NPJ Digit Med. 2025 May 17;8(1):291. doi: 10.1038/s41746-025-01641-y.

ABSTRACT

Deep learning techniques have significantly enhanced the convenience and precision of ultrasound image diagnosis, particularly in the crucial step of lesion segmentation. However, recent studies reveal that both train-from-scratch models and pre-trained models often exhibit performance disparities across sex and age attributes, leading to biased diagnoses for different subgroups. In this paper, we propose APPLE, a novel approach designed to mitigate unfairness without altering the parameters of the base model. APPLE achieves this by learning fair perturbations in the latent space through a generative adversarial network. Extensive experiments on both a publicly available dataset and an in-house ultrasound image dataset demonstrate that our method improves segmentation and diagnostic fairness across all sensitive attributes and various backbone architectures compared to the base models. Through this study, we aim to highlight the critical importance of fairness in medical segmentation and contribute to the development of a more equitable healthcare system.

PMID:40382499 | DOI:10.1038/s41746-025-01641-y

Eur Radiol. 2025 May 17. doi: 10.1007/s00330-025-11666-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.

METHODS: This retrospective study included 618 patients diagnosed with SAH. The dataset was divided into a training and internal validation cohort (533 cases: aSAH = 305, naSAH = 228) and an external test cohort (85 cases: aSAH = 55, naSAH = 30). Hemorrhage regions were automatically segmented using a U-Net + + architecture. A ResNet-based deep learning model was trained to classify the etiology of SAH.

RESULTS: The model achieved robust performance in distinguishing aSAH from naSAH. In the internal validation cohort, it yielded an average sensitivity of 0.898, specificity of 0.877, accuracy of 0.889, Matthews correlation coefficient (MCC) of 0.777, and an area under the curve (AUC) of 0.948 (95% CI: 0.929-0.967). In the external test cohort, the model demonstrated an average sensitivity of 0.891, specificity of 0.880, accuracy of 0.887, MCC of 0.761, and AUC of 0.914 (95% CI: 0.889-0.940), outperforming junior radiologists (average accuracy: 0.836; MCC: 0.660).

CONCLUSION: The study presents a deep learning architecture capable of accurately identifying SAH etiology from NCCT scans. The model's high diagnostic performance highlights its potential to support rapid and precise clinical decision-making in emergency settings.

KEY POINTS: Question Differentiating aneurysmal from naSAH is crucial for timely treatment, yet existing imaging modalities are not universally accessible or convenient for rapid diagnosis. Findings A ResNet-variant-based deep learning model utilizing non-contrast CT scans demonstrated high accuracy in classifying SAH etiology and enhanced junior radiologists' diagnostic performance. Clinical relevance AI-driven analysis of non-contrast CT scans provides a fast, cost-effective, and non-invasive solution for preoperative SAH diagnosis. This approach facilitates early identification of patients needing aneurysm surgery while minimizing unnecessary angiography in non-aneurysmal cases, enhancing clinical workflow efficiency.

PMID:40382487 | DOI:10.1007/s00330-025-11666-2

Sci Rep. 2025 May 17;15(1):17142. doi: 10.1038/s41598-025-98496-w.

ABSTRACT

The emergence of Deep Learning provides an opportunity for traffic flow prediction. However, uncertainty and volatility exhibited by nonlinearity and instability of traffic flow pose challenges to Deep Learning models. Therefore, a combined prediction model, VMD-GAT-MGTCN, based on variational modal decomposition (VMD), graph attention network (GAT), and multi-gated attention time convolutional network (MGTCN) is proposed to enhance short-term traffic flow prediction accuracy. In the VMD-GAT-MGTCN, VMD decomposes traffic flow data to obtain the modal components, the GAT and MGTCN are integrated to design the spatio-temporal feature model to obtain the temporal and spatial features of traffic flow. The predicted value of traffic flow modal components by spatio-temporal feature model are stacked to obtain the ultimate traffic flow prediction results. The simulation experiments with the compared models and the baseline models show that the VMD-GAT-MGTCN have superior prediction accuracy and effect. It also verifies the enhancement effect of the VMD algorithm on the prediction performance of the VMD-GAT-MGTCN and the good prediction results obtained by the VMD-GAT-MGTCN in the traffic flow mutation region.

