Hasil untuk "Internal medicine"

Fengxian Chen, Zhilong Tao, Jiaxuan Li et al.

Retrieval-augmented generation (RAG) promises grounded question answering, yet domain settings with multiple heterogeneous knowledge bases (KBs) remain challenging. In Chinese Tibetan medicine, encyclopedia entries are often dense and easy to match, which can dominate retrieval even when classics or clinical papers provide more authoritative evidence. We study a practical setting with three KBs (encyclopedia, classics, and clinical papers) and a 500-query benchmark (cutoff $K{=}5$) covering both single-KB and cross-KB questions. We propose two complementary methods to improve traceability, reduce hallucinations, and enable cross-KB verification. First, DAKS performs KB routing and budgeted retrieval to mitigate density-driven bias and to prioritize authoritative sources when appropriate. Second, we use an alignment graph to guide evidence fusion and coverage-aware packing, improving cross-KB evidence coverage without relying on naive concatenation. All answers are generated by a lightweight generator, \textsc{openPangu-Embedded-7B}. Experiments show consistent gains in routing quality and cross-KB evidence coverage, with the full system achieving the best CrossEv@5 while maintaining strong faithfulness and citation correctness.

Deepjyoti Chetia, Sanjib Kr Kalita, Prof Partha Pratim Baruah et al.

Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of these plants from nonmedicinal plants requires human expert intervention. To solve the issue of accurate plant identification and reduce the need for a human expert in the collection process; employing computer vision methods will be efficient and beneficial. In this paper, we have proposed a model that solves such issues. The proposed model is a custom convolutional neural network (CNN) architecture with 6 convolution layers, max-pooling layers, and dense layers. The model was tested on three different datasets named Indian Medicinal Leaves Image Dataset,MED117 Medicinal Plant Leaf Dataset, and the self-curated dataset by the authors. The proposed model achieved respective accuracies of 99.5%, 98.4%, and 99.7% using various optimizers including Adam, RMSprop, and SGD with momentum.

Ayyüce Begüm Bektaş, Mithat Gönen

This paper claims that machine learning models deployed in high stakes domains such as medicine must be interpretable, shareable, reproducible and accountable. We argue that these principles should form the foundational design criteria for machine learning algorithms dealing with critical medical data, including survival analysis and risk prediction tasks. Black box models, while often highly accurate, struggle to gain trust and regulatory approval in health care due to a lack of transparency. We discuss how intrinsically interpretable modeling approaches (such as kernel methods with sparsity, prototype-based learning, and deep kernel models) can serve as powerful alternatives to opaque deep networks, providing insight into biomedical predictions. We then examine accountability in model development, calling for rigorous evaluation, fairness, and uncertainty quantification to ensure models reliably support clinical decisions. Finally, we explore how generative AI and collaborative learning paradigms (such as federated learning and diffusion-based data synthesis) enable reproducible research and cross-institutional integration of heterogeneous biomedical data without compromising privacy, hence shareability. By rethinking machine learning foundations along these axes, we can develop medical AI that is not only accurate but also transparent, trustworthy, and translatable to real-world clinical settings.

Manuel Baltieri, Martin Biehl, Matteo Capucci et al.

The internal model principle, originally proposed in the theory of control of linear systems, nowadays represents a more general class of results in control theory and cybernetics. The central claim of these results is that, under suitable assumptions, if a system (a controller) can regulate against a class of external inputs (from the environment), it is because the system contains a model of the system causing these inputs, which can be used to generate signals counteracting them. Similar claims on the role of internal models appear also in cognitive science, especially in modern Bayesian treatments of cognitive agents, often suggesting that a system (a human subject, or some other agent) models its environment to adapt against disturbances and perform goal-directed behaviour. It is however unclear whether the Bayesian internal models discussed in cognitive science bear any formal relation to the internal models invoked in standard treatments of control theory. Here, we first review the internal model principle and present a precise formulation of it using concepts inspired by categorical systems theory. This leads to a formal definition of ``model'' generalising its use in the internal model principle. Although this notion of model is not a priori related to the notion of Bayesian reasoning, we show that it can be seen as a special case of possibilistic Bayesian filtering. This result is based on a recent line of work formalising, using Markov categories, a notion of ``interpretation'', describing when a system can be interpreted as performing Bayesian filtering on an outside world in a consistent way.

