Basal cell carcinoma (BCC) frequently involves the external nose, necessitating radical surgical excision that often results in complex soft-tissue defects. While the nasolabial fold retrograde island flap offers an aesthetically superior reconstructive option due to its excellent color and texture match, its reliance on a reverse-flow subcutaneous pedicle makes it highly susceptible to venous congestion, which can compromise flap survival. The goal of this protocol is to demonstrate a modified surgical technique that incorporates immediate postoperative local microinjection of heparin sodium to mitigate this specific vascular risk. The procedure involves the radical excision of the nasal tumor, followed by the harvesting and rotation of a subcutaneous pedicle island flap from the ipsilateral nasolabial fold. To address the challenge of venous stasis, heparin sodium is micro-injected into the full thickness of the flap in a multi-point grid pattern immediately after suturing. This intervention utilizes both pharmacological anticoagulation to prevent microthrombosis and mechanical decompression via needle puncture to facilitate venous drainage. In a clinical application involving 24 patients, this protocol achieved a 100% flap survival rate. Early signs of severe venous congestion observed in three cases (12.5%) were successfully reversed within one week through continuous local heparin therapy. Advanced clinical data analysis indicated that flap size was not an independent predictor of venous congestion, demonstrating the robustness of this technique even for larger defects. Furthermore, systemic safety assessments confirmed no clinically significant coagulation abnormalities or adverse events. This method offers a safe, reproducible, and effective strategy for ensuring high-quality flap survival and favorable aesthetic outcomes in nasal reconstruction, avoiding the risks associated with systemic anticoagulation.
COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.
Fetal growth restriction (FGR) is associated with significant perinatal morbidity and mortality, yet its identification remains challenging due to the limited sensitivity of conventional biometric assessment and the lack of a reliable tool for assessing placental function. Placental function depends on uteroplacental perfusion which, in turn reflects maternal cardiovascular adaptation to pregnancy. In this context, umbilical venous flow (UVF) has emerged as a quantitative parameter reflecting fetal blood supply and a potential surrogate marker of placental function. This narrative review aims to provide an overview of UVF and maternal hemodynamics, and to explore their interaction within the framework of the cardiac-fetal-placental unit. The current literature indicates that UVF is reduced in pregnancies complicated by FGR, and it correlates significantly with maternal cardiac output (CO) and systemic vascular resistance (SVR). A hypodynamic maternal profile (high SVR, low CO) is consistently associated with reduced UVF and impaired placental perfusion, regardless of fetal biometry. The integration of maternal cardiovascular assessment and UVF evaluation provides a functional perspective on placental insufficiency and may improve the identification of pregnancies at risk. Furthermore, this approach offers a potential framework for understanding the effects of maternal hemodynamic interventions on fetal growth.
Pulmonary vascular obstruction causes dyspnea in chronic thromboembolic pulmonary disease (CTEPD) and chronic thromboembolic pulmonary hypertension (CTEPH). Conventional assessments, like 6-minute walk test (6MWT) and World Health Organization (WHO) functional class (WHO-FC), poorly discriminate the mechanism of dyspnea. Cardiopulmonary exercise testing (CPET) offers direct evaluation of ventilatory efficiency and gas-exchange abnormalities. The purpose of this study was to evaluate the impact of balloon pulmonary angioplasty (BPA) and pulmonary thromboendarterectomy (PTE) on CPET-derived gas-exchange parameters. In this prospective study, patients with CTEPD or CTEPH evaluated by a multidisciplinary team underwent outpatient point-of-care CPET (SHAPE-HF system) before and after BPA/PTE. The primary endpoint was change in ventilatory efficiency, assessed by the VE/VCO2 (minute ventilation/carbon dioxide production) slope. Secondary endpoints included changes in WHO-FC and 6MWT. Sixty patients were evaluated; 8 served as controls, and 52 underwent revascularization (20 PTE and 32 BPA). The VE/VCO2 slope improved from 43 to 31 (P < 0.001) after BPA and from 42 to 32 (P = 0.02) after PTE. WHO-FC improved from class III to I in both groups (P < 0.001). The mean 6MWT increased 38 m (381 ± 146 vs 419 ± 151 m; P = 0.03) in the BPA and PTE group (349 ± 162 vs 358 ± 140 m; P = 0.7). VE/VCO2 and Shape-HF Severity Score positively correlated (P < 0.001), but not with 6MWT (P = 0.83). Before and after revascularization, the Shape-HF score improved in the BPA (2.4 vs 1.5; P < 0.001) and the PTE (2.3 vs 1.6; P = 0.01) groups. Survival at 1-year follow-up was a 100%. Point-of-care CPET provides objective assessment of gas-exchange improvements after pulmonary revascularization in mild-to-moderate CTEPH/CTEPD.