PMID:40382484 | DOI:10.1038/s41598-025-98496-w

Sci Rep. 2025 May 17;15(1):17150. doi: 10.1038/s41598-025-94086-y.

ABSTRACT

Differential equations-based epidemiological compartmental systems and deep neural networks-based artificial intelligence can effectively analyze and combat monkeypox (MPV) transmission with Poisson random measure noise into a stochastic SEIQR (susceptible, exposed, infected, quarantined, recovered) model human population and SEI (susceptible, exposed, infected) for rodent population. Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV's progression and lack elucidation. This research introduces Levenberg Marquardt backpropagation neural networks (LMBNNS) in training, a new approach that combines compartmental frameworks with artificial neural networks (ANNs) to explain the complex mechanisms of MPV. Meanwhile, a model description proves the existence and uniqueness of a global positive solution. A threshold parameter is determined and employed to identify the factors that lead to infection in the general public. Furthermore, other criteria are developed to eliminate the infection within the entire population. The MPV is eliminated if [Formula: see text], but continues if [Formula: see text]. The study depends on two functional scenarios to quantitatively clarify the theoretical results. An adapted dataset is generated employing the Adam algorithm to minimize the mean square error (MSE) by setting its data effectiveness to 81% for training, 9% for testing, and 10% for validation. The solver's accuracy is validated by minimal absolute error and complementing responses to every hypothetical situation. In order to verify the adaptation's reliability and precision, productivity is measured using the error histogram, changeover state, and prediction for addressing the MPV model. Visual representations are used to illustrate the investigation and compare results. Utilizing this hybrid approach, we want to increase our comprehension of disease propagation, strengthen forecasting competencies, and influence more efficient public health actions. The combination of stochastic processes and machine learning approaches creates a powerful tool for capturing the inherent uncertainties in infectious disease dynamics, as well as a more accurate framework for real-time epidemic prediction and prevention.

PMID:40382439 | DOI:10.1038/s41598-025-94086-y

Sci Rep. 2025 May 17;15(1):17135. doi: 10.1038/s41598-025-01830-5.

ABSTRACT

With the continuous development of intelligent technology, robots have entered various industries. Firefighting robots have become a hot topic in the field of firefighting and rescue equipment. For firefighting robots, autonomous firefighting technology is the core capability. It includes three steps: flame recognition, fire source location, autonomous firefighting. At present, flame recognition has been widely studied, but the adaptability is poor for different flames. For fire source location technology only rough positioning has been achieved. For autonomous firefighting, the time-consuming water point feedback adjustment method is often used. Those are not suitable for the actual fire rescue. So we proposed to use the visual information, thermal imaging morphological and thermal data of the flame for deep learning, which greatly improves the adaptability of flame recognition. The centimeter-level high-precision positioning of the fire source is achieved. Finally, the proposed water cannon fire source projection method is used to realize rapid water cannon movement instruction generation, and achieve rapid autonomous firefighting. The test results show that the proposed fire source identification algorithm can identify all fire sources up to 15 m at a speed about 15Fps. It can rapid autonomous firefighting within 0.5 s.

PMID:40382425 | DOI:10.1038/s41598-025-01830-5

Cell Signal. 2025 May 15:111867. doi: 10.1016/j.cellsig.2025.111867. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by irreversible scarring of the lungs, predominantly affecting older adults. The limited therapeutic options available are largely due to an insufficient understanding of IPF etiology and pathogenesis. This study investigated potential biomarkers to enhance IPF diagnosis and treatment strategies. Through single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing analyses of public datasets, four hub genes-FTH1, FABP5, DCXR, and IGFBP7-were identified as strongly associated with IPF. Subsequent validation in in vivo and in vitro models confirmed IGFBP7 as a novel biomarker. Double immunofluorescence staining and scRNA-seq analysis revealed that IGFBP7 expression is elevated in IPF epithelial cells. IGFBP7 shows potential for early diagnosis of IPF and can differentiate IPF from other diseases. Gene set enrichment analysis revealed the involvement of IGFBP7 in IPF pathogenesis, particularly through its strong connection to the TGF-β signaling pathway, which drives inflammation and fibrosis. In vitro studies with the TGF-β inhibitor SB431542 showed that inhibition of the TGF-β pathway significantly reduced IGFBP7 expression. Furthermore, IGFBP7 knockdown decreased the expression of markers associated with epithelial-mesenchymal transition and fibrosis while suppressing TGF-β1 expression. These results suggest that IGFBP7 forms a positive feedback loop with TGF-β1. In conclusion, this research identified IGFBP7 as a promising biomarker with significant diagnostic and therapeutic potential for IPF. These insights pave the way for improved diagnostics and the development of targeted antifibrosis therapies, while deepening our understanding of IPF mechanisms.

