J Vitreoretin Dis. 2025 Feb 27:24741264251322213. doi: 10.1177/24741264251322213. Online ahead of print.

ABSTRACT

Purpose: To evaluate the early real-world clinical outcomes regarding the safety and efficacy after administration of a ranibizumab biosimilar (Ranieyes). Methods: This multicenter retrospective uncontrolled observational study incorporated data from 7 centers in India. All patients were treated with at least 1 intravitreal injection of 0.5 mg of ranibizumab biosimilar between July 2022 and July 2023 for various indications. Results: A total of 474 ranibizumab biosimilar injections were given in 268 eyes of 254 patients. Indications were diabetic macular edema (DME) (n = 112), macular neovascularization (MNV) (n = 92), retinal vein occlusion (RVO) (n = 54), cystoid macular edema (n = 4), and proliferative diabetic retinopathy with vitreous hemorrhage (n = 6). The mean logMAR BCVA (±SD) improved significantly from baseline to the last follow-up as follows: DME cases, from 0.77 ± 0.37 (Snellen equivalent, 6/36) to 0.43 ± 0.25 (6/15) (z = -8.0; r = -0.8); MNV cases, from 0.95 ± 0.53 (6/60) to 0.59 ± 0.42 (6/24) (z = -7.1; r = -0.8); RVO cases, from 0.83 ± 0.40 (6/45) to 0.44 ± 0.32 (6/15) (z = -5.5; r = -0.8) (all P < .001). All groups also had significant improvement in the central subfield thickness (all P < .001). No site reported drug-related adverse events (eg, inflammation, vasculitis, systemic adverse effects). Conclusions: The preliminary real-world data from this limited early series suggest that Ranieyes has clinical efficacy and is safe as a ranibizumab biosimilar across the approved indications.

PMID:40028177 | PMC:PMC11869221 | DOI:10.1177/24741264251322213

Cureus. 2025 Jan 29;17(1):e78185. doi: 10.7759/cureus.78185. eCollection 2025 Jan.

ABSTRACT

Introduction A combination of ipilimumab/nivolumab has demonstrated a median overall survival (mOS) of 71.9 months in advanced melanoma, establishing it as the standard first-line (1L) therapy. However, the approval of this combination by the Portuguese Regulatory Authority occurred 76 months after its approval by the European Authority, leaving tyrosine kinase inhibitors as the only 1L option available for the BRAF-mutated melanoma population. Our study aims to evaluate real-world data from patients with advanced melanoma and assess the potential prognostic impact of the delayed availability of ipilimumab/nivolumab combination therapy on this population. Methods This was an observational, retrospective cohort study conducted at a Portuguese Comprehensive Cancer Center. The study included adult patients with melanoma who received innovative therapies in the 1L between May 2016 and December 2021 and who would meet the criteria for treatment with ipilimumab/nivolumab. The primary outcome measure was mOS; secondary outcome measures included median progression-free survival (mPFS), objective response rate (ORR), and safety data. Results Our study included 172 patients, of which 50% were male, and 32.6% (n = 56) had BRAF-mutated melanoma. In 1L setting, 70.9% received anti-programmed cell death protein 1 (anti-PD-1) monotherapy, while the rest were treated with targeted therapies. The median follow-up time was 57 months. Patients treated with anti-PD-1 had ORR of 36.0%, mPFS of seven months (95% CI 2.9-11.1), and mOS of 19 months (95% CI 7.5-30.4). Among patients treated with targeted therapies, the ORR was 56.0%, mPFS seven months (95% CI 5.1-8.9), and mOS 14 months (95% CI 5.9-22.1). In our population, 10% presented grade 3 or higher adverse events, with no drug-related deaths reported. Conclusion These findings underscore significant disparities in access to innovative therapies in Portugal, which may have adversely impacted patients' outcomes. The delay raises ethical concerns regarding equity in healthcare access and highlights the need for policy measures to expedite the approval and availability of life-extending treatments.

PMID:40027067 | PMC:PMC11870778 | DOI:10.7759/cureus.78185

Respir Med Case Rep. 2025 Feb 10;54:102179. doi: 10.1016/j.rmcr.2025.102179. eCollection 2025.

