY1485 (an activator of mTOR and inhibitor of autophagy) were used to determine whether QYSL-induced autophagy was regulated by the mTOR pathway. RESULTS: QYSL serum inhibited the cell viability of A549 cells in a concentration-dependent manner. In vivo, the QYSL formula inhibited xenograft growth. The QYSL formula promoted apoptosis in A549 cells and induced autophagosome formation in vitro and in vivo. In addition, the QYSL formula downregulated the expression of mTOR and p62, while it upregulated the expression of ATG-7 and Beclin-1 and increased the LC3-II/LC3-I ratio. QYSL serum inhibited p-mTOR in a similar manner to rapamycin while reducing the activating effects of MHY1485 on p-mTOR. CONCLUSION: The QYSL formula has anti-lung cancer effects and promotes autophagy through the mTOR signaling pathway.

Riddle of the Sphinx: Emerging Role of Transfer RNAs in Human Cancer.

The dysregulation of transfer RNA (tRNA) expression contributes to the diversity of proteomics, heterogeneity of cell populations, and instability of the genome, which may be related to human cancer susceptibility. However, the relationship between tRNA dysregulation and cancer susceptibility remains elusive because the landscape of cancer-associated tRNAs has not been portrayed yet. Furthermore, the molecular mechanisms of tRNAs involved in tumorigenesis and cancer progression have not been systematically understood. In this review, we detail current knowledge of cancer-related tRNAs and comprehensively summarize the basic characteristics and functions of these tRNAs, with a special focus on their role and involvement in human cancer. This review bridges the gap between tRNAs and cancer and broadens our understanding of their relationship, thus providing new insights and strategies to improve the potential clinical applications of tRNAs for cancer diagnosis and therapy.

[Bioinformatical analysis of differentially expressed genes in alveolar macrophages of chronic obstructive pulmonary disease(COPD)].

Objective To analyze the differentially expressed genes (DEGs) in alveolar macrophages (AMs) of patients with chronic obstructive pulmonary disease (COPD) and their potential roles in the pathogeneses of COPD using bioinformatics. Methods Gene chip and RNA sequencing data sets of AMs in patients with COPD were downloaded from GEO. Limma and Degseq2 packages in R software were applied to obtain DEGs, and the GO enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction analysis (PPI), and the hub gene analysis were performed to predict the molecular mechanisms of DEGs. Results Through the integration of three data sets, a total of 43 DEGs of AMs were obtained, and the function predictive analysis found that the 43 DEGs were primarily related to chemokines, cytokines, complement, cytochrome P450, etc., which mainly included the significantly low expression of C-X-C motif chemokine ligand 9 (CXCL9), CXCL11, etc. and the significantly high expression of cytochrome P450 family 1 subfamily B member 1 (CYP1B1). Conclusion The DEGs of AMs in patients with COPD are related to the molecular mechanisms of immunity and inflammation and might be involved in the pathogenesis of chronic inflammation of COPD.

Pingchuanning decoction attenuates airway inflammation by suppressing autophagy via phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin signaling pathway in rat models of asthma.

BACKGROUND: Pingchuanning decoction is a well-known traditional Chinese medicine for the treatment of airway inflammatory diseases, including asthma. However, the potential mechanism by which Pingchuanning decoction contributes to the amelioration of airway inflammation remains unknown. METHODS: A rat model of asthma was well established by inducing ovalbumin. Lipopolysaccharide-stimulated rat tracheal epithelial (RTE) cells were used as cellular model. Lung histopathology and goblet cell hyperplasia were assessed by hematoxylin-eosin (HE) and periodic acid Schiff staining, respectively. Total inflammatory cells count and RTE cell apoptosis were analyzed by flow cytometry. The autophagic activities were evaluated by immunohistochemical and immunofluorescence analysis and Western blot analysis of autophagy-related proteins. We also detected the effects of Pingchuanning decoction on phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) and high-mobility group box 1 (HMGB1)-mediated toll-like receptor 4 (TLR4)/NF-κB pathways-related proteins and inflammatory cytokines using the Western blot analysis and enzyme-linked immunosorbent assay. RESULTS: Pingchuanning decoction effectively attenuated pulmonary pathology and autophagy. Treatment with Pingchuanning decoction activated PI3K/Akt/mTOR pathway and inhibited HMGB1/TLR4/NF-κB pathway, which could be overturned by LY294002, a PI3K antagonist, or rapamycin (Rapa), an autophagy inducer. CONCLUSION: Pingchuanning decoction exerted a therapeutic effect on asthma by inhibiting autophagy via PI3K/Akt /mTOR signaling pathway.

Huaier Granule extract inhibit the proliferation and metastasis of lung cancer cells through down-regulation of MTDH, JAK2/STAT3 and MAPK signaling pathways.

