Asian J Androl. 2025 Mar 11. doi: 10.4103/aja2024124. Online ahead of print.

ABSTRACT

Individuals with congenital absence of the vas deferens (CAVD) may transmit cystic fibrosis (CF)-causing variants of the cystic fibrosis transmembrane conductance regulator (CFTR) gene to their offspring through assisted reproductive technology (ART). We aimed to delineate the spectrum and estimate the prevalence of CF-causing variants in Chinese individuals with CAVD through a cohort analysis and meta-analysis. CFTR was sequenced in 145 Chinese individuals with CAVD. CFTR variants were classified as CF-causing or non-CF-causing variants regarding clinical significance. A comprehensive genotype analysis was performed in Chinese individuals with CAVD, incorporating previous studies and our study cohort. The prevalence of CF-causing variants was estimated through meta-analysis. In our cohort, 56 different CFTR variants were identified in 108 (74.5%) patients. Twenty variants were categorized as CF-causing and were detected in 28 (19.3%) patients. A comprehensive genotype analysis of 867 patients identified 174 different CFTR variants. Sixty-four were classified as CF-causing variants, 56.3% of which had not been previously reported in Chinese patients with CF. Meta-analysis showed that 14.8% (95% confidence interval [CI]: 11.0%-18.9%) CAVD cases harbored one CF-causing variant, and 68.6% (95% CI: 65.1%-72.0%) CAVD cases carried at least one CFTR variant. Our study underscores the urgent need for extensive CFTR screening, including sequencing of whole exons and flanking regions and detection of large rearrangements and deep intronic CF-causing variants, in Chinese individuals with CAVD before undergoing ART. The established CF-causing variants spectrum may aid in the development of genetic counseling strategies and preimplantation diagnosis to prevent the birth of a child with CF.

PMID:40065563 | DOI:10.4103/aja2024124

Nutr Hosp. 2025 Feb 26. doi: 10.20960/nh.05692. Online ahead of print.

ABSTRACT

ChatGPT and other artificial intelligence (AI) tools can modify nutritional management in clinical settings. These technologies, based on machine learning and deep learning, enable the identification of risks, the proposal of personalized interventions, and the monitoring of patient progress using data extracted from clinical records. ChatGPT excels in areas such as nutritional assessment by calculating caloric needs and suggesting nutrient-rich foods, and in diagnosis, by identifying nutritional issues with technical terminology. In interventions, it offers dietary and educational strategies but lacks critical abilities such as interpreting non-verbal cues or performing physical examinations. Recent studies indicate that ChatGPT achieves high accuracy in questions related to clinical guidelines but shows deficiencies in integrating multiple medical conditions or ensuring the accuracy of meal plans. Additionally, generated plans may exhibit significant caloric deviations and imbalances in micronutrients such as vitamin D and B12. Despite its limitations, this AI has the potential to complement clinical practice by improving accessibility and personalization in nutritional care. However, its effective implementation requires professional supervision, integration with existing healthcare systems, and constant updates to its databases. In conclusion, while it does not replace nutrition experts, ChatGPT can serve as a valuable tool to optimize nutrition education and management of our patiens, always under the guidance of trained professionals.

PMID:40066572 | DOI:10.20960/nh.05692

Front Cardiovasc Med. 2025 Feb 24;12:1473544. doi: 10.3389/fcvm.2025.1473544. eCollection 2025.

ABSTRACT

BACKGROUND: Congenital heart disease (CHD) is a major contributor to morbidity and infant mortality and imposes the highest burden on global healthcare costs. Early diagnosis and prompt treatment of CHD contribute to enhanced neonatal outcomes and survival rates; however, there is a shortage of proficient examiners in remote regions. Artificial intelligence (AI)-powered ultrasound provides a potential solution to improve the diagnostic accuracy of fetal CHD screening.

METHODS: A literature search was conducted across seven databases for systematic review. Articles were retrieved based on PRISMA Flow 2020 and inclusion and exclusion criteria. Eligible diagnostic data were further meta-analyzed, and the risk of bias was tested using Quality Assessment of Diagnostic Accuracy Studies-Artificial Intelligence.

FINDINGS: A total of 374 studies were screened for eligibility, but only 9 studies were included. Most studies utilized deep learning models using either ultrasound or echocardiographic images. Overall, the AI models performed exceptionally well in accurately identifying normal and abnormal ultrasound images. A meta-analysis of these nine studies on CHD diagnosis resulted in a pooled sensitivity of 0.89 (0.81-0.94), a specificity of 0.91 (0.87-0.94), and an area under the curve of 0.952 using a random-effects model.