Facile and quantitative detection of liquid biopsy biomarkers such as microRNAs offers significant potential for precision healthcare; however, conventional biosensing methods rely on enzyme- or label-based workflows that are costly, time-consuming, and labor intensive. Microwave biosensors, particularly split-ring resonators (SRRs), offer an attractive alternative as they enable label-free, noncontact electromagnetic detection through permittivity measurements and are compatible with printed-circuit-board manufacturing. However, the sensitivity of conventional SRR platforms remains insufficient for clinically relevant biomarker detection. Here, we introduce an enzyme-free, label-free microwave biosensing architecture that integrates SRRs with microfluidic channels containing localized bioreceptor-functionalized hydrogel micropillars. Target hybridization within the hydrogel micropillars induces localized changes in complex permittivity, which are transduced into concentration-dependent shifts in the resonant frequency of the SRR capacitive gap. As a proof of concept, the platform is applied to detect the cancer-associated biomarker miR-16-5p using peptide nucleic acid (PNA) probes, which were selected for their neutral backbone, enzymatic stability, and strong hybridization affinity. The hydrogel micropillars act as three-dimensional scaffolds that enhance probe loading and maximize volumetric electromagnetic interaction, representing a departure from conventional planar biointerfaces. Compared with equivalent planar systems, this architecture achieves approximately a 20-fold improvement in detection limit, reaching subnanomolar sensitivity without any amplification or labeling while maintaining single-nucleotide specificity and strong device reproducibility. Beyond being the first demonstration of SRR-based miRNA detection, this work establishes a general strategy for three-dimensional microwave biosensing and positions hydrogel-interfaced resonators as a next-generation platform for sensitive, selective, label-free, and reusable biosensors.
Here, we describe a protocol for generating Cell Line-Derived Tumor Organoids (CDTOs) from the A549 human lung adenocarcinoma cell line. The protocol involves embedding 2D-expanded A549 cells in Matrigel and maintaining them in 3D culture medium for long-term culture. Recommended seeding density of 500 cells/µL was determined to support consistent organoid formation. The resulting CDTOs were characterized by hematoxylin and eosin (H&E) staining and immunofluorescence (IF). The organoids maintained high expression of the lung adenocarcinoma markers Thyroid Transcription Factor 1 (TTF-1), adhesion protein E-Cadherin (ECAD), and cytoskeleton protein Keratin 7 (KRT7). Furthermore, tight junction protein Zona Occludens 1 (ZO-1) expression showed dysregulated polarity of tumor organoids. This protocol offers a technically straightforward, cost-effective, and purely tumorous organoid platform for lung adenocarcinoma research. Its simplicity and reproducibility also make it suitable for undergraduate laboratory teaching, where it can help students acquire fundamental 3D tumor organoid culture techniques within a limited lab schedule.
A carbon-free energy system might use ammonia as a hydrogen carrier owing to its high hydrogen concentration, good liquefaction, and worldwide production and transportation infrastructure. Catalytic ammonia breakdown provides a CO2-free hydrogen-generating method, but high temperatures, catalyst deactivation, and expense restrict its practicality. This study covers important aspects of ammonia decomposition catalysts, including Ru-, Ni-, and Co-based systems supported by carbon, Al2O3, and perovskite oxides. To develop structure-activity connections and identify rate-limiting stages under various operating regimes, thermodynamic, kinetic, and fundamental reaction processes are examined. Ru-based catalysts, especially those supported on conductive carbons and tailored perovskites, are standards for low-temperature activity, but their high cost and stability prevent large-scale application. Combining Ni-based catalysts with Al2O3 or basic perovskite supports offers a good balance between activity, cost, and commercial practicality, making them the dominant non-noble alternatives. Due to strong metal-support interactions and alternate reaction routes, Co-based catalysts in perovskite formulations seem promising despite being inherently less active. Support chemistry, promoters, metal dispersion, and oxygen vacancies are discussed, along with low-temperature operation and scale-up problems and research prospects.