ABSTRACT

Pembrolizumab is an immune checkpoint inhibitor (ICI) of programmed cell death-1 category, used for treating various types of cancer. ICIs can sometimes result in immune-related adverse events. Allergic bronchopulmonary mycosis (ABPM) induced by ICI has rarely been reported. We hereby report the case of an 83-year-old woman who experienced non-Aspergillus ABPM with eosinophilia induced by pembrolizumab that had been prescribed for treating bladder cancer. Steroid monotherapy with prednisone was successful and pembrolizumab could be resumed. Through the present case report, we urge the clinicians to be aware of the potential risk of ABPM as a T-helper type 2-associated immune-related adverse event.

PMID:40026847 | PMC:PMC11871464 | DOI:10.1016/j.rmcr.2025.102179

Antimicrob Steward Healthc Epidemiol. 2025 Feb 17;5(1):e53. doi: 10.1017/ash.2025.24. eCollection 2025.

ABSTRACT

OBJECTIVE: To compare the clinical outcomes of patients with lower limb osteomyelitis (LLOM) and negative methicillin-resistant Staphylococcus aureus (MRSA) cultures treated with anti-MRSA therapy (AMT) versus those treated with no-anti-MRSA therapy (NAMT).

DESIGN: Retrospective cohort study.

PATIENTS: Hospitalized adult (≥18 yr of age) patients admitted to multiple tertiary referral centers in a single healthcare system between April 1, 2017 and April 1, 2023, with LLOM and planned intravenous antibiotics for at least four weeks.

METHODS: Electronic medical records were queried for demographic information, admission dates, treatment strategies, imaging and culture results, and discharge diagnoses. Descriptive statistics measured baseline characteristics, imaging, and culture results.

RESULTS: Out of 473 patients, 64 met the inclusion criteria and 409 were excluded. Of the 64 patients, 26 (40%) had AMT and 38 (59%) had NAMT. A larger but statistically insignificant portion of patients in the NAMT cohort failed therapy (23% AMT vs 32% NAMT, P = 0.325). However, hospital readmission for LLOM within 180 days of antibiotic completion (46.2% vs 47%, P = 0.92), hospital length of stay (median (IQR): 6 (5-9) d vs 7 (5-12.5) d, P = 0.285), incidence of new renal replacement therapy initiation (0% vs 2.6%, P = 0.594), creatinine kinase levels (0 vs 2.6%, P = 0.594), and drug-induced immune thrombocytopenia (0% vs 5.3% P = 0.349) were comparable between the two cohorts.

CONCLUSIONS: Treatment failure rates and adverse events did not differ significantly among patients with LLOM treated with AMT or NAMT. Further investigation of determinants of clinical failures in LLOM may help optimize overall treatment.

PMID:40026767 | PMC:PMC11869046 | DOI:10.1017/ash.2025.24

J Brown Hosp Med. 2024 Oct 1;3(4):21-24. doi: 10.56305/001c.122729. eCollection 2024.

ABSTRACT

Turmeric and its active compound, curcumin, has gained popularity as an herbal supplement due to its anti-inflammatory properties. However, the lack of standardized regulation for herbal supplements raises concerns about potential side effects and toxicity. This case report presents a 53-year-old woman with Behçet disease who developed biopsy-proven drug-induced liver injury (DILI) after initiating a turmeric supplement, with resolution of laboratory abnormalities after a positive supplement de-challenge. This case highlights the importance of noting herbal supplementation during medication reconciliation and underscores the need for rigorous regulatory oversight to ensure the safety of such products.

PMID:40026546 | PMC:PMC11864403 | DOI:10.56305/001c.122729

Cardiovasc Ther. 2025 Feb 21;2025:5711316. doi: 10.1155/cdr/5711316. eCollection 2025.