PMID:40381971 | DOI:10.1016/j.cellsig.2025.111867

Lancet Rheumatol. 2025 May 14:S2665-9913(25)00026-8. doi: 10.1016/S2665-9913(25)00026-8. Online ahead of print.

ABSTRACT

BACKGROUND: In patients with systemic sclerosis, it is common practice to treat interstitial lung disease (ILD) in patients in whom progression has already occurred. We sought to clarify whether observed progression of systemic sclerosis-associated ILD confers risk for subsequent progression.

METHODS: In this multicentre observational cohort study, based on an analysis of prospectively collected data, we included patients with systemic sclerosis-associated ILD aged 18 years or older at diagnosis, who fulfilled the 2013 American College of Rheumatology-European Association of Alliances in Rheumatology systemic sclerosis classification criteria. The main cohort (diagnosed between January 2001 and December 2019) was consecutively followed up annually over 4 years at the Department of Rheumatology at the Oslo University Hospital, Norway, and the Department of Rheumatology at the University Hospital Zurich, Switzerland. We applied four definitions of ILD progression: the primary definition was forced vital capacity (FVC) decline of 5% or more, and secondary definitions included FVC decline of 10% or more, progressive pulmonary fibrosis (PPF), and progressive fibrosing ILD (PF-ILD). We applied these definitions at each annual visit after the first (visit 1). We validated our findings in an enriched cohort that included patients from the main cohort with systemic sclerosis-associated ILD and short disease duration of less than 3 years along with patients diagnosed between January 2003 and September 2019 from the Division of Rheumatology, University of Michigan, Ann Arbor, MI, USA. Multivariable logistic regression analyses were applied to predict ILD progression and its effect on mortality. There was no involvement of people with lived experience in this study.

FINDINGS: Of 231 patients with systemic sclerosis-associated ILD from the main cohort (mean age 48·0 years [SD 14·6], 176 [76%] female and 55 [24%] male), 71 (31%) had ILD progression as defined by an FVC decline of 5% or more between visit 1 and visit 2, 38 (16%) as defined by an FVC decline of 10% or more, 39 (17%) as defined by PPF, and 89 (39%) defined by PF-ILD. In multivariable logistic regression analyses, adjusted for risk factors for progressive systemic sclerosis-associated ILD and immunosuppressive treatment, we found that ILD progression, defined by FVC decline of 5% or more, from visit 1 to visit 2 reduced the risk for further progression from visit 2 to visit 3 (odds ratio [OR] 0·28 [95% CI 0·12-0·63]; p=0·002) and that there was no risk for subsequent progression using the other definitions (FVC decline of ≥10%: 0·57 [0·16-1·99; p=0·38]; PPF: 0·93 [0·39-2·22; p=0·88]; and PF-ILD: 0·69 [0·35-1·36]; p=0·28]). Using the primary definition of progression, we found the same results in the enriched systemic sclerosis-associated ILD cohort, wherein 41 (34%) of 121 patients had progression defined by an FVC decline of 5% or more (OR 0·22 [95% CI 0·06-0·87]; p=0·031). FVC decline of 5% or more was significantly associated with mortality (hazard ratio 1·66 [95% CI 1·05-2·62]; p=0·030) adjusted for other risk factors.

