Electromagn Biol Med. 2025 Jan 21:1-18. doi: 10.1080/15368378.2024.2401540. Online ahead of print.

ABSTRACT

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

PMID:39835842 | DOI:10.1080/15368378.2024.2401540

J Acoust Soc Am. 2025 Jan 1;157(1):382-395. doi: 10.1121/10.0034857.

ABSTRACT

Underwater acoustic propagation is a complex phenomenon in the ocean environment. Traditional methods for calculating acoustic propagation loss rely on solving complex partial differential equations. Deep learning methods, leveraging their robust nonlinear approximation capabilities, can model various physical phenomena effectively, significantly reducing computation time and cost. Despite considerable advancements in the study of various inverse underwater acoustic problems, research focused on forward physical modeling is still nascent. This study proposes an end-to-end architecture for predicting underwater acoustic transmission loss (TL). This architecture employs a data-driven approach capable of swiftly and accurately predicting the complete acoustic field. It employs a U-Net model integrated with an adaptive multi-scale dilated module, named MultiScale-DUNet, which effectively predicts by assimilating multi-scale acoustic field information. It is demonstrated that the MultiScale-DUNet is capable of predicting acoustic TL in complex two-dimensional ocean environments within the end-to-end framework. The results indicate that the MultiScale-DUNet can rapidly predict the acoustic TL while maintaining high accuracy under computationally inexpensive conditions. This end-to-end technology for predicting underwater acoustic TL holds broad application prospects in fields such as underwater exploration and real-time underwater monitoring.

PMID:39835828 | DOI:10.1121/10.0034857

Hum Brain Mapp. 2025 Feb 1;46(2):e70136. doi: 10.1002/hbm.70136.

ABSTRACT

Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( R 2 ' = R 2 * - R 2 $$ {R}_2^{\prime }={R}_2^{\ast }-{R}_2 $$ ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R 2 $$ {R}_2 $$ (e.g., multi-echo spin-echo) in addition to multi-echo GRE data for R 2 * $$ {R}_2^{\ast } $$ . To address these challenges, we develop a new deep learning network, χ-sepnet, and propose two deep learning-based susceptibility source separation pipelines, χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ for inputs with multi-echo GRE and multi-echo spin-echo (or turbo spin-echo) and χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ for input with multi-echo GRE only. The neural network is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality χ-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from the conventional regularization-based reconstruction methods. In quantitative analysis, χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ achieves the best outcomes followed by χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ , outperforming the conventional methods. When the lesions of multiple sclerosis patients are classified into subtypes, most lesions are identified as the same subtype in the maps from χ-sepnet- R 2 ' $$ {R}_2^{\prime } $$ and χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ (paramagnetic susceptibility: 99.6% and diamagnetic susceptibility: 98.4%; both out of 250 lesions). The χ-sepnet- R 2 * $$ {R}_2^{\ast } $$ pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.

PMID:39835664 | DOI:10.1002/hbm.70136

Mol Biol Evol. 2025 Jan 21:msaf017. doi: 10.1093/molbev/msaf017. Online ahead of print.

ABSTRACT

Bats have adapted to pathogens through diverse mechanisms, including increased resistance - rapid pathogen elimination, and tolerance - limiting tissue damage following infection. In the Egyptian fruit bat (an important model in comparative immunology) several mechanisms conferring disease tolerance were discovered, but mechanisms underpinning resistance remain poorly understood. Previous studies on other species suggested that elevated basal expression of innate immune genes may lead to increased resistance to infection. Here, we test whether such transcriptional patterns occur in Egyptian fruit bat tissues through single-cell and spatial transcriptomics of gut, lung and blood cells, comparing gene expression between bat, mouse and human. Despite numerous recent loss and expansion events of interferons in the bat genome, interferon expression and induction are remarkably similar to that of mouse. In contrast, central complement system genes are highly and uniquely expressed in key regions in bat lung and gut epithelium, unlike in human and mouse. Interestingly, the unique expression of these genes in the bat gut is strongest in the crypt, where developmental expression programs are highly conserved. The complement system genes also evolve rapidly in their coding sequence across the bat lineage. Finally, the bat complement system displays strong hemolytic activity. Together, these results indicate a distinctive transcriptional divergence of the complement system, which may be linked to bat resistance, and highlight the intricate evolutionary landscape of bat immunity.