ABSTRACT

Background: Cardiovascular diseases (CVDs) encompass a group of diseases that affect the heart and/or blood vessels, making them the leading cause of global mortality. In our study, we performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel therapeutic protein targets for CVDs and evaluate the potential drug-related protein side effects. Methods: We conducted a comprehensive proteome-wide MR study to assess the causal relationship between plasma proteins and the risk of CVDs. Summary-level data for 4907 circulating protein levels were extracted from a large-scale protein quantitative trait loci (pQTL) study involving 35,559 individuals. Additionally, genome-wide association study (GWAS) data for CVDs were extracted from the UK Biobank and the Finnish database. Colocalization analysis was utilized to identify causal variants shared between plasma proteins and CVDs. Finally, we conducted a comprehensive phenome-wide association study (PheWAS) using the R10 version of the Finnish database. This study was aimed at examining the potential drug-related protein side effects in the treatment of CVDs. A total of 2408 phenotypes were included in the analysis, categorized into 44 groups. Results: The research findings indicate the following associations: (1) In coronary artery disease (CAD), the plasma proteins A4GNT, COL6A3, KLC1, CALB2, KPNA2, MSMP, and ADH1B showed a positive causal relationship (p-fdr < 0.05). LAYN and GCKR exhibited a negative causal relationship (p-fdr < 0.05). (2) In chronic heart failure (CHF), PLG demonstrated a positive causal relationship (p-fdr < 0.05), while AZGP1 displayed a negative causal relationship (p-fdr < 0.05). (3) In ischemic stroke (IS), ALDH2 exhibited a positive causal relationship (p-fdr < 0.05), while PELO showed a negative causal relationship (p-fdr < 0.05). (4) In Type 2 diabetes (T2DM), the plasma proteins MCL1, SVEP1, PIP4K2A, RFK, HEXIM2, ALDH2, RAB1A, APOE, ANGPTL4, JAG1, FGFR1, and MLN demonstrated a positive causal relationship (p-fdr < 0.05). PTPN9, SNUPN, VAT1, COMT, CCL27, BMP7, and MSMP displayed a negative causal relationship (p-fdr < 0.05). Colocalization analysis conclusively identified that AZGP1, ALDH2, APOE, JAG1, MCL1, PTPN9, PIP4K2A, SNUPN, and RAB1A share a single causal variant with CVDs (PPH3 + PPH4 > 0.8). Further phenotype-wide association studies have shown some potential side effects of these nine targets (p-fdr < 0.05). Conclusions: This study identifies plasma proteins with significant causal associations with CVDs, providing a more comprehensive understanding of potential therapeutic targets. These findings contribute to our knowledge of the underlying mechanisms and offer insights into potential avenues for treatment.

PMID:40026415 | PMC:PMC11870767 | DOI:10.1155/cdr/5711316

Vet Dermatol. 2025 Mar 3. doi: 10.1111/vde.13332. Online ahead of print.

ABSTRACT

BACKGROUND: Early and accurate diagnosis of otitis externa is crucial for correct management yet can often be challenging. Artificial intelligence (AI) is a valuable diagnostic tool in human medicine. Currently, no such tool is available in veterinary dermatology/otology.

OBJECTIVES: As a proof-of-concept, we developed and evaluated a novel YOLOv5 object detection model for identifying healthy ear canals, otitis or masses in the canine ear canal.

ANIMALS: Digital images of ear canals from dogs with healthy ears, otitis and masses in the ear canal were used.

MATERIALS AND METHODS: Four variants of the YOLOv5 model were trained, each using a different training dataset. The prediction performance metrics used to evaluate them include F1/confidence-curves, mean average precision (mAP50), precision (P), recall (R) and average precision (AP) for accuracy. These are quantifiable performance metrics used to evaluate the efficacy of each variant.

RESULTS: All four variants were capable of detecting and classifying the ear canal. However, training datasets with many duplicates (A and C) inflated performance metrics as a consequence of data leakage, potentially compromising their effectiveness on unseen images. Additionally, larger datasets (without duplicates) demonstrated superior performance metrics compared to model variants trained on smaller datasets (without duplicates).

CONCLUSIONS AND CLINICAL RELEVANCE: This novel AI object detection model has the potential for application in the field of veterinary dermatology. An external validation study is needed prior to clinical deployment.

PMID:40026191 | DOI:10.1111/vde.13332

Adv Mater. 2025 Mar 3:e2416480. doi: 10.1002/adma.202416480. Online ahead of print.

