Relationship Between Family History and Quality of Life in Patients with Psoriasis: A Cross-Sectional Study from China.

Purpose: The purpose of this study was to investigate the comprehensive impact of family history of psoriasis, lesion size, disease severity, and the possibility of joint involvement on patients' quality of life(QoL). Patients and Methods: Data from 5961 patients with psoriasis recruited from 440 hospitals throughout China were analyzed. The effects of family history of psoriasis, Body Surface Area(BSA), Psoriasis Area and Severity Index(PASI), and Psoriasis Epidemiology Screening Tool(PEST) on their Dermatology Life Quality Index(DLQI) were studied using a moderated chained mediated effects test. Results: A total of 912 patients (15.30%) had a family history of psoriasis, and 5071 patients (85.10%) had plaque psoriasis. In patients with plaque psoriasis, the variables of family history, PASI, PEST, and DLQI were positively correlated with each other. Additionally, in patients with other types of psoriasis, PASI was positively correlated with PEST and DLQI. Age was positively correlated with PASI and PEST and negatively correlated with DLQI in patients with plaque psoriasis; their Body Mass Index(BMI) and disease duration were in positive correlation with PASI and PEST. The mediation effect of PASI and PEST between family history and DLQI was remarkable in patients with plaque psoriasis and not in those with other types of psoriasis. BSA moderated the association between family history and PASI in patients with plaque psoriasis. Conclusion: PASI and PEST play a chain mediating role in the relationship between family history and DLQI in patients with plaque psoriasis, and high levels of BSA increase the ability of family history to positively predict PASI in plaque psoriasis, thereby affecting the patient's QoL.

Expression and function of VISTA on myeloid cells.

V-domain containing Ig Suppressor of T cell Activation (VISTA) is predominantly expressed on myeloid cells and functions as a ligand/receptor/soluble molecule. In inflammatory responses and immune responses, VISTA regulates multiple functions of myeloid cells, such as chemotaxis, phagocytosis, T cell activation. Since inflammation and immune responses are critical in many diseases, VISTA is a promising therapeutic target. In this review, we will describe the expression and function of VISTA on different myeloid cells, including neutrophils, monocytes, macrophages, dendritic cells (DCs), myeloid-derived suppressor cells (MDSCs). In addition, we will discuss whether the functions of VISTA on these cells impact the disease processing.

A novel risk signature based on liquid-liquid phase separation-related genes reveals prognostic and tumour microenvironmental features in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma(ccRCC) is one of the most common malignancies. However, there are still many barriers to its underlying causes, early diagnostic techniques and therapeutic approaches. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA)- Kidney renal clear cell (KIRC) cohort differentially analysed liquid-liquid phase separation (LLPS)-related genes from the DrLLPS website. Univariate and multivariate Cox regression analyses and LASSO regression analyses were used to construct prognostic models. The E-MTAB-1980 cohort was used for external validation. Then, potential functions, immune infiltration analysis, and mutational landscapes were analysed for the high-risk and low-risk groups. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) experiments as well as single-cell analyses validated the genes key to the model. RESULTS: We screened 174 LLPS-related genes in ccRCC and constructed a risk signature consisting of five genes (CLIC5, MXD3, NUF2, PABPC1L, PLK1). The high-risk group was found to be associated with worse prognosis in different subgroups. A nomogram constructed by combining age and tumour stage had a strong predictive power for the prognosis of ccRCC patients. In addition, there were differences in pathway enrichment, immune cell infiltration, and mutational landscapes between the two groups. The results of qRT-PCR in renal cancer cell lines and renal cancer tissues were consistent with the biosignature prediction. Three single-cell data of GSE159115, GSE139555, and GSE121636 were analysed for differences in the presence of these five genes in different cells. CONCLUSIONS: We developed a risk signature constructed based on the five LLPS-related genes and can have a high ability to predict the prognosis of ccRCC patients, further providing a strong support for clinical decision-making.

Cytokines and soluble mediators as architects of tumor microenvironment reprogramming in cancer therapy.

Navigating the intricate landscape of the tumor microenvironment (TME) unveils a pivotal arena for cancer therapeutics, where cytokines and soluble mediators emerge as double-edged swords in the fight against cancer. This review ventures beyond traditional perspectives, illuminating the nuanced interplay of these elements as both allies and adversaries in cancer dynamics. It critically evaluates the evolving paradigms of TME reprogramming, spotlighting innovative strategies that target the sophisticated network of cytokines and mediators. Special focus is placed on unveiling the therapeutic potential of novel cytokines and mediators, particularly their synergistic interactions with extracellular vesicles, which represent underexplored conduits for therapeutic targeting. Addressing a significant gap in current research, we explore the untapped potential of these biochemical players in orchestrating immune responses, tumor proliferation, and metastasis. The review advocates for a paradigm shift towards exploiting these dynamic interactions within the TME, aiming to transcend conventional treatments and pave the way for a new era of precision oncology. Through a critical synthesis of recent advancements, we highlight the imperative for innovative approaches that harness the full spectrum of cytokine and mediator activities, setting the stage for breakthrough therapies that offer heightened specificity, reduced toxicity, and improved patient outcomes.