Although the effect of Huaier has been widely studied, its role and its molecular mechanism in lung cancer are not clear. In this study, we explored the inhibitory effect of Huaier on lung cancer cells and its molecular mechanism. The cell viability, migration and invasion were analyzed by CCK-8 and BrdU cell proliferation assay kits, Transwell and colony forming assay. The cell cycle and apoptosis were analyzed by flow cytometry. The experimental results showed that the viability, migration and invasion of A549 and NCI-H1650 cells were inhibited by Huaier in a dose and time-dependent manner. Huaier induced cell apoptosis and the cells were blocked in the S phase to inhibit cell proliferation. Western blotting results showed that Huaier inhibited the expression of MTDH and increased the proportion of Bax/Bcl-2, it could also promote the expression of Cleaved Caspase-3 and increase the activity of Caspase-3, promote cell apoptosis and inhibit cell proliferation. Huaier inhibited the metastasis and invasion of lung cancer cells by inhibiting the expression levels of EMT related proteins, it also inhibited the expression of JAK2/STAT3 and MAPK signaling pathways. Therefore, our results showed that Huaier may inhibit the proliferation and metastasis of lung cancer cells through multiple targets, it had the potential for treatment of lung cancer.

Qiyusanlong decoction suppresses lung cancer in mice via Wnt/ß-catenin pathway.

Lung cancer is one of the most fatal cancers due to its high metastatic rate. Traditional Chinese medicine has been used in cancer patients for decades to improve quality of life and prolong survival time. The present study used a novel Qiyusanlong (QYSL) decoction composed of 10 kinds of Chinese medicine including astragalus membranaceus (Huangqi), polygonatumod oratum (yuzu), scolopendra (tianlong), pberetima (dilong), solanum nigrum (longkui), herbahedyotis (baihushecao), semen coicis (yiyiren), euphorbia helioscopia (zeqi), curcuma longa (eshu) and tendril-leaved fritillary bulb (chuanbei). The effects and function of the QYSL decoction remain to be elucidated. The present study established a mouse xenograft model using Lewis lung carcinoma cell injection and administered different doses of QYSL decoction to the mice. It was demonstrated that the chemotherapy drug Cisplatin (DDP) and QYSL decoction repressed lung tumor growth, and the inhibitory effect of DDP was more significant. Furthermore, QYSL decoction and DDP modulated the expression of regulatory proteins in the Wnt/ß­catenin pathway, including Wnt1, Wnt2, Wnt5a and glycogen synthase kinase 3ß, detected by western blotting, and affected the signals of cluster of differentiation 44 variation 6 and Survivin in tumor tissues, examined via immunohistochemistry. The combination of QYSL decoction and DDP enhanced the inhibitory effect. These data demonstrated that the QYSL decoction repressed lung tumor development via the Wnt/ß­catenin pathway. The therapeutic effect of QYSL decoction alone was milder compared with DDP, however the combination of QYSL decoction and chemotherapy exhibited an increased the rapeutic effect compared with the treatments administered alone. These findings revealed the function of QYSL decoction as a lung cancer treatment and provided insight for a novel lung cancer therapy.

Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization.

As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers' attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.

A review of protocol implementations and energy efficient cross-layer design for wireless body area networks.

The issues inherent in caring for an ever-increasing aged population has been the subject of endless debate and continues to be a hot topic for political discussion. The use of hospital-based facilities for the monitoring of chronic physiological conditions is expensive and ties up key healthcare professionals. The introduction of wireless sensor devices as part of a Wireless Body Area Network (WBAN) integrated within an overall eHealth solution could bring a step change in the remote management of patient healthcare. Sensor devices small enough to be placed either inside or on the human body can form a vital part of an overall health monitoring network. An effectively designed energy efficient WBAN should have a minimal impact on the mobility and lifestyle of the patient. WBAN technology can be deployed within a hospital, care home environment or in the patient’s own home. This study is a review of the existing research in the area of WBAN technology and in particular protocol adaptation and energy efficient cross-layer design. The research reviews the work carried out across various layers of the protocol stack and highlights how the latest research proposes to resolve the various challenges inherent in remote continual healthcare monitoring.

Wireless mesh networks.

Wireless telemedicine using GSM and GPRS technologies can only provide low bandwidth connections, which makes it difficult to transmit images and video. Satellite or 3G wireless transmission provides greater bandwidth, but the running costs are high. Wireless networks (WLANs) appear promising, since they can supply high bandwidth at low cost. However, the WLAN technology has limitations, such as coverage. A new wireless networking technology named the wireless mesh network (WMN) overcomes some of the limitations of the WLAN. A WMN combines the characteristics of both a WLAN and ad hoc networks, thus forming an intelligent, large scale and broadband wireless network. These features are attractive for telemedicine and telecare because of the ability to provide data, voice and video communications over a large area. One successful wireless telemedicine project which uses wireless mesh technology is the Emergency Room Link (ER-LINK) in Tucson, Arizona, USA. There are three key characteristics of a WMN: self-organization, including self-management and self-healing; dynamic changes in network topology; and scalability. What we may now see is a shift from mobile communication and satellite systems for wireless telemedicine to the use of wireless networks based on mesh technology, since the latter are very attractive in terms of cost, reliability and speed.