CONCLUSION: Although several limitations must be addressed before AI models can be implemented in clinical practice, AI has shown promising results in CHD diagnosis. Nevertheless, prospective studies with bigger datasets and more inclusive populations are needed to compare AI algorithms to conventional methods.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461738, PROSPERO (CRD42023461738).

PMID:40066351 | PMC:PMC11891181 | DOI:10.3389/fcvm.2025.1473544

Heliyon. 2025 Feb 19;11(4):e42802. doi: 10.1016/j.heliyon.2025.e42802. eCollection 2025 Feb 28.

ABSTRACT

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors.

PMID:40066024 | PMC:PMC11891688 | DOI:10.1016/j.heliyon.2025.e42802

J Korean Med Sci. 2025 Mar 10;40(9):e25. doi: 10.3346/jkms.2025.40.e25.

ABSTRACT

BACKGROUND: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories.

METHODS: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups.

RESULTS: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08-21.98).

CONCLUSION: Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia. Appropriate therapeutic strategies should be tailored based on these disease trajectories.

PMID:40065711 | DOI:10.3346/jkms.2025.40.e25

J Korean Med Sci. 2025 Mar 10;40(9):e20. doi: 10.3346/jkms.2025.40.e20.

ABSTRACT

BACKGROUND: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia.

METHODS: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests. These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split.

RESULTS: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934. Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts.

CONCLUSION: The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

PMID:40065710 | DOI:10.3346/jkms.2025.40.e20

Cancer Imaging. 2025 Mar 10;25(1):27. doi: 10.1186/s40644-025-00849-1.

ABSTRACT

BACKGROUND: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).

METHODS: This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.

RESULTS: On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745-0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.

CONCLUSIONS: The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.

PMID:40065444 | DOI:10.1186/s40644-025-00849-1

Breast Cancer Res. 2025 Mar 10;27(1):34. doi: 10.1186/s13058-025-01981-3.

ABSTRACT

BACKGROUND: Traditional surgical education is based on observation and assistance in surgical practice. Recently introduced deep learning (DL) techniques enable the recognition of the surgical view and automatic identification of surgical landmarks. However, there was no previous studies have conducted to develop surgical guide for robotic breast surgery. To develop a DL model for guiding the dissection plane during robotic mastectomy for beginners and trainees.

METHODS: Ten surgical videos of robotic mastectomy procedures were recorded. Video frames taken at 1-s intervals were converted to PNG format. The ground truth was manually delineated by two experienced surgeons using ImageJ software. The evaluation metrics were the Dice similarity coefficient (DSC) and Hausdorff distance (HD).

RESULTS: A total of 8,834 images were extracted from ten surgical videos of robotic mastectomies performed between 2016 and 2020. Skin flap dissection during the robotic mastectomy console time was recorded. The median age and body mass index of the patients was 47.5 (38-52) years and 22.00 (19.30-29.52) kg/m2, respectively, and the median console time was 32 (21-48) min. Among the 8,834 images, 428 were selected and divided into training, validation, and testing datasets at a ratio of 7:1:2. Two experts determined that the DSC of our model was 0.828[Formula: see text]5.28 and 0.818[Formula: see text]6.96, while the HDs were 9.80[Formula: see text]2.57 and 10.32[Formula: see text]1.09.

CONCLUSION: DL can serve as a surgical guide for beginners and trainees, and can be used as a training tool to enhance surgeons' surgical skills.

PMID:40065440 | DOI:10.1186/s13058-025-01981-3

Genome Biol. 2025 Mar 10;26(1):51. doi: 10.1186/s13059-025-03516-7.

ABSTRACT

BACKGROUND: Identifying transcriptional cis-regulatory elements (CREs) and understanding their role in gene expression are essential for the precise manipulation of gene expression and associated phenotypes. This knowledge is fundamental for advancing genetic engineering and improving crop traits.

RESULTS: We here demonstrate that CREs can be accurately predicted and utilized to precisely regulate gene expression beyond the range of natural variation. We firstly build two sequence-to-expression deep learning models to respectively identify distal and proximal CREs by combining them with interpretability methods in multiple crops. A large number of distal CREs are verified for enhancer activity in vitro using UMI-STARR-seq on 12,000 synthesized sequences. These comprehensively characterized CREs and their precisely predicted effects further contribute to the design of in silico editing schemes for precise engineering of gene expression. We introduce a novel concept of "editingplasticity" to evaluate the potential of promoter editing to alter expression of each gene. As a proof of concept, both exhaustive prediction and random knockout mutants are analyzed within the promoter region of ZmVTE4, a key gene affecting α-tocopherol content in maize. A high degree of agreement between predicted and observed expression is observed, extending the range of natural variation and thereby allowing the creation of an optimal phenotype.

