Liver stiffness in hepatocellular carcinoma and chronic hepatitis patients: Hepatitis B virus infection and transaminases should be considered.

BACKGROUND: Liver condition is a crucial prognostic factor for patients with hepatocellular carcinoma (HCC), but a convenient and comprehensive method to assess liver condition is lacking. Liver stiffness (LS) measured by two-dimensional shear wave elastography may help in assessing liver fibrosis and liver condition. Chronic hepatitis B (CHB) is an important risk factor for HCC progression, but LS was found to be less reliable in assessing liver fibrosis following hepatitis viral eradication. We hypothesize that the status of hepatitis virus infection would affect the accuracy of LS in assessing the liver condition. AIM: To test the feasibility and impact factors of using LS to assess liver condition in patients with HCC and CHB. METHODS: A total of 284 patients were retrospectively recruited and classified into two groups on the basis of serum CHB virus hepatitis B virus (HBV)-DNA levels [HBV-DNA ≥ 100.00 IU/mL as Pos group (n = 200) and < 100.00 IU/mL as Neg group (n = 84)]. Correlation analyses and receiver operating characteristic analyses were conducted to evaluate the relationship between LS and liver condition. RESULTS: A significant correlation was found between LS and most of the parameters considered to have the ability to evaluate liver condition (P < 0.05). When alanine aminotransferase (ALT) concentrations were normal (≤ 40 U/L), LS was correlated with liver condition indices (P < 0.05), but the optimal cutoff of LS to identify a Child-Pugh score of 5 was higher in the Neg group (9.30 kPa) than the Pos group (7.40 kPa). When ALT levels were elevated (> 40 U/L), the correlations between LS and liver condition indices were not significant (P > 0.05). CONCLUSION: LS was significantly correlated with most liver condition indices in patients with CHB and HCC. However, these correlations varied according to differences in HBV-DNA and transaminase concentrations.

H19 lncRNA triggers ferroptosis, exacerbating ox-LDL-induced artery endothelial cell damage in vitro.

BACKGROUND: The precise association between lncRNA H19 and ferroptosis in the context of atherosclerosis remains uncertain. OBJECTIVE: This study is to clarify the underlying process and propose novel approaches for the advancement of therapeutic interventions targeting atherosclerosis. METHODS: Assessment of ferroptosis, which entails the evaluation of cell viability using CCK-8 and the quantification of intracellular MDA, GSH, and ferrous ions. Simultaneously, the protein expression levels of assessed by western blot analysis, while the expression level of lncRNA H19 was also determined. Furthermore, HAECs that were cultured with ox-LDL were subjected to Fer-1 interference. HAECs were exposed to ox-LDL and then transfected with H19 shRNA and H19 overexpression vector pcDNA3.1. The level of ferroptosis in the cells was then measured. Then, HAECs were subjected to incubation with ox-LDL, followed by transfection with H19 shRNA and treated with Erastin to assess the levels of ferroptosis, cell viability, and inflammatory factor production. and the ability for blood vessel development. RESULTS: The survival rate of HAECs in the ox-LDL group was much lower. Ox-LDL resulted in an upregulation of ACSL4 expression in HAECs, while the expression of SLC7A11 and GPX4 decreased. CONCLUSIONS: lncRNA H19 enhances ferroptosis and exacerbates arterial endothelial cell damage induced by LDL.

A double-blind pilot study of oral baricitinib in adult patients with lupus erythematosus panniculitis.