Chronic non-communicable diseases require high-quality primary healthcare management, yet existing evaluation systems often fail to capture the multidimensional nature of patient-perceived service quality in chronic care settings. This study developed and validated a service quality assessment system (SERVQUAL) tailored for primary chronic disease management based on the SERVQUAL framework. A multi-stage methodology integrating the Delphi method and analytic hierarchy process (AHP) was employed to construct and weight the scale. The instrument was empirically validated using cross-sectional data from 433 patients across five primary healthcare institutions. Construct validity and reliability were evaluated through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Furthermore, the predictive performance of the AHP-SERVQUAL model was compared with that of alternative models (KANO, TOPSIS-RSR) for patient compliance, problem resolution rates, and complaint frequencies. Psychometric evaluation demonstrated that the adapted five-dimensional scale (tangibility, reliability, responsiveness, assurance, and empathy) possessed high internal consistency and structural validity. Application across the centers revealed significant variations in service quality, particularly in the assurance and empathy dimensions. Comparative modeling indicated that the AHP-SERVQUAL framework yielded higher predictive power for patient adherence and overall service quality than the alternative models. Additionally, the quality indices generated by this model were significantly correlated with higher problem-resolution rates and lower complaint frequencies. The AHP-SERVQUAL-based system offers a reliable and valid metric for evaluating chronic disease management in primary care. By accurately capturing patient-perceived quality, this instrument provides administrators with an evidence-based tool to target quality improvement initiatives.
Cutaneous metastases in patients with incurable cancer represent a significant problem as they often cause pain, discomfort, and emotional distress that affect everyday life. Finding treatment options that are both effective and gentle is essential. ECT offers one such possibility. Here, short, high-voltage electrical pulses are applied directly to the tumor, briefly opening tumor cells, allowing chemotherapy to enter more effectively and kill cancer cells. Traditionally, patients receive 15.000 IU/m2 of bleomycin intravenously, but emerging evidence suggests that a lower dose may be just as effective while causing fewer side effects. This protocol describes an ongoing double-blinded, randomized clinical trial that tests whether ECT with half the standard bleomycin dose is non-inferior to the conventional regimen for tumor control in patients with cutaneous metastases. The article outlines randomization and blinding procedures, pretreatment evaluation, bleomycin preparation and administration, electrode placement, pulse delivery, and response evaluation using the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. In addition to clinical response at three months, the protocol includes pharmacokinetic blood sampling and qualitative interviews with the patients to enable a comprehensive evaluation of treatment impact. Baseline tumor characteristics from the first 15 enrolled patients and an example of how mRECIST is applied are presented. Critical steps to ensure methodological rigor are discussed, including standardized tumor measurements, consistent electrode positioning, and predefined management of confluent or poorly demarcated tumors. By visually outlining the procedural workflow and key methodological considerations, this article provides a reproducible framework for dose optimization in ECT. It supports future implementation of reduced-dose regimens in clinical oncology practice.
