Search Results (1,697)

An Analog-to-Digital Converter (ADC) is an indispensable part of image sensor systems. This paper presents a silicon-based 13-bit 100 kS/s two-step single-slope analog-to-digital converter (TS-SS ADC) for infrared image sensors with a frame rate of 100 Hz. For the charge leakage and offset voltage issues inherent in conventional TS-SS ADC, a four-terminal comparator was employed to resolve the fine ramp voltage offset caused by charge redistribution in storage and parasitic capacitors. In addition, a current-steering digital-to-analog converter (DAC) was adopted to calibrate the voltage reference of the dynamic comparator and mitigate differential nonlinearity (DNL)/integral nonlinearity (INL). To eliminate quantization dead zones, a 1-bit redundancy was incorporated into the fine quantization circuit. Finally, the quantization scheme consisted of 7-bit coarse quantization followed by 7-bit fine quantization. The ADC was implemented using an SMIC 55 nm processSemiconductor Manufacturing International Corporation, Shanghai, China. The post-simulation results show that when the power supply is 3.3 V, the ADC achieves a quantization range of 1.3 V–3 V. Operating at a 100 kS/s sampling rate, the proposed ADC exhibits an effective number of bits (ENOBs) of 11.86, a spurious-free dynamic range (SFDR) of 97.45 dB, and a signal-to-noise-and-distortion ratio (SNDR) of 73.13 dB. The power consumption of the ADC was 22.18 mW. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

Background/Objectives: Solanezumab is a humanized monoclonal antibody designed to bind soluble amyloid-beta (Aβ) and facilitate its clearance from the brain, aiming to slow the progression of Alzheimer’s disease (AD). Methods: A systematic search was applied in four medical databases through October 2024 to identify phase 2 or 3 randomized controlled trials evaluating solanezumab in patients aged ≥50 years with mild AD or in preclinical stages. The primary outcomes were changes in cognitive and functional scales, including ADAS-cog14, MMSE, ADCS-ADL, and CDR-SB. Data were pooled using a random-effects model, and certainty of evidence was assessed using GRADE. Results: Seven trials involving 4181 participants were included. Solanezumab did not significantly reduce cognitive decline based on ADAS-cog14 (MD = −0.75; 95% CI: −2.65 to 1.15; very low certainty) or improve functional scores on ADCS-ADL (MD = 0.85; 95% CI: −1.86 to 3.56; very low certainty) and CDR-SB (MD = −0.15; 95% CI: −0.89 to 0.60; very low certainty). A modest but statistically significant improvement was observed in MMSE scores (MD = 0.59; 95% CI: 0.33 to 0.86; moderate certainty). Conclusions: While solanezumab may offer slight benefits in general cognitive performance, its overall impact on clinically meaningful outcomes remains limited. The results do not support its use as a disease-modifying therapy for Alzheimer’s disease in either preclinical or symptomatic stages. Full article

Background and Clinical Significance: Breast cancer is a heterogeneous disease with different spread of metastases. In particular, skin metastases are common in HER2-positive metastatic breast cancer (mBC). However, anti-HER2 therapies have shown limited activity in this context. Recently, Trastuzumab Deruxtecan (T-DXd), a novel potent anti-HER2 antibody–drug conjugate (ADC), has revolutionized the therapeutic armamentarium of HER2 mBC with unprecedented evidence of efficacy in pretreated patients. However, the activity of this drug in patients with skin involvement is largely unknown. Case Presentation: Here, we report a case of extensive cutaneous involvement in a heavily pretreated patient who achieved a long-lasting complete response to T-DXd, which, unexpectedly, remained sustained for more than three years following treatment discontinuation. Conclusions: Skin toxicity is not a common adverse event with this agent, and, as demonstrated in the present case, it might not be drug-related, and additional causes might be ruled out before treatment discontinuation. However, the possibility of discontinuing anti-Her2 treatment in a patient who has achieved a complete response could represent a field of research, potentially using liquid biopsy or other new technologies. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate and compare the performance of three advanced multimodal LLMs (ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) in classifying brain MRI sequences. Methods: A total of 130 brain MRI images from adult patients without pathological findings were used, representing 13 standard MRI series. Models were tested using zero-shot prompts for identifying modality, anatomical region, imaging plane, contrast-enhancement status, and MRI sequence. Accuracy was calculated, and differences among models were analyzed using Cochran’s Q test and McNemar test with Bonferroni correction. Results: ChatGPT-4o and Gemini 2.5 Pro achieved 100% accuracy in identifying the imaging plane and 98.46% in identifying contrast-enhancement status. MRI sequence classification accuracy was 97.7% for ChatGPT-4o, 93.1% for Gemini 2.5 Pro, and 73.1% for Claude 4 Opus (p < 0.001). The most frequent misclassifications involved fluid-attenuated inversion recovery (FLAIR) sequences, often misclassified as T1-weighted or diffusion-weighted sequences. Claude 4 Opus showed lower accuracy in susceptibility-weighted imaging (SWI) and apparent diffusion coefficient (ADC) sequences. Gemini 2.5 Pro exhibited occasional hallucinations, including irrelevant clinical details such as “hypoglycemia” and “Susac syndrome.” Conclusions: Multimodal LLMs demonstrate high accuracy in basic MRI recognition tasks but vary significantly in specific sequence classification tasks. Hallucinations emphasize caution in clinical use, underlining the need for validation, transparency, and expert oversight. Full article