Exogenous drugs-induced mouse models of atopic dermatitis.

Atopic dermatitis (AD) is an inflammatory skin disease characterized by intense pruritus. AD is harmful to both children and adults, but its pathogenic mechanism has yet to be fully elucidated. The development of mouse models for AD has greatly contributed to its study and treatment. Among these models, the exogenous drug-induced mouse model has shown promising results and significant advantages. Until now, a large amount of AD-related research has utilized exogenous drug-induced mouse models, leading to notable advancements in research. This indicates the crucial significance of applying such models in AD research. These models exhibit diverse characteristics and are highly complex. They involve the use of various strains of mice, diverse types of inducers, and different modeling effects. However, there is currently a lack of comprehensive comparative studies on exogenous drug-induced AD mouse models, which hinders researchers' ability to choose among these models. This paper provides a comprehensive review of the features and mechanisms associated with various exogenous drug-induced mouse models, including the important role of each cytokine in AD development. It aims to assist researchers in quickly understanding models and selecting the most suitable one for further investigation.

Transformer guided self-adaptive network for multi-scale skin lesion image segmentation.

BACKGROUND: In recent years, skin lesion has become a major public health concern, and the diagnosis and management of skin lesions depend heavily on the correct segmentation of the lesions. Traditional convolutional neural networks (CNNs) have demonstrated promising results in skin lesion segmentation, but they are limited in their ability to capture distant connections and intricate features. In addition, current medical image segmentation algorithms rarely consider the distribution of different categories in different regions of the image and do not consider the spatial relationship between pixels. OBJECTIVES: This study proposes a self-adaptive position-aware skin lesion segmentation model SapFormer to capture global context and fine-grained detail, better capture spatial relationships, and adapt to different positional characteristics. The SapFormer is a multi-scale dynamic position-aware structure designed to provide a more flexible representation of the relationships between skin lesion characteristics and lesion distribution. Additionally, it increases skin lesion segmentation accuracy and decreases incorrect segmentation of non-lesion areas. INNOVATIONS: SapFormer designs multiple hybrid transformers for multi-scale feature encoding of skin images and multi-scale positional feature sensing of the encoded features using a transformer decoder to obtain fine-grained features of the lesion area and optimize the regional feature distribution. The self-adaptive feature framework, built upon the transformer decoder module, dynamically and automatically generates parameterizations with learnable properties at different positions. These parameterizations are derived from the multi-scale encoding characteristics of the input image. Simultaneously, this paper utilizes the cross-attention network to optimize the features of the current region according to the features of other regions, aiming to increase skin lesion segmentation accuracy. MAIN RESULTS: The ISIC-2016, ISIC-2017, and ISIC-2018 datasets for skin lesions are used as the basis for the experiment. On these datasets, the proposed model has accuracy values of 97.9 %, 94.3 %, and 95.7 %, respectively. The proposed model's IOU values are, in order, 93.2 %, 86.4 %, and 89.4 %. The proposed model's DSC values are 96.4 %, 92.6 %, and 94.3 %, respectively. All three metrics surpass the performance of the majority of state-of-the-art (SOTA) models. SapFormer's metrics on these datasets demonstrate that it can precisely segment skin lesions. Notably, our approach exhibits remarkable noise resistance in non-lesion areas, while simultaneously conducting finer-grained regional feature extraction on the skin lesion image. CONCLUSIONS: In conclusion, the integration of a transformer-guided position-aware network into semantic skin lesion segmentation results in a notable performance boost. The ability of our proposed network to capture spatial relationships and fine-grained details proves beneficial for effective skin lesion segmentation. By enhancing lesion localization, feature extraction, quantitative analysis, and classification accuracy, the proposed segmentation model improves the diagnostic efficiency of skin lesion analysis on dermoscopic images. It assists dermatologists in making more accurate and efficient diagnoses, ultimately leading to better patient care and outcomes. This research paves the way for advances in diagnosing and treating skin lesions, promoting better understanding and decision-making in the clinical setting.