S. Naderi, K. Samadi, A. A. Zeraati et al.

Introduction. COVID-19 has been associated with both acute and chronic extrapulmonary complications, including renal dysfunction. Understanding the long-term effects of COVID-19 on renal function is essential for managing recovery in affected individuals.Objective. This study aimed to evaluate the long-term renal outcomes in patients who recovered from COVID-19, focusing on changes in glomerular filtration rate (GFR), blood urea nitrogen (BUN), and serum creatinine levels in Iran.Materials & methods. A retrospective cohort study was conducted using data from the Mashhad University of Medical Sciences cohort. The study included patients who had confirmed COVID-19 and a minimum follow-up period of six months post-recovery. Renal function was assessed by measuring the Glomerular Filtration Rate (GFR), Blood Urea Nitrogen (BUN), and serum creatinine levels both at baseline (when COVID-19 was initially diagnosed) and at follow-up. Statistical analysis was performed to explore the associations between renal outcomes and various factors, including gender, the severity of COVID-19, and blood pressure status.Results. In the study, 55.3% were male, and the mean age of 51.38 ± 13.41. Among the patients, 55.3% were male and 44.7% were female. The difference in mean creatinine level between baseline and follow-up was significant (p < 0.001). The difference in mean GFR between baseline and follow-up was significant (p < 0.001). In men, the mean blood urea nitrogen at the first visit and at the follow-up difference was not statistically significant (p = 0.241). In women, the mean blood urea nitrogen was a statistically significant decrease (p = 0.003). Other parameters, including creatinine and GFR, did not differ significantly in both male and female groups at the time of hospitalization and follow-up.Conclusion. Overall, the results of this study suggest that COVID-19 can affect kidney function, especially in association with underlying factors such as hypertension and diabetes, and female gender, which may be risk factors for more severe renal complications in patients with COVID-19. The decrease in GFR in patients with hypertension and diabetes highlights the importance of controlling these diseases in patients with COVID-19. Overall, this study showed that COVID-19 can have lasting effects on patients' kidney function.

Ashot A. Avagimyan, Mohammad Sheibani, Artem I. Trofimenko et al.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are now uncovering new possibilities in the field of internal medicine owing to their diverse protective effects. In the second part of the literature review, we explore potential applications of SGLT2i in hepatology, neurology, ophthalmology, and oncology, mechanisms of action of such drugs as dapagliflozin, empagliflozin, canagliflozin, etc, and their effect on different organs and systems.

Ku Bean Jeong, Hee Seok Moon, Kyung Ryun In et al.

Abstract Background The incidence of lower gastrointestinal bleeding is on the rise, prompting the creation of various scoring systems to forecast patient’s outcomes. But there is no single unified scoring system and these scoring systems clinical data are small and not worldwide. Aims To evaluate how different scoring systems predict mortality and prolonged hospital stay (≥ 10 days). Methods A retrospective review was conducted on the medical records of 4417 patients who presented with hematochezia at the emergency department from January 2016 to December 2022. We evaluated the predictive accuracy of various scoring systems for 30-day mortality and prolonged hospital stay (≥ 10 days) by analyzing the areas under the receiver-operating characteristic curves, taking into account factors such as patient age, laboratory findings, and comorbidities (ABC); AIMS 65; Glasgow-Blatchford; Oakland; Rockall(pre-endoscopy); SHA2PE; and CHAMPS scores. Results We analyzed data from 1000 patients (mean age 66 years, 56.1% men, mean hospital stay 9.4 days) with lower gastrointestinal bleeding confirmed by any other means including DRE, colonoscopy and CT. The 30-day mortality rate was 3.7%. The primary etiologies of lower gastrointestinal bleeding were identified as ischemic colitis and diverticular bleeding, accounting for 18.8% and 18.5% of cases, respectively. In terms of forecasting 30-day mortality, the AIMS 65, CHAMPS, and ABC scoring systems demonstrated superior performance (p < 0.001). For predicting prolonged hospital stay, the SHA2PE score exhibited the highest accuracy among all evaluated systems (p < 0.001). Conclusions The newly developed scoring systems demonstrated superior accuracy in forecasting outcomes for patients with lower gastrointestinal bleeding, and the results of this study demonstrate that these scoring systems can be applied in clinical practice.