Lupus erythematosus panniculitis (LEP) is a chronic inflammatory skin disease with a significant impact on the overall well-being of patients. The safety and efficacy of oral baricitinib for the treatment of LEP have not been studied. This study aimed to explore the efficacy of oral baricitinib in patients with LEP who are recalcitrant or intolerant to conventional therapies. Patients (aged ≥18 years) with active LEP (with a revised cutaneous lupus erythematosus disease area and severity index [RCLASI]-active score ≥4] were randomly assigned 2:1 to baricitinib (4 mg) or placebo (once daily for 20 weeks). The placebo group was switched to baricitinib (4 mg) at week 13, and the final evaluation was conducted at week 24. The primary endpoint was the proportion of patients with an RCLASI-A score decreased by 20% at week 12. The secondary endpoints included the changes in the Cutaneous Lupus Erythematosus Disease Area and Severity Index active-(CLASI-A) score, the Dermatology Life Quality Index (DLQI), the Physician's Global Assessment (PGA) score, and safety. Five patients were enrolled. Three patients received baricitinib (4 mg), and two patients were treated with placebo. Two patients in the baricitinib treatment group showed a significant RCLASI-A decrease at week 12 and week 24. Two patients in the placebo group had no change in RCLASI-A at week 12 and a significant decrease at week 24. No new safety events were observed. Treatment with baricitinib was effective and well tolerated in patients with LEP.

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.

Molecular recognition with resolution below 0.2 angstroms through thermoregulatory oscillations in covalent organic frameworks.

Crystalline materials with uniform molecular-sized pores are desirable for a broad range of applications, such as sensors, catalysis, and separations. However, it is challenging to tune the pore size of a single material continuously and to reversibly distinguish small molecules (below 4 angstroms). We synthesized a series of ionic covalent organic frameworks using a tetraphenoxyborate linkage that maintains meticulous synergy between structural rigidity and local flexibility to achieve continuous and reversible (100 thermal cycles) tunability of "dynamic pores" between 2.9 and 4.0 angstroms, with resolution below 0.2 angstroms. This results from temperature-regulated, gradual amplitude change of high-frequency linker oscillations. These thermoelastic apertures selectively block larger molecules over marginally smaller ones, demonstrating size-based molecular recognition and the potential for separating challenging gas mixtures such as oxygen/nitrogen and nitrogen/methane.

Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods. Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases. Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated. Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.

Safety and adherence of bictegravir/emtricitabine/tenofovir alafenamide for HIV post-exposure prophylaxis among adults in Guiyang China: a prospective cohort study.

BACKGROUND: The effectiveness of post-exposure prophylaxis (PEP) depends on participants adherence, making it crucial to assess and compare regimen options to enhance human immunodeficiency virus (HIV) prophylaxis strategies. However, no prospective study in China has shown that the completion rate and adherence of single-tablet regimens in HIV PEP are higher than those of multi-tablet preparations. Therefore, this study aimed to assess the completion rate and adherence of two HIV PEP regimens. METHODS: In this single-center, prospective, open-label cohort study, we included 179 participants from May 2022 to March 2023 and analyzed the differences in the 28-day medication completion rate, adherence, safety, tolerance, and effectiveness of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and tenofovir disoproxil fumarate, emtricitabine, and dolutegravir (TDF/FTC + DTG). RESULTS: The PEP completion rate and adherence were higher in the BIC/FTC/TAF group than in the TDF/FTC + DTG group (completion rate: 97.8% vs. 82.6%, P = 0.009; adherence: 99.6 ± 2.82% vs. 90.2 ± 25.29%, P = 0.003). The incidence of adverse reactions in the BIC/FTC/TAF and TDF/FTC + DTG groups was 15.2% and 10.3% (P = 0.33), respectively. In the TDF/FTC + DTG group, one participant stopped PEP owing to adverse reactions (1.1%). No other participants stopped PEP due to adverse events. CONCLUSIONS: BIC/FTC/TAF and TDF/FTC + DTG have good safety and tolerance as PEP regimens. BIC/FTC/TAF has a higher completion rate and increased adherence, thus, is recommended as a PEP regimen. These findings emphasize the importance of regimen choice in optimizing PEP outcomes. TRIAL REGISTRATION: The study was registered in the Chinese Clinical Trial Registry (registration number: ChiCTR2200059994(2022-05-14), https://www.chictr.org.cn/bin/project/edit?pid=167391 ).

