ABSTRACT Background Psoriatic hard‐to‐treat areas (scalp, palmoplantar, genital and nails) are associated with lower efficacy, treatment discontinuation and impaired quality of life. Recent biotherapies offer a new perspective for their treatment. Among them brodalumab is indicated for the treatment of moderate‐to‐severe plaque psoriasis in adults requiring systemic therapy. No French data are available on the effectiveness of brodalumab on hard‐to‐treat areas. Objectives To assess the effectiveness and safety of brodalumab in real life on psoriatic hard‐to‐treat areas. Methods Retrospective multicenter study including 42 psoriasis adults which received at least one injection of brodalumab from January 2022 through May 2024 and were followed for a minimum period of 2 months after initiation. Scores were collected at baseline and between 2 and 20 months after treatment initiation: BSA (body surface area), PASI (psoriasis area severity index), PGA‐G (Physician's Global Assessment of genitalia), ppPASI (Palmoplantar Psoriasis Area and Severity Index), NAPSI (Nail Psoriasis Severity Index) and PSSI (Psoriasis Scalp Severity Index). DLQI (Dermatology Life Quality Index) was calculated to assess the impact on quality of life. Tolerance and potential adverse events were reported. Results The mean BSA at baseline was 16.3%; mean baseline scores: PASI 11.9; PGA‐G 2.3; ppPASI 14.9; NAPSI 16.3; PSSI 20.2. 92.9% of patients had DLQI ≥ 10. All patients had hard‐to‐treat areas: scalp 69.0%; nail 50.0%; genital 50.0%; palmoplantar 35.7%. At the end of the follow‐up, the mean scores were, respectively: PASI 0.8; PGA‐G 0.3; ppPASI 1.1; NAPSI 2.2; PSSI 1.2. PASI < 1 was achieved by 69.0% of patients. Adverse events were reported in 9.5% of patients. Conclusions Brodalumab demonstrated clinical efficacy on hard‐to‐treat areas in just 3 months of treatment, with a median treatment duration of 4.0 months. This important and rapid clinical efficacy was associated with an improvement in quality of life and good tolerance.
Dermatology, Diseases of the genitourinary system. Urology
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewards (RLVR), Agentic planning prior to reasoning, Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware, all using publicly available open-source datasets. K2-Think excels in mathematical reasoning, achieving state-of-the-art scores on public benchmarks for open-source models, while also performing strongly in other areas such as Code and Science. Our results confirm that a more parameter-efficient model like K2-Think 32B can compete with state-of-the-art systems through an integrated post-training recipe that includes long chain-of-thought training and strategic inference-time enhancements, making open-source reasoning systems more accessible and affordable. K2-Think is freely available at k2think.ai, offering best-in-class inference speeds of over 2,000 tokens per second per request via the Cerebras Wafer-Scale Engine.
This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framework. While classical AI approaches rely on symbolic processing and data driven models, Physical AI understands intelligence as an emergent phenomenon of real interaction between body, environment, and experience. The six fundamentals presented here are embodiment, sensory perception, motor action, learning, autonomy, and context sensitivity, and form the conceptual basis for designing and evaluating physically intelligent systems. Theoretically, it is shown that these six principles do not represent loose functional modules but rather act as a closed control loop in which energy, information, control, and context are in constant interaction. This circular interaction enables a system to generate meaning not from databases, but from physical experience, a paradigm shift that understands intelligence as an physical embodied process. Physical AI understands learning not as parameter adjustment, but as a change in the structural coupling between agents and the environment. To illustrate this, the theoretical model is explained using a practical scenario: An adaptive assistant robot supports patients in a rehabilitation clinic. This example illustrates that physical intelligence does not arise from abstract calculation, but from immediate, embodied experience. It shows how the six fundamentals interact in a real system: embodiment as a prerequisite, perception as input, movement as expression, learning as adaptation, autonomy as regulation, and context as orientation.
Umaima Rahman, Abhishek Basu, Muhammad Uzair Khattak
et al.
