Search Results (1,551)

A programmable gain amplifier (PGA) is commonly used to optimize the input dynamic range of high-performance systems such as headphones and biomedical sensors. But PGA is rather sensitive to electromagnetic interference (EMI), which limits the precision of these systems. Many capacitor-less low-dropout regulator (LDO) schemes with high power supply rejection have been proposed to act as the independent power supply for PGA, which consumes additional power and area. This paper proposed a PGA with a high power supply rejection ratio (PSRR) and low power consumption, which serves as the analog front-end amplifier in the 20-bit sigma-delta ADC. The PGA is a two-stage amplifier with hybrid compensation. The first stage is the recycling folded cascode amplifier with the gain-boost technique, while the second stage is the class-AB output stage. The PGA was implemented in the 0.18 μm CMOS technology and achieved a 9.44 MHz unity-gain bandwidth (UGBW) and a 57.8° phase margin when driving the capacitor of 5.9 pF. An optimum figure-of-merit (FoM) value of 905.67 has been achieved with the proposed PGA. As the front-end amplifier of a high-precision ADC, it delivers a DC gain of 162.1 dB, the equivalent input noise voltage of 301.6 nV and an offset voltage of 1.61 μV. Within the frequency range below 60 MHz, the measured PSRR of ADC is below −70 dB with an effective number of bits (ENOB), namely 20 bits. Full article

Background/Objectives: Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor’s histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin’s tumors (WT) and malignant tumors (MT), two entities that proved to present overlapping imaging features on conventional and functional MRI sequences. Methods: In this retrospective study, a total of 106 PGT (66 WT, 40 MT) with confirmed histology were eligible for radiomic analysis, which were randomly split into a training group (79 PGT; 49 WT; 30 MT) and a testing group (27 PGT; 17 WT, 10 MT). The radiomic features were extracted from 3D segmentations of PGT performed on the following sequences: PROPELLER T2-weighted images and the ADC map, using a dedicated software. First- and second-order features were derived for each lesion, using original and filtered images. Results: After employing several feature reduction techniques, including LASSO regression, three final radiomic parameters were identified to be the most significant in distinguishing between the two studied groups, with fair AUC values that ranged between 0.703 and 0.767. All three radiomic features were used to construct a Radiomic Score that presented the highest diagnostic performance in distinguishing between WT and MT, achieving an AUC of 0.785 in the training set, and 0.741 in the testing set. Conclusions: MRI-based radiomic features have the potential to serve as promising novel imaging biomarkers for discriminating between Warthin’s tumors and malignant tumors in the parotid gland. Nevertheless, it is still to prove how radiomic features can consistently achieve higher diagnostic performance, and if they can outperform alternative imaging methods, ideally in larger, multicentric studies. Full article

The design of conventional digital predistortion (DPD) requires an analogue-to-digital converter (ADC) with a sampling frequency that is multiple times the signal bandwidth, which is extremely challenging for sub-Nyquist sampling systems with undersampled signals. To address this, this paper proposes a neural network (NN)-assisted wideband power amplifier (PA) DPD method for sub-Nyquist sampling systems, wherein a dual-stage architecture is designed to handle the ambiguity caused by subsampled communications signals. In the first stage, the time-delayed polynomial reconstruction method is employed to estimate the wideband DPD nonlinearity coarsely with the undersampled signals with limited pilots. In the second stage, an NN-based DPD method is proposed for the virtual training of the DPD, which learns the up-sampled DPD behavior by taking advantage of the pre-estimated DPD model and the input data signals, which reduces the length of the training sequence significantly and refines the DPD behavior efficiently. Simulation results demonstrate the efficacy of the proposed method in tackling the wideband PA nonlinearity and its ability to outperform the conventional method in terms of power spectrum, error vector magnitude, and bit error rate. Full article