CD4+CD57+ senescent T cells as promoters of systemic lupus erythematosus pathogenesis and the therapeutic potential of senolytic BCL-2 inhibitor.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by persistent activation of immune cells and overproduction of autoantibodies. The accumulation of senescent T and B cells has been observed in SLE and other immune-mediated diseases. However, the exact mechanistic pathways contributing to this process in SLE remain incompletely understood. In this study, we found that in SLE patients: (1) the frequency of CD4+CD57+ senescent T cells was significantly elevated and positively correlated with disease activity; (2) the expression levels of B-lymphoma-2 (BCL-2) family and interferon-induced genes (ISGs) were significantly upregulated; and (3) in vitro, the cytokine IL-15 stimulation increased the frequency of senescent CD4+ T cells and upregulated the expression of BCL-2 family and ISGs. Further, treatment with ABT-263 (a senolytic BCL-2 inhibitor) in MRL/lpr mice resulted in decreased: (1) frequency of CD4+CD44hiCD62L-PD-1+CD153+ senescent CD4+ T cells; (2) frequency of CD19+CD11c+T-bet+ age-related B cells; (3) level of serum antinuclear antibody; (4) proteinuria; (5) frequency of Tfh cells; and (6) renal histopathological abnormalities. Collectively, these results indicated a dominant role for CD4+CD57+ senescent CD4+ T cells in the pathogenesis of SLE and senolytic BCL-2 inhibitor ABT-263 may be the potential treatment in ameliorating lupus phenotypes.

Nanogalvanic Cells Release Highly Reactive Electrons in Tumors to Effectively Eliminate Tumors.

External stimuli triggering chemical reactions in cancer cells to generate highly reactive chemical species are very appealing for cancer therapy, in which external irradiation activating sensitizers to transfer energy or electrons to surrounding oxygen or other molecules is critical for generating cytotoxic reactive species. However, poor light penetration into tissue, low activity of sensitizers, and reliance on oxygen supply restrict the generation of cytotoxic chemical species in hypoxic tumors, which lowers the therapeutic efficacy. Here, this work presents galvanic cell nanomaterials that can directly release highly reactive electrons in tumors without external irradiation or photosensitizers. The released reactive electrons directly react with surrounding biomolecules such as proteins and DNA within tumors to destroy them or react with other surrounding (bio)molecules to yield cytotoxic chemical species to eliminate tumors independent of oxygen. Administering these nanogalvanic cells to mice results in almost complete remission of subcutaneous solid tumors and deep metastatic tumors. The results demonstrate that this strategy can further arouse an immune response even in a hypoxic environment. This method offers a promising approach to effectively eliminate tumors, similar to photodynamic therapy, but does not require oxygen or irradiation to activate photosensitizers.

Comparative Studies on the Efficacy and Safety of Ainuovirine-Based Versus Efavirenz-Based Antiretroviral Therapy in the Management of Persons Living with HIV: A Real-World Study in Guizhou, China.

In China, non-nucleoside reverse transcriptase inhibitors (NNRTIs) are integral to the antiretroviral therapy (ART) regimen for persons living with HIV (PWH), comprising over 80% of such treatments. To broaden treatment options and improve therapeutic effectiveness, Ainuovirine (ANV), a new NNRTI, was authorized for ART in 2021. Nevertheless, the clinical efficacy of ANV and its impact on blood biochemical markers remain somewhat underexplored. This study seeks to evaluate ANV's clinical performance in ART and its influence on relevant treatment parameters. A retrospective analysis was performed on 208 patients treated with an ANV-based regimen from July 2021 to July 2023, monitoring indicator changes from baseline to week 24. The primary endpoint was the proportion of participants achieving HIV-1 RNA levels of less than 50 copies/mL by week 24. Secondary endpoints involved assessing variations in CD4+ T cell counts and blood biochemical markers from baseline. These outcomes were also compared with data from 241 patients treated with an Efavirenz (EFV)-based regimen in the same time frame. The findings suggest that the ANV-based regimen is as effective as the EFV-based regimen in viral suppression (p > .05) and offers superior improvements in lipid profiles, liver function, and immune system indicators, alongside fewer adverse reactions. These results affirm ANV's efficacy and safety as an antiretroviral therapy option, offering Acquired Immune Deficiency Syndrome patients a wider array of treatment possibilities and the potential for better treatment outcomes.