Novel Lanthanide Complexes Synthesized from 3-Dimethylamino Benzoic Acid and 5,5'-Dimethyl-2,2' Bipyridine Ligand: Crystal Structure, Thermodynamics, and Fluorescence Properties.

Two isostructural lanthanide complexes were synthesized by solvent evaporation with 3-dimethylaminobenzoic acid and 5,5'-dimethyl-2,2'-bipyridine as ligands. The general formula of the structure is a [Ln(3-N,N-DMBA)3(5,5'-DM-2,2'-bipy)]2·2(3-N,N-DMHBA), Ln = (Gd(1), Tb(2)), 3-N,N-DMBA = 3-Dimethylamino benzoate, 5,5'-DM-2,2'-bipy = 5,5'-dimethyl-2,2' bipyridine. Both complexes exhibited dimeric structures based on X-ray diffraction analysis. At the same time, infrared spectroscopy and Raman spectroscopy were used to measure the spectra of the complex. A thermogravimetric infrared spectroscopy experiment was performed to investigate the thermal stability and decomposition mechanism of the complexes. Measurements of the low-temperature heat capacity of the complexes were obtained within the temperature range of 1.9 to 300 K. The thermodynamic function was calculated by heat capacity fitting. In addition, the fluorescence spectra of complex 2 were studied and the fluorescence lifetime values were determined, and the energy transfer mechanism of complex 2 was elucidated.

Polysaccharide extracted from the Sargassum fusiforme induces cell cycle arrest and apoptosis of B16F10 melanoma cells through the PI3K/AKT pathway.

BACKGROUND: SARGASSUM FUSIFORME: (S. fusiforme) is a brown alga that has been utilized as a medicine for a long time. Polysaccharides extracted from S. fusiforme demonstrate antitumor activities. METHODS: The impact of S. fusiforme polysaccharides (SFPS 191,212) on the proliferation, apoptosis, and cell cycle kinetics of B16F10 murine melanoma cells were thoroughly investigated in this work. The anticancer activities of the SFPS 191,212 compounds were assayed in the B16F10 cells at both transcriptional and translational levels. RESULTS: The compound exhibited concentration-dependent effects. Moreover, SPFS 191,212 increased the numbers of apoptotic cells and arrested the cell cycle in the S phase of the quantitative real-time PCR. From western blotting, it was verified that the SFPS 191,212 treatment improved the expression of Bax, Caspase-9, and Caspase-3 genes and proteins, while it reduced phosphatidylinositol 3 kinase and Bcl-2 genes and proteins, suggesting the involvement of mitochondria. CONCLUSION: Overall, SFPS 191,212 can be further explored as a potential functional food or adjuvant agent for the prevention or treatment of melanoma.

Fecal microbiota transplantation affects the recovery of AD-skin lesions and enhances gut microbiota homeostasis.

BACKGROUND: Accumulating evidence has shown that gut microbiota plays a key role in the progression of atopic dermatitis (AD). Fecal microbiota transplantation (FMT), as an effective method to restore gut microbiota homeostasis, has been successfully applied for treating many inflammatory diseases. However, the therapeutic effect of FMT on AD remains unclear. The following study examined the effect and mechanism of FMT on AD-skin lesions in an AD mouse model. METHODS: In this study, we exposed the shaved back skin of BALB/c mice to calcipotriol (MC903) to induce AD model. Mice were then treated with FMT, which was performed with gut microbiota from healthy mice. The gut microbiota of treated mice was tracked by 16S rRNA gene sequencing. Mice skin tissues were examined by histopathology and inflammatory cytokines change in serum by ELISA. RESULTS: FMT had a faster trend on the reversion of the increases in skin epidermal layer thicknesses and suppressed some of the representative inflammatory cytokines. The gut microbial community in the natural recovery process varied significantly in the FMT group at day 7 (ANOSIM P = 0.0229, r = 0.2593). Notably, FMT had a long-lasting and beneficial impact on the gut microbial compositions of AD mice by increasing the ratio of Firmicutes to Bacteroidetes and the amount of butyric-producing bacteria (BPB), including Erysipelotrichaceae, Lactobacillaceae, and Eubacteriacea. Furthermore, the relative abundances of gut microbiota-mediated functional pathways involved in the cell growth and death, amino acid, energy, lipid, and carbohydrate metabolisms, and immune system increased after FMT treatment. CONCLUSION: FMT modulated the gut microbiota homeostasis and affected the recovery from AD-related inflammations, suggesting that it could be used as a treatment strategy for AD patients in the clinic.

Chemotherapy induces immune checkpoint VISTA expression in tumor cells via HIF-2alpha.