When chips perform numerous computational tasks or process complex instructions, they generate substantial heat, potentially affecting their long-term reliability and performance. Thus, accurate and effective temperature measurement and management are crucial to ensuring chip performance and lifespan. This paper presents a multi-channel temperature measurement system based on a diode temperature-sensitive array thermal test chip (TTC). The thermal test chip accurately emulates the heat power and thermal distribution of the target chip, providing signal output through row and column address selection. The multi-channel temperature measurement system centers around a microcontroller and includes voltage signal acquisition circuits and host computer software. It enables temperature acquisition, storage, and real-time monitoring of 16 channels in a 4 × 4 array thermal test chip. During experiments, the system uses a constant current source to drive temperature-sensitive diodes, collects diode output voltage through multiplexers and high-precision amplification circuits, and converts analog signals to digital signals via a high-speed ADC. Data transmission occurs via the USB 2.0 protocol, with the host computer software handling data processing and real-time display. The test results indicate that the system accurately monitors chip temperature changes in both steady-state and transient thermal response tests, closely matching measurements from a semiconductor device analyzer, with an error of about 0.67%. Therefore, this multi-channel temperature measurement system demonstrates excellent accuracy and real-time monitoring capability, providing an effective solution for the thermal design and evaluation of high power density integrated circuits. Full article

Objective: This study evaluates the accuracy of a Machine Learning model of Random Forest (RF) type, using MRI data and radiomic features to predict lymph node involvement in prostate cancer (PCa). Methods: Ninety-five patients who underwent mp-MRI, prostatectomy, and lymphadenectomy at the Fondazione Policlinico Campus Bio-medico Radiological Department from 2016 to 2022 were analyzed. Radiomic features were extracted from T2-weighted, DWI, and ADC sequences and processed using a Random Forest (RF) model. Clinical data such as PSA levels and Gleason scores were also considered. Results: The RF model demonstrated significant accuracy in predicting lymph node involvement, achieving 84% accuracy for nodules in the peripheral zone (80% for predicting positive lymph node involvement and 85% for negative lymph node involvement) and 87% for those in the transitional zone (86% for predicting positive lymph node involvement and 88% for negative lymph node involvement). In the peripheral zone, key features included ADC shape maximum 2D diameter row and T2 noduloglcm difference variance, while in the transitional zone, DWI glcm difference average and DWI glcm Idm were important. DWI and ADC sequences were particularly crucial for accurate lymph node assessment. First-order features emerged as the most significant in whole-gland analysis, indicating fundamental differences in tumor composition and density critical for identifying malignancies with higher metastatic potential. Conclusions: AI-driven radiomic analysis, especially using DWI- and ADC-derived features, effectively predicts lymph node involvement in PCa patients, in particular in negative linfonode status patients, offering a promising tool for preoperative linfonode sparing patient selection. Further validation with larger cohorts is needed. Some limitations of this study are a relatively small sample size and it being a retrospective study. Full article

Background/Objectives: Diabetes-related pancreatic changes on MRI remain unclear. Thus, we evaluated the pancreatic changes on MRI in patients with both type 1 diabetes (T1D) and type 2 diabetes (T2D) using multiparametric MRI. Methods: This prospective study involved patients with T1D or T2D who underwent upper abdominal 3-T MRI. Additionally, patients without impaired glucose metabolism were retrospectively included as a control. The imaging data included pancreatic anteroposterior (AP) diameter, pancreas-to-muscle signal intensity ratio (SIR) on fat-suppressed T1-weighted image (FS-T1WI), apparent diffusion coefficient (ADC) value, T1 value on T1 map, proton density fat fraction (PDFF), and mean secretion grade of pancreatic juice flow on cine-dynamic magnetic resonance cholangiopancreatography (MRCP). The MR measurements were compared using one-way analysis of variance and the Kruskal–Wallis test. Results: Sixty-one patients with T1D (n = 7) or T2D (n = 54) and 21 control patients were evaluated. The pancreatic AP diameters were significantly smaller in patients with T1D than in patients with T2D (p < 0.05). The average SIR on FS-T1WI was significantly lower in patients with T1D than in controls (p < 0.001). The average ADC and T1 values of the pancreas were significantly higher in patients with T1D than in patients with T2D (p < 0.01) and controls (p < 0.05). The mean secretion grade of pancreatic juice flow was significantly lower in patients with T1D than in controls (p = 0.019). The average PDFF of the pancreas was significantly higher in patients with T2D than in controls (p = 0.029). Conclusions: Patients with T1D had reduced pancreas size, increased pancreatic T1 and ADC values, and decreased pancreatic juice flow on cine-dynamic MRCP, whereas patients with T2D had increased pancreatic fat content. Full article