The lunar mare region is rich in basaltic minerals, and in situ resource utilization (ISRU) is a fundamental strategy for sustainable extraterrestrial construction. However, combining electroless plating with basalt fibers for such construction leads to a significant mismatch of coefficient of thermal expansion (CTE) between the fiber substrate and metal coating. Under extreme temperature alternations, this mismatch induces interfacial thermal stress concentration, causing coating peeling and performance failure. To address this issue, this study, using commercial terrestrial basalt fiber as an analogue for lunar basaltic materials, proposes an ISRU-inspired metallized fiber composite suitable for wide-temperature-range applications. By sequential electroless nickel plating and copper electroplating on basalt fibers, a nickel-copper-coated basalt fiber fabric (BF@Ni@Cu) was successfully fabricated, exhibiting high electrical conductivity, excellent electromagnetic interference shielding effectiveness (62.59 dB), and significant joule heating performance. The Ni interlayer forms a CTE gradient transition between the basalt substrate and the outer Cu layer, mitigating interfacial thermal stress. After annealing and PDMS encapsulation, the surface reflection characteristics are effectively regulated. To verify reliability under lunar diurnal temperature variations, cold-thermal shock cycle tests simulating the lunar range (from -196 to 130 °C) are conducted. After 30 cycles, the material maintained structural integrity without cracking or peeling, successfully overcoming interfacial thermal stress concentration. Consequently, the EMI shielding and joule heating performance showed only slight degradation, demonstrating excellent temperature shock resistance. This study not only provides a fiber metallization strategy that retains high performance under extreme temperature alternations but also offers a potential technical pathway inspired by ISRU for multifunctional protection and thermal management materials in future lunar base construction, through the design concept of thermal stress alleviation and failure-mode control via a gradient interlayer.
The gut microbiome plays a key role in animal health, productivity, and environmental sustainability. As it represents a valuable proxy for animal welfare, its investigation has become increasingly important in livestock studies. With the growing focus on promoting sustainable livestock, supporting rural areas at risk of abandonment is receiving particular attention. Indeed, sheep grazing offers a promising strategy for improving sustainability, biodiversity, and land management. This study focuses on the interconnected dynamics between the sheep gut and soil microbiomes, assessing how seasonal changes and grazing activity shape microbial diversity and community structure across the animal-soil interface. Fecal and soil samples were collected throughout 2024 in a commercial farm in Tuscany, Italy: 215 fecal and 46 soil samples (23 pasture and 23 meadow - i.e., not grazed) were stored. Alpha and Beta diversity were assessed using the Kruskal-Wallis test and PERMANOVA, respectively, and the differential abundance analysis was also performed. The relative abundance analysis at the family and genus level revealed an increase in the number of taxa from winter to autumn in both fecal and soil samples. When the Chao1 index was considered, alpha diversity was higher in fecal samples, followed by soils. Principal Coordinate Analysis revealed distinct clustering between animal and soil microbiota, with slightly reduced differentiation in Summer. In fecal samples, the five most abundant bacterial families were Ruminococcaceae, Spirochaetaceae, Porphyromonadaceae, Lachnospiraceae, and Rikenellaceae, whose abundance varied seasonally. Ruminococcaceae, Lachnospiraceae, and Rikenellaceae decreased in Summer, while Spirochaetaceae and Porphyromonadaceae increased. The increased abundance of these families during Summer may reflect heat stress in animals. Differential abundance analysis also suggested potential microbial transfer from animals to soil: Peptostreptococcaceae and Erysipelotrichaceae were enriched in grazed soils across multiple seasons. Repeated cross-sectional studies like this are essential for understanding microbiome dynamics and animal-soil interactions in grazing systems.
Autonomous driving offers a promising way to tackle the rising number of fatalities from traffic accidents. An autonomous vehicle includes many features, but the ability to detect pedestrians is crucial, challenging, and relevant to various real-time situations like surveillance, tracking people, and monitoring. Accurately identifying pedestrians is difficult because they can appear in different shapes, positions, and postures. They can wear various types of clothing and sometimes be partially hidden or blend in with nearby objects. This paper focuses on the real-time detection of pedestrians for self-driving cars using a popular hardware platform: The field programmable gate array (FPGA), Ultra 96 v2. The study implements a method for pedestrian detection based on a histogram of oriented gradients (HOG) combined with a support vector machine (SVM) classifier to recognize individuals on the FPGA board, leveraging high-level synthesis (HLS) tools. The effectiveness of the system has been tested on both still images and live video. The results show that advanced FPGA boards like the Ultra 96 v2 significantly improve performance metrics. The system operates at a clock frequency of 150 MHz while using less than half of the available resources and consuming around 2.5 W of power. Also, the system reports the pedestrian detection accuracy close to 95% and other efficient metrics for detection evaluation, like precision (78.6%), recall (88.3%), and F1 Score (83.1%). In summary, the developed system can detect pedestrians in real-time and has the potential to significantly improve the development of a smart and safe transportation environment.