Kyung Hye Park, Ki-Byung Lee, HyeRin Roh

Purpose Most of studies about racial or ethnic biases among medical students have been conducted in English-speaking developed countries. This study explores the hybridity and transformation of Korean medical students’ biases, arguing that a nation’s identity and culture are constantly in a state of ever-changing hybridity. Methods This research used a qualitative document analysis. The study participants were 600 pre-clinical medical students at two medical colleges in Korea, who enrolled in anti-bias programs and subsequently submitted self-reflection essays. Data collection focused on biases related to race, ethnicity, nationality, and medical practices as doctors. Bhabha’s cultural hybridity concepts guided the coding of the data in order to explore the hybridity and transformation of the students’ biases. Results The students presented cultural biases toward patients and doctors with ambivalence related to a person’s high socioeconomic status and open-mindedness, as well as doctors’ excellence and superiority as Korean authoritative figures. Since the students had ambivalent and complex biases toward patients and doctors, they felt unhomeliness as Korean doctors encountering international patients in Korean clinics. However, after discovering their contradictory assumptions, they transformed their unhomeliness into new hybrid identities. The students’ biases were rarely based on race but instead were based on nationality, specifically national class by national income. Conclusion Understanding the changing hybrid nature of identities and culture from a cultural hybridity perspective could help clarify medical students’ complex and changing biases and improve anti-bias education. Korean medical students’ hybridized positions suggest that anti-bias education goes beyond focusing on prestige or racism.

Yasutoshi Makino, Hsin-Ni Ho, Seokhee Jeon

The sixth international conference AsiaHaptics 2024 took place at Sunway University, Malaysia on 28-30 October 2024. AsiaHaptics is an exhibition type of international conference dedicated to the haptics domain, engaging presentations accompanied by hands-on demonstrations. It presents the state-of-the-art of the diverse haptics (touch)-related research, including perception and illusion, development of haptics devices, and applications to a wide variety of fields such as education, medicine, telecommunication, navigation and entertainment. This proceedings volume is a valuable resource not only for active haptics researchers, but also for general readers wishing to understand the status quo in this interdisciplinary area of science and technology.

Zhihui Xie, Jizhou Guo, Tong Yu et al.

Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks, aided by techniques like chain-of-thought prompting that elicits verbalized reasoning. However, LLMs often generate text with obvious mistakes and contradictions, raising doubts about their ability to robustly process and utilize generated rationales. In this work, we investigate reasoning in LLMs through the lens of internal representations, focusing on how these representations are influenced by generated rationales. Our preliminary analysis reveals that while generated rationales improve answer accuracy, inconsistencies emerge between the model's internal representations in middle layers and those in final layers, potentially undermining the reliability of their reasoning processes. To address this, we propose internal consistency as a measure of the model's confidence by examining the agreement of latent predictions decoded from intermediate layers. Extensive empirical studies across different models and datasets demonstrate that internal consistency effectively distinguishes between correct and incorrect reasoning paths. Motivated by this, we propose a new approach to calibrate reasoning by up-weighting reasoning paths with high internal consistency, resulting in a significant boost in reasoning performance. Further analysis uncovers distinct patterns in attention and feed-forward modules across layers, providing insights into the emergence of internal inconsistency. In summary, our results demonstrate the potential of using internal representations for self-evaluation of LLMs. Our code is available at github.com/zhxieml/internal-consistency.

Sydney Anuyah, Mallika K Singh, Hope Nyavor

The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformerbased models, to stratify patients, forecast adverse events, and personalize treatment plans. Furthermore, predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates. To address challenges in analysing unstructured clinical data, such as patient notes and trial protocols, natural language processing [NLP] techniques are utilized for extracting actionable insights. A custom dataset comprising structured patient demographics, genomic data, and unstructured text is curated for training and validating these models. Key metrics, including precision, recall, and F1 scores, are used to evaluate model performance, while trade-offs between accuracy and computational efficiency are examined to identify the optimal model for clinical deployment. This research underscores the potential of AI-driven methods to streamline clinical trial workflows, improve patient-centric outcomes, and reduce costs associated with trial inefficiencies. The findings provide a robust framework for integrating predictive analytics into precision medicine, paving the way for more adaptive and efficient clinical trials. By bridging the gap between technological innovation and real-world applications, this study contributes to advancing the role of AI in healthcare, particularly in fostering personalized care and improving overall trial success rates.

Peng Xu, Hongjin Wu, Jinle Wang et al.