Issues related to increasing the number of material recognition classes in cargo inspection by the X-ray dual high-energy method through introducing a class of heavy organic materials that include basic explosives are considered. A mathematical model of material recognition by the dual-energy method based on the parameters of level lines and effective atomic numbers has been proposed. Estimates of the parameters of the level lines and effective atomic numbers of explosives and their physical counterparts for monoenergetic and classical high-energy implementations of the dual-energy method were made. The use of a simulation model to demonstrate the ability to detect and correctly identify explosives and their physical counterparts using the dual high-energy method is illustrated. An algorithmic methodological approach is proposed to improve the accuracy of effective atomic number estimation. It has been demonstrated theoretically and by simulation that it is possible to distinguish materials in cargo inspection from the following classes of materials: light organics (typical representative—polyethylene); heavy organics (explosives), light minerals and heavy plastics (fluoropolymers); light metals (aluminum, Z = 13), heavy minerals (calcium oxide, Z = 19); metals (iron, Z = 26); heavy metals (tin, Z = 50); and radiation insensitive metals (Z > 57). Full article

Background: The pyrrolobenzodiazepine (PBD) dimer SJG-136 reached Phase II clinical trials in ovarian cancer and leukaemia in the UK and USA in the 2000s. Several structural analogues of SJG-136 are currently in clinical development as payloads for Antibody-Drug Conjugates (ADCs). There is growing evidence that PBDs exert their pharmacological effects through inhibition of transcription factors (TFs) in addition to arrest at the replication fork, DNA strand breakage, and inhibition of enzymes including endonucleases and RNA polymerases. Hence, PBDs can be used to target specific DNA sequences to inhibit TFs as a novel anticancer therapy. Objective: To explore the ability of SJG-136 to bind to the cognate sequences of transcription factors using a previously described HPLC/MS method, to obtain preliminary mechanistic evidence of its ability to inhibit transcription factors (TF), and to determine its effect on TF-dependent gene expression. Methods: An HPLC/MS method was used to assess the kinetics and thermodynamics of adduct formation between the PBD dimer SJG-136 and the cognate recognition sequence of the TFs NF-κB, EGR-1, AP-1, and STAT3. CD spectroscopy, molecular dynamics simulations, and gene expression analyses were used to rationalize the findings of the HPLC/MS study. Results: Notable differences in the rate and extent of adduct formation were observed with different DNA sequences, which might explain the variations in cytotoxicity of SJG-136 observed across different tumour cell lines. The differences in adduct formation result in variable downregulation of several STAT3-dependent genes in the human colon carcinoma cell line HT-29 and the human breast cancer cell line MDA-MB-231. Conclusions: SJG-136 can disrupt transcription factor-mediated gene expression, which contributes to its exceptional cytotoxicity in addition to the DNA-strand cleavage initiated by its ability to crosslink DNA. Full article