PMID:39836373 | DOI:10.1093/molbev/msaf017

Appl Microbiol Biotechnol. 2025 Jan 21;109(1):14. doi: 10.1007/s00253-024-13390-1.

ABSTRACT

Butenyl-spinosyn, derived from Saccharopolyspora pogona, is a broad-spectrum and effective bioinsecticide. However, the regulatory mechanism affecting butenyl-spinosyn synthesis has not been fully elucidated, which hindered the improvement of production. Here, a high-production strain S. pogona H2 was generated by Cobalt-60 γ-ray mutagenesis, which showed a 2.7-fold increase in production compared to the wild-type strain S. pogona ASAGF58. A comparative transcriptomic analysis between S. pogona ASAGF58 and H2 was performed to elucidate the high-production mechanism that more precursors and energy were used to synthesize of butenyl-spinosyn. Fortunately, a PurR family transcriptional regulator TF00350 was discovered. TF00350 overexpression strain RS00350 induced morphological differentiation and butenyl-spinosyn production, ultimately leading to a 5.5-fold increase in butenyl-spinosyn production (141.5 ± 1.03 mg/L). Through transcriptomics analysis, most genes related to purine metabolism pathway were downregulated, and the butenyl-spinosyn biosynthesis gene was upregulated by increasing the concentration of c-di-GMP and decreasing the concentration of c-di-AMP. These results provide valuable insights for further mining key regulators and improving butenyl-spinosyn production. KEY POINTS: • A high production strain of S. pogona H2 was obtained by 60Co γ-ray mutagenesis. • Positive regulator TF00350 identified by transcriptomics, increasing butenyl-spinosyn production by 5.5-fold. • TF00350 regulated of butenyl-spinosyn production by second messengers.

PMID:39836216 | DOI:10.1007/s00253-024-13390-1

Biochem Soc Trans. 2025 Jan 21:BST20240678. doi: 10.1042/BST20240678. Online ahead of print.

ABSTRACT

Many prokaryotic and eukaryotic cells store inorganic phosphate in the form of polymers called polyphosphate (polyP). There has been an explosion of interest in polyP over the past decade, in part due to newly suggested roles related to diverse aspects of human health. The physical interaction of polyP chains with specific proteins has been proposed to regulate cellular homeostasis and modulate signaling pathways in response to environmental changes. Recently, several studies have challenged existing models for how polyP interacts with its protein targets, while identifying new motifs that are capable of binding to polyP. In this review, we summarize these findings, delineate the functional implications for polyP-protein interactions at the molecular level, and define open questions that should be addressed to propel the field forward.

PMID:39836110 | DOI:10.1042/BST20240678

Clin Transl Gastroenterol. 2025 Jan 21. doi: 10.14309/ctg.0000000000000803. Online ahead of print.

ABSTRACT

INTRODUCTION: X842 is a new type of gastric acid-suppressing agent with a rapid onset of action and a long duration of effect. We aim to investigate the efficacy and safety of different doses of X842 versus lansoprazole in the treatment of patients with erosive esophagitis (EE).

METHODS: This phase 2 study included 90 patients with EE (Los Angeles grades A-D) who were randomized (1:1:1) to receive oral low-dose X842 (50 mg/day, n=31), high-dose X842 (100 mg/day, n=31), or lansoprazole (30 mg/day, n=30) for 4 weeks. The main efficacy endpoint was the EE healing rate, which was the proportion of patients who achieved endoscopic healing after 4 weeks of treatment.

RESULTS: For ITT analysis, the EE healing rates at 4 weeks were 93.6% (29/31), 79.3% (23/29), and 80.0% (24/30) for the X842 50 mg, the X842 100 mg and the lansoprazole 30 mg groups. For PP analysis, the EE healing rates at 4 weeks were 93.6% (29/31), 80.8% (21/26), and 82.1% (23/28) in the three groups, respectively. The EE healing rate did not significantly differ among the three groups in either the ITT (p = 0.2351) or PP (p = 0.3320) analysis. The incidence of drug-related treatment-emergent adverse events (TEAEs) did not differ among groups. No severe drug-related TEAEs occurred in the X842 group.

CONCLUSIONS: Our findings confirmed that X842 had efficacy and a favourable safety profile similar to those of lansoprazole. Therefore, X842, a novel P-CAB, is expected to become a promising therapeutic agent for EE.