Neuromodulation, a therapeutic approach to the dysfunction of various organs, has proven effective in treating lower urinary tract dysfunctions. Despite its clinical success, the mechanisms underlying neuromodulation remain incompletely understood, emphasizing the need for standardized and reproducible animal models. Preclinical models of urinary bladder neuromodulation using sacral and peripheral nerve stimulation have been established for more than two decades; however, the existing literature provides limited methodological detail regarding the surgical procedures. This article offers a detailed description and visual representation of well-established techniques for sacral nerve stimulation. It also describes a modified approach to tibial nerve stimulation and introduces a novel bladder neuromodulation method using peroneal nerve stimulation in a rat model. The modified tibial nerve stimulation approach involves exposing and stimulating the tibial nerve at its origin, as a branch of the sciatic nerve, rather than at the medial ankle, as described in previous studies. Given the recent introduction of peroneal nerve stimulation for bladder neuromodulation in clinical practice, this article proposes a unique rat model to investigate its mechanisms in the control of lower urinary tract function. Step-by-step guidance for all three neuromodulation methods is outlined for nerve exposure, electrode placement, and verification of successful electrode placement based on characteristic motor responses. A representative example of the effect of peroneal nerve stimulation on bladder function in a rat model of acetic acid-induced bladder overactivity is included to demonstrate its applicability. This article presents a standardized, technically accessible protocol for studying neuromodulation mechanisms, including alterations in bladder sensory signaling, spinal and supraspinal regulatory pathways, optimization of stimulation parameters, and comparison of the efficacy of different stimulation targets.
Hepatitis C virus (HCV) infection remains a major public health challenge, with significant gaps in diagnosis and treatment in resource-limited settings. Hepatitis C self-testing (HCVST) offers a potential strategy to expand access, particularly in HIV clinical settings. We evaluated feasibility and acceptability of HCVST among high-risk populations in Nasarawa State, Nigeria when provided at antiretroviral (ART) clinics and one-stop shops (OSS) serving key populations (KP). 2,000 participants were enrolled between May 2023 and December 2023. Participants tested with either finger-prick blood-based or oral fluid antibody HCVST, and with or without health worker support. Follow-up documented results and linkage to qualitative RNA PCR confirmatory testing and treatment. Case-complete feasibility analyses were conducted across the HCV care cascade using chi-square tests (N = 1995). Acceptability was evaluated using a post-test survey score derived through case-complete factor analysis (N = 1868); associations between lowest decile acceptability score and participant characteristics were explored through regression analyses. Outcomes were compared by facility type (ART clinic vs. OSS). Free-text responses were thematically analyzed to contextualize findings. HCVST was feasible and acceptable. Of 226 reactive HCVST results (11.3%), 99.1% received RNA PCR testing. Among those with detectable RNA, 92% initiated treatment and 97% completed therapy. However, differences were observed by facility type. Participants in ART clinics were older, more likely to be female, and showed higher reactivity (15% vs. 8%) and treatment uptake (96% vs. 83%) than OSS clients. Acceptability was higher in ART clinics than OSS. HCVST was both feasible and acceptable in Nasarawa State, with some observed variations by facility type. These findings suggest that with differentiated service delivery models and adequate support for linkage, HCVST can increase HCV diagnosis, linkage to care, and treatment among high-risk groups in Nigeria, supporting integration of HCVST into national viral hepatitis elimination strategies.
