Search Results (561)

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

Background: We evaluated the utility of HNSCC LN R2* relaxation times to infer the oxygenation status of LN non-invasively at baseline and when breathing air and 100% oxygen to predict chemoradiotherapeutic locoregional response at 2 years. Hypoxia within LNs has been associated with poorer outcomes following CRT. Deoxyhaemoglobin decreases MRI transverse relaxation time (T2*) (lengthening inverse, R2*). Methods: A total of 54 patients underwent 1.5T-MRI before CRT. Conventional MR sequences were supplemented with T2* sequences breathing both air and 100% oxygen; pathological nodes identified in consensus were volumetrically contoured to T2* parametric maps. Results: Patients followed-up with for >2 years were categorised by multidisciplinary consensus into post-therapy complete local response (CR; n = 32/54) and local nodal disease relapse (RD; n = 22/54). Our data demonstrated, by R2*, that nodes that sustained post-therapy CR are significantly more hypoxic compared with relapsing nodes and paradoxically demonstrate a significant increase in hypoxia on 100% oxygen. Pre-treatment LN short axis diameter, various qualitative descriptors of malignancy, and quantitative DWI were not useful in discriminating successful response to CRT. Conclusions: This study demonstrates that a significant differential response to 100% oxygen and higher baseline R2* LN measurements could be exploited in risk stratification prior to CRT, and future work could be directed towards understanding the contrast mechanisms of R2* imaging, underpinning the observed differences in the context of hypoxia. Full article

Pneumonia is a respiratory condition caused by various microorganisms, including bacteria, viruses, fungi, and parasites. It manifests with symptoms such as coughing, chest pain, fever, breathing difficulties, and fatigue. Early and accurate detection is crucial for effective treatment, yet traditional diagnostic methods often fall short in reliability and speed. Chest X-rays have become widely used for detecting pneumonia; however, current approaches still struggle with achieving high accuracy and interpretability, leaving room for improvement. PneumoNet, an artificial intelligence assistant for X-ray pneumonia detection, is proposed in this work. The framework comprises (a) a new deep learning-based classification model for the detection of pneumonia, which expands on the AlexNet backbone for feature extraction in X-ray images and a new head in its final layers that is tailored for (X-ray) pneumonia classification. (b) GPT-Neo, a large language model, which is used to integrate the results and produce medical reports. The classification model is trained and evaluated on three publicly available datasets to ensure robustness and generalisability. Using multiple datasets mitigates biases from single-source data, addresses variations in patient demographics, and allows for meaningful performance comparisons with prior research. PneumoNet classifier achieves accuracy rates between 96.70% and 98.70% in those datasets. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

Context: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted. Full article

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment. Full article

Background: This work presents the procedures and application of the deep inspiration breath-hold (DIBH) technique for mediastinal lymphoma patients at a proton therapy (PT) center. It also discusses the implementation and validation of the surface-guided radiotherapy (SGRT) protocol in terms of positioning accuracy. Methods: This study included six lymphoma patients. Dedicated computed tomography (CT) protocols and a treatment workflow based on international guidelines were developed. Clinical data from the treatment planning system (TPS) were used to assess the difference between DIBH and free-breathing irradiation. Additionally, data from an optical patient positioning system and kilovoltage (kV) imaging system were used to estimate positioning shifts. The new CT protocol reduced the volume CT dose index by over six times compared with the standard protocol. Results: The DIBH method decreased the mean dose to the heart and lungs by up to 7.02 Gy(RBE) and 0.83 Gy(RBE), respectively. The median magnitude of patient setup errors and repeatability in DIBH positioning was 0.4 cm and 0.18 cm (mean for males and females) for the SGRT protocol. The kV imaging showed a setup error of over 0.3 cm for both groups. Conclusions: Despite the small size of the patient cohort, the relatively large number of individual positioning sessions enabled the detection of statistically significant differences (p < 0.05) in certain areas between male and female patients; however, no significant difference in the displacement vector magnitude was observed. DIBH treatment with SGRT offers high reproducibility for patient positioning. Full article

Recent cognitive literary studies of fiction have begun to reveal patterns in the ways authors engage readers’ bodily and environmentally grounded imaginations. This study brings fiction writers’ craft knowledge into conversation with neuroscientific, cognitive, and literary studies of multimodal imagery and other embodied responses to fiction reading. Developed through years of literary experiments, craft knowledge involves using language not just to engage readers’ senses but to broaden their understandings of how senses work. A close analysis of Edwidge Danticat’s craft techniques in Breath, Eyes, Memory (1994) affirms some recent literary and scientific findings on how language can activate readers’ sensory and motor systems. Danticat’s cues to readers’ imaginations present a relational, environmentally engaged kind of sensorimotor experience that may widen scientific understandings of how sensory and motor systems collaboratively ground cognition. By helping diverse readers imagine a young Haitian American woman’s movements, sensations, and emotions, Danticat’s craft also does political work, depicting the inner lives of characters under-represented in widely published fiction. Full article

