RESUMEN

Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy.

Asunto(s)

COVID-19 , Aprendizaje Automático , Radar , Humanos , Masculino , SARS-CoV-2 , Terapia por Ejercicio/métodos , Algoritmos , Adulto

RESUMEN

This study demonstrates the potential of using biological nanoparticles to deliver RNA therapeutics targeting programmed death-ligand 1 (PD-L1) as a treatment strategy for cholangiocarcinoma (CCA). RNA therapeutics offer prospects for intracellular immune modulation, but effective clinical translation requires appropriate delivery strategies. Milk-derived nanovesicles were decorated with epithelial cellular adhesion molecule (EpCAM) aptamers and used to deliver PD-L1 small interfering RNA (siRNA) or Cas9 ribonucleoproteins directly to CCA cells. In vitro, nanovesicle treatments reduced PD-L1 expression in CCA cells while increasing degranulation, cytokine release, and tumor cell cytotoxicity when tumor cells were co-cultured with T cells or natural killer cells. Similarly, immunomodulation was observed in multicellular spheroids that mimicked the tumor microenvironment. Combining targeted therapeutic vesicles loaded with siRNA to PD-L1 with gemcitabine effectively reduced tumor burden in an immunocompetent mouse CCA model compared with controls. This proof-of-concept study demonstrates the potential of engineered targeted nanovesicle platforms for delivering therapeutic RNA cargoes to tumors, as well as their use in generating effective targeted immunomodulatory therapies for difficult-to-treat cancers such as CCA.

Asunto(s)

Antígeno B7-H1 , Colangiocarcinoma , Inmunoterapia , ARN Interferente Pequeño , Colangiocarcinoma/terapia , Colangiocarcinoma/metabolismo , Colangiocarcinoma/inmunología , Colangiocarcinoma/tratamiento farmacológico , Colangiocarcinoma/patología , Antígeno B7-H1/metabolismo , Antígeno B7-H1/antagonistas & inhibidores , Antígeno B7-H1/genética , Animales , Humanos , Ratones , Línea Celular Tumoral , Inmunoterapia/métodos , ARN Interferente Pequeño/genética , ARN Interferente Pequeño/administración & dosificación , Nanopartículas/química , Neoplasias de los Conductos Biliares/terapia , Neoplasias de los Conductos Biliares/patología , Neoplasias de los Conductos Biliares/metabolismo , Neoplasias de los Conductos Biliares/inmunología , Microambiente Tumoral/inmunología , Modelos Animales de Enfermedad , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacología , Gemcitabina

RESUMEN

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Asunto(s)

Inteligencia Artificial , Conducción de Automóvil , Redes Neurales de la Computación , Radar , Máquina de Vectores de Soporte , Humanos , Algoritmos , Aprendizaje Automático

RESUMEN

Extensive mechanical stress frequently causes micro-traumas in skeletal muscle, followed by a regeneration period. The effective removal of dead myofibers is a prerequisite for proper regeneration, and several cell types, including professional phagocytes, were reported to be active in this process. Myoblasts express several molecules of the phagocytic machinery, such as BAI1, stabilin-2, and TAM (Tyro3, Axl, Mertk) tyrosine kinase receptors, but these molecules were reported to serve primarily cell fusion and survival, and their role in the phagocytosis was not investigated. Therefore, we aimed to investigate the in vitro phagocytic capacity of the C2C12 mouse myoblast cell line. RNA sequencing data were analyzed to determine the level and changes of phagocytosis-related gene expression during the differentiation process of C2C12 cells. To study the phagocytic capacity of myoblasts and the effect of dexamethasone, all-trans retinoic acid, hemin, and TAM kinase inhibitor treatments on phagocytosis, C2C12 cells were fed dead thymocytes, and their phagocytic capacity was determined by flow cytometry. The effect of dexamethasone and all-trans retinoic acid on phagocytosis-related gene expression was determined by quantitative PCR. Both undifferentiated and differentiated cells engulfed dead cells being the undifferentiated cells more effective. In line with this, we observed that the expression of several phagocytosis-related genes was downregulated during the differentiation process. The phagocytosis could be increased by dexamethasone and all-trans retinoic acid and decreased by hemin and TAM kinase inhibitor treatments. Our results indicate that myoblasts not only express phagocytic machinery genes but are capable of efficient dead cell clearance as well, and this is regulated similarly, as reported in professional phagocytes.