ABSTRACT

The interlayer displacement has recently emerged as a crucial tuning parameter to control diverse physical properties in layered crystals. Transmission electron microscopy (TEM), an exceptionally powerful tool for structural analysis, directly observes the interlayer stacking and strain fields in various crystals. However, conventional analysis methods based on high-resolution phase-contrast TEM images are inadequate for recognizing spatially varying unit-cell patterns and their associated structure factors, hindering precise determination of interlayer displacements. Here, a deep learning-based analysis is introduced for atomic resolution TEM images, enabling unit-cell pattern recognition and precise identification of interlayer stacking displacement in bilayer phosphorene. The deep learning model applied to bilayer phosphorene accurately determines stacking displacement, with an error level of 3.3% displacement within the unit cell and a spatial resolution approaching the individual unit-cell level. Additionally, the model successfully processes a large set of in situ TEM data, capturing spatially varying, time-dependent interlayer displacement dynamics associated with edge reconstruction, demonstrating its potential for processing large-scale microscopy datasets.

PMID:40026027 | DOI:10.1002/adma.202416480

J Xray Sci Technol. 2025 Mar 3:8953996251317412. doi: 10.1177/08953996251317412. Online ahead of print.

ABSTRACT

BACKGROUND:: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses.

OBJECTIVES:: This study investigated synthesizing MBF from simulated static myocardial CTP to explore dose reduction potential, bypassing the traditional dynamic input function.

METHODS:: The study included 253 subjects with intermediate-to-high pretest probabilities of obstructive coronary artery disease (CAD). MBF was reconstructed from dynamic myocardial CTP. A deep neural network (DNN) converted simulated static CTP into synthetic MBF. Beyond the usual image quality evaluation, the synthetic MBF was segmented and a clinical functional assessment was conducted, with quantitative analysis for consistency and correlation.

RESULTS:: Synthetic MBF closely matched the referenced MBF, with an average structure similarity (SSIM) of 0.87. ROC analysis of ischemic segments showed an area under curve (AUC) of 0.915 for synthetic MBF. This method can theoretically reduce the radiation dose for MBF significantly, provided satisfactory static CTP is obtained, reducing reliance on high time resolution of dynamic CTP.

CONCLUSIONS:: The proposed method is feasible, with satisfactory clinical functionality of synthetic MBF. Further investigation and validation are needed to confirm actual dose reduction in clinical settings.

PMID:40026015 | DOI:10.1177/08953996251317412

J Xray Sci Technol. 2025 Mar 3:8953996241308759. doi: 10.1177/08953996241308759. Online ahead of print.

ABSTRACT

Computed tomography (CT) image reconstruction is faced with challenge of balancing image quality and radiation dose. Recent unrolled optimization methods address low-dose CT image quality issues using convolutional neural networks or self-attention mechanisms as regularization operators. However, these approaches have limitations in adaptability, computational efficiency, or preservation of beneficial inductive biases. They also depend on initial reconstructions, potentially leading to information loss and error propagation. To overcome these limitations, Kernel Basis Attention Primal-Dual Network (KBA-PDNet) is proposed. The method unrolls multiple iterations of the proximal primal-dual optimization process, replacing traditional proximal operators with Kernel Basis Attention (KBA) modules. This design enables direct training from raw measurement data without relying on preliminary reconstructions. The KBA module achieves adaptability by learning and dynamically fusing kernel bases, generating customized convolution kernels for each spatial location. This approach maintains computational efficiency while preserving beneficial inductive biases of convolutions. By training end-to-end from raw projection data, KBA-PDNet fully utilizes all original information, potentially capturing details lost in preliminary reconstructions. Experiments on simulated and clinical datasets demonstrate that KBA-PDNet outperforms existing approaches in both image quality and computational efficiency.

PMID:40026009 | DOI:10.1177/08953996241308759

Small Methods. 2025 Mar 3:e2401616. doi: 10.1002/smtd.202401616. Online ahead of print.