Why do people punish wrongdoers when they are not personally affected? Researchers on costly third-party punishment have long debated whether such behavior reflects strategic self-interest or a moral commitment to fairness and justice. Recent developmental evidence offers important insights into this question. We argue that the origins of costly third-party punishment in early childhood are best explained by nonstrategic moral concerns. Young children selectively punish norm violators, incur personal costs to do so, and intervene even when they stand to gain nothing-often without reputational incentives or expectations of future benefit. Empirical studies indicate that children's punishment is driven by egalitarian norms, retributive motives, and efforts to alleviate victims' distress. In contrast, strategic motivations, such as reputation management and self-protection, appear only later in development. These findings challenge the view that third-party punishment is grounded in self-interest and instead support the idea that a concern for justice underlies the earliest forms of human norm enforcement. We conclude that whereas strategic considerations may shape punishment in adolescence and adulthood, they build upon an early-emerging moral foundation centered on fairness and justice.
Globalization and the development of information technology have driven a surge in English text data, and there is an urgent need for efficient classification. To improve the generalization ability of English text classification in small-sample and cross-domain scenarios and to achieve lightweight models, this study combines complex systems theory with deep learning to construct a graph neural network model that fully accounts for the distributional characteristics of text samples. A two-level meta-distillation method, combined with meta-learning strategies, dynamically adjusts the teacher model's parameters and optimizes the entire knowledge-transfer process. The experimental results show that the classification performance of the proposed model in few-shot and cross-domain text classification tasks is significantly superior to that of traditional graph neural networks and other mainstream comparative models. In terms of lightweighting, the SM generated by the two-stage meta-distillation mechanism maintains an extremely high level of performance retention on multi-topic text classification datasets, while significantly reducing computational time. In addition, this method can maintain high efficiency and stable classification performance in long text processing scenarios and offers clear advantages in computational efficiency compared with traditional distillation methods and other methods reported in the literature. The proposed method effectively enhances the classification performance of English text in low-sample and cross-domain application scenarios while significantly reducing the computational cost, thus providing a feasible and efficient technical solution for English text classification in resource-constrained environments.
Blended therapy (BT) combines digital applications with face-to-face treatment and has become an increasingly important component of psychiatric care. Evidence indicates that BT can achieve outcomes comparable to or even superior to those of traditional face-to-face therapy. Despite certain advantages, routine implementation of BT remains challenging, and clinical practice suggests that while some inpatients engage with BT, many either discontinue early or do not initiate its use at all. To better understand these patterns, this multicentric, retrospective observational study investigates factors associated with noninitiation and dropout among inpatients who are offered BT.  In this study, data from 278 inpatients were analyzed to examine the influence of sociodemographic variables, comorbidities, and symptom severity on the uptake and continued use of BT. The objective was to identify predictors of noninitiation and dropout.  Multivariable logistic regression models were conducted to identify significant predictors of noninitiation and dropout among inpatients using the transdiagnostic, cognitive behavioral therapy-based electronic mental health platform Minddistrict, which offers modules targeting psychoeducation, cognitive restructuring, and behavioral activation. Data were collected from 2 psychiatric hospitals between January 2020 and May 2024. The sample consisted predominantly of patients diagnosed with depression (182/278, 65.7%) and posttraumatic stress disorder (61/278, 21.9%), alongside various comorbid conditions. The findings indicate distinct patterns of association for noninitiation and dropout. Of the 278 patients, only 5 (1.8%) completed all the assigned modules, and one-third of the patients never initiated the platform at all. Specifically, increasing age was linked to a lower risk of noninitiation (odds ratio [per year age difference] 0.98, 95% CI 0.96-1.00; P=.01), while the presence of a comorbid anxiety disorder was associated with a reduced risk of dropout (odds ratio 0.23, 95% CI 0.08-0.66; P=.007). Several variables showed no association with either noninitiation or dropout across all analyses, including sex, overall symptom severity, and certain comorbidities such as personality disorders and depression.  In this preselected inpatient sample, uptake of BT was very limited. Older age was associated with lower noninitiation, and comorbid anxiety disorders were associated with a lower likelihood of dropout. These findings may help inform future prospective studies on how BT can be introduced and supported more effectively in inpatient psychiatric care. As access to BT was granted selectively by therapists, the results should be interpreted as predictors of engagement within a selected sample rather than general predictors of BT uptake among all psychiatric inpatients.