CONCLUSIONS: Our study provides a robust computational framework that advances knowledge-guided gene editing for precise regulation of gene expression and crop improvement. By reliably predicting and validating CREs, we offer a tool for targeted genetic modifications, enhancing desirable traits in crops.

PMID:40065399 | DOI:10.1186/s13059-025-03516-7

BMC Med Inform Decis Mak. 2025 Mar 10;25(1):124. doi: 10.1186/s12911-025-02961-5.

ABSTRACT

BACKGROUND: As part of qualitative research, the thematic analysis is time-consuming and technical. The rise of generative artificial intelligence (A.I.), especially large language models, has brought hope in enhancing and partly automating thematic analysis.

METHODS: The study assessed the relative efficacy of conventional against AI-assisted thematic analysis when investigating the psychosocial impact of cutaneous leishmaniasis (CL) scars. Four hundred forty-eight participant responses from a core study were analysed comparing nine A.I. generative models: Llama 3.1 405B, Claude 3.5 Sonnet, NotebookLM, Gemini 1.5 Advanced Ultra, ChatGPT o1-Pro, ChatGPT o1, GrokV2, DeepSeekV3, Gemini 2.0 Advanced with manual expert analysis. Jamovi software maintained methodological rigour through Cohen's Kappa coefficient calculations for concordance assessment and similarity measurement via Python using Jaccard index computations.

RESULTS: Advanced A.I. models showed impressive congruence with reference standards; some even had perfect concordance (Jaccard index = 1.00). Gender-specific analyses demonstrated consistent performance across subgroups, allowing a nuanced understanding of psychosocial consequences. The grounded theory process developed the framework for the fragile circle of vulnerabilities that incorporated new insights into CL-related psychosocial complexity while establishing novel dimensions.

CONCLUSIONS: This study shows how A.I. can be incorporated in qualitative research methodology, particularly in complex psychosocial analysis. Consequently, the A.I. deep learning models proved to be highly efficient and accurate. These findings imply that the future directions for qualitative research methodology should focus on maintaining analytical rigour through the utilisation of technology using a combination of A.I. capabilities and human expertise following standardised future checklist of reporting full process transparency.

PMID:40065373 | DOI:10.1186/s12911-025-02961-5

J Transl Med. 2025 Mar 10;23(1):298. doi: 10.1186/s12967-025-06254-3.

ABSTRACT

BACKGROUND: The presence of tumour-infiltrating lymphocytes (TILs) is a well-established prognostic biomarker across multiple cancer types, with higher TIL counts being associated with lower recurrence rates and improved patient survival. We aimed to examine whether an automated intraepithelial TIL (iTIL) assessment could stratify patients by risk, with the ability to generalise across independent patient cohorts, using routine H&E slides of colorectal cancer (CRC). To our knowledge, no other existing fully automated iTIL system has demonstrated this capability.

METHODS: An automated method employing deep neural networks was developed to enumerate iTILs in H&E slides of CRC. The method was applied to a Stage III discovery cohort (n = 353) to identify an optimal threshold of 17 iTILs per-mm2 tumour for stratifying relapse-free survival. Using this threshold, patients from two independent Stage II-III validation cohorts (n = 1070, n = 885) were classified as "TIL-High" or "TIL-Low".

RESULTS: Significant stratification was observed in terms of overall survival for a combined validation cohort univariate (HR 1.67, 95%CI 1.39-2.00; p < 0.001) and multivariate (HR 1.37, 95%CI 1.13-1.66; p = 0.001) analysis. Our iTIL classifier was an independent prognostic factor within proficient DNA mismatch repair (pMMR) Stage II CRC cases with clinical high-risk features. Of these, those classified as TIL-High had outcomes similar to pMMR clinical low risk cases, and those classified TIL-Low had significantly poorer outcomes (univariate HR 2.38, 95%CI 1.57-3.61; p < 0.001, multivariate HR 2.17, 95%CI 1.42-3.33; p < 0.001).