Asunto(s)

Hemina , Fagocitosis , Ratones , Animales , Hemina/farmacología , Diferenciación Celular , Mioblastos/metabolismo , Tretinoina/farmacología , Tretinoina/metabolismo , Expresión Génica , Dexametasona/farmacología , Dexametasona/metabolismo

RESUMEN

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals.

RESUMEN

Extracellular vesicles (EVs) show promise for targeted drug delivery but face production challenges with low yields. Cell-derived nanovesicles (CDNVs) made by reconstituting cell membranes could serve as EV substitutes. In this study, CDNVs were generated from mesenchymal stem cells by extrusion. Their proteomic composition, in vitro and in vivo toxicity, and capacity for loading RNA or proteins were assessed. Compared with EVs, CDNVs were produced at higher yields, were comprised of a broader range of proteins, and showed no detrimental effects on cell proliferation, DNA damage, or nitric oxide production in vitro or on developmental toxicity in vivo. CDNVs could be efficiently loaded with RNA and engineered to modify surface proteins. The feasibility of generating immunomodulatory CDNVs was demonstrated by preparing CDNVs with enhanced surface expression of PD1, which could bind to PD-L1 expressing tumor cells, enhance NK and T cell degranulation, and increase immune-mediated tumor cell death. These findings demonstrate the adaptability and therapeutic promise of CDNVs as promising substitutes for natural EVs that can be engineered to enhance immunomodulation.

RESUMEN

A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.

RESUMEN

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient's respiration rate. However, it is crucial to consider a patient's medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

RESUMEN

The tumor microenvironment (TME) in liver cancers such as hepatocellular cancer (HCC) consists of a complex milieu of liver tissue-resident cells, infiltrated immune cells, and secreted factors that collectively serve to promote tumor growth and progression. Intercellular crosstalk contributes to tissue homeostasis, and perturbations during injury, inflammation and tumorigenesis that are important for tumor progression. Extracellular vesicle (EV)-mediated transfer of a payload of RNA molecules that serve as an intercellular signaling is an important contributor to tissue homeostasis within the TME. Several types of RNA have been implicated in EV-mediated signaling. Biological processes that can be modulated by EV RNA signaling within the liver include tumor growth, invasion, metastasis, angiogenesis, and modulation of the immune cell activities. This mini-review describes the liver TME, and the biological effects of EV RNA-mediated signaling within the liver to highlight the role of EV RNA in intercellular communication.

Asunto(s)

Carcinoma Hepatocelular , Vesículas Extracelulares , Neoplasias Hepáticas , Humanos , Neoplasias Hepáticas/patología , Carcinoma Hepatocelular/patología , ARN , Microambiente Tumoral/genética , Vesículas Extracelulares/genética

RESUMEN

Extracellular vesicles (EVs) and their contents are gaining recognition as important mediators of intercellular communication through the transfer of bioactive molecules, such as non­coding RNA. The present study comprehensively assessed the microRNA (miRNA/miR) content within EVs released from HepG2 liver cancer (LC) cells and LX2 hepatic stellate cells (HSCs) and determined the contribution of EV miRNA to intercellular communication. Using both transwell and spheroid co­cultures of LC cells and HSCs, miR­126­3p within EV was established as a mediator of HSC to LC cell communication that influenced tumor cell migration and invasion, as well as the growth of multicellular LC/HSC spheroids. Manipulation of miR­126­3p either by enforced expression using pre­miR­126­3p or by inhibition using antimiR­126­3p did not alter tumor cell viability, proliferation or sensitivity to either sorafenib or regorafenib. By contrast, enforced expression of miR­126­3p decreased tumor­cell migration. Knockdown of miR­126­3p in tumor cells increased disintegrin and metalloproteinase domain­containing protein 9 (ADAM9) expression and in HSCs increased collagen­1A1 accumulation with an increase in compactness of multicellular spheroids. Within LC/HSC spheroids, ADAM9 and vascular endothelial growth factor expression was increased by silencing of miR­126­3p but diminished with the restoration of miR­126­3p. These studies implicate miR­126­3p in functional effects on migration, invasion and spheroid growth of tumor cells in the presence of HSCs, and thereby demonstrate functional EV­RNA­based intercellular signaling between HSCs and LC cells that is directly relevant to tumor­cell behavior.