INTERPRETATION: Systemic sclerosis-associated ILD progression does not predict further ILD progression at the next annual follow-up visit, even in an enriched population, but progression was associated with mortality. These results have implications for clinical practice because they support a paradigm shift in treatment strategy, advocating for initiating therapy in patients at risk of progression. Further research is needed to confirm these findings.

FUNDING: None.

TRANSLATIONS: For the German and Norwegian translations of the abstract see Supplementary Materials section.

PMID:40381640 | DOI:10.1016/S2665-9913(25)00026-8

Nat Commun. 2025 May 17;16(1):4607. doi: 10.1038/s41467-025-59780-5.

ABSTRACT

Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are predominantly sequence-based and typically focus on individual CRE types, limiting insights into their cell-type-specific functions and regulatory dynamics. Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. CREATE integrates genomic sequences, chromatin accessibility, and chromatin interaction data to generate discrete CRE embeddings, enabling accurate multi-class classification and robust characterization of CREs. CREATE excels in identifying cell-type-specific CREs, and provides quantitative and interpretable insights into CRE-specific features, uncovering the underlying regulatory codes. By facilitating large-scale prediction of CREs in specific cell types, CREATE enhances the recognition of disease- or phenotype-associated biological variabilities of CREs, thus advancing our understanding of gene regulatory landscapes and their roles in health and disease.

PMID:40382355 | DOI:10.1038/s41467-025-59780-5

Trends Genet. 2025 May 16:S0168-9525(25)00103-9. doi: 10.1016/j.tig.2025.04.011. Online ahead of print.

ABSTRACT

LINE-1 (long interspersed nuclear element 1, L1) retrotransposons constitute ~17% of human DNA (~0.5 million genomic L1 copies) and exhibit context-dependent expression in different cell lines. Recent studies reveal that L1 is under multilayered control by diverse factors that either collaborate or compete with each other to ensure precise L1 activity. Remarkably, L1s have been co-opted as various transcription-dependent regulatory elements, such as promoters, enhancers, and topologically associating domain (TAD) boundaries, that regulate gene expression in zygotic genome activation, aging, cancer, and other disorders. This review highlights the regulation of L1 and its regulatory functions that influence disease and development.

PMID:40382218 | DOI:10.1016/j.tig.2025.04.011

Int J Radiat Oncol Biol Phys. 2025 Jun 1;122(2):524. doi: 10.1016/j.ijrobp.2025.03.057.

NO ABSTRACT

PMID:40382175 | DOI:10.1016/j.ijrobp.2025.03.057

J Mol Biol. 2025 May 15:169222. doi: 10.1016/j.jmb.2025.169222. Online ahead of print.

NO ABSTRACT

PMID:40381984 | DOI:10.1016/j.jmb.2025.169222

Neurobiol Dis. 2025 May 15:106954. doi: 10.1016/j.nbd.2025.106954. Online ahead of print.

ABSTRACT

Pathogenic variants in the neuronal Na+/K+ ATPase transmembrane ion transporter (ATP1A3) cause a spectrum of neurological disorders including alternating hemiplegia of childhood (AHC). The most common de novo pathogenic variants in AHC are p.D801N (~40 % of patients) and p.E815K (~25 % of patients), which lead to early mortality by spontaneous death in mice. Nevertheless, knowledge of the development of clinically relevant neurological phenotypes without the obstacle of premature death, is critical for the identification of pathophysiological mechanisms and ultimately, for the testing of therapeutic strategies in disease models. Here, we used hybrid vigor attempting to mitigate the fragility of AHC mice and then performed behavioral, electrophysiological, biochemical, and molecular testing to comparatively analyze mice that carry either of the two most common AHC patient observed variants in the Atp1a3 gene. Collectively, our data reveal the presence but also the differential impact of the p.D801N and p.E815K variants on disease relevant alterations such as spontaneous and stress-induced paroxysmal episodes, motor function, behavioral and neurophysiological activity, and neuroinflammation. Our alternate AHC mouse models with their phenotypic deficits open novel avenues for the investigation of disease biology and therapeutic testing for ATP1A3 research.

PMID:40381892 | DOI:10.1016/j.nbd.2025.106954

Antiviral Res. 2025 May 15:106190. doi: 10.1016/j.antiviral.2025.106190. Online ahead of print.