CONCLUSIONS: Our deep learning method is the first fully automated system to stratify patient outcome by analysing TILs in H&E slides of CRC, that has shown generalisation capabilities across multiple independent cohorts.

PMID:40065354 | DOI:10.1186/s12967-025-06254-3

Cureus. 2025 Mar 9;17(3):e80290. doi: 10.7759/cureus.80290. eCollection 2025 Mar.

ABSTRACT

OBJECTIVE: This retrospective study aimed to determine the prevalence of progression in fibrotic interstitial lung disease (ILD) and the findings at diagnosis most associated with progression after two years of follow-up in a large Brazilian cohort.

METHODS: This was a retrospective multicenter observational study in Brazil. Progression was defined after two years of follow-up. We excluded patients with an initial peripheral oxygen saturation (SpO2) of less than 88% or an initial forced vital capacity (FVC) of less than 45%. Diagnoses were made by multidisciplinary discussion. Patients with idiopathic pulmonary fibrosis were included for comparison. At least one of the following events was indicative of progressive ILD: (1) a relative decrease in FVC of 10% or more, (2) worsening dyspnea, (3) a greater extent of fibrotic findings on high-resolution computed tomography (HRCT), (4) initiation of oxygen, and (5) death attributed to ILD. Logistic regression analysis was used to identify risk factors for progressive fibrosis.

RESULTS: The mean age of patients was 61.7±12.3 years, and 69.5% had Velcro crackles. The mean FVC was 71.6±15.8%, and 26.1% showed honeycombing on HRCT. After two years of follow-up, 40.5% of patients (n=154) showed disease progression. Fibrotic hypersensitivity pneumonitis (FHP) was the most progressive disease (52%), and connective tissue disease-associated ILD (CTD-ILD) was the least progressive (25%). Multivariate analysis showed that a higher score for dyspnea, crackles, and SpO2 at rest ≤94% and ≤85% at the end of exercise were significant indicators of progression. Diffusing lung capacity for carbon monoxide (DLCO) was measured in 172 cases, with values <55% predicting a high odds ratio for progression (OR=4.03; 2.10-7.69).

CONCLUSION: In Brazil, FHP is the most progressive disease and CTD-ILD is the least progressive after two years of follow-up. The degree of dyspnea, crackles, SpO2 at rest and during exercise, and DLCO at baseline are associated with progressive disease.

PMID:40066320 | PMC:PMC11892080 | DOI:10.7759/cureus.80290

Front Med (Lausanne). 2025 Feb 24;12:1542400. doi: 10.3389/fmed.2025.1542400. eCollection 2025.

ABSTRACT

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects millions of people worldwide and is characterized by persistent inflammation, pain, and joint destruction. In RA, the dysregulation of the immune system is well documented. However, the genetic basis of the disease is not fully understood, especially when extra-articular organs are involved. Interstitial lung disease (ILD) is a major cause of morbidity and mortality in patients with RA. Notably, RA-ILD shares several risk factors with idiopathic pulmonary fibrosis (IPF), namely male gender, smoking history, usual interstitial pneumonia (UIP) pattern of fibrosis, and association with the MUC5B rs35705950 polymorphism. In addition, other genetic susceptibilities are reported in RA-ILD for some HLA alleles and other less studied polymorphisms. However, the pathobiology of RA-ILD, particularly whether and to what extent genetic and environmental factors interact to determine the disease, remains elusive. In this review, we summarize and critically discuss the most recent literature on the genetics and pathogenesis of RA-ILD. The main clinical aspects of RA-ILD are also discussed.

PMID:40066169 | PMC:PMC11891064 | DOI:10.3389/fmed.2025.1542400

Clin Transl Sci. 2025 Mar;18(3):e70179. doi: 10.1111/cts.70179.