PMID:39836012 | DOI:10.14309/ctg.0000000000000803

J Inflamm Res. 2025 Jan 16;18:637-652. doi: 10.2147/JIR.S503118. eCollection 2025.

ABSTRACT

PURPOSE: Immunometabolism is pivotal in rheumatoid arthritis (RA) pathogenesis, yet the intricacies of its pathological regulatory mechanisms remain poorly understood. This study explores the complex immunometabolic landscape of RA to identify potential therapeutic targets.

PATIENTS AND METHODS: We integrated genome-wide association study (GWAS) data involving 1,400 plasma metabolites, 731 immune cell traits, and RA outcomes from over 58,000 participants. Mendelian randomization (MR) and mediation analyses were applied to evaluate causal relationships among plasma metabolites, immune cells, and RA. We further analyzed single-cell and bulk transcriptomes to investigate differential gene expression, immune cell interactions, and relevant biological processes. Machine learning models identified hub genes, which were validated via quantitative real-time PCR (qRT-PCR). Then, potential small-molecule drugs were screened using the Connectivity Map (CMAP) and molecular docking. Finally, a phenome-wide association study (PheWAS) was conducted to evaluate potential side effects of drugs targeting the hub genes.

RESULTS: Causalities were found between six plasma metabolites, five immune cells and RA in genetically determined levels. Notably, DC mediated 18% of the protective effect of PE on RA. Autophagy-related scores were elevated in both RA and DC subsets in PE-associated biological processes. Through observation in the functional differences in cellular interactions between the identified clusters, DCs with high autophagy scores may process such as necroptosis and the activation of the Jak-STAT signaling pathway in contributing the pathogenesis of RA. Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). CMAP, molecular docking, and PheWAS analysis further highlighted vismodegib as a promising therapeutic candidate.

CONCLUSION: This study clarifies the key immunometabolic mechanisms in RA, pinpointing promising paths for better prevention, diagnosis, and treatment.

PMID:39835297 | PMC:PMC11745140 | DOI:10.2147/JIR.S503118

Int J Nanomedicine. 2025 Jan 16;20:605-624. doi: 10.2147/IJN.S500487. eCollection 2025.

ABSTRACT

INTRODUCTION: Androgenetic alopecia (AGA) is a multifactorial and age-related dermatological disease that affects both males and females, usually at older ages. Traditional hair repair drugs exemplified by minoxidil have limitations such as skin irritation and hypertrichosis. Thus, attention has been shifted to the use of repurposing drugs. Metformin is an anti-diabetic drug, that can promote hair follicle regeneration via upregulation of the hair-inductive capability. Hence, the current study aims to fabricate a safe and effective nanoemulsion to improve metformin efficacy in targeting AGA.

METHODS: Rosemary oil was selected as the oily phase due to its ability to increase blood flow and hair growth. Rosemary-based nanoemulsions were statistically optimized by Box-Behnken experimental design, loaded with metformin, and incorporated into a hydrogel to form a nanoemulgel. Metformin-loaded nanoemulsions were assessed for their diametric size, uniformity, zeta potential, and metformin characteristics within the formulated nanosystem. The nanoemulgel was then evaluated in terms of its pH, percentage drug content, and in-vitro release performance. In-vivo study assessed the nanoemulgel's ability to augment hair growth in rats.

RESULTS: The experimental design displayed that using 50%w/w, 20%w/w, and 10%w/w of Cremophor®, Labrafil®, and deionized water, respectively, resulted in nanoemulsion formulation with the smallest globule size (125.01 ± 0.534 nm), unimodal size distribution (PDI=0.103), negative surface charge (-19.9 ± 2.01 mV) with a spherical morphological structure. Rosemary-based nanoemulgel displayed acceptable physicochemical characterizations namely; a neutral pH value of 6.7±0.15, high drug content (92.9± 2.3%), and controlled metformin in-vitro release. Besides, the formulated nanoemulgel significantly increased the number of hair follicles in the animal model compared with other controls and tested groups.

CONCLUSION: The designed nanoemulgel is a promising approach for treating androgenic alopecia.

PMID:39835177 | PMC:PMC11745075 | DOI:10.2147/IJN.S500487

Front Pharmacol. 2025 Jan 6;15:1503490. doi: 10.3389/fphar.2024.1503490. eCollection 2024.