ABSTRACT

LP-98 is a lipopeptide-based HIV fusion inhibitor with exceptional potency and long-acting antiviral activity, currently in phase II clinical trials. In this study, we elucidated the structural basis of LP-98's antiviral activity and resistance mechanisms. Using AlphaFold3, we first predicted the six-helical bundle (6-HB) structure formed by LP-98 and the gp41-derived NHR peptide N44, identifying key residues mediating interhelical interactions. Subsequent crystallographic analysis of the LP-98/N44 complex confirmed these binding features, revealing that a cluster of hydrophobic residues in LP-98, along with a network of 15 hydrogen bonds, two electrostatic interactions and a salt bridge, critically stabilizes the 6-HB structure. Superposition analyses of the LP-98/N44 crystal structure with either the predicted 6-HB model or the LP-40/N44 crystal structure provided further mechanistic insights into LP-98's binding mode. Additionally, structural and functional characterization of the N-terminal Tyr-127 residue using a truncated variant (LP-98-Y) demonstrated its essential role in inhibitor binding and antiviral activity. Notably, LP-98 exhibited significantly reduced efficacy against T20-resistant HIV strains harboring single or double mutations in NHR. Our structural models shed light on the molecular basis of this resistance, offering critical insights for drug optimization. Collectively, these findings provide a detailed structural understanding of LP-98's antiviral mechanism, supporting its continued development as a promising next-generation HIV fusion inhibitor.

PMID:40381660 | DOI:10.1016/j.antiviral.2025.106190

Neuron. 2025 May 13:S0896-6273(25)00298-3. doi: 10.1016/j.neuron.2025.04.015. Online ahead of print.

ABSTRACT

We report a role for activity in the development of the primary sensory neurons that detect touch. Genetic deletion of Piezo2, the principal mechanosensitive ion channel in somatosensory neurons, caused profound changes in the formation of mechanosensory end-organ structures. Peripheral-nervous-system-specific deletion of the voltage-gated sodium channel Nav1.6 (Scn8a), which resulted in altered electrophysiological responses to mechanical stimuli, also disrupted somatosensory neuron morphologies, supporting a role for neuronal activity in end-organ formation. Single-cell RNA sequencing of Piezo2 mutants revealed changes in gene expression in sensory neurons activated by light mechanical forces, whereas other neuronal classes were minimally affected, and genetic deletion of Piezo2-dependent genes partially reproduced the defects in mechanosensory neuron structures observed in Piezo2 mutants. These findings indicate that mechanically evoked neuronal activity acts early in life to shape the maturation of mechanosensory end-organs that underlie our sense of gentle touch.

PMID:40381613 | DOI:10.1016/j.neuron.2025.04.015

Pediatr Neurol. 2025 Apr 25;168:67-81. doi: 10.1016/j.pediatrneurol.2025.04.010. Online ahead of print.

ABSTRACT

Developmental and epileptic encephalopathies (DEEs) are severe neurological disorders characterized by childhood-onset seizures and significant developmental impairments. Seizures are often refractory to treatment with traditional antiseizure medications, which fail to address the underlying genetic and molecular mechanisms. This comprehensive review explores the evolving landscape of precision therapeutics for DEEs, focusing on mechanism-driven interventions across key pathophysiologic categories. Targeted approaches for channelopathies include antisense oligonucleotides and gene therapies, such as zorevunersen and ETX101 for SCN1A-related Dravet syndrome, alongside novel small molecules for other ion channel disorders. Advances in targeting neurotransmitter receptor dysfunctions, including γ-aminobutyric acid and glutamate receptor variants, highlight the use of modulators such as gaboxadol, radiprodil, and l-serine, alongside emerging gene therapies. For synaptic dysfunctions, innovative treatments such as chemical chaperones for STXBP1-related disorders and Ras-Raf-MEK-ERK inhibitors for SYNGAP1 pathologies are discussed. The review also examines precision interventions targeting cellular signaling pathways in tuberous sclerosis complex, epigenetic regulation in Rett syndrome, and metabolic interventions like ketogenic diets and targeted supplementation for specific genetic etiologies. Additionally, the importance of enhancing access to genetic testing, conducting robust natural history studies, and employing innovative clinical trial designs is emphasized. Future directions focus on addressing the challenges in developing and implementing gene-based therapies, integrating systems biology, leveraging artificial intelligence for data analysis, and fostering collaboration among stakeholders. The rapidly advancing field of precision therapeutics for DEEs holds promise to improve outcomes through tailored, equitable, and patient-centered care.