This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details, pathogenesis, syndromes, and annotations. Using the $Baidu\_ERNIE\_Speed\_128K$ API, we removed redundant information and generated the final answers through the $DeepSeekv2$ API, outputting results in standard JSON format. We optimized data recall with RAG and rerank techniques during retrieval and developed a hybrid matching scheme. By combining two-stage retrieval method with keyword matching via Jieba, we significantly enhanced the accuracy of model outputs.

Stephanie Bond, Renaud Léguillette

Abstract Background Nebulized administration of dexamethasone on cytokine regulation in horses with moderate asthma has not been investigated. Objective To investigate the changes in expression of inflammatory cytokine mRNA after nebulized administration of dexamethasone treatment of horses with moderate asthma. Animals Horses with naturally occurring moderate asthma (n = 16) and healthy control horses (n = 4). All horses were kept in a dusty environment during the study. Methods Prospective, parallel, randomized, controlled, blinded clinical trial. Blood endogenous cortisol, tracheal mucus, and bronchoalveolar lavage (BAL) were sampled before and after 13 days treatment with either nebulized administration of dexamethasone (15 mg once daily) or 0.9% saline (3 mL). Treatment groups were randomly allocated via randomization function (Microsoft Excel). Amplification of target mRNA in BAL fluid (IL‐1β, IL‐4, IL‐5, IL‐6, IL‐8, IL‐10, IL‐12, IL‐17, IL‐23, IFN‐γ, Eotaxin‐2, and TNF‐α) was achieved by qPCR, and the relative expression software tool was used to analyze BAL inflammatory cytokine mRNA. Results Horses treated with nebulized administration of dexamethasone had increased relative expression of IL‐5 (1.70‐fold), IL‐6 (1.71‐fold), IL‐17 (3.25‐fold), IL‐12 (1.66‐fold), and TNF‐α (1.94‐fold), and decreased relative expression of IL‐23 (1.76‐fold; P = .04) in samples collected on Day 14, in comparison to samples collected on Day 0 (all P < .05). Horses treated with nebulized administration of saline had no significant difference in the relative expression of any gene (all P > .05). Conclusions and Clinical Importance Nebulized administration of dexamethasone was associated with increased expression of inflammatory cytokine mRNA. There was no improvement in inflammatory airway cytology associated with either dexamethasone or saline treatment.

Lyne Moser, Maru Sarazola, Paula Verdugo

Given a cartesian closed category $\mathcal{V}$, we introduce an internal category of elements $\int_\mathcal{C} F$ associated to a $\mathcal{V}$-functor $F\colon \mathcal{C}^{\mathrm{op}}\to \mathcal{V}$. When $\mathcal{V}$ is extensive, we show that this internal Grothendieck construction gives an equivalence of categories between $\mathcal{V}$-functors $\mathcal{C}^{\mathrm{op}}\to \mathcal{V}$ and internal discrete fibrations over $\mathcal{C}$, which can be promoted to an equivalence of $\mathcal{V}$-categories. Using this construction, we prove a representation theorem for $\mathcal{V}$-categories, stating that a $\mathcal{V}$-functor $F\colon \mathcal{C}^{\mathrm{op}}\to \mathcal{V}$ is $\mathcal{V}$-representable if and only if its internal category of elements $\int_\mathcal{C} F$ has an internal terminal object. We further obtain a characterization formulated completely in terms of $\mathcal{V}$-categories using shifted $\mathcal{V}$-categories of elements. Moreover, in the presence of $\mathcal{V}$-tensors, we show that it is enough to consider $\mathcal{V}$-terminal objects in the underlying $\mathcal{V}$-category $\mathrm{Und}\int_\mathcal{C} F$ to test the representability of a $\mathcal{V}$-functor $F$. We apply these results to the study of weighted $\mathcal{V}$-limits, and also obtain a novel result describing weighted $\mathcal{V}$-limits as certain conical internal limits.

Bo Cai, Shixue Chang, Yuhan Tian et al.

Abstract Hepatitis B virus (HBV) infection remains a global health challenge. Despite the availability of effective preventive vaccines, millions of people are at risk of cirrhosis and hepatocellular carcinoma. Current drug therapies inhibit viral replication, slow the progression of liver fibrosis and reduce infectivity, but they rarely remove the covalently sealed circular DNA (cccDNA) of the virus that causes HBV persistence. Alternative treatment strategies, including those based on CRISPR/cas9 knockout virus gene, can effectively inhibit HBV replication, so it has a good prospect. During chronic infection, some virus gene knockouts based on CRISPR/cas9 may even lead to cccDNA inactivation. This paper reviews the progress of different HBV CRISPR/cas9, vectors for delivering to the liver, and the current situation of preclinical and clinical research.