ABSTRACT

Hepatocellular carcinoma (HCC) is a major medical challenge due to its high incidence and poor prognosis. 5-Fluorouracil (5-FU), although extensively studied in the treatment of HCC and other solid tumors, has limited application as a first-line therapy for HCC due to its resistance and significant inter-patient variability. To address these issues, researchers have explored drug repurposing. One of our key findings in this endeavour was the potent anti-HCC effect of the natural product Salidroside (Sal) when co-administered with 5-FU. Sal was found to inhibit mitosis and promote cellular senescence in HCC cells via a mechanism distinct from 5-FU, specifically by inducing excessive mitophagy that led to cellular mitochondrial dysfunction. Importantly, YIPF5 was confirmed as a potential molecular target of Sal. This natural product modulated YIPF5-induced mitophagy and influenced both mitosis and senescence in HCC cells. The combination of Sal and 5-FU demonstrated significant therapeutic effects in a mouse HCC model. In conclusion, our study was not only in line with the innovative strategy of drug repurposing, but also important for drug design and natural product screening targeting the relevant pathways.

PMID:39834805 | PMC:PMC11743563 | DOI:10.3389/fphar.2024.1503490

Front Nutr. 2025 Jan 6;11:1503389. doi: 10.3389/fnut.2024.1503389. eCollection 2024.

ABSTRACT

INTRODUCTION: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.

METHODS: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge. This research is a novel contribution because it allows for the interconnection and complementation of food composition data with knowledge and takes what has been done in the past a step further by enabling the integration of knowledge. NutriBase focuses on two important challenges; data (semantic) harmonization by using the existing ontologies, and reducing missing data by semi-automatic data imputation made from conflating with existing databases.

RESULTS AND DISCUSSION: The developed web-based tool is highly modifiable and can be further customized to meet national or international requirements. It can help create and maintain the quality management system needed to assure data quality. Newly generated data and knowledge can continuously be added, as interoperability with other systems is enabled. The tool is intended for use by domain experts, food compilers, and researchers who can add and edit food-relevant data and knowledge. However, the tool is also accessible to food manufacturers, who can regularly update information about their products and thus give consumers access to current data. Moreover, the traceability of the data and knowledge provenance allows the compilation of a trustworthy management system. The system is designed to allow easy integration of data from different sources, which enables data borrowing and reduction of missing data. In this paper, the feasibility of NutriBase is demonstrated on Slovenian food-related data and knowledge, which is further linked with international resources. Outputs such as matched food components and food classifications have been integrated into semantic resources that are currently under development in various international projects.

PMID:39834464 | PMC:PMC11743969 | DOI:10.3389/fnut.2024.1503389

Front Endocrinol (Lausanne). 2025 Jan 6;15:1537014. doi: 10.3389/fendo.2024.1537014. eCollection 2024.

ABSTRACT

[This corrects the article DOI: 10.3389/fendo.2023.1184024.].

PMID:39835259 | PMC:PMC11744269 | DOI:10.3389/fendo.2024.1537014

Cureus. 2024 Dec 20;16(12):e76089. doi: 10.7759/cureus.76089. eCollection 2024 Dec.

ABSTRACT

Shock is a state of inadequate perfusion that affects vital organs. Cardiogenic shock (CS) predisposes patients to various arrhythmias. The adverse effect depends on intervention and pharmacogenomics. This narrative review sheds light on treatment strategies for arrhythmias caused by milrinone and dobutamine when managing CS. Dobutamine, through beta-1 agonism, and milrinone, by phosphodiesterase-3 inhibition, increase cardiac contractility by enhancing the availability of calcium to the myocardium. Dobutamine is also a beta-2 agonist, and milrinone is a phosphodiesterase-3 inhibitor; both result in peripheral vasodilation, leading to their use preferentially in patients with CS with normotensive blood pressure. To narrow down relevant literature, various electronic databases, including PubMed, Google Scholar, and Cochrane Library, were searched. The review revealed limited evidence favoring either milrinone or dobutamine as the preferred inotropic agent for managing CS, but it did reveal that though hospital stays using dobutamine were shorter, mortality from its induced arrhythmias led to an increase in all-cause mortality rates. Both proarrhythmic agents triggered ventricular and supraventricular tachyarrhythmias, some requiring cardioversion while others are non-sustained and managed medically or symptomatically. Though neither agent has a specific reversal agent, the effect of dobutamine was seen to be successfully aborted using intravenous ultrashort half-life beta-blockers (such as esmolol). The findings accentuated the critical need for a tailored approach to managing these iatrogenic arrhythmias, emphasizing clinical vigilance and individualized patient care.