This study explores the concept of cross-disease transferability (XDT) in medical imaging, focusing on the potential of binary classifiers trained on one disease to perform zero-shot classification on another disease affecting the same organ. Utilizing chest X-rays (CXR) as the primary modality, we investigate whether a model trained on one pulmonary disease can make predictions about another novel pulmonary disease, a scenario with significant implications for medical settings with limited data on emerging diseases. The XDT framework leverages the embedding space of a vision encoder, which, through kernel transformation, aids in distinguishing between diseased and non-diseased classes in the latent space. This capability is especially beneficial in resource-limited environments or in regions with low prevalence of certain diseases, where conventional diagnostic practices may fail. However, the XDT framework is currently limited to binary classification, determining only the presence or absence of a disease rather than differentiating among multiple diseases. This limitation underscores the supplementary role of XDT to traditional diagnostic tests in clinical settings. Furthermore, results show that XDT-CXR as a framework is able to make better predictions compared to other zero-shot learning (ZSL) baselines.
Kohei Iwano, Shogo Shibuya, Satoshi Kagiwada
et al.
Recently, object detection methods (OD; e.g., YOLO-based models) have been widely utilized in plant disease diagnosis. These methods demonstrate robustness to distance variations and excel at detecting small lesions compared to classification methods (CL; e.g., CNN models). However, there are issues such as low diagnostic performance for hard-to-detect diseases and high labeling costs. Additionally, since healthy cases cannot be explicitly trained, there is a risk of false positives. We propose the Hierarchical object detection and recognition framework (HODRF), a sophisticated and highly integrated two-stage system that combines the strengths of both OD and CL for plant disease diagnosis. In the first stage, HODRF uses OD to identify regions of interest (ROIs) without specifying the disease. In the second stage, CL diagnoses diseases surrounding the ROIs. HODRF offers several advantages: (1) Since OD detects only one type of ROI, HODRF can detect diseases with limited training images by leveraging its ability to identify other lesions. (2) While OD over-detects healthy cases, HODRF significantly reduces these errors by using CL in the second stage. (3) CL's accuracy improves in HODRF as it identifies diagnostic targets given as ROIs, making it less vulnerable to size changes. (4) HODRF benefits from CL's lower annotation costs, allowing it to learn from a larger number of images. We implemented HODRF using YOLOv7 for OD and EfficientNetV2 for CL and evaluated its performance on a large-scale dataset (4 crops, 20 diseased and healthy classes, 281K images). HODRF outperformed YOLOv7 alone by 5.8 to 21.5 points on healthy data and 0.6 to 7.5 points on macro F1 scores, and it improved macro F1 by 1.1 to 7.2 points over EfficientNetV2.
The incidence rate of voice diseases is increasing year by year. The use of software for remote diagnosis is a technical development trend and has important practical value. Among voice diseases, common diseases that cause hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and vocal cord polyp. This paper presents a voice disease detection method that can be applied in a wide range of clinical. We cooperated with Xiangya Hospital of Central South University to collect voice samples from sixty-one different patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are extracted as input features to describe the voice in the form of data. An innovative model combining MFCC parameters and single convolution layer CNN is proposed for fast calculation and classification. The highest accuracy we achieved was 92%, it is fully ahead of the original research results and internationally advanced. And we use Advanced Voice Function Assessment Databases (AVFAD) to evaluate the generalization ability of the method we proposed, which achieved an accuracy rate of 98%. Experiments on clinical and standard datasets show that for the pathological detection of voice diseases, our method has greatly improved in accuracy and computational efficiency.
Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain
et al.
Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems, drugs interact with protein targets in cells, influencing target activities and subsequently impacting biological pathways to promote healthy functions and treat diseases. Moving beyond binary relationships and exploring tighter triple relationships is essential to understanding drugs' mechanism of action (MoAs). Moreover, identifying the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, is critical to model these complex interactions appropriately. To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}). \texttt{HeTriNet} introduces a novel triplet attention mechanism within this heterogeneous graph structure. Beyond pairwise attention as the importance of an entity for the other one, we define triplet attention to model the importance of pairs for entities in the drug-target-disease triplet prediction problem. Experimental results on real-world datasets show that \texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable proficiency in uncovering novel drug-target-disease relationships.