Pneumonitis is characterized as inflammation of the lung parenchyma, and a potential adverse effect of several anti-cancer therapies. Diagnosing pneumonitis can be particularly challenging in lung cancer patients due to inherent similarities in symptoms and radiological presentation associated with pneumonitis, as well as other common conditions such as infection or disease progression. Furthermore, many lung cancer patients have underlying pulmonary conditions that might render them more susceptible to severe or fatal outcomes from pneumonitis. Novel anti-cancer agents, such as antibody–drug conjugates (ADCs) and bispecific antibodies (BsAbs), are being incorporated into the treatment of lung cancer; therefore, understanding the risk and mechanisms underlying the potential development of pneumonitis with these new therapies is important to ensure continuous improvements in patient care. This narrative review provides an overview of the incidence of pneumonitis observed with novel anti-cancer agents, characterizes potential pathophysiological mechanisms underlying pneumonitis risk and emerging predictive biomarkers, highlights management strategies, and explores future directions for minimizing the risk of pneumonitis for lung cancer patients. Full article

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

Cancer has remained one of the leading causes of death worldwide throughout history despite significant advancements in drug development, radiation therapy, and surgery. Traditional chemotherapeutic small molecules are often hindered by narrow therapeutic indices and limited specificity, leading to suboptimal clinical outcomes. On the other hand, more advanced approaches, such as antibody–drug conjugates (ADCs), frequently encounter obstacles, including poor tumor penetration and prohibitive production costs. The tumor-forming and metastatic capacity of cancer further challenges currently available cancer therapies by creating a biochemical milieu known as the tumor microenvironment (TME). Although solid tumor development presents significant obstacles, it also opens new avenues for innovative therapeutic approaches. It is well-documented that as tumors grow beyond 1–2 mm³ in size, they undergo profound changes in their microenvironment, including alterations in oxygen levels, pH, enzymatic activity, surface antigen expression, and the cellular composition of the stroma. These changes create unique opportunities that can be exploited to develop novel and innovative therapeutics. Currently, numerous ADCs, small-molecule–drug conjugates (SMDCs), and prodrugs are being developed to target specific aspects of these microenvironmental changes. In this review, we explore five TME parameters in detail, with a focus on their relevance to specific cancer types, phenotypic identifiers, and preferred methods of therapeutic targeting. Additionally, we examine the chemical moieties available to target these changes, providing a framework for design strategies that exploit the dynamics of the tumor microenvironment. Full article