PMID:39835019 | PMC:PMC11743927 | DOI:10.7759/cureus.76089

Front Immunol. 2025 Jan 6;15:1505752. doi: 10.3389/fimmu.2024.1505752. eCollection 2024.

ABSTRACT

BACKGROUND: Factors leading to severe COVID-19 remain partially known. New biomarkers predicting COVID-19 severity that are also causally involved in disease pathogenesis could improve patient management and contribute to the development of innovative therapies. Autophagy, a cytosolic structure degradation pathway is involved in the maintenance of cellular homeostasis, degradation of intracellular pathogens and generation of energy for immune responses. Acyl-CoA binding protein (ACBP) is a key regulator of autophagy in the context of diabetes, obesity and anorexia. The objective of our work was to assess whether circulating ACBP levels are associated with COVID-19 severity, using proteomics data from the plasma of 903 COVID-19 patients.

METHODS: Somalogic proteomic analysis was used to detect 5000 proteins in plasma samples collected between March 2020 and August 2021 from hospitalized participants in the province of Quebec, Canada. Plasma samples from 903 COVID-19 patients collected during their admission during acute phase of COVID-19 and 295 hospitalized controls were assessed leading to 1198 interpretable proteomic profiles. Levels of anti-SARS-CoV-2 IgG were measured by ELISA and a cell-binding assay.

RESULTS: The median age of the participants was 59 years, 46% were female, 65% had comorbidities. Plasma ACBP levels correlated with COVID-19 severity, in association with inflammation and anti-SARS-CoV-2 antibody levels, independently of sex or the presence of comorbidities. Samples collected during the second COVID-19 wave in Quebec had higher levels of plasma ACBP than during the first wave. Plasma ACBP levels were negatively correlated with biomarkers of T and NK cell responses interferon-γ, tumor necrosis factor-α and interleukin-21, independently of age, sex, and severity.

CONCLUSIONS: Circulating ACBP levels can be considered a biomarker of COVID-19 severity linked to inflammation. The contribution of extracellular ACBP to immunometabolic responses during viral infection should be further studied.

PMID:39835130 | PMC:PMC11743960 | DOI:10.3389/fimmu.2024.1505752

Cureus. 2024 Dec 19;16(12):e76002. doi: 10.7759/cureus.76002. eCollection 2024 Dec.

ABSTRACT

Aquagenic syringeal acrokeradermatoma (ASA) is a dermatological condition characterized by the transient appearance of edematous, white, translucent papules on the palms, typically triggered by water exposure. While ASA is most commonly associated with cystic fibrosis (CF) and predominantly affects young females, there has been a significant increase in ASA cases since the most recent update in 2015. The COVID-19 pandemic increased the number of patients diagnosed with ASA following exposure to the viral infection. The growing body of literature suggests a multifactorial etiology for ASA, with potential links to CF, medication use, and possibly COVID-19-related behavioral changes. Due to the recent increase in cases of ASA, an updated review seeks to quantify the existing literature that has been published on the prevalence of this condition. This review sought to find those newly diagnosed cases between the years 2014 and 2024. Through a literature review, we were able to find 57 cases of ASA since the last significant update to the total number of cases found in the literature. This review includes the prevalence of CF, a known etiology of ASA, as well as demographic information and known status of exposure to COVID-19.

PMID:39835050 | PMC:PMC11743320 | DOI:10.7759/cureus.76002

Respir Med Case Rep. 2024 Dec 27;53:102159. doi: 10.1016/j.rmcr.2024.102159. eCollection 2025.

ABSTRACT

INTRODUCTION: Acute fibrinous and organizing pneumonia (AFOP) is a severe form of acute lung injury which can occur after lung transplantation. Treatment is empiric, based on immunosuppressive regimens and the mortality rate is very high.

CASE PRESENTATION: We report the case of a young lung transplant (LT) recipient who developed AFOP following a respiratory viral infection while on suboptimal maintenance immunosuppression due to adherence issues. Diagnosis was confirmed by cryobiopsies showing intra-alveolar fibrin balls. Despite high dose systemic corticosteroids, the patient developed severe respiratory failure requiring mechanical ventilation. IV infliximab and tocilizumab were administered. The patient was extubated 11 days later and discharged to home 42 days after intubation with 1L/min O2. She developed severe pleuritic pain needing opioid treatment and died 4 months later.