Multi-omics study reveals different pathogenesis of the generation of skin lesions in SLE and IDLE patients.

Lupus erythematosus (LE) is a heterogeneous, antibody-mediated autoimmune disease. Isolate discoid LE (IDLE) and systematic LE (SLE) are traditionally regarded as the two ends of the spectrum, ranging from skin-limited damage to life-threatening multi-organ involvement. Both belong to LE, but IDLE and SLE differ in appearance of skin lesions, autoantibody panels, pathological changes, treatments, and immunopathogenesis. Is discoid lupus truly a form of LE or is it a completely separate entity? This question has not been fully elucidated. We compared the clinical data of IDLE and SLE from our center, applied multi-omics technology, such as immune repertoire sequencing, high-resolution HLA alleles sequencing and multi-spectrum pathological system to explore cellular and molecular phenotypes in skin and peripheral blood from LE patients. Based on the data from 136 LE patients from 8 hospitals in China, we observed higher damage scores and fewer LE specific autoantibodies in IDLE than SLE patients, more uCDR3 sharing between PBMCs and skin lesion from SLE than IDLE patients, elevated diversity of V-J recombination in IDLE skin lesion and SLE PBMCs, increased SHM frequency and class switch ratio in IDLE skin lesion, decreased SHM frequency but increased class switch ratio in SLE PBMCs, HLA-DRB1*03:01:01:01, HLA-B*58:01:01:01, HLA-C*03:02:02:01, and HLA-DQB1*02:01:01:01 positively associated with SLE patients, and expanded Tfh-like cells with ectopic germinal center structures in IDLE skin lesions. These findings suggest a significant difference in the immunopathogenesis of skin lesions between SLE and IDLE patients. SLE is a B cell-predominate systemic immune disorder, while IDLE appears limited to the skin. Our findings provide novel insights into the pathogenesis of IDLE and other types of LE, which may direct more accurate diagnosis and novel therapeutic strategies.

Image-based remote evaluation of PASI scores with psoriasis by the YOLO-v4 algorithm.

As a chronic relapsing disease, psoriasis is characterized by widespread skin lesions. The Psoriasis Area and Severity Index (PASI) is the most frequently utilized tool for evaluating the severity of psoriasis in clinical practice. Nevertheless, long-term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time-consuming, subjective and prone to evaluation bias. To develop a deep learning system with high accuracy and speed to assist PASI evaluation, we collected 2657 high-quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO-v4 algorithm to establish the model via four modules, we also conducted a human-computer comparison through quadratic weighted Kappa (QWK) coefficients and intra-class correlation coefficients (ICC). The YOLO-v4 algorithm was selected for model training and optimization compared with the YOLOv3, RetinaNet, EfficientDet and Faster_rcnn. The model evaluation results of mean average precision (mAP) for various lesion features were as follows: erythema, mAP = 0.903; scale, mAP = 0.908; and induration, mAP = 0.882. In addition, the results of human-computer comparison also showed a median consistency for the skin lesion severity and an excellent consistency for the area and PASI score. Finally, an intelligent PASI app was established for remote disease assessment and course management, with a pleasurable agreement with dermatologists. Taken together, we proposed an intelligent PASI app based on the image YOLO-v4 algorithm that can assist dermatologists in long-term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time-saving and objective manner.