Samantha So, Teng Qing Wang, Brian Edward Yu et al.

Introduction: The COVID-19 pandemic has created a chronically stressful work environment for healthcare workers, increasing the negative psychological effects experienced. Aims: The authors of this systematic review and meta-analysis aimed to assess the impact of COVID-19 on frontline healthcare workers’ mental health, using various psychological outcomes. Methods: A systematic literature search was conducted up until June 30th, 2022 on MEDLINE, EMBASE, CINAHL, Cochrane Library, Web of Science, ClinicalTrials.gov, and Dissertations and Theses. Results: This meta-analysis includes 22 cross-sectional studies with a total of 32,690 participants. Anxiety (ES = 0.23, CI: [0.18, 0.28]), depression (ES = 0.17, CI: [0.10, 0.24]), PTSD (ES = 0.28, CI: [0.08, 0.48]), and stress (ES = 0.35, CI: [0.17, 0.53]) was significantly prevalent among frontline healthcare workers. Conclusions: Our results suggested that European healthcare workers were experiencing high psychological symptoms associated with the COVID-19 pandemic. The monitoring of their psychological symptoms, preventative interventions, and treatments should be implemented to prevent, reduce, and treat the worsening of their mental health.

Ahmad Chaddad, Qizong lu, Jiali Li et al.

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. (1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. (2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. (3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.

FELIPE RAMALHO GUEDES, FERNANDA MINUTTI NAVARRO, RODRIGO YUITI NAKAO et al.

ABSTRACT Objective: To evaluate the prevalence of low back pain in nurses at a university hospital in São Paulo and establish a relationship with social aspects. Methods: A cross-sectional study was carried out, through the application of a questionnaire containing social questions(weight, age, height, work sector, working hours, physical activity, presence and frequency of low back pain) in addition to the Oswestry questionnaire. Results: One hundred fifty-three nurses participated in the study. Of these, 92.30% of the women and 73.91% of the men presented low back pain, with a third classifying the pain as sporadic. In relation to BMI, pain is lower in those who are underweight (60%) and higher among those who are overweight (96.77%). Most of the sample was sedentary (66%), and of these, 96% had low back pain. There was no difference in the comparison by working hours, in relation to work sector, pain was more present in the following sectors: coordination (100%); children’s ward (92%); adult emergency room (90%) and adult ICU (31%). Thirty nurses worked double shifts, and of these, 90% reported low back pain, while among those who worked only at the university hospital, 89.4% reported pain. In relation to working hours, the longer the working day, the greater the pain. In the function assessment (Oswestry), 99 participants obtained a value of up to 30% disability. Conclusion: Based on the results of this work, it is concluded that there is a high prevalence of low back pain in nurses at the Hospital Universitário; however, it was not possible to determine a direct risk factor associated with this high prevalence. Level of Evidence IV; Cross-sectional study.