Camilla M. Grunewald, Vera Feldmeier, Tillmann Supprian
et al.
Background: Impaired cognitive function of bladder cancer patients plays a role in coping with the kind of urinary diversion and may impact perioperative morbidity. In this study we therefore aimed to assess the prevalence of mild cognitive impairment in patients undergoing radical cystectomy. Secondary objectives included correlation of common cognition tests, assessment of the admitting physician, and perioperative complication rates. Methods: Patients undergoing radical cystectomy for bladder cancer were prospectively screened by neuropsychological tests including cognition tests [DemTect (Dementia Detection test), MMSE (Mini-Mental State Examination), clock drawing test] prior to surgery. Besides, clinical characteristics and perioperative outcomes were documented. Frequency of mild cognitive impairment as assessed by DemTect was correlated with the results of MMSE and clock drawing test, the occurrence of anxiety and depression, the assessment of the admitting physician, and perioperative complication rates as calculated by Spearman rank correlation coefficient. Comparative analysis (parametric and nonparametric) of patient characteristics (nonpathological versus pathological DemTect suggestive of mild cognitive impairment) was performed. Results: A total of 51 patients (80% male, median age 69 years) were analyzed. DemTect was suspicious of mild cognitive impairment in 27% (14/51) of patients, whereas MMSE and clock drawing test showed pathological results only in 10/51 and 6/51 patients, respectively. We found no correlation between mild cognitive impairment and anxiety/depression status. In all, 5/20 patients (25%) with suspicious DemTect results were considered suitable for a continent diversion neobladder by the admitting physician. Suspicious DemTect results were predictive for higher perioperative complication rates (29% versus 5%). Study limitations include small sample size and missing long-term follow-up. Conclusions: Mild cognitive impairment was observed in more than a quarter of radical cystectomy patients prior to surgery. Preoperative assessment should be supplemented by neuropsychological testing such as the DemTect as mild cognitive impairment is often underestimated and associated with significantly higher perioperative complication rates.
Abstract Objectives To evaluate S-phase fraction as a predictor of invasiveness and cancer-specific survival in upper tract urothelial carcinoma (UTUC). Patients and methods One hundred and fifteen patients having undergone radical nephroureterectomy were analysed with histology in radical nephroureterectomy specimens as reference test and S-phase fraction as index test. Ploidy and S-phase were determined using flow cytometry. Differences in S-phase fraction were calculated between stages, grades (WHO 1999 and 2004 classifications), ploidy and patients that died of UTUC and those who did not. Five- and 10-year-cancer-specific survivals were calculated. Areas under the ROC curve (AUCs) of S-phase fraction in relation to tumour stage and to death from UTUC were measured. Multiple Cox regression was performed. Results Independent prognostic markers of death from UTUC were S-phase fraction and stage. Correlation between S-phase fraction and risk of dying from UTUC was strong, with a 17% greater risk of death from UTUC with every 1% increase in S-phase fraction, hazard ratio = 1.17, 95% CI = 1.10–1.25, p
Joana Tavares, João Pedro Oliveira, Pedro Reis
et al.