ABSTRACT

Structured illumination microscopy (SIM) has emerged as a pivotal super-resolution technique in biological imaging. This review aims to introduce the fundamental principles of SIM, primarily focuses on the latest developments in super-resolution SIM imaging, such as the light illumination and modulation devices, and the image reconstruction algorithms. Additionally, the application of deep learning (DL) technology in SIM imaging is explored, which is employed to enhance image quality, accelerate imaging and reconstruction speed or replace the current image reconstruction method. Furthermore, the key evaluation metrics are proposed and discussed for assessment of deep-learning neural networks, especially for their employment in SIM. Finally, the future integration of artificial intelligence (AI) with SIM system and the perspective of smart microscope are also discussed.

PMID:40025917 | DOI:10.1002/smtd.202401616

J Appl Clin Med Phys. 2025 Mar 2:e70066. doi: 10.1002/acm2.70066. Online ahead of print.

ABSTRACT

PURPOSE: Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in 177Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)/CT imaging during 177Lu DOTATATE therapy.

METHODS: The 177Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of 177Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PDdose) for spleen, right kidney, left kidney, and liver.

RESULTS: Before denoising, the mean ± standard deviation of PDdose for all critical organs were 2.31 ± 2.94%, 4.86 ± 9.42%, 8.39 ± 14.76%, 12.95 ± 19.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69 ± 2.25%, 2.84 ± 4.46%, 3.72 ± 4.22%, 7.98 ± 15.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.

CONCLUSION: As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of 177Lu DOTATATE therapy. Improving CT image quality through denoising could enhance 177Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT/CT imaging during treatment.

PMID:40025651 | DOI:10.1002/acm2.70066

Int J Lab Hematol. 2025 Mar 2. doi: 10.1111/ijlh.14455. Online ahead of print.

ABSTRACT

INTRODUCTION: Von Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter-rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.

METHODS: We trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.

RESULTS: The model achieved 91% accuracy compared to Expert 1 (macro-averaged precision = 0.851, recall = 0.757, F1-score = 0.786) and 87% accuracy compared to Expert 2 (macro-averaged precision = 0.653, recall = 0.653, F1-score = 0.641). Inter-rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.

CONCLUSION: Automated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.

PMID:40025642 | DOI:10.1111/ijlh.14455

Development. 2025 Feb 15;152(4):DEV204514. doi: 10.1242/dev.204514. Epub 2025 Mar 3.

ABSTRACT

The formation of a new head during Hydra regeneration involves the establishment of a head organizer that functions as a signaling center and contains an aster-shaped topological defect in the organization of the supracellular actomyosin fibers. Here, we show that the future head region in regenerating tissue fragments undergoes multiple instances of extensive stretching and rupture events from the onset of regeneration. These recurring localized tissue deformations arise due to transient contractions of the supracellular ectodermal actomyosin fibers that focus mechanical strain at defect sites. We further show that stabilization of aster-shaped defects is disrupted by perturbations of the Wnt signaling pathway. We propose a closed-loop feedback mechanism promoting head organizer formation, and develop a biophysical model of regenerating Hydra tissues that incorporates a morphogen source activated by mechanical strain and an alignment interaction directing fibers along morphogen gradients. We suggest that this positive-feedback loop leads to mechanical strain focusing at defect sites, enhancing local morphogen production and promoting robust organizer formation.

PMID:40026208 | DOI:10.1242/dev.204514

Small Methods. 2025 Mar 3:e2401416. doi: 10.1002/smtd.202401416. Online ahead of print.

ABSTRACT

Tandem repeats of a certain DNA sequence can be generated using rolling circle amplification (RCA), where a circular template is continuously amplified by a polymerase with strand displacement activity. In leveraging the linear repetition of the target sequence, enhanced accuracy is achievable by consensus calling in nanopore sequencing. However, traditional multiply-primed RCA produces branched products with limited length, which may not be optimal for nanopore sequencing. In this study, an enhanced RCA protocol is introduced using sequence-specific primers to produce longer and less branched amplicons. Taking advantage of the RCA amplicons of tandem repeats, custom-primed rolling circle amplification sequencing (CPRSeq) is developed, a highly accurate nanopore sequencing pipeline. Utilizing CPRSeq, this successfully sequence standard samples of tumor-associated single nucleotide variants at low mutation frequency and accomplished the whole-genome sequencing and assembly of E. coli.