In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. Detecting human vital signs remains a challenge, especially when obstacles interfere with the attempt to detect the presence of life. The sensitivity of a measurement system’s perception of vital signs is highly dependent on the monitoring systems and antennas that are used. The current work proposes a computational approach that aims to extract an empirical law of the dependence of the phase shift of the transmission coefficient (S₂₁) on the sensitivity at reception, based upon a set of four parameters. These variables are as follows: (a) the frequency of the continuous wave utilized; (b) the antenna type and its gain/directivity; (c) the electric field strength distribution on the chest surface (and its average value); and (d) the type of material (dielectric properties) impacted by the incident wave. The investigated frequency range is (1–20) GHz, while the simulations are generated using a doublet of dipole or gain-convenient identical Yagi antennas. The chest surface is represented by a planar rectangle that moves along a path of only 3 mm, with a step of 0.3 mm, mimicking respiration movement. The antenna–target system is modeled in the computational space in each new situation considered. The statistics illustrate the multiple regression function, empirically extracted. This enables the subsequent building of a continuous-wave bio-radar Doppler system with controlled and improved sensitivity. Full article

Gas sensors assume a crucial role in the medical domain, offering substantial support for disease diagnosis, treatment, medical environment management, and the operation of medical equipment by virtue of their distinctive gas detection capabilities. This paper presents an overview of the key research and development orientations for gas sensors, encompassing the exploration and optimization of novel sensitive materials, such as nanomaterials and metal oxides, to augment sensor sensitivity, selectivity, and stability. The innovation in sensor structural design, particularly the integration of micro-electromechanical systems (MEMS) technology to attain miniaturization and integration, is also addressed. The applications of gas sensors in health safety are expounded, covering the real-time monitoring of indoor air quality for harmful gases such as formaldehyde, as well as the detection of toxic gases in industrial environments to guarantee the safety of living and working spaces and prevent occupational health hazards. In the sphere of medical detection and diagnosis, this paper focuses on the detection of biomarkers in human exhaled breath by gas sensors, which facilitates the early diagnosis of diseases such as lung cancer. Additionally, the existing challenges and future development trends in this field are analyzed, with the aim of providing a comprehensive reference for the in-depth research and extensive application of gas sensors in the health, safety, and medical fields. Full article

Fluid dynamics modeling was conducted for the supply unit of a Powered Air-Purifying Respirator (PAPR) consisting of a nonwoven fabric filter and a pump, as well as for the exhaust filter (nonwoven fabric). The supply flow rate Q₁ was modeled as a function of the differential pressure ΔP and the duty value d of the PWM control under a constant pump voltage of V = 12.0 [V]. In contrast, the exhaust flow rate Q₂ was modeled solely as a function of ΔP. To simulate the pressurized hood compartment of the PAPR, a pressure buffer and a connected “respiratory airflow simulator” (a piston–cylinder mechanism) were developed. The supply unit and exhaust filter were connected to this pressure buffer, and simulated respiratory flow was introduced as an external disturbance flow. Under these conditions, it was demonstrated that the respiratory state—i.e., the expiratory state (flow from the simulator to the pressure buffer) and the inspiratory state (flow from the pressure buffer to the simulator)—can be estimated from the differential pressure ΔP, the pump voltage V, and the PWM duty value d, with respect to the disturbance flow generated by the respiratory airflow simulator. It was also confirmed that such respiratory state estimation remains valid even when the duty value d of the pump is being actively modulated to control the internal pressure of the PAPR hood. Furthermore, based on the estimated respiratory states, a theoretical investigation was conducted on constant pressure control inside the PAPR and on the inverse pressure control aimed at supporting respiratory activity—namely, pressure control that assists breathing by depressurizing when expiratory motion is detected and pressurizing when inspiratory motion is detected. This study was conducted as part of a research and development project on public-oriented PAPR systems, which are being explored as alternatives to lockdown measures in response to airborne infectious diseases such as COVID-19. The present work specifically focused on improving the wearing comfort of the PAPR. Full article

Temporomandibular joint (TMJ) ankylosis compromises essential functions such as chewing, phonation, and breathing. Surgical treatment aims to restore mandibular mobility and prevent the recurrence of joint fusion. This article describes a technical variation based on Puricelli biconvex arthroplasty, using surgical cement, screw fixation, and 3D-printed cutting guides based on virtual planning, allowing for greater precision in joint reconstruction. In this work, we present the step-by-step process used in the customization of cutting guides, virtual planning, and the production of the interposition material with PMMA associated with fixation with titanium screws as a hybrid method for the treatment of recurrent TMJ ankylosis. This reported technique is demonstrated to be reproducible, low-cost, and effective. Full article