Deciphering protein function is fundamental to advancements in medicine and biotechnology. However, conventional experimental characterization remains resource-intensive. Public large language models (LLMs), though proficient in natural language processing, often fail to accurately interpret and predict the functional and structural properties of proteins, limiting their utility in bioinformatics. To address this gap, we introduce BetaDescribe, designed to generate detailed and rich textual descriptions of proteins, including their function, catalytic activity, involvement in specific metabolic pathways, subcellular localizations, and the presence of specific domains. The trained BetaDescribe model receives protein sequences as input and outputs a textual description of these properties. BetaDescribe starting point was the LLAMA2 model, which was trained on trillions of tokens. Our model was next trained on datasets containing both biological and English text, which allowed the incorporation of biological knowledge. In addition to the description generator, BetaDescribe comprises multiple validator models and a judge, which together enable accurate ranking of alternative generated descriptions. We demonstrate the utility of BetaDescribe by providing descriptions for proteins that share little to no sequence similarity to proteins with functional descriptions in public datasets. Using in silico mutagenesis, we further show that BetaDescribe relies on functionally important regions, as part of its prediction, suggesting that the model identifies regions of importance for the protein functionality without needing homologous sequence. BetaDescribe offers a powerful tool to explore protein functionality, augmenting existing approaches such as annotation transfer based on sequence or structure similarity.
Spiking Neural Networks (SNNs) have emerged as a promising paradigm for brain-inspired edge computing. Leveraging binary spikes and local learning rules, SNNs enable energy-efficient on-chip learning and rapid adaptation to changing environments, which is crucial for edge AI that needs to learn continuously from new data. However, many SNN processors enabling on-chip learning for edge computing confront a trade-off: small-scale task-specific designs offer low power but poor multi-task inference accuracy, while large-scale general-purpose designs achieve high multi-task accuracy at the cost of large memory and poor energy efficiency. To overcome this challenge, this paper presents ANP-R, a 22nm asynchronous SNN-based edge AI processor with coarse-grained reconfigurable architecture enabling one-shot, few-shot, batch and incremental on-chip learning. The processor integrates 64 cores containing 4096 neurons and 0.262 million synapses. Two key features are proposed: 1) An asynchronous coarse-grained reconfigurable architecture that supports various STDP-based SNN topologies. These topologies enable over 95% average accuracy across four sensory tasks; 2) an energy-efficient asynchronous training method incorporating a self-adaptive synaptic weight update mechanism reducing up to 65% redundant updates without accuracy loss, and a trained weights low-bit width coding method reducing up to 50% storage cost with 0.3% accuracy loss. Measurement results demonstrate 92.1% accuracy for hand gesture classification, 93.9% for keyword spotting, 98.6% for object recognition and 99.2% for gas identification. Compared with state-of-the-art SNN-based chips, this work achieves up to 6.02x, 8.61x and 7.1% improvement in energy efficiency, energy per step, and accuracy, respectively.
The incidence and mortality rates of lung cancer remain high, posing a serious threat to human health. This study aims to investigate the role of resveratrol in lung cancer and its regulatory mechanism on the immune microenvironment. Human lung cancer A549 cells were treated with different concentrations of resveratrol, and a syngeneic mouse model of lung cancer was established to evaluate the effects of resveratrol and the NLRP3 inhibitor MCC950 on cell growth and the expression of NLRP3/IL-1β. Meanwhile, tumor growth, cell apoptosis, T cell infiltration, and the expression of key signaling proteins were examined in vivo. The results showed that resveratrol significantly inhibited the proliferation of A549 cells, induced cell cycle arrest and apoptosis, and was accompanied by reduced expression of NLRP3 and IL-1β. In the syngeneic mouse model of lung cancer, resveratrol treatment suppressed tumor cell proliferation, improved tumor morphology, enhanced T lymphocyte infiltration, and concurrently decreased the expression of NLRP3, IL-1β, ASC, and cleaved-caspase-1. These findings indicate that resveratrol inhibits tumor growth and remodels the immune microenvironment by targeting the NLRP3/IL-1β signaling axis, offering new insights and potential strategies for lung cancer immunotherapy.