PMID:40381457 | DOI:10.1016/j.pediatrneurol.2025.04.010

Neoplasia. 2025 May 16;66:101173. doi: 10.1016/j.neo.2025.101173. Online ahead of print.

ABSTRACT

Obesity-related oesophageal adenocarcinoma (OAC), arising from Barrett's oesophagus (BO), incidence rates are rising coincident with high-fat diets. However, adipose tissue phenotype drives metabolic characteristics. Prior feeding studies demonstrated that obesogenic diets enriched in saturated fatty acids (SFA) induce a more adverse metabolic and pro-inflammatory adipose phenotype, compared to monounsaturated fatty acids (MUFA) enriched high-fat diets, despite equal obesity. We hypothesise that different fatty acids may alter the progression of BO to OAC, wherein SFA may be more pathogenic compared to MUFA. Proteomic analysis shows that SFA, not MUFA, increases fatty acid metabolism, oncogenic signalling, and mitochondrial respiratory chain to a greater extent in BO but not in OAC cells. Cellular metabolic analysis validated proteomic findings to show mitochondrial dysfunction in BO but showed an increase in glycolysis in OAC following SFA treatment compared to MUFA. Additionally, it showed a decrease in mitochondrial ATP production following treatment of SFA in BO and OAC cells. Reduction of SFA intake may be beneficial as a supplementary treatment approach to manage and/or prevent OAC progression.

PMID:40381373 | DOI:10.1016/j.neo.2025.101173

ISME J. 2025 May 17:wraf096. doi: 10.1093/ismejo/wraf096. Online ahead of print.

ABSTRACT

Phytoplankton account for nearly half of global photosynthetic carbon fixation, and the fate of that carbon is regulated in large part by microbial food web processing. We currently lack a mechanistic understanding of how interactions among heterotrophic bacteria impact the fate of photosynthetically fixed carbon. Here, we used a set of bacterial isolates capable of growing on exudates from the diatom Phaeodactylum tricornutum to investigate how bacteria-bacteria interactions affect the balance between exudate remineralization and incorporation into biomass. With exometabolomics and genome-scale metabolic modeling, we estimated the degree of resource competition between bacterial pairs. In a sequential spent media experiment, we found that pairwise interactions were more beneficial than predicted based on resource competition alone, and 30% exhibited facilitative interactions. To link this to carbon fate, we used single-cell isotope tracing in a custom cultivation system to compare the impact of different "primary" bacterial strains in close proximity to live P. tricornutum on a distal "secondary" strain. We found that a primary strain with a high degree of competition decreased secondary strain carbon drawdown by 51% at the single-cell level, providing a quantitative metric for the "cost" of competition on algal carbon fate. Additionally, a primary strain classified as facilitative based on sequential interactions increased total algal-derived carbon assimilation by 7.6 times, integrated over all members, compared to the competitive primary strain. Our findings suggest that the degree of interaction between bacteria along a spectrum from competitive to facilitative is directly linked to algal carbon drawdown.

PMID:40381217 | DOI:10.1093/ismejo/wraf096

Cell Rep. 2025 May 15;44(5):115710. doi: 10.1016/j.celrep.2025.115710. Online ahead of print.

ABSTRACT

The importance of serine as a metabolic regulator is well known for tumors and is also gaining attention in degenerative diseases. Recent data indicate that de novo serine biosynthesis is an integral component of the metabolic response to mitochondrial disease, but the roles of the response have remained unknown. Here, we report that glucose-driven de novo serine biosynthesis maintains metabolic homeostasis in energetic stress. Pharmacological inhibition of the rate-limiting enzyme, phosphoglycerate dehydrogenase (PHGDH), aggravated mitochondrial muscle disease, suppressed oxidative phosphorylation and mitochondrial translation, altered whole-cell lipid profiles, and enhanced the mitochondrial integrated stress response (ISRmt) in vivo in skeletal muscle and in cultured cells. Our evidence indicates that de novo serine biosynthesis is essential to maintain mitochondrial respiration, redox balance, and cellular lipid homeostasis in skeletal muscle with mitochondrial dysfunction. Our evidence implies that interventions activating de novo serine synthesis may protect against mitochondrial failure in skeletal muscle.