PMID:40025906 | DOI:10.1002/smtd.202401416

Sci Rep. 2025 Mar 1;15(1):7266. doi: 10.1038/s41598-025-91786-3.

ABSTRACT

Cervical cancer, arising from the cells of the cervix, the lower segment of the uterus connected to the vagina-poses a significant health threat. The microscopic examination of cervical cells using Pap smear techniques plays a crucial role in identifying potential cancerous alterations. While developed nations demonstrate commendable efficiency in Pap smear acquisition, the process remains laborious and time-intensive. Conversely, in less developed regions, there is a pressing need for streamlined, computer-aided methodologies for the pre-analysis and treatment of cervical cancer. This study focuses on the classification of squamous cells into five distinct classes, providing a nuanced assessment of cervical cancer severity. Utilizing a dataset comprising over 4096 images from SimpakMed, available on Kaggle, we employed ensemble technique which included the Convolutional Neural Network (CNN), AlexNet, and SqueezeNet for image classification, achieving accuracies of 90.8%, 92%, and 91% respectively. Particularly noteworthy is the proposed ensemble technique, which surpasses individual model performances, achieving an impressive accuracy of 94%. This ensemble approach underscores the efficacy of our method in precise squamous cell classification and, consequently, in gauging the severity of cervical cancer. The results represent a promising advancement in the development of more efficient diagnostic tools for cervical cancer in resource-constrained settings.

PMID:40025091 | DOI:10.1038/s41598-025-91786-3

Sci Rep. 2025 Mar 1;15(1):7291. doi: 10.1038/s41598-025-89612-x.

ABSTRACT

The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the patient's data to the central machine or server may create severe privacy and security issues. In recent years, the progression of deep federated learning (DFL) and its remarkable success in many domains has guided as a potential solution in this field. Therefore, we proposed a dependable and privacy-preserving DFL-based identification model of new infections from GSs. The unique contributions include automatic effective feature selection, which is best suited for identifying new infections, designing a dependable and privacy-preserving DFL-based LeNet model, and evaluating real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Our proposed model has an overall accuracy of 99.12% after independently and identically distributing the dataset among six clients. Moreover, the proposed model has a precision of 98.23%, recall of 98.04%, f1-score of 96.24%, Cohen's kappa of 83.94%, and ROC AUC of 98.24% for the same configuration, which is a noticeable improvement when compared to the other benchmark models. The proposed dependable model, along with empirical results, is encouraging enough to recognize as an alternative for identifying new infections from other virus strains by ensuring proper privacy and security of patients' data.

PMID:40025035 | DOI:10.1038/s41598-025-89612-x

Biochem Pharmacol. 2025 Feb 28:116839. doi: 10.1016/j.bcp.2025.116839. Online ahead of print.

ABSTRACT

Dengue viruses (DENV) pose significant health threats, with no approved antiviral drugs currently available, creating an urgent need for new therapies. This study screened FDA-approved drugs for their antiviral ability against DENV and identified three promising candidates: darunavir (DRV), domperidone, and tetracycline. DRV demonstrated the highest efficacy against three DENV serotypes, with half-maximal effective concentrations (EC50) below 1 µM, surpassing the performance of tetracycline and domperidone. It effectively blocked DENV envelope (E) protein attachment to two type cells with EC50 values less than 0.2 μM. Domperidone reduced DENV-2 attachment to TE671 cells (EC50 = 3.08 μM) but was less effective in BHK-21 cells, while tetracycline inhibited NS3 protease (IC50 = 1.12 μM). Among DRV's structurally related drugs, fosamprenavir (FPV) significantly reduced DENV infectivity and virus yield, with EC50 values below 0.5 µM. In vivo, DRV at 1, 2, and 5 mg/kg achieved 100 % survival in suckling mice, compared to 83.5 % with FPV. Real-time RT-PCR showed DRV more effectively reduced DENV-2 RNA in mouse brains than FPV. Molecular docking showed DRV and FPV bind tightly to the DENV-2 E protein's N-octyl-β-D-glucoside (βOG) hydrophobic pocket, with DRV forming stronger interactions than FPV. Chimeric DENV-2 single-round infectious particle tests confirmed DRV's effective targeting of this pocket, though mutations at K128, L198, Q200, I270, and T280 reduced its efficacy. These findings highlight DRV as a potent antiviral agent against DENV, targeting the E protein's βOG hydrophobic pocket, with the potential for rapid deployment in treating and preventing infections.

