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Abstract Background Monitoring Intraabdominal Pressure (IAP) is essential in critical care, as elevated IAP can lead to severe complications, including Abdominal Compartment Syndrome (ACS). Advances in technology, such as digital capsules, have opened new avenues for measuring IAP non-invasively. This study assesses the feasibility and effectiveness of using a capsular device for IAP measurement in an animal model. Method In our controlled experiment, we anesthetized pigs and simulated elevated IAP conditions by infusing CO2 into the peritoneal cavity. We compared IAP measurements obtained from three different methods: an intravesical catheter (IAPivp), a capsular device (IAPdot), and a direct peritoneal catheter (IAPdir). The data from these methods were analyzed to evaluate agreement and accuracy. Results The capsular sensor (IAPdot) provided continuous and accurate detection of IAP over 144 h, with a total of 53,065,487 measurement triplets recorded. The correlation coefficient (R²) between IAPdot and IAPdir was excellent at 0.9241, demonstrating high agreement. Similarly, IAPivp and IAPdir showed strong correlation with an R² of 0.9168. Conclusion The use of capsular sensors for continuous and accurate assessment of IAP marks a significant advancement in the field of critical care monitoring. The high correlation between measurements from different locations and methods underscores the potential of capsular devices to transform clinical practices by providing reliable, non-invasive IAP monitoring.

Abstract This study demonstrates a novel use of the U-Net convolutional neural network (CNN) for modeling pixel-based electrostatic potential distributions in GaN metal–insulator-semiconductor high-electron mobility transistors (MIS-HEMTs) with various gate and source field plate designs and drain voltages. The pixel-based images of the potential distribution are successfully modeled from the developed U-Net CNN with an error of less than 1% error relative to a TCAD simulated reference of a 500-V electrostatic potential distribution in the AlGaN/GaN interface. Furthermore, the modeling time of potential distributions by U-Net takes about 80 ms. Therefore, the U-Net CNN is a promising approach to efficiently model the pixel-based distributions characteristics in GaN power devices.

Fibromyalgia is a chronic illness usually accompanied by long-lasting, general pain throughout the body, often accompanied by anxiety, depression, fatigue, and sleep disruption. Meanwhile, doctors and scientists have not entirely discovered detailed mechanisms; patients always have an exaggerated sensation to pervasive pain without satisfied medical service. Given the lack of knowledge on its underlying mechanism, current treatments aim to provide pain and/or symptom relief. The present study aimed to clarify the role of cannabinoid receptor 1 (CB1) signaling in a mouse fibromyalgia pain model. To develop the mouse fibromyalgia model, mice were subjected to intermittent cold stress (ICS). Our results indicated that mechanical (2.09 ± 0.09 g) and thermal hyperalgesia (4.77 ± 0.29 s), which were evaluated by von Frey and Hargraves’ tests, were induced by ICS, suggesting successful modeling. The hurting replies were then provoked by electroacupuncture (EA) but not for sham EA mice. Further, in a Western blot analysis, we found significantly decreased CB1 protein levels in the thalamus, somatosensory cortex, and anterior cingulate cortex. In addition, the levels of pain-related protein kinases and transcription factor were increased. Treatment with EA reliably increased CB1 expression in various brain regions sequentially alleviated by nociceptive mediators. Furthermore, the administration of a CB1 agonist significantly attenuated fibromyalgia pain, reversed EA analgesia by the CB1 antagonist, and further reversed the chemogenetic inhibition of SSC. Our innovative findings evidence the role of CB1 signaling in the interaction of EA and fibromyalgia, suggesting its potential for clinical trials and as a treatment target.

Purpose To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm.Materials and methods We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps.Results The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures.Conclusion The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.

The introduction of hydrophilic segments into the main-chain of polymer photocatalysts allows water to efficiently enter the interior through these hydrophilic segments, and results in effective water/polymer interfaces for hydrogen evolution.

Abstract Hypertension and pain are both prevalent conditions in the older adult population. We aimed to report the prevalence of pain discomforts and investigated the association between hypertension and pain discomforts among older adults in the United States. Data from the 2011 National Health and Aging Trends Study were analyzed. In-person interviews were conducted in 7601 adults ages ≥ 65 years. Prevalence of bothersome pain, activity-limiting pain, locations of pain and usage of pain medicine were evaluated. Demographics, comorbidities, and other covariates were compared between older adults with hypertension and those without. Multivariate regression was further performed to yield adjusted odd ratios. Among 6825 older adults, 4533 of them had a history of hypertension while 2272 of them had not. Prevalence of bothersome pain (57.12% versus 44.81%, p