This paper proposes a current harmonic suppression strategy that combines harmonic synchronous rotating frame (HSRF) current feedback control and back-electromotive force harmonic (BEMFH) feedforward compensation to suppress the fifth and seventh current harmonics of a six-phase permanent magnet synchronous motor (PMSM). The current harmonics of six-phase PMSMs vary with the current due to manufacturing imperfections and the inverter nonlinearity effect. Using fixed-parameter BEMFH feedforward compensation cannot completely eliminate current harmonics. This paper integrates a closed-loop harmonic current control strategy, using HSRF in the differential mode of the six-phase PMSM rotor rotating frame to effectively mitigate current harmonic variations caused by load changes. The controller adapts a Texas Instrument microcontroller featuring encoder interfaces, complementary pulse width modulation (PWM), and analog–digital converters (ADC) to simplify the board design. The rotor angle feedback is provided by a 12-pole resolver in conjunction with an Analog Device resolver-to-digital converter (RDC). The specifications of the six-phase PMSM are as follows: 12 poles, 1200 rpm, 200 A (rms), and 600 V DC bus. The total harmonic distortion (THD) of the phase current for harmonics below the 21st order was reduced from 31.71% to 4.84% under the test conditions of 1200 rpm rotor speed and 200 A peak phase current. Specifically, the fifth and seventh harmonics were reduced from 29.98% and 9.72% to 2.74% and 1.21%, respectively. These results validate the feasibility of the proposed current harmonic suppression strategy. Full article

Technology has increasingly influenced the provision of healthcare services by enhancing patient safety, optimising workflows, and improving efficiency. Large healthcare facilities have adopted automated dispensing cabinets (ADCs) as an advanced technological solution. A key gap exists in understanding the ADC implementation experience in different contexts. Therefore, this study seeks to fill this literature gap by exploring key stakeholders’ perspectives on the benefits, barriers, and improvement opportunities related to ADCs, offering valuable insights to support their effective integration across various healthcare settings. This qualitative study was conducted in Saudi Arabia. The implementation of ADCs generally has positive outcomes for all staff. The system has brought about enhanced medication tracking, greater time efficiency, along with reduced workload and medication errors. However, there are barriers to their implementation, including changes in workflow and workload distribution, cabinet design, technical medication management challenges, and the need for staff training. To maximise the effectiveness of ADCs, healthcare organisations should focus on improving operational workflows, providing ongoing staff training, and maintaining robust system monitoring. Additionally, manufacturers should focus on advancing technology to further enhance the efficiency and functionality of ADCs. Full article

The emergence of antibody–drug conjugates (ADCs) has transformed the treatment landscape of a variety of cancers. ADCs typically consist of three main components: monoclonal antibody, chemical linker, and cytotoxic payload. These integrated therapeutic modalities harness the benefits of each component to provide a therapeutic response that cannot be achieved by conventional chemotherapy. Antibodies play roles in determining tumor specificity through target-mediated uptake, prolonging the circulation half-life of cytotoxic payloads, and providing additional mechanisms of action inherent to the original antibody, thus significantly contributing to the overall performance of ADCs. However, ADCs have unique safety concerns, such as drug-induced adverse events related to the target-mediated uptake of the ADC in normal tissues (so-called “on-target, off-tumor toxicity”) and platform toxicity, which are partially derived from limited tumor uptake of antibodies. Identifying suitable target antigens thus impacts the clinical success of ADCs and requires careful consideration, given the multifaceted aspects of this unique treatment modality. This review briefly summarizes the representative roles that antibodies play in determining the efficacy and safety of ADCs. Key considerations for selecting suitable cell surface target antigens for ADC therapy are also highlighted. Full article

Wireless surface electromyography (sEMG) sensors are very practical in that they can be worn freely, but the radio link between them and the receiver might cause unpredictable latencies that hinder the accurate synchronization of time between multiple sensors, which is an important aspect to study, e.g., the correlation between signals sampled at different sites. Moreover, to minimize power consumption, it can be useful to design a sensor with multiple clock domains so that each subsystem only runs at the minimum frequency for correct operation, thus saving energy. This paper presents the design, implementation, and test results of an sEMG sensor that uses Bluetooth Low Energy (BLE) communication and operates in three different clock domains to save power. In particular, this work focuses on the synchronization problem that arises from these design choices. It was solved through a detailed study of the timings experimentally observed over the BLE connection, and through the use of a dual-stage filtering mechanism to remove timestamp measurement noise. Time synchronization through three different clock domains (receiver, microcontroller, and ADC) was thus achieved, with a resulting total jitter of just 47 µs RMS for a 1.25 ms sampling period, while the dedicated ADC clock domain saved between 10% to 50% of power, depending on the selected data rate. Full article