Magnetic superhydrophobic cellulose nanofibril based aerogel with rope-ladder like structure incorporating both superelasticity and excellent oil absorption.

Achieving an equilibrium between exceptional oil absorption and remarkable elasticity has emerged as a formidable challenge for magnetic porous materials designed for oil absorption. Here, we propose an original, magnetic and superhydrophobic cellulose nanofibril (CNF) based aerogel system with a rope-ladder like skeleton by to greatly improve the issue. Within this system, CNF as the skeleton was combined with multiwalled carbon nanotubes (MWCNT)@Fe3O4 as the magnetic and enhanced component, both methyltrimethoxysilane (MTMS) and acetonitrile-extracted lignin (AEL) as the soft-hard associating constituents. The resultant CNF based aerogel shows a rope-ladder like pore structure to contribute to high elasticity and excellent oil absorption (28.34-61.09 g/g for various oils and organic solvents) under the synergistic effect of Fe3O4@MWCNT, AEL and MTMS, as well as good specific surface area (27.97 m2/g), low density (26.4 mg/cm3). Notably, despite the introduced considerable proportion (0.5 times of mass-CNF) of Fe3O4@MWCNT, the aerogel retained an impressive compression-decompression rate (88%) and the oil absorption efficiency of above 87% for various oils due to the soft-hard associating structure supported by both MTMS and AEL. This study provides a prospective strategy to balance between high elasticity and excellent oil absorption of CNF based aerogel doping inorganic particles.

Clinical benefits of novel non-nucleoside reverse transcriptase inhibitors: A prospective cohort study.

INTRODUCTION: The efficacy and safety of ainuovirine+lamivudine+tenofovir (ANV+3TC+TDF) and efavirenz+lamivudine+tenofovir (EFV+3TC+TDF) have been confirmed in previous clinical trials; however, there are no related studies on patient-reported outcomes. This study aimed to evaluate the effectiveness and safety of these 2 antiretroviral therapy regimens and to understand the patient's symptom experience and subjective experience of sleep quality through patient-reported outcomes. METHODS: This is a single-center prospective cohort study with 243 patients evaluated from October 1, 2021 to June 30, 2022. Virological effectiveness and patient-reported outcomes results were analyzed. The primary endpoint was the proportion of HIV viral load <50 copies/mL (virological suppression rate) at 48 weeks and the changes in the HIV symptom index and Pittsburgh sleep quality index. RESULTS: The virological suppression rates in the ANV+3TC+TDF and EFV+3TC+TDF groups were 83.6% (102/122) and 87.6% (106/121), respectively, at 48 weeks. In the ANV+3TC+TDF group, the scores of HIV symptom index and pittsburgh sleep quality index in the 48th week were lower than the baseline level (p < 0.05). Logistic regression results showed that the baseline regimen EFV+3TC+TDF was a risk factor for dizziness/lightheadedness (odds ratio = 3.153, 95% confidence interval: 1.473-6.748, p = 0.003), sadness/depression odds ratio = 2.404, 95% confidence interval:1.188-4.871, p = 0.015), and difficulty sleeping (odds ratio = 2.802, 95% confidence interval: 1.437-5.463, p = 0.002) at 48 weeks. CONCLUSIONS: Both regimens showed good virological effectiveness; however, compared with ANV+3TC+TDF, the EFV+3TC+TDF regimen reduced the prevalence of HIV-related symptoms.

Efficacy and effect on lipid profiles of Ainuovirine-based regimen versus Efavirenz-based regimen in treatment-naïve people with HIV-1 at week 24: A real-world, retrospective, multi-center cohort study.