Tumor cells can evade the innate and adaptive immune systems, which play important roles in tumor recurrence and metastasis. Malignant tumors that recur after chemotherapy are more aggressiveciscis, suggesting an increased ability of the surviving tumor cells to evade innate and adaptive immunity. Therefore, in order to reduce patient mortality, it is important to discover the mechanisms by which tumor cells develop resistance to chemotherapeutics. In the present study we focused on the tumor cells that survived chemotherapy. We found that chemotherapy could promote the expression of VISTA in tumor cells, and that this change was mediated by HIF-2α. In addition, VISTA overexpression on melanoma cells promoted immune evasion, and the application of the VISTA-blocking antibody 13F3 enhanced the therapeutic effect of carboplatin. These results offer an insight into the immune evasion of chemotherapy-resistant tumors, and provide a theoretical basis for the combined application of chemotherapy drugs and VISTA inhibitors to treat tumors.

Mechanisms underlying pathological scarring by fibroblasts during wound healing.

Pathological scarring is an abnormal outcome of wound healing, which often manifests as excessive proliferation and transdifferentiation of fibroblasts (FBs), and excessive deposition of the extracellular matrix. FBs are the most important effector cells involved in wound healing and scar formation. The factors that promote pathological scar formation often act on the proliferation and function of FB. In this study, we describe the factors that lead to abnormal FB formation in pathological scarring in terms of the microenvironment, signalling pathways, epigenetics, and autophagy. These findings suggest that understanding the causes of abnormal FB formation may aid in the development of precise and effective preventive and treatment strategies for pathological scarring that are associated with improved quality of life of patients.

Clinical and HRCT features of amyopathic dermatomyositis associated with interstitial lung disease: A retrospective study of 128 patients with connective tissue disease-related interstitial lung disease.

BACKGROUND: This study retrospectively analyzed the laboratory data and chest images of patients with amyopathic dermatomyositis associated with interstitial lung disease (ADM-ILD) and patients with other connective tissue disease-related ILDs (CTD-ILDs) to find a characteristic index for the early recognition of ADM-ILD and help clinicians consider the possibility of ADM-ILD as soon as possible. METHODS: In our cohort study, the records of 128 Chinese patients with CTD-ILD, including 33 ADM-ILD patients, 37 rheumatoid arthritis (RA)-ILD patients, 33 primary Sjogren's syndrome (pSS)-ILD patients, 14 systemic sclerosis (SSc)-ILD patients and 11 systemic lupus erythematosus (SLE)-ILD patients. The patients' clinical features, laboratory parameters, and chest HRCT findings were analyzed. RESULTS: ADM-ILD patients generally had significantly higher LDH (333.52±160.21 U/L), AST (66.21±83.66 U/L), and CK-MB (18.23±8.28 U/L) levels than other CTD-ILD patients. A total of 90.91% (30/33) of ADM-ILD patients had elevated LDH. Patients with ADM-ILD were more prone to organizing pneumonia radiologic patterns on chest HRCT scans than patients with other CTD-ILDs (χ2=37.39, p < 0.001) and were found in 18 of 33 ADM-ILD patients. Anti-MDA5 (45.45%) was the most commonly detected autoantibody in ADM-ILD patients, followed by anti-PL-7 (21.21%), anti-Jo-1 (12.12%), and anti-PL-12 (9.09%), and levels of ALT (96.93±119.79 vs. 17.50±6.218 U/L), AST (113.00±106.13 vs. 23.56±6.91 U/L), LDH (415.00±198.51 vs. 261.94±67.75 U/L) and CK-MB (22.57±5.91 vs. 14.61±8.36 U/L) were significantly higher in anti-MDA5-positive patients, but these patients had significantly lower WBC counts (4.82±2.61 vs. 7.14±3.00 × 109/L), lymphocyte counts (0.72±0.20 vs. 1.23±0.53 × 109/L), and ALB levels (31.90±4.76 vs. 35.49±4.71 g/L). CONCLUSIONS: ADM-ILD patients have higher serum LDH, AST and CK-MB levels, especially serum LDH levels, and are more prone to organizing pneumonia radiologic patterns on chest HRCT scans than other CTD-ILD patients. A high level of serum LDH with ILD may be a useful characteristic index for recognizing ADM-ILD.

Serum Leukocyte Cell-Derived Chemotaxin 2 (LECT2) Level Is Associated with Osteoporosis.