Cisplatin combined with gemcitabine, a doublet regimen, is the first-line treatment for patients with advanced lung adenocarcinoma (ADC); however, the treatment response remains poor. This study aimed to identify potential biomarkers for predicting response to cisplatin and gemcitabine. Tissue transcriptome and blood proteome analyses were conducted on 27 patients with lung ADC. Blood-derived proteins that reflected tissue-specific biomarkers were obtained using Venn diagrams. The candidate proteins were validated by Western blotting. Lentivirus-mediated short hairpin RNA interference was used to verify the functional roles of the candidate proteins in human A549 cells. We identified 417 differentially expressed genes, including 52 upregulated and 365 downregulated genes, and 31 differentially expressed proteins, including 26 upregulated and 5 downregulated proteins. Integrative analysis revealed the presence of alpha-1-acid glycoprotein 1 (A1AG1) and fibrinogen alpha chain (FGA or FIBA) in both the tissue and serum. FGA levels were elevated in responders compared to non-responders, and reduced serum FGA levels were correlated with resistance to this regimen. Moreover, FGA knockdown in A549 cells resulted in resistance to the doublet regimen. Our findings indicate that FGA is a tissue-specific serum protein that may function as a blood-based biomarker to predict the response of patients with lung ADC to cisplatin plus gemcitabine chemotherapy. Full article

Background/Objectives: As the global population ages, it is becoming increasingly important to create sustainable, health-promoting environments that support healthy aging. This study explores seniors’ mealtime experiences in adult day centers (ADCs) in southern Israel, focusing on identifying health and well-being needs related to eating and dining behaviors through the lens of the healthy placemaking approach. Methods: Thematic analysis was used to analyze data from focus groups and interviews with ADC attendees and leaders across a multicultural sample of ADCs in southern Israel between April and November 2022. Results: Three main themes emerged from the study: (1) individual-level needs, which are met through meals or during mealtimes and include positive experiences, a sense of empowerment, and the cultivation of warmth and domesticity; (2) social needs, which are addressed through interactions during mealtimes and food-related behaviors, including building social connections, fostering community, and encouraging social engagement; and (3) sustainability, health, and environmental aspects, including promoting a healthy and disease-appropriate diet, alongside addressing ecological and food security concerns. Conclusions: We demonstrate the pivotal role of ADC meals in facilitating social engagement and fostering a sense of community among attendees. Additionally, we highlight the importance of centering attendees’ concerns and needs in the dining experience and promoting their active participation in decision-making processes. Transforming ADC meals through the healthy placemaking approach can promote healthy eating, enhance social interactions, and support sustainable environments. Full article

The U.S. Food and Drug Administration (FDA) has authorized 50 new drugs in 2024, which matches the average figure for recent years (2018–2023). The approval of 13 monoclonal antibodies (mAbs) sets a new record, with these molecules accounting for more than 25% of all drugs authorized this year. Three proteins have been added to the list of biologics, and with the inclusion of four TIDES (two oligonucleotides and two peptides), only one in three approved drugs this year is a small molecule. As of 2023, no antibody-drug conjugates (ADCs) have reached the market this year. Two deuterated drugs have been approved, bringing the total approvals for this class of compounds to four. This year saw the authorization of two more PEGylated drugs—both peptides—highlighting a renewed interest in this strategy for extending drug half-life, despite the setback caused by the withdrawal of peginesatide from the market in 2014 due to adverse side effects. N-aromatic heterocycles and fluorine atoms are present in two-thirds of all the small molecules approved this year. Herein, the 50 new drugs authorized by the FDA in 2024 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins), TIDES (oligonucleotides and peptides), combined drugs, natural products, F-containing molecules, nitrogen aromatic heterocycles, aromatic compounds, and other small molecules. Full article