Asunto(s)

Vesículas Extracelulares , Neoplasias Hepáticas , MicroARNs , Humanos , Microambiente Tumoral/genética , Factor A de Crecimiento Endotelial Vascular/metabolismo , Neoplasias Hepáticas/patología , Comunicación Celular/genética , MicroARNs/metabolismo , Células Estrelladas Hepáticas/metabolismo , Células Estrelladas Hepáticas/patología , Vesículas Extracelulares/metabolismo , Proteínas de la Membrana/metabolismo , Proteínas ADAM/metabolismo , Proteínas ADAM/farmacología

RESUMEN

Phytoremediation is a new technology for cleaning contaminated soil with crude oil. Oil pollution is a serious problem worldwide; the aim of this study was to use the plant for Phytoremediation. Leguminous plant Acacia seiberiana Tausch was tested for it is efficiency to remediate soil polluted with 0.5, 1, 1.5 and 2% (w/w) crude oil and it is the ability to enhance the activity of soil microorganisms. Plant parameters, degradation percentage, Total Petroleum Hydrocarbon and bacterial count were measured. Results showed that the concentration of the crude oil did not affect plant growth indicating the significant success of the Phytoremediation process. Shoot length and shoot and root weights have not been negatively affected by oil, compared to the control plant, up to a concentration of 1.5% for up to six months. Crude oil degradation percentages were found to be in the range of 49-79%. The highest degradation percentage was recorded for the soil collected from underneath A. sieberiana Tausch seedlings (79%). A total of 81 different hydrocarbons were detected in soil samples at zero time and most of them were found to be of long (≥30 carbon atoms) and moderate (10-29 C) hydrocarbon chains. Fraction analyses were conducted in plant A. sieberiana Tausch seedlings at intervals of 60, 120 and 180 days of incubation, six different hydrocarbons were detected. The most abundant hydrocarbon detected were Heneicosane (21 C), Tetracosane (24 C) and Octacosane (28 C). The viable microorganism's count in oil-contaminated soil at any of the concentrations tested was significantly (P ≤ 0.01) higher than that in oil-free soil at any sampling interval. High efficiency of biodegradation was achieved using A. seiberiana Tausch indicating the unique mechanism of this plant in remediating contaminated soil with crude oil.

Asunto(s)

Acacia , Fabaceae , Petróleo , Contaminantes del Suelo , Acacia/metabolismo , Petróleo/metabolismo , Biodegradación Ambiental , Suelo , Hidrocarburos/metabolismo , Contaminantes del Suelo/metabolismo , Microbiología del Suelo

RESUMEN

Congenital nasal dermoid and sinus cysts (NDSCs) are rare congenital deformities with a prevalence rate of 0.005% to 0.0025%. Early diagnosis is usually made during the first three years of life, but in some cases, the diagnosis may be delayed. The present case study elaborates the treatment course of a 22-year-old adult with a rare congenital nasal midline dermoid cyst. The patient had no family history of the deformity, and intracranial extensions were also ruled out before surgery. Open rhinoplasty technique was used along with osteotomies during the surgical process. The cyst was removed in entirety. The total operating time was six hours, and no complications were observed during the intra-operative or post-operative period. In conclusion, the case presentation focuses on various techniques and methods that can be used during surgery that have not been practiced before to correct the deformity while achieving a good aesthetic result as well.

RESUMEN

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user's choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.

Asunto(s)

Audífonos , Pérdida Auditiva , Percepción del Habla , Humanos , Ruido , Pérdida Auditiva/terapia , Acústica

RESUMEN

Alpha-Synuclein (α-Syn) accumulation is central to the pathogenesis of Parkinson's disease (PD), hence the quest for finding potential therapeutics that may promote the α-Syn clearance is the need of the hour. To this, activation of the evolutionarily conserved protein and key regulator of the autophagy, 5'AMP-activated protein kinase (AMPK) is well-known to induce autophagy and subsequently the clearance of α-Syn aggregates. Alpha-mangostin (AM) a polyphenolic xanthone obtained from Garcinia Mangostana L. was previously reported to activate AMPK-dependent autophagy in various pre-clinical cancer models. However, no studies evidenced the effect of AM on AMPK-dependent autophagy activation in the PD. Therefore, the present study aimed to investigate the neuroprotective activity of AM in the chronic rotenone mouse model of PD against rotenone-induced α-Syn accumulation and to dissect molecular mechanisms underlying the observed neuroprotection. The findings showed that AM exerts neuroprotection against rotenone-induced α-Syn accumulation in the striatum and cortex by activating AMPK, upregulating autophagy (LC3II/I, Beclin-1), and lysosomal (TFEB) markers. Of note, an in-vitro study utilizing rat pheochromocytoma cells verified that AM conferred the neuroprotection only through AMPK activation, as the presence of inhibitors of AMPK (dorsomorphin) and autophagy (3-methyl adenine) failed to mitigate rotenone-induced α-Syn accumulation. Moreover, AM also counteracted rotenone-induced behavioral deficits, oxidative stress, and degeneration of nigro-striatal dopaminergic neurons. In conclusion, AM provided neuroprotection by ameliorating the rotenone-induced α-Syn accumulation through AMPK-dependent autophagy activation and it can be considered as a therapeutic agent which might be having a higher translational value in the treatment of PD.