Background/purpose: The palate has become a popular site for the placement of temporary anchorage devices (TADs) owing to its bone quantity and quality. This study aimed to investigate total and cortical bone thicknesses in the whole palate as well as palatal width using a standard grid system and cone-bean computed tomography (CBCT) images. Materials and methods: The CBCT images of 43 samples were selected. The total bone and cortical bone thicknesses of the palate were surveyed on 64 points per patient. The palatal width was measured. The difference between the age and sex groups was analyzed. Results: The total palatal bone thickness in the adult group ranged from 9.85 ± 2.04 to 1.87 ± 0.79 mm. In the adolescent group, we found one-third of the incisor roots in the area 3 mm distal to the incisive foramen and 8 mm lateral to the mid-palatal suture. The cortical bone thickness in adults was significantly thicker in the posterior paramedian area than that in adolescents. Conclusion: The thickest vertical bone is located in the zone 3 mm distal to the incisive foramen and 4–8 mm lateral to the midpalate. The zone 6 mm posterior to the incisive foramen and 2–8 mm lateral to the midpalate exhibited optimal thickness and was away from the incisor roots. This region could be a safe zone for adolescent patients to place TADs. When TADs are to be inserted at the posterior palate, the 2-mm paramedian area should be the first region of choice.

Radiation-induced stenosis of the carotid artery is a significant risk factor for large-vessel ischemic stroke, which usually leads to significant impairment of neurological function. We performed intra-arterial thrombectomy on a 63-year-old male patient who had laryngeal cancer and postradiation carotid stenosis. He presented with acute-onset dysarthria and left hemiplegia. Brain computed tomography perfusion scan showed right middle cerebral artery ischemic change. Angiography confirmed total occlusion of the right internal carotid artery. Intra-arterial mechanical thrombectomy with carotid stenting was performed immediately, and recanalization was achieved. The patient fully recovered and was discharged after a 1-week hospitalization. Our experience suggests that early intervention for radiation-related carotid stenosis might be essential and beneficial for the outcome of large-vessel ischemic stroke.

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.

(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost.

In this work, we demonstrated the thermal analysis of different flip-chip bonding designs for high power GaN HEMT developed for power electronics applications, such as power converters or photonic driver applications, with large gate periphery and chip size, as well as an Au metal heat-spreading layer deposited on top of a planarized dielectric/passivation layer above the active region. The Au bump patterns can be designed with high flexibility to provide more efficient heat dissipation from the large GaN HEMT chips to an AlN package substrate heat sink with no constraint in the alignment between the HEMT cells and the thermal conduction bumps. Steady-state thermal simulations were conducted to study the channel temperatures of GaN HEMTs with various Au bump patterns at different levels of current and voltage loadings, and the results were compared with the conventional face-up GaN die bonding on an AlN package substrate. The simulations were started from a single finger isolated HEMT cell and then extended to multiple fingers HEMT cells (total gate width > 40 mm) to investigate the “thermal cross-talk” effect from neighboring devices. Thermal analysis of the GaN HEMT under pulse operation was also performed to better reflect the actual conditions in power conversion or pulsed laser driver applications. Our analysis provides a combinational assessment of power GaN HEMT dies under a working condition (e.g., 1MHz, 25% duty cycle) with different flip chip packaging schemes. The analysis indicated that the channel temperature rise (∆T) of a HEMT cell in operation can be reduced by 44~46% by changing from face-up die bonding to a flip-chip bonding scheme with an optimized bump pattern design.

MEMS based 3D double stacked tower pixel biosensor 10 × 10 array with integration of readout circuit for detection of saliva pH ion is demonstrated. The pixel biosensor comprised a driving electrode, sensing electrode and double stack tower pixel structure. The sensitivity of double stacked tower biosensor can be auxiliary enhanced by proposed lower-jitter low dropout regulator circuit and dual offset cancellation comparator. The double stacked tower sensor is fabricated by MEMS backend-of-line CMOS process, it is compatible with CMOS frontend readout circuits and integrated as a system-on-chip (SoC). The double stacked tower pixel by MEMS process is to obtain a larger volume ratio of charge groups in a pixel of biosensor to enhance the sensitivity and linearity for ion detection. With the double stacked tower structure in biosensor, the sensitivity is improved by 31% than that of single tower structure proved by simulation. A wide-range linearity from pH 2.0 to pH 8.3, high sensitivity of −21 ADC counts/pH (or 212 mV/pH), response time of 5 s, repetition of 98.9%, and drift over time of 0.5 mV are achieved. Furthermore, the proposed biosensor was performed to confirm the artificial saliva from healthy gingiva, chronic gingivitis and chronic periodontitis, the measured ADC counts from proposed biosensor SoC was in consistent of that measured cyclic voltametric (CV) method very well. The proposed 3D double stack tower biosensor and readout circuit can be further integrated with internet-of-thing (IoT) device and NFC for data transmission for continuous pH sensing to facilitate the chronic gingiva disease health care at home.