L. D. Vorobyeva, E. Yu. Loginova, Yu. L. Korsakova et al.

Objective – validation of the Russian-language version of the PsAID-12 questionnaire in patients with psoriatic arthritisMaterials and methods. The study included 187 patients, mostly men (50.2%), with a reliable diagnosis of psoriatic arthritis (PsA) according to the CASPAR criteria (2006), who consistently sought medical help at the V.A. Nasonova Research Institute of Rheumatology and those who signed informed consent to participate in the study. Average age was 45.6±11.7 years, duration of PsA – 113.8±76.7 months, duration of psoriasis – 241±144 months, disease activity according to DAPSA (Disease Activity in Psoriatic Arthritis) – 29.1±22.6. At the initial visit and after 12 months of therapy, all patients underwent a standard rheumatologic examination and assessment of the quality of life. The number of tender joint count (TJC 68), the number of swollen joint count (SJC 66), PGA (patient global assessments) were assessed using a visual analogue scale (VAS) (0–10 cm), VAS pain (0–10 cm), BASDAI (Bath Ankylosing Spondylitis Disease Activity Index), PsAID-12 (Psoriatic Arthritis Impact of Disease-12) and EQ-5D (EuroQoL-5D). The EQ-5D was considered the “gold standard” for assessing quality of life. The reliability of the PsAID-12 questionnaire was studied on the basis of its reproducibility by test-retest analysis and internal constancy by calculating the Cronbach’s alpha for each scale. We assessed the validity, i. e., the ability of the PsAID-12 questionnaire to reliably measure its characteristics based on criterional and constructive validity. The criterion validity was calculated by assessing the relationship between PsAID-12 and “external criteria”| reflecting the activity of PsA and interchangeability with the EQ-5D questionnaire using correlation analysis. A moderate and strong bond was considered r≥0.30. Constructive validity was assessed by the method of “known groups” and factor analysis. The study of the reliability of the PsAID-12 questionnaire was carried out in 30 PsA patients. Sensitivity – in 172 patients in dynamics after 12 months of various PsA therapy regimens.Results. The study of the reliability of the PsAID-12 questionnaire included 30 patients. According to the results of the test-retest analysis, it was revealed that there were no statistically significant differences between the initial and repeated assessments on all 12 PsAID-12 scales (p&gt;0.05). To assess the internal constancy, the Cronbach’s alpha was calculated for each of the 12 scales of the questionnaire. The value of this coefficient ranged from 0.8 to 0.9 and was quite high. Validity was assessed in 187 patients with PsA. The analysis of the criterion validity of the PsAID-12 questionnaire was studied by assessing the relationship of its scales with the scales of the EQ-5D questionnaire, where it demonstrated a close correlation (r&gt;0.3). That testifies to the good interchangeability of this questionnaire. Also, the criterion validity was carried out by assessing the “external criteria” (TJC 68, SJC 66, DAPSA, VAS global assessments, VAS global pain, BASDAI). Where were identified direct correlations of external criteria with all scales of the questionnaire. The highest correlation coefficient (r=0.8) was found between the BASDAI index and the “Physical performance” scale. When assessing constructive validity by the “known groups” method, patients were divided into 2 groups according to disease activity: with DAPSA index ≥5 and DAPSA index ≤4. Significant differences were revealed between the group of patients with active PsA and the absence of PsA activity on all scales of the questionnaire (p&lt;0.001). Factor analysis revealed two main factors – physical and emotional health; a high level of correlation of the scales with their factor was also shown. To assess the sensitivity of the questionnaire, its changes were analyzed depending on the achieved effect on therapy after 12 months: group I of patients in whom MDA was achieved – 50 patients; group II – 43 patients REM/LDA; group III – 79 patients with no effect on therapy. It was revealed that in groups I and II there was a statistically significant difference on all scales of questionnaires, in group III, “non-responders” to therapy, there were no statistically significant improvements in the scales “Pain” (p=0.37), “Fatigue” (p=0.15), “Skin problems” (p=0.23), “Work and/or leisure activities” (p=0.056), “Functional capacity” (p=0.44). Thus, during treatment, it was noted that the PsAID-12 questionnaire may reflect the dynamics depending on the activity of the disease during treatment, which proves its good sensitivity.Conclusion. The Russian version of the PsAID-12 has good psychometric properties and is able to reflect changes in the patient’s health status over time, along with disease activity and laboratory manifestations.

Xiaohu Zhou, Xiaohu Zhou, Qiang Sun et al.

Peroxidasin (PXDN), also known as vascular peroxidase-1, is a newly discovered heme-containing peroxidase; it is involved in the formation of extracellular mesenchyme, and it catalyzes various substrate oxidation reactions in humans. However, the role and specific mechanism of PXDN in tumor are unclear, and no systematic pan-cancer studies on PXDN have been reported to date. This study employed data from multiple databases, including The Cancer Genome Atlas and The Genotype-Tissue Expression, to conduct a specific pan-cancer analysis of the effects of PXDN expression on cancer prognosis. Further, we evaluated the association of PXDN expression with DNA methylation status, tumor mutation burden, and microsatellite instability. Additionally, for the first time, the relationship of PXDN with the tumor microenvironment and infiltration of fibroblasts and different immune cells within different tumors was explored, and the possible molecular mechanism of the effect was also discussed. Our results provide a comprehensive understanding of the carcinogenicity of PXDN in different tumors and suggest that PXDN may be a potential target for tumor immunotherapy, providing a new candidate that could improve cancer clinical diagnosis and treatment.