Asunto(s)

Enfermedad de Parkinson , Rotenona , Animales , Ratas , Ratones , Rotenona/toxicidad , alfa-Sinucleína/metabolismo , Enfermedad de Parkinson/tratamiento farmacológico , Enfermedad de Parkinson/metabolismo , Proteínas Quinasas Activadas por AMP , Neuroprotección , Autofagia

RESUMEN

Parkinson's disease (PD) is a chronic motor disorder, characterized by progressive loss of dopaminergic neurons. Numerous studies suggest that glucagon-like peptide-1 (GLP-1) secretagogue has a neuroprotective role in PD models. The present study evaluated potential of coffee bioactive compounds in terms of their ability to bind GPR-40/43 and tested the neuroprotective effect of best candidate on rotenone-induced PD mice acting via GLP-1 release. In silico molecular docking followed by binding free energy calculation revealed that chlorogenic acid (CGA) has a strong binding affinity for GPR-40/43 in comparison to other bioactive polyphenols. Molecular dynamics simulation studies revealed stable nature of GPR40-CGA and GPR43-CGA interaction and also provided information about the amino acid residues involved in binding. Subsequently, in vitro studies demonstrated that CGA-induced secretion of GLP-1 via enhancing cAMP levels in GLUTag cells. Furthermore, in vivo experiments utilizing rotenone-induced mouse model of PD revealed a significant rise in plasma GLP-1 after CGA administration (50 mg/kg, orally for 13 weeks) with concomitant increase in colonic GPR-40 and GPR-43 mRNA expression. CGA treatment also prevented rotenone-induced motor and cognitive impairments and significantly restored the rotenone-induced oxidative stress. Meanwhile, western blot results confirmed that CGA treatment downregulated rotenone-induced phosphorylated alpha-synuclein levels by upregulating PI3K/AKT signaling and inactivating GSK-3ß through the release of GLP-1. CGA treatment ameliorated rotenone-induced dopaminergic nerve degeneration and alpha-synuclein accumulation in substantia nigra and augmented mean density of dopaminergic nerve fibers in striatum. These findings demonstrated novel biological function of CGA as a GLP-1 secretagogue. An increase in endogenous GLP-1 may render neuroprotection against a rotenone mouse model of PD and has the potential to be used as a neuroprotective agent in management of PD.

Asunto(s)

Ácido Clorogénico , Péptido 1 Similar al Glucagón , Fármacos Neuroprotectores , Enfermedad de Parkinson , Aminoácidos , Animales , Ácido Clorogénico/farmacología , Ácido Clorogénico/uso terapéutico , Café/química , Neuronas Dopaminérgicas/metabolismo , Péptido 1 Similar al Glucagón/metabolismo , Glucógeno Sintasa Quinasa 3 beta , Ratones , Simulación del Acoplamiento Molecular , Fármacos Neuroprotectores/farmacología , Fármacos Neuroprotectores/uso terapéutico , Enfermedad de Parkinson/tratamiento farmacológico , Fosfatidilinositol 3-Quinasas , Polifenoles/farmacología , Polifenoles/uso terapéutico , Proteínas Proto-Oncogénicas c-akt , ARN Mensajero , Rotenona/toxicidad , Secretagogos/farmacología , alfa-Sinucleína/metabolismo