CONCLUSION: While high-dose systemic corticosteroids remain the first line of treatment, the use of anti TNF-α has shown promising results in case reports. Furthermore, we propose prompt realization of a cytokine panel analysis in both blood and bronchoalveolar lavage to better guide the adjuvant administration of a targeted anti-inflammatory therapy.

PMID:39834689 | PMC:PMC11743898 | DOI:10.1016/j.rmcr.2024.102159

Cureus. 2025 Jan 19;17(1):e77681. doi: 10.7759/cureus.77681. eCollection 2025 Jan.

ABSTRACT

Bronchiectasis is a well-recognized chronic respiratory disease characterized by a productive cough and multi-microbial activation syndrome (MMAS) of various respiratory infections due to what can be the permanent dilatation of the bronchi. Bronchiectasis represents an ongoing challenge to conventional antibiotic treatment as the damaged bronchial environment remains conducive to ongoing opportunistic infections and microbial mutations, leading to multi-drug resistance. Standard treatment guidelines are designed to promptly identify and address the primary infection. Despite the strong focus on identification of the primary infection in each new episode, by combining clinical history, and high-resolution computed tomography (HRCT), a high proportion of patients remain classified as "idiopathic". Important underlying infections, such as Aspergillus and other mold infections, Pseudomonas aeruginosa, Mycobacterium, Mycoplasma, and various viruses, are frequently not identified for prolonged periods of time, and selected broad-spectrum antibiotics are often ineffective. The introduction of Induced Native Phage Therapy in 2021 and Induced Native Phage cocktails in 2024 provides a new treatment alternative that induces naturally occurring phages to eliminate specifically targeted acute and chronic mixed infections even in cases of multi-drug resistant infections as seen in chronic bronchiectasis. This article will present the successful long-term results in a case study demonstrating the speed, gentleness, and effectiveness of induced native phage cocktails in a 45-year-old male with life-long asthma resulting in multi-microbial activation syndrome in severe non-cystic fibrosis bronchiectasis for the last 20 years.

PMID:39834667 | PMC:PMC11744022 | DOI:10.7759/cureus.77681

Front Public Health. 2025 Jan 6;12:1442114. doi: 10.3389/fpubh.2024.1442114. eCollection 2024.

ABSTRACT

INTRODUCTION: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone. Therefore, plenty of automated screening technique have been developed to address this task.

METHODS: Among these techniques, the deep learning models have demonstrated promising outcomes in various types of machine vision tasks. However, most of the medical image analysis-oriented deep learning approaches are built upon the convolutional operations, which might neglect the global dependencies between long-range pixels in the medical images. Therefore, the vision transformer models, which can unveil the associations between global pixels, have been gradually employed in medical image analysis. However, the quadratic computation complexity of attention mechanism has hindered the deployment of vision transformer in clinical practices. Bearing the analysis above in mind, this study introduces an integrated self-attention mechanism with both softmax and linear modules to guarantee efficiency and expressiveness, simultaneously. To be specific, a portion of query and key tokens, which are much less than the original query and key tokens, are adopted in the attention module by adding a set of proxy tokens. Note that the proxy tokens can fully utilize both the advantages of softmax and linear attention.

RESULTS: To evaluate the performance of the presented approach, the comparison experiments between state-of-the-art algorithms and the proposed approach are conducted. Experimental results demonstrate that the proposed approach achieves superior outcome over the state-of-the-art algorithms on the publicly available datasets.

DISCUSSION: Accordingly, the proposed approach can be taken as a potentially valuable instrument in clinical practices.

PMID:39835306 | PMC:PMC11743363 | DOI:10.3389/fpubh.2024.1442114

Front Med (Lausanne). 2025 Jan 6;11:1510792. doi: 10.3389/fmed.2024.1510792. eCollection 2024.

ABSTRACT

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI's transformative potential in sepsis care.

PMID:39835096 | PMC:PMC11743359 | DOI:10.3389/fmed.2024.1510792

Front Med (Lausanne). 2025 Jan 6;11:1478842. doi: 10.3389/fmed.2024.1478842. eCollection 2024.

ABSTRACT

BACKGROUND: The conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.

OBJECTIVE: This study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.

METHODS: Five models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.

RESULTS: A total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72-0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73-0.96).

CONCLUSION: BITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.

PMID:39835092 | PMC:PMC11744519 | DOI:10.3389/fmed.2024.1478842