This study aimed to compare the efficacy and effect on lipid profiles of Ainuovirine (ANV)- and efavirenz (EFV) -based regimens in treatment-naïve people living with HIV-1 (PLWH) at week 24. The proportion of PLWH achieving HIV-1 RNA < the limit of quantification in the ANV group was significantly higher than that in the EFV group (89.18% vs. 76.04%, P = 0.002). The mean change of log10 HIV-1 RNA from baseline was greater (-4.34 vs. -4.18, P < 0.001), the median change from baseline in CD4+ T cell count increased more (106.00 cells/µL vs. 92.00 cells/µL, P = 0.007) in the ANV group, while the CD4+/CD8+ ratio was similar (0.15 vs. 0.20, P = 0.167) between the two groups. The mean changes from baseline in total cholesterol (-0.02 for ANV vs. 0.25 mmol/L for EFV, P < 0.001), triglyceride (-0.14 for ANV vs. 0.11 mmol/L for EFV, P = 0.024), and low-density lipoprotein cholesterol (-0.07 for ANV vs. 0.15 mmol/L for EFV, P < 0.001) was significantly different between the two groups. The percentage of patients with improved lipid profiles was significantly higher in the ANV group (37.44 %) than in the EFV group (29.55%, P = 0.0495). The incidence of any adverse events in the ANV group was significantly lower than that in the EFV group at week 12 (6.2% vs. 30.7%, P < 0.001) and was comparable at week 24 (3.6% vs. 5.5%, P = 0.28). The ANV-based regimen was well tolerated and lipid-friendly in treatment-naïve PLWH.

Exploring Blood Cell Count-Derived Ratios as Practical Diagnostic Tools for Scabies in Vulnerable Populations.

Scabies is a neglected tropical disease and represents a considerable global burden. Although consensus diagnostic criteria for scabies have been recently published, diagnosing scabies infestation remains challenging in clinical practice. We investigated the diagnostic utility of complete blood cell count (CBC) and CBC-derived ratios obtained at diagnosis in a set of 167 patients who are Vietnamese with confirmed scabies. These parameters were compared with those of patients with dermatophytosis (N = 800) and urticaria (N = 2023), two diseases frequent in Vietnam, which can present with similar skin manifestations to scabies and tend to pose a diagnostic challenge in vulnerable populations. Our analysis revealed that white blood cell, monocyte, and eosinophil counts were significantly higher among patients with scabies than the other two diseases. Similarly, the monocyte-to-lymphocyte ratio (MLR) and eosinophil-to-lymphocyte ratio (ELR) were significantly higher among patients with scabies. The optimal cut-off values to distinguish scabies from dermatophytosis and urticaria were 0.094 for ELR (sensitivity: 74.85%, specificity: 70.7%) and 0.295 for MLR (sensitivity: 52.69%, specificity: 73.54%). CBC, ELR, and MLR are low-cost and easily calculated parameters that may be helpful for the diagnosis of scabies.

Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.

BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. RESULTS: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). CONCLUSIONS: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.

Biological factors driving colorectal cancer metastasis.

Approximately 20% of colorectal cancer (CRC) patients present with metastasis at diagnosis. Among Stage I-III CRC patients who undergo surgical resection, 18% typically suffer from distal metastasis within the first three years following initial treatment. The median survival duration after the diagnosis of metastatic CRC (mCRC) is only 9 mo. mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue, allowing cancer cells to spread from primary to distant organs; however, increasing evidence suggests that the mCRC process can begin early in tumor development. CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations. Different genomic and nongenomic events can induce subclone diversity, which leads to cancer and metastasis. Throughout the course of mCRC, metastatic cascades are associated with invasive cancer cell migration through the circulatory system, extravasation, distal seeding, dormancy, and reactivation, with each step requiring specific molecular functions. However, cancer cells presenting neoantigens can be recognized and eliminated by the immune system. In this review, we explain the biological factors that drive CRC metastasis, namely, genomic instability, epigenetic instability, the metastatic cascade, the cancer-immunity cycle, and external lifestyle factors. Despite remarkable progress in CRC research, the role of molecular classification in therapeutic intervention remains unclear. This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.