PMID:40024350 | DOI:10.1016/j.bcp.2025.116839

Syst Rev. 2025 Mar 1;14(1):52. doi: 10.1186/s13643-025-02793-4.

ABSTRACT

INTRODUCTION: Empathy is a crucial skill that enhances the quality of patient care, reduces burnout among healthcare professionals, and fosters professionalism in medical students. Clinical practice and standardized patient-based education provide opportunities to enhance empathy, but a lack of consistency and reproducibility as well as significant dependency on resources are impediments. The COVID-19 pandemic has further restricted these opportunities, highlighting the need for alternative approaches. Virtual patients through standardized scenarios ensure consistency and reproducibility while offering safe, flexible, and repetitive learning opportunities unconstrained by time or location. Empathy education using virtual patients could serve as a temporary alternative during the COVID-19 pandemic and address the limitations of traditional face-to-face learning methods. This review aims to comprehensively map existing literature on the use of virtual patients in empathy education and identify research gaps.

METHODS: This scoping review will follow the Joanna Briggs Institute's guidelines and be reported according to PRISMA-P. The search strategy includes a comprehensive search across databases such as PubMed (MEDLINE), CINAHL, Web of Science, Scopus, ERIC, Google, Google Scholar, and Semantic Scholar, covering both published and gray literature without language restrictions. Both quantitative and qualitative studies will be included. Two independent researchers will screen all titles/abstracts and full texts for eligibility. Data will be extracted to summarize definitions of empathy, characteristics of virtual patient scenarios, and methods for measuring their impact on empathy development. Results will be presented in narrative and tabular formats to highlight key findings and research gaps.

DISCUSSION: As this review analyzes existing literature, ethical approval is not required. Findings will be actively disseminated through academic conferences and peer-reviewed publications, providing educators and researchers with valuable insights into the potential of virtual patients to enhance empathy in medical education. This study goes beyond the mere synthesis of academic knowledge by contributing to the advancement of medical education and clinical practice by clarifying virtual patient scenario design and evaluation methods in empathy education. The findings provide a critical foundation for our ongoing development of a medical education platform aimed at enhancing empathy through the use of virtual patients.

PMID:40025554 | DOI:10.1186/s13643-025-02793-4

Wiad Lek. 2024;78(1):90-99. doi: 10.36740/WLek/200331.

ABSTRACT

OBJECTIVE: Aim: The aims of this study are to detect the genetic polymorphisms of FSHR rs6166 (C> T) and rs6165 (C> T) gene particularly that associated with the response to FSH treatment and their effects on the pathogenesis of infertility in Iraqi women.

PATIENTS AND METHODS: Materials and Methods: 210 Iraqi women, aged 20 to 34, who had just been diagnosed with infertility were included in this prospective case control research, whereas the control group consisted of 50 clinically healthy women who were free of any disorders. Following the guidelines for inclusion and exclusion in the study, each of the participating women saw a gynecologist to confirm. The time frame for this From November 2021 to June 2022, the investigation was carried out.

RESULTS: Results: The findings of this study in infertile women, clearly indicates that multiple genotypes of FSHR gene particularly (rs6166) (C>T) and (rs6165) (C>T), that include the homozygous wild genotype (CC), homozygous mutant (TT) and heterozygous (CT) genotype. The T allele was significantly increased (P<0.05) in poor responder infertile women for both rs6166 and rs6165 in FSHR which associated significantly with poor response to FSH in Iraqi infertile women.

CONCLUSION: Conclusions: Polymorphisms in FSHR gene may be associated with decrease in response to FSH treatment and it was associated with pathogenesis of infertility in Iraqi women/ Kerbala province.

PMID:40023860 | DOI:10.36740/WLek/200331