ABSTRACT

SC1011 (sufenidone) is a novel pyridone derivative with therapeutic potential for idiopathic pulmonary fibrosis (IPF). Two Phase 1 studies evaluated the safety and pharmacokinetics of single (SAD) and multiple ascending doses (MAD) of SC1011 immediate-release (IR) and modified-release (MR) oral formulations in healthy adult subjects. In Phase 1a, subjects were randomized to receive oral SC1011 IR or placebo in SAD (50 mg-300 mg) or MAD (100 mg and 200 mg) twice daily for 7 days. The Phase 1b study consisted of three treatment groups that received 100, 150, or 200 mg SC1011 MR twice daily for 7 days. SC1011 IR was absorbed rapidly (mean time to maximum concentration, Tmax ≤ 1 h) and eliminated rapidly (mean terminal half-life, t1/2: 1.23-2.64 h) following 50-300 mg single-dose administrations. Reduced maximum plasma concentration (Cmax), delayed Tmax, and comparable total exposure were observed with the MR formulation compared with the IR formulation. Both formulations demonstrated dose-proportional pharmacokinetics at the applied dose ranges, and no obvious accumulation of systemic exposure was observed upon repeated administration. All treatment-emergent adverse events (TEAEs) with both formulations were mild or moderate in severity, and gastrointestinal reactions were the most frequently reported TEAEs. The tolerability of SC1011 was markedly improved with the MR formulation. Exposure-adverse event (AE) analysis with the most frequent AEs identified Cmax rather than total exposure as a good predictor of AEs. Compared to the IR formulation, SC1011 MR demonstrated improved exposure and tolerability, supporting its further development in patients with IPF.

PMID:40065557 | DOI:10.1111/cts.70179

Respir Res. 2025 Mar 10;26(1):97. doi: 10.1186/s12931-025-03164-2.

ABSTRACT

BACKGROUND/RATIONALE: Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive disease of unknown origin. Establishing the epidemiology of IPF has been challenging due to diagnostic complexity, poor survival, low prevalence, and heterogeneity of ascertainment methodologies.

OBJECTIVES: This research aimed to estimate the rates of IPF in central and western Pennsylvania and to pilot the use of capture recapture (CR) methods to estimate the disease incidence.

METHODS: We identified adults ≥ 30 years old diagnosed with IPF (by ICD-9/10 coding) between 2013 to 2021 from two health systems (UPMC Health System and Penn State Health) participating in the PaTH Clinical Research Network. We extracted information on patients' sex, race, date of birth and 3-digit zip code from electronic health records (EHR). Incidence rate of IPF among Pennsylvania residents was calculated using three case definitions (broad and two restricted) and piloted the use of CR in estimating IPF incidence.

RESULTS: IPF incidence rates were 8.42, 6.95 and 4.4 per 100,000 person-years for the unrestricted (n = 3148), partially restricted (n = 2598) and fully restricted (n = 1661) samples, respectively. Low case overlap between two sites resulted in a highly inflated estimate of IPF incidence, using the CR methodology.

CONCLUSIONS: The rate of IPF in central and western Pennsylvania was similar to previously published statistics. The application of CR to IPF epidemiology could be further investigated in health systems with greater overlap of patients utilizing more than one system.

PMID:40065350 | DOI:10.1186/s12931-025-03164-2

Front Plant Sci. 2025 Feb 24;15:1442324. doi: 10.3389/fpls.2024.1442324. eCollection 2024.

ABSTRACT

Cassava is a crucial source of daily calorie intake for millions of people in sub-Saharan Africa (SSA) but has an inferior protein content. Despite numerous attempts utilizing both traditional and biotechnological methods, efforts to address protein deficiency in cassava have yet to meet with much success. We aim to leverage modern biotechnologies to enhance cassava's nutritional value by creating bioengineered cassava cultivars with increased protein and starch content. In this study, we utilized Qui-Quine Starch (QQS), a novel orphan gene unique to Arabidopsis thaliana, to develop transgenic cassava plants with increased protein and starch accumulation in their tissues. A total of 10 independent transgenic cassava lines expressing QQS were successfully regenerated in this study, among which line R7 (F) demonstrated superior growth vigor. Quantitative RT-PCR verified the expression of the QQS gene in the transgenic lines. Data showed that QQS expression in cassava plants increased leaf protein content by 36% in line R''' (LA) L2 and root protein by 17% for the same line compared to their wild-type and empty vector (NPTII) control plants. Moreover, leaf-soluble total carbohydrates increased by 51.76% in line R (G) L2, and root-soluble total carbohydrates increased by 46.75% in line R7 (F). The novel function of QQS in increasing the starch content in the transgenic biomass is demonstrated. No significant change in the content of specific amino acids was observed among the lines and various plant parts. In addition, QQS expression revealed increased biomass, plant vigor, and early In vitro mini-tubers production for line R7 (F). Gene interaction study between AtQQS and 59 interacting partners generated 184 interactions or edges. These gene networks comprised several functional categories regulating the starch metabolic and auxin biosynthetic processes. The role of QQS in imparting starch and protein content of transgenic cassava plants is validated. The next logical step is the evaluation of biochemical profiles of cassava lines expressing QQS that reach maturity and the transferability of these findings to consumer-preferred cassava cultivars and local landraces grown in SSA. This study represents the first biotechnological report demonstrating a simultaneous increase of protein and starch content in bioengineered cassava.