OBJECTIVE: The aim of this study was to examine serum leukocyte cell-derived chemotaxin 2 (LECT2) levels in osteoporosis subjects to confirm its association with osteoporosis. METHODS: A total of 204 adult subjects were recruited. Bone mineral densities (BMD) were assessed and blood samples were collected for measurements of biomedical parameters and the bone turnover markers. Serum LECT2 levels were measured by enzyme-linked immunosorbent assay. The relationships between serum LECT2 levels and other parameters were analyzed using the Spearman correlation coefficient. RESULTS: Serum LECT2 levels were significantly increased in osteoporosis subjects over controls. We found a significantly negative correlation of serum LECT2 with BMD, 25-hydroxy-vitamin D, and creatinine and a significantly positive correlation with C-terminal telopeptide of type 1 collagen and total cholesterol. CONCLUSION: Serum LECT2 levels were significantly upregulated in osteoporosis subjects and correlated with the severity of bone loss. Serum LECT2 could be a potential biomarker to assess the risk of bone loss.

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer.

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

An improved transformer network for skin cancer classification.

BACKGROUND: Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts. OBJECTIVES: As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed. INNOVATIONS: To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible. MAIN RESULTS: The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans. CONCLUSIONS: The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.

Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution.

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.

Value of a Signature of Immune-Related Genes in Predicting the Prognosis of Melanoma and Its Responses to Immune Checkpoint Blocker Therapies.

Melanoma is becoming increasingly common worldwide, with high rates of transformation into malignancy compared to other skin lesions. The prognosis of patients with melanoma at an advanced stage is highly unsatisfying despite the development of immunotherapy, target therapy, or combinative therapy. The major barrier to exploiting immune checkpoint therapies and achieving the best benefits clinically is resistance that can easily develop if regimens are not selected appropriately. In this study, we investigated the possibility of using immune-related genes to predict patient survival and their responses to immune checkpoint blocker therapies with the expression profiles available at The Cancer Genome Atlas (TCGA) Program plus expression data from the Gene Expression Omnibus (GEO) for validation. A five gene signature that is highly correlated with the local infiltration of cytotoxic lymphocytes in the tumor microenvironment was identified, and a scoring model was developed with stepwise regression after multivariate Cox analyses. The score calculated strongly correlates with Breslow depth, and this model effectively predicts the prognosis of patients with melanoma, whether primary or metastasized. It also depicts the heterogenous immune-related nature of melanoma by revealing different predicted responses to immune checkpoint blocker therapies through its correlation to tumor immune dysfunction and exclusion (TIDE) score.

Adverse and unconventional reactions related to immune checkpoint inhibitor therapy for cancer.

Immunotherapy is an emerging method for the treatment of cancer. Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that block immune checkpoint pathways and release the body's anti-tumor immunity. They consist mainly of antibodies against cytotoxic T lymphocyte associated antigen-4 (CTLA-4), programmed death receptor 1 (PD-1), and programmed death ligand 1 (PD-L1). Although ICI therapy has been shown to be effective at treating cancer, it can also destroy immune tolerance and lead to organ toxicity. These unwanted side effects are known as immune related adverse events (irAEs). ICI treatment can also cause unconventional reactions such as pseudoprogression and hyperprogression. Pseudoprogression looks like an increase in the tumor parenchyma but is actually a temporary inflammation in the tumor; hyperprogression refers to the acceleration of tumor growth after the start of immunotherapy. Understanding the mechanisms of these two phenomena and distinguishing their differences are necessary for the effective prevention and treatment of unconventional reactions.

Anxiety and depression are associated with reduced quality of life and increased cough severity in chronic cough.

BACKGROUND: Chronic cough has led to a substantial socioeconomic burden globally. Psychiatric comorbidities are reported in many chronic diseases. However, the relationship between mental disorders and chronic cough remains unclear. OBJECTIVE: This study aims to explore the relationship between anxiety, depression and chronic cough. METHODS: 238 patients (96 males and 142 females) with chronic cough were enrolled in this study. Responses were collected using the Cough Visual Analog Scale, the Hospital Anxiety and Depression Scale (HADS), and the Leicester Cough Questionnaire. RESULTS: According to the HADS, 9.2% and 6.3% of patients were identified as having anxiety and depression, respectively. Patients with anxiety and depression were more likely to have a reduced quality of life. Cough duration, cough severity and history of anaphylaxis were found to be positively associated with reduced quality of life in patients with chronic cough. Cough severity was considered as a dependent risk factor for symptoms of anxiety and depression. Also, more severe symptoms of anxiety were observed in patients reported that a history of anaphylaxis. More female patients had a history of anaphylaxis and reduced cough-related quality of life. CONCLUSIONS: Symptoms of anxiety and depression, longer cough duration, more severe cough and a history of anaphylaxis may reduce the quality of life in patients with chronic cough. Cough severity and a history of anaphylaxis are associated with symptoms of anxiety.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.

Introduction: Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. Methods: This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. Results: To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. Discussion: The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.