PMID:40381195 | DOI:10.1016/j.celrep.2025.115710

Mol Neurobiol. 2025 May 17. doi: 10.1007/s12035-025-05047-5. Online ahead of print.

ABSTRACT

Disruption of the Wnt signaling pathway (WSP), a highly conserved pathway essential for growth and organ development, has been proven to play a role in the pathogenesis of Alzheimer's disease (AD). This study focused on repurposing the FDA-approved drug, Voglibose to target the DKK1-LRP6 site with the goal of upregulating WSP in in vitro as well as rodent model of AD. Based on our previous computational approach, Voglibose was evaluated for the DKK1 binding, neuroprotective effects were examined using SHSY5Y cells, while WSP activation was analyzed through RTPCR in the HEK293/LRP6 cell line. Rodent model of AD was developed using intracerebroventricular administration of Aβ25-35. Male Wistar rats were randomly assigned to receive oral doses of Voglibose (1 and 10 mg/kg) for 28 days, after which behavioral assessments, biochemical analyses, RT-PCR, and histopathological evaluations were conducted. Voglibose showed significant reduction in the DKK1 binding, neuroprotective property in SHSY5Y as well as activation of WSP in LRP6 overexpressed HEK293 cells. There was a significant decrease in the island latency in rats treated with lower dose (p < 0.01) and higher dose (p < 0.05) of Voglibose when compared to the disease control rats. Similarly, in the behavioral tests, Voglibose significantly improved cognition. The deposition of amyloid plaques was found to be considerably more in the disease control rats which got reduced in the treatment groups as observed in the histopathological slides stained with Congo red. Significant alterations in mRNA levels and protein expression of glycogen synthase kinase-β (GSK-3β), β-catenin (β-cat) was observed in rat brain homogenates indicating upregulation of WSP. In conclusion, Voglibose demonstrated significant neuroprotective potential in a cell line study and showed potential cognitive benefits in a rat model of AD. Furthermore, its ability to activate WSP highlights its immense potential as AD therapeutic to enhance memory and modulate key neuroprotective mechanisms.

PMID:40381169 | DOI:10.1007/s12035-025-05047-5

Biotechnol Lett. 2025 May 17;47(3):56. doi: 10.1007/s10529-025-03596-2.

ABSTRACT

Orphan diseases comprise a range of disorders that individually affect a small percentage of people, but collectively impact millions of people worldwide. Patients with this disorder often face significant challenges in diagnosis, treatment, and access to care due to their rare nature and limited understanding and treatment options. In recent years, significant advancements have been made in the global healthcare in addressing the accessibility of essential treatments and medicines, but still challenges persist particularly related to orphan drugs (to treat rare diseases) in the developing world. The accessibility of orphan drugs remains a major challenge, where patients face barriers such as high costs, limited availability, and inadequate healthcare infrastructure. The high cost associated with orphan drugs presents a barrier to affordability for both patients and healthcare systems, causing disparities in access to life-saving treatments. The molecular farming approach utilizing plant-based production systems for recombinant protein production offers a hope for overcoming barriers to orphan drug access in resource-constrained settings. Molecular farming has the potential to produce a wide range of therapeutic proteins and biologics for the treatment of various rare diseases. The FDA approval of plant-derived proteins for the treatment of Gaucher disease (Elelyso) and Fabry disease (Elfabrio) highlights the potential of plant-based expression systems for the development of suitable drugs targeting niche and orphan diseases. This review examines the potential of the plant system in producing orphan drugs and also highlights the opportunities and challenges related to orphan drug manufacturing.

PMID:40381123 | DOI:10.1007/s10529-025-03596-2