PMID:40066347 | PMC:PMC11891011 | DOI:10.3389/fpls.2024.1442324

NPJ Drug Discov. 2025;2(1):3. doi: 10.1038/s44386-025-00006-5. Epub 2025 Mar 4.

ABSTRACT

Identification of novel drug targets is a key component of modern drug discovery. While antimalarial targets are often identified through the mechanism of action studies on phenotypically derived inhibitors, this method tends to be time- and resource-consuming. The discoverable target space is also constrained by existing compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction, we systematically assessed the Plasmodium falciparum genome and identified 867 candidate protein targets with evidence of small-molecule binding and blood-stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 27 high-priority antimalarial target candidates. This study also provides a genome-wide data resource for P. falciparum and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

PMID:40066064 | PMC:PMC11892419 | DOI:10.1038/s44386-025-00006-5

J Infect Dis. 2025 Mar 11:jiaf125. doi: 10.1093/infdis/jiaf125. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets ciliated cells during the initial infection of the upper respiratory tract. Since uncertainties persist regarding other involved epithelial cell types, we here utilized viral replication analysis, single-cell RNA sequencing, and spectral microscopy on infected air-liquid interface cultures of human primary nasal and bronchial epithelial cells to discern cell type proportions in relation to SARS-CoV-2 tropism and immune activation. We revealed that, next to ciliated and secretory cells, SARS-CoV-2 (wild type and lineage B1.1.7 [Alpha variant]) strongly infects basal cells, significantly contributing to the epithelial immune response in a donor-specific manner. Moreover, local Camostat mesylate treatment was effective on both the basal and apical cell compartment, resulting in a notable reduction in viral load and reduced immune activation. Collectively, our data emphasize the critical role of basal cells in facilitating SARS-CoV-2 dissemination within the upper respiratory tract and their substantial contribution to the epithelial immune response. Furthermore, our results highlight the potential of local application of Camostat mesylate as an effective strategy for inhibiting SARS-CoV-2 infection and mitigating associated immune activation early on.

PMID:40065723 | DOI:10.1093/infdis/jiaf125

Nat Rev Genet. 2025 Mar 10. doi: 10.1038/s41576-025-00821-6. Online ahead of print.

ABSTRACT

Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances - from single-cell and spatial profiling to CRISPR genome editing and machine learning - have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases. However, adapting systems-inspired approaches to dissect human disease complexity is challenging, given that discrepancies between the biological features of human tissues and the experimental models typically used to probe function (which we term 'translational distance') can confound insight. Here we review how samples, measurements and analyses can be contextualized within overall multiscale human disease processes to mitigate data and representation gaps. We then examine ways to bridge the translational distance between systems-inspired human discovery loops and model system validation loops to empower precision interventions in the era of single-cell genomics.

PMID:40065155 | DOI:10.1038/s41576-025-00821-6

EMBO J. 2025 Mar 10. doi: 10.1038/s44318-025-00406-3. Online ahead of print.

ABSTRACT

Bacteria in polymicrobial habitats are constantly exposed to biotic threats from bacteriophages (or "phages"), antagonistic bacteria, and predatory eukaryotes. These antagonistic interactions play crucial roles in shaping the evolution and physiology of bacteria. To survive, bacteria have evolved mechanisms to protect themselves from such attacks, but the fitness costs of resisting one threat and rendering bacteria susceptible to others remain unappreciated. Here, we examined the fitness consequences of phage resistance in Salmonella enterica, revealing that phage-resistant variants exhibited significant fitness loss upon co-culture with competitor bacteria. These phage-resistant strains display varying degrees of lipopolysaccharide (LPS) deficiency and increased susceptibility to contact-dependent interbacterial antagonism, such as the type VI secretion system (T6SS). Utilizing mutational analyses and atomic force microscopy, we show that the long-modal length O-antigen of LPS serves as a protective barrier against T6SS-mediated intoxication. Notably, this competitive disadvantage can also be triggered independently by phages possessing LPS-targeting endoglycosidase in their tail spike proteins, which actively cleave the O-antigen upon infection. Our findings reveal two distinct mechanisms of phage-mediated LPS modifications that modulate interbacterial competition, shedding light on the dynamic microbial interplay within mixed populations.

PMID:40065098 | DOI:10.1038/s44318-025-00406-3