Abstract Introduction: Kidney transplant (KT) recipients have a high risk for adverse outcomes from infections, such as COVID-19. Methods: We have retrospectively reviewed all KT recipients with documented COVID-19 between March 1, 2020, and March 15, 2021, and analyzed patients’ characteristics, clinical course, treatment, and outcomes. Results: We identified 123 patients, 72% were male, with a mean age of 54.5±13.0 years. Twenty percent were asymptomatic, 7% had a nosocomial transmission, and 36% of the remainder required hospitalization. Almost all admitted patients received oxygen, 30% required invasive mechanical ventilation (IMV), more than a half had acute kidney injury, with 10% requiring dialysis, and 20% died. Incidence was comparable to that of the Portuguese population, but the mortality rate was almost four times higher (SMR of 3.768 (95% CI:1.723-7.154). Higher body mass index (OR 1.275, P=0.001), lower baseline graft function (OR 0.968, P=0.015), and nosocomial transmission (OR 13.836, P=0.019) were associated with oxygen demand, whereas female gender (OR 3.801, P=0.031) and lower baseline kidney graft function (OR 0.955, P=0.005), but not body mass index, were associated with IMV and/or death. Conclusion: Mortality rate in KT patients was higher than in the general population and lower baseline kidney function was the most consistent marker for adverse outcomes.
This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into the field of digital interaction design and User Experience (UX). We ended up with the concept of interactive cockpits, not because pilots interacted with mechanical things, but because they interacted using pointing devices on computer displays. Since the early 2000s, complexity and organizational issues gained prominence to the point that complex systems design and management found itself center stage, with the spotlight on the role of the human element and organizational setups. Today, Human Systems Integration (HSI) is no longer only a single-agent problem, but a multi-agent research field. Systems are systems of systems, considered as representations of people and machines. They are made of statically and dynamically articulated structures and functions. When they are at work, they are living organisms that generate emerging functions and structures that need to be considered in evolution (i.e., in their constant redesign). This chapter will more specifically, focus on human factors such as human-centered systemic representations, life critical systems, organizational issues, complexity management, modeling and simulation, flexibility, tangibility and autonomy. The discussion will be based on several examples in civil aviation and air combat, as well as aerospace.
Ryosuke Saiki, Kan Katayama, Yosuke Hirabayashi
et al.
Abstract Background Multicentric Castleman’s disease is a life-threatening disorder involving a systemic inflammatory response and multiple organ failure caused by the overproduction of interleukin-6. Although renal complications of Castleman’s disease include AA amyloidosis, thrombotic microangiopathy, and membranoproliferative glomerulonephritis, membranous nephropathy is relatively rare. We experienced a case of secondary membranous nephropathy associated with Castleman’s disease. Case presentation The patient was a 43-year-old Japanese man who had shown a high zinc sulfate value in turbidity test, polyclonal hypergammaglobulinemia, anemia, and proteinuria. A physical examination revealed diffuse lymphadenopathy, an enlarged spleen and papulae of the body trunk. A skin biopsy of a papule on the patient’s back showed plasma cells in the perivascular area and he was diagnosed with multicentric Castleman’s disease, plasma cell variant. Kidney biopsy showed the appearance of bubbling in the glomerular basement membranes in Periodic acid methenamine silver stain and electron microscopy revealed electron dense deposits within and outside the glomerular basement membranes. Since immunofluorescence study showed predominant granular deposition of IgG1 and IgG2, he was diagnosed with secondary membranous nephropathy associated with Castleman’s disease. He was initially treated with prednisolone alone, however his biochemical abnormalities did not improve. After intravenous tocilizumab (700 mg every 2 weeks) was started, his C-reactive protein elevation, anemia, and polyclonal gammopathy improved. Furthermore, his urinary protein level declined from 1.58 g/gCr to 0.13 g/gCr. The prednisolone dose was gradually tapered, then discontinued. He has been stable without a recurrence of proteinuria for more than 6 months. Conclusions Tocilizumab might be a treatment option for secondary membranous nephropathy associated with Castleman’s disease.
Tone Andersen-Hollekim, Bodil J. Landstad, Marit Solbjør
et al.