There is a growing body of research showing that Alzheimer’s disease (AD) is related to enteric dysbacteriosis. Exercise can be effective in alleviating AD, but the effects that exercise has on the gut microbiota in AD patients needs to be further studied. Through this study, we aimed to investigate the differences in the diversity of gut microorganisms between AD model mice and wild-type mice and the effect that treadmill exercise has on the composition of the gut microbiota in both types of mice. C57BL/6 wild-type mice were randomly divided into a sedentary control group (WTC) and an exercise group (WTE); APP/PS1 double transgenic mice were also randomly divided into a sedentary control group (ADC) and an exercise group (ADE). After the control group remained sedentary for 12 weeks and a 12-week treadmill exercise intervention was adopted for the exercise group, the rectal contents were collected so that they could undergo V3-V4 16S rDNA sequencing, and a comparative analysis of the microbial composition and diversity was also performed. The alpha diversity of the gut microbiota in AD mice was lower than that in wild-type mice, but exercise increased the gut microbial diversity in both types of mice. At the phylum level, the dominant microorganisms in all four groups of mice were Bacteroidetes and Firmicutes. There was an increase in the Bacteroidetes phylum in AD mice. Treadmill exercise reduced the abundance of Bacteroidetes in both groups of mice, whereas the abundance of Firmicutes increased. At the genus level, Muribaculaceae, the Lachnospiraceae_NK4A136_group, Alloprevotella, and Alistipes were in relatively high abundance. Muribaculaceae and Alloprevotella were in greater abundance in AD mice than in wild-type mice, but both decreased after treadmill exercise. Through performing linear discriminant analysis effect size (LEfSe), we found that the dominant strains in AD mice were Campilobacterota, Helicobacteraceae, Escherichia–Shigella, and other malignant bacteria, whereas exercise resulted in an increase in probiotics among the dominant strains in both types of mice. Although gut microbial diversity decreases and malignant bacteria increase in AD mice, treadmill exercise can increase gut microbial diversity and lead to the development of dominant strains of probiotics in both types of mice. These findings provide a basis for applying exercise as a treatment for AD. Full article

Background/Objectives: To develop a DL-based model predicting recurrence risk in HER2-low breast cancer patients and to compare performance of the MRI-alone, clinicopathologic-alone, and combined models. Methods: We analyzed 453 patients with HER2-low breast cancer who underwent surgery and preoperative breast MRI between May 2018 and April 2022. Patients were randomly assigned to either a training cohort (n = 331) or a test cohort (n = 122). Imaging features were extracted from DCE-MRI and ADC maps, with regions of interest manually annotated by radiologists. Clinicopathological features included tumor size, nodal status, histological grade, and hormone receptor status. Three DL prediction models were developed: a CNN-based MRI-alone model, a clinicopathologic-alone model based on a multi-layer perceptron (MLP) and a combined model integrating CNN-extracted MRI features with clinicopathological data via MLP. Model performance was evaluated using AUC, sensitivity, specificity, and F1-score. Results: The MRI-alone model achieved an AUC of 0.69 (95% CI, 0.68–0.69), with a sensitivity of 37.6% (95% CI, 35.7–39.4), specificity of 87.5% (95% CI, 86.9–88.2), and F1-score of 0.34 (95% CI, 0.33–0.35). The clinicopathologic-alone model yielded the highest AUC of 0.92 (95% CI, 0.92–0.92) and sensitivity of 93.6% (95% CI, 93.4–93.8), but showed the lowest specificity (72.3%, 95% CI, 71.8–72.8) and F1-score of 0.50 (95% CI, 0.49–0.50). The combined model demonstrated the most balanced performance, achieving an AUC of 0.90 (95% CI, 0.89–0.91), sensitivity of 80.0% (95% CI, 78.7–81.3), specificity of 83.2% (95% CI: 82.7–83.6), and the highest F1-score of 0.55 (95% CI, 0.54–0.57). Conclusions: The DL-based model combining MRI and clinicopathological features showed superior performance in predicting recurrence in HER2-low breast cancer. This multimodal approach offers a framework for individualized risk assessment and may aid in refining follow-up strategies. Full article

Following lung cancer, prostate cancer is the leading cause of cancer death in men. High-risk localized tumor burden or metastatic disease often progresses, refractory to initial treatment regimens. There is ongoing development of technology to appropriately identify high-risk patients, stage them correctly, and offer appropriate treatments to obtain the best clinical outcomes. Prostate cancer-specific membrane antigen (PSMA) is a transmembrane glutamate carboxypeptidase, which helps regulate folate absorption, and its overexpression is pathologically directly proportional and associated with prostate cancer. Increased PSMA expression is a known independent risk factor for poorer survival, and most metastatic lesions in CRPC are PSMA positive. Over the last decade, several PSMA-based PET radiopharmaceuticals have demonstrated superior sensitivities and specificities compared to traditional imaging methods. These outcomes have been demonstrated by several large clinical trials. As the data emerges, these diagnostics are being integrated into standard of care protocol to facilitate nuanced identification of malignant lesions. PSMA is also being targeted through several therapeutics, including radioligands and immunotherapies such as CAR-T, BiTEs, and ADCs. This review will discuss the landscape of PSMA-based theranostics in the context of prostate cancer. Full article

Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide, with human epidermal growth factor receptor 2 (HER2)-positive disease representing a clinically relevant subset. Trastuzumab combined with chemotherapy is the standard first-line treatment in advanced settings, following the landmark ToGA trial. However, resistance to trastuzumab has emerged as a significant limitation, prompting the need for more effective second-line therapies. Trastuzumab deruxtecan, a novel antibody–drug conjugate (ADC) composed of trastuzumab linked to a cytotoxic payload, has demonstrated promising efficacy in trastuzumab-refractory, HER2-positive GC, including cases with heterogeneous HER2 expression. Other HER2-targeted ADCs are also under investigation as potential alternatives. In addition, strategies to overcome resistance include HER2-specific immune-based therapies, such as peptide vaccines and chimeric antigen receptor T cell therapies, as well as antibodies targeting distinct HER2 domains or downstream signaling pathways like PI3K/AKT. These emerging approaches aim to improve efficacy in both HER2-high and HER2-low GC. As HER2-targeted treatments evolve, addressing resistance mechanisms and optimizing therapy for broader patient populations is critical. This review discusses current and emerging HER2-directed strategies in GC, focusing on trastuzumab deruxtecan and beyond, and outlines future directions to improve outcomes for patients with HER2-positive GC across all clinical settings. Full article

Background/Objectives: All current FDA-approved antibody–drug conjugates (ADCs) are single-target and single-payload molecules that have limited efficacy in patients due to drug resistance. Therefore, our goal was to generate a novel ADC that was less susceptible to single points of resistance to reduce the likelihood of patient relapse. Methods: We developed a dual-targeting, dual-payload ADC by conjugating a bispecific EGFR x cMET antibody to two payloads (MMAF and SN38) that had separate mechanisms of action using a novel tri-functional linker. This dual-payload ADC was tested for potency and efficacy in dividing and nondividing in vitro cell models using multiple tumor cell types. Efficacy of the dual-payload ADC was confirmed using in vivo models. Results: Our ADC with dual MMAF and SN38 payloads was more efficacious in inhibiting cell proliferation than single-payload ADCs across multiple cancer cell lines. In addition, the dual-payload molecule inhibited nondividing cells, which were more resistant to traditional ADC payloads. The dual-payload ADC also exhibited more potent tumor growth inhibition in vivo compared to that of single-payload ADCs. Conclusions: Overall, the bispecific antibody conjugated with both the MMAF and SN38 payloads inhibited tumor growth more strongly than ADCs conjugated with MMAF or SN38 alone. Developing dual-payload ADCs could limit the impact of acquired resistance in patients as well as lower the effective dose of each payload. Full article

Background and Objectives: Accurate noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) remains a clinical challenge. This study aimed to assess the dignostic performance of apparent diffusion coefficient (ADC) values from diffusion-weighted MRI in distinguishing between HCC and ICC, with histological confirmation as the gold standard. Materials and Methods: A retrospective analysis was performed on 61 patients (41 HCC, 20 ICC) who underwent liver MRI and percutaneous biopsy between 2019 and 2024. ADC values were measured from diffusion-weighted sequences (b-values of 0, 500, and 1000 s/mm²), and regions of interest were placed over solid tumor areas. Statistical analyses included t-tests, one-way ANOVA, and ROC curve analysis. Results: Mean ADC values did not differ significantly between HCC (1.09 ± 0.19 × 10⁻³ mm²/s) and ICC (1.08 ± 0.11 × 10⁻³ mm²/s). ROC analysis showed poor discriminative ability (AUC = 0.520; p = 0.806). In HCC, ADC values decreased with lower differentiation grades (p = 0.008, η² = 0.224). No significant trend was observed in ICC (p = 0.410, η² = 0.100). Immunohistochemical markers such as CK-7, Glypican 3, and TTF-1 showed significant diagnostic value between tumor subtypes. Conclusions: ADC values have limited utility for distinguishing HCC from ICC but may aid in HCC grading. Immunohistochemistry remains essential for accurate diagnosis, especially in poorly differentiated tumors. Further studies with larger cohorts are recommended to improve noninvasive diagnostic protocols. Full article

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article