RESUMEN

In this paper, a self-threshold voltage (Vth) compensated Radio Frequency to Direct Current (RF-DC) converter operating at 900 MHz and 2.4 GHz is proposed for RF energy harvesting applications. The threshold voltage of the rectifying devices is compensated by the bias voltage generated by the auxiliary transistors and output DC voltage. The auxiliary transistors compensate the threshold voltage (Vth) of the PMOS rectifying device while the threshold voltage (Vth) of the NMOS rectifying device is compensated by the output DC voltage. The proposed RF-DC converter was implemented in 180 nm Complementary Metal-Oxide Semiconductor (CMOS) technology. The experimental results show that the proposed design achieves better performance at both 900 MHz and 2.4 GHz frequencies in terms of PCE, output voltage, sensitivity, and effective area. The peak power conversion efficiency (PCE) of 38.5% at -12 dBm across a 1 MΩ load for 900 MHz frequency was achieved. Similarly, for 2.4 GHz frequency, the proposed circuit achieves a peak PCE of 26.5% at -6 dBm across a 1 MΩ load. The proposed RF-DC converter circuit shows a sensitivity of -20 dBm across a 1 MΩ load and produces a 1 V output DC voltage.

RESUMEN

Diabetic wounds are one of the debilitating complications that affect up to 20% of diabetic patients. Despite the advent of extensive therapies, the recovery rate is unsatisfactory, and approximately, 25% of patients undergo amputation, thereby demanding alternative therapeutic strategies. On the basis of the individual therapeutic roles of the miR-155 inhibitor and mesenchymal stem cells (MSC)-derived exosomes, we conjectured that the combination of the miR-155 inhibitor and MSC-derived exosomes would have synergy in diabetic wound healing. Herein, miR-155-inhibitor-loaded MSC-derived exosomes showed synergistic effects in keratinocyte migration, restoration of FGF-7 levels, and anti-inflammatory action, leading to accelerated wound healing mediated by negative regulation of miR-155, using an in vitro co-culture model and in vivo mouse model of the diabetic wound. Furthermore, treatment with miR-155-inhibitor-loaded MSC-derived exosomes led to enhanced collagen deposition, angiogenesis, and re-epithelialization in diabetic wounds. This study revealed the therapeutic potential of miR-155-inhibitor-loaded MSC-derived exosomes in diabetic wound healing and opened the doors for encapsulating miRNAs along with antibiotics within the MSC-derived exosomes toward improved management of chronic, nonhealing diabetic wounds.

Asunto(s)

Diabetes Mellitus , Exosomas , Células Madre Mesenquimatosas , MicroARNs , Animales , Células Endoteliales de la Vena Umbilical Humana , Humanos , Ratones , MicroARNs/genética , MicroARNs/farmacología , Cicatrización de Heridas

RESUMEN

Introduction and objective The fracture of hand bones is very common among manual hand workers and a fractured hand imparts a great effect on a person's productivity both socioeconomically and from a body image point of view. The most common method of hand fractures fixation is with the help of Kirschner wires. Kirchner wires can be inserted in exposed or in buried manner. There are a few studies that provide a comparative analysis of rate of infection between these two techniques. This study aimed to assess the rate of infection in buried versus exposed Kirschner (K)-wires for hand fractures. Material and method The study was designed as a randomized controlled trial with consecutive non-random sampling. It was conducted in the Department of Plastic Surgery, Holy Family Hospital, Rawalpindi, Pakistan, and lasted from June to December 2019. Blinding was not possible as both the operating surgeon and patient were aware of the procedure being done; however, the assessor was blinded and was not aware which group got which treatment. Total 122 patients with fractures of metacarpals and phalanges of hand were included in the study and were divided into two groups with 61 patients in each. Group A was treated with buried K-wires and group B with exposed K-wires. The patients were followed for one month for the outcomes in terms of infection in the patients. Results Group A had 24 females (39.3%) and 37 males (60.7%). Group B had 16 females (26.2%) and 45 males (73.8%). In group A, nine (14.8%) patients had ages between 10 and 20 years, 18 (29.5%) patients between 21 and 30 years, 14 (23.0%) patients between 31 and 40 years, 11 (18.0%) patients between 41 and 50 years, and nine (14.8%) were between 51 and 60 years. The mean duration of surgery was 35.16 minutes for group A and 27.30 minutes for group B. Based on modified Oppenheim scoring system for pin site infection, out of 61 patients, seven (11.5%) with buried K-wires while 14 (23%) with exposed K-wires developed pin site infection. Conclusion Rate of infection is low in buried K-wires as compared to exposed K-wires though not statistically significant (p>0.05) for the fractures of metacarpals and phalangeal fractures of hand.

RESUMEN

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

Asunto(s)

Radar , Procesamiento de Señales Asistido por Computador , Emociones , Femenino , Humanos , Aprendizaje Automático , Masculino , Respiración