Abstract Background For individuals in need of dialysis, patient participation is important when determining care goals and in decision making regarding dialysis modality. Nephrologists hold a key role in delivering evidence-based healthcare that integrates patient preferences and values throughout the trajectory, and their experiences with patient participation are important for improving health care. The aim of this study was to explore nephrologists’ experiences with patient participation in different phases of the end-stage renal disease trajectory for working-age individuals who require dialysis. Methods This explorative study comprised interviews with ten nephrologists from four different dialysis units in Central Norway. We analysed the interviews by applying an interpretive phenomenological approach. Results Nephrologists had varied experiences with patient participation throughout the different phases of the treatment trajectory. During decision making on the dialysis modality, nephrologists emphasised patients’ choices in two approaches. In the first approach, they expected patients to choose the modality based on the provided information, which could be actively steered. In the second approach, they recognised the patients’ values and lifestyle preferences through shared decision-making. Within hospital haemodialysis, nephrologists considered patients’ self-care activities equivalent to patient participation, seeing self-care as a source of patient empowerment. They identified divergent patient–professional values and organisational structures as barriers to patient participation. Conclusion Our study shows that nephrologists have different approaches to patient participation in different phases of the end-stage renal disease trajectory. Individual understanding as well as organisational structures are important factors to address to increase patient participation in end-stage renal disease care. Shared decision making, in which patient values are balanced against biomedical treatment targets, allows for mutual agreement between patients and healthcare professionals concerning medical plans and minimises the potential for patient–professional tensions.
Approximately 25% of patients who have undergone extensive systemic therapy for advanced metastatic castration-resistant prostate cancer (mCRPC) develop treatment associated neuroendocrine prostate cancer (NEPC). 177Lu-prostate specific membrane antigen (-PSMA) is an emerging alternative therapy for mCRPC patients who have exhausted other systemic therapy options; however, cells with neuroendocrine differentiation do not express PSMA and are not affected by this treatment. This case highlights an exceptional response of skeletal metastases to 177LuPSMA that is undermined by neuroendocrine transformation in the liver.
This article explores mathematical models for understanding the evolution of contagious diseases. The most widely known set of models are the compartmental ones, which are based on a set of differential equations. But these are not the only models. This review visits many different families of models. Additionally, we show these families, not as unrelated entities, but following a common thread in which the problems or assumptions of a model are solved or generalized by another model. In this way, we can understand their relationships, assumptions, simplifications, and, ultimately, limitations. Prompted by the current Covid19 pandemic, we have a special focus on spread forecasting. We illustrate the difficulties encountered to do realistic predictions. In general, they are only approximations to a reality whose biological and societal complexity is much larger. Particularly troublesome are the large underlying variability, the problem's time-varying nature, and the difficulty to estimate the required parameters for a faithful model. Additionally, we will also see that these models have a multiplicative nature implying that small errors in the system parameters cause a huge uncertainty in the prediction. Stochastic or agent-based models can overcome some of the modeling problems of systems based on differential or stochastic equations. Their main difficulty is that they are as accurate and realistic as the data available to estimate their detailed parametrization, and very often this detailed data is not at the modeller's disposal. Although the predictive power of mathematical models to forecast the evolution of a contagious disease is very limited, these models are still very useful to plan interventions as they can calculate their impact if all other parameters stay fixed. They are also very useful to understand the properties of disease propagation in complex systems.
The purpose of this paper is to analyze the mechanism for the interplay of deterministic and stochastic models for contagious diseases. Deterministic models for contagious diseases are prone to predict global stability. Small natural birth and death rates in comparison to disease parameters like the contact rate and the removal rate ensures that the globally stable endemic equilibrium corresponds to a tiny average proportion of infected individuals. Asymptotic equilibrium levels corresponding to low numbers of individuals invalidate the deterministic results. Diffusion effects force frequency functions of the stochastic model to possess similar stability properties as the deterministic model. Particular simulations of the stochastic model predict, however, oscillatory patterns. Small and isolated populations show longer periods, more violent oscillations, and larger probabilities of extinction. We prove that evolution maximizes the infectiousness of the disease as measured by the ability to increase the proportion of infected individuals. This holds provided the stochastic oscillations are moderate enough to keep the proportion of susceptible individuals near a deterministic equilibrium. We close our paper with a discussion of the herd-immunity concept and stress its close relation to vaccination-programs.