Background/Objectives: Pediatric obstructive sleep apnea (POSA) is a long-term sleep disorder characterized by repeated interruptions in breathing during sleep among children. These interruptions result from blockages in the upper airways, causing decreased oxygen intake and disturbed sleep. Assessing parents’ awareness level and the factors affecting their knowledge is vital for enhancing early diagnosis and management of POSA. Methods: This was a cross-sectional study that utilized data from a sample of 838 parents in the Central Region of the Kingdom of Saudi Arabia. The participants completed self-administered online surveys, which ensured anonymity. Results: Only 320 (38.2%) of the parents demonstrated good knowledge about POSA, while the majority, 518 (61.8%), had poor knowledge. Nearly a third, 261 (31.2%), of the parents indicated that their primary sources of information on POSA were the internet and social media platforms. The prevalence of OSA among children was quite significant, with nearly a quarter, 236 (28.2%), of parents having a child affected by the condition. Commonly recognized symptoms included snoring, reported by 425 (50.7%), and mouth breathing, reported by 156 (18.6%). Frequently cited risk factors included obesity, mentioned by 373 (44.5%), and enlarged tonsils, mentioned by 175 (20.9%). A statistically significant association was found between age, gender, marital status, education level, specialization/work, source of knowledge about POSA, knowing someone with OSA, and having a child with OSA and the level of knowledge about POSA (p < 0.05). The study identified several significant factors predicting poor parental awareness of pediatric obstructive sleep apnea (POSA) including gender [AOR = 1.65; 95% CI = 1.220–2.223; p = 0.001], source of knowledge about pediatric obstructive sleep apnea [AOR = 1.35; 95% CI = 1.167–1.572; p < 0.001], and knowing someone with OSA [AOR = 1.92; 95% CI = 1.301–2.832; p = 0.001]. Conclusions: The study revealed that parents in the Central Region had limited knowledge about POSA. There were notable gaps in recognizing and understanding the symptoms of POSA and its impact on children’s mental health and academic performance. This underscores the importance of introducing targeted educational programs and initiatives for both parents and healthcare providers to enhance children’s mental health and overall well-being. Full article

Monitoring trace toluene exposure is critical for early-stage lung cancer screening via breath analysis, yet conventional chemiresistive sensors face fundamental limitations, including compromised selectivity in complex VOC matrices and humidity-induced signal drift, with prevailing p–n heterojunction architectures suffering from inherent charge recombination and environmental instability. Herein, we pioneer a 2D core–shell n–n heterojunction strategy through rational design of TiO₂@MoS₂ heterostructures, where vertically aligned MoS₂ nanosheets are epitaxially grown on 2D TiO₂ derived from graphene-templated synthesis, creating built-in electric fields at the heterojunction interface that dramatically enhance charge carrier separation efficiency. At 240 °C, the TiO₂@MoS₂ sensor exhibits a superior response (R_a/R_g = 9.8 to 10 ppm toluene), outperforming MoS₂ (R_a/R_g = 2.8). Additionally, the sensor demonstrates rapid response/recovery kinetics (9 s/16 s), a low detection limit (50 ppb), and excellent selectivity against interfering gases and moisture. The enhanced performance is attributed to unidirectional electron transfer (TiO₂ → MoS₂) without hole recombination losses, methyl-specific adsorption through TiO₂ oxygen vacancy alignment, and steric exclusion of non-target VOCs via size-selective MoS₂ interlayers. This work establishes a transformative paradigm in gas sensor design by leveraging n–n heterojunction physics and 2D core–shell synergy, overcoming long-standing limitations of conventional architectures. Full article

The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (${\mathrm{CO}}_2$), nitrogen dioxide (${\mathrm{NO}}_2$), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics), such as changes in electrical activity in the brain, measured via electroencephalogram (EEG) and physiological changes, including skin temperature, galvanic skin response (GSR), and blood oxygen saturation (${\mathrm{SpO}}_2$). These biometrics can be used to estimate pollutants, in particularly ${\mathrm{PM}}_1$ and ${\mathrm{CO}}_2$, with high degree of accuracy using machine learning. Our previous work made use of the Welch method (WM) to obtain a power spectral density (PSD) from the time series of EEG data. In this study, we introduce a novel approach for obtaining a PSD from the EEG time series, developed by Astrapi, called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors were measured using a biometric suite, and ambient ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO were measured simultaneously. We combined physiological responses with the PSD from the EEG time series using both the WM and the ASA to estimate the inhaled concentrations of ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO. This work shows that the PSD obtained from the ASA, when combined with other physiological responses, provides much better results (RMSE = 9.28 ppm in an independent test set) in estimating inhaled ${\mathrm{CO}}_2$ compared to making use of the same physiological responses and the PSD obtained by the WM (RMSE = 17.55 ppm in an independent test set). Small improvements were also seen in the estimation of ${\mathrm{NO}}_2$ and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset. Full article