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PubMed2026

Anatomy and Biomechanics of the Deltoid Ligament Complex in Healthy Ankles: Protocol for a Systematic Review and Meta-Analysis.

INTRODUCTION: Up to one in two individuals who have a history of an ankle injury will develop chronic ankle instability, which subsequently increases the risk of osteoarthritis development. Although lateral ankle ligament injuries are the most common, recent research shows that concomitant injuries to the deltoid (medial collateral) ligament complex may be more prevalent than previously recognised. However, the anatomy and biomechanics of the deltoid ligament complex are reported inconsistently in the literature. This systematic review will summarise current evidence on the anatomy and biomechanics of the deltoid ligament complex in healthy adult ankles. METHODS: Searches will be conducted in Scopus, MEDLINE, Embase, CINAHL and SPORTDiscus. Our search strategy will cover terms associated with 'deltoid ligament', 'anatomy' and 'biomechanics'. We will only include dissection and imaging studies published in English that report any of the listed clinically relevant properties of the deltoid ligament in healthy adult human ankles. Two reviewers will independently perform screening and assess study quality using the anatomical quality assessment (AQUA) tool. One reviewer will extract relevant data, which will be independently verified by co-authors. Primary outcomes include band prevalence, length, width, cross-sectional area, maximum and/or failure load and elastic modulus. If three or more studies report a primary outcome, we will conduct a meta-analysis and report findings as pooled means with 95% confidence intervals. If a meta-analysis is not feasible, outcomes will be summarised as a narrative analysis. Measures will be taken during data synthesis to address anticipated methodological heterogeneity across included studies, and pooled estimates will be interpreted with caution. DISCUSSION: This protocol details a systematic review that aims to summarise the anatomy and biomechanics of the deltoid ligament complex. Our findings will inform computational modelling, clinical management and biomechanics for ankle pathologies, as well as identifying research gaps and directions for future research. TRIAL REGISTRATION: PROSPERO: CRD420251142867.

Humans↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

PubMed2026

The radiographic effect of cage subsidence on neuroforamina after anterior cervical discectomy and fusion.

STUDY DESIGN: Retrospective Cohort Study. OBJECTIVE: The objective of this study is to investigate the effect of cage subsidence on neuroforaminal area after anterior cervical discectomy and fusion (ACDF) utilizing computed tomography (CT). SUMMARY OF BACKGROUND DATA: Restoration of disc height via implantation of an interbody device provides an indirect decompression of the cervical neuroforamina. Interbody cage subsidence is a potential postoperative occurrence, but the effect of this on neuroforaminal area has yet to be characterized. METHODS: A retrospective review was conducted of patients who underwent one- to four-levels of ACDF utilizing an interbody device with anterior plating. Cage subsidence, neuroforaminal area, height and width were measured on CT scans preoperatively and at least 6 months postoperatively. Levels with a cumulative sum of cranial and caudal subsidence greater than 4 mm were classified as severely subsided, while levels with cumulative subsidence less than 4 mm were classified as non-severely subsided. RESULTS: A total of 83 patients (151 levels) were included in this retrospective analysis. Average endplate subsidence was 3.2 ± 1.9 mm. Non-severely subsided levels demonstrated a greater perioperative increase in neuroforaminal area (7.9 vs 2.1 mm2, p < 0.001), neuroforaminal height (1.1 vs 0.4 mm, p < 0.001) and neuroforaminal width (0.7 vs 0.1 mm, p < 0.001) compared to severely subsided levels. Interbody subsidence significantly predicted a decreased change in neuroforaminal height, width and area (p < 0.001). Severe subsidence was associated with an increased rate of pseudarthrosis, but similar reoperation rates and recurrent neurologic deficits between the two groups. CONCLUSIONS: Severe subsidence of interbody cages after an ACDF was associated with a decreased perioperative change in neuroforaminal dimensions. This decrease in the size of the neuroforamen may reduce the effect of indirect decompression of the nerve root.

Humans↗

PubMed2026

Screening for neurofibromatosis type 1-related optic pathway gliomas: a systematic review.

BACKGROUND: Neurofibromatosis-type 1 (NF1) is a genetic disorder characterized by developing optic pathway gliomas (OPGs) in 15%-20% of patients with higher estimates where consanguinity is prevalent. Clinically, NF1-OPG might be unpredictable with the risk of OPG progression and visual impairment. The optimal time for screening is controversial. We aim to identify the mean/median age at diagnosis of NF1-OPG and its clinical spectrum. METHODS: A systematic review of PubMed, Web of Science, and Embase databases was conducted for English-language publications from January 1993 to October 2025, exploring the visual screening of OPGs in NF1 patients, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and registered in the Prospective Register of Systematic Reviews (PROSPERO ID: CRD420251036244). Inclusion criteria focused on studies reporting the age at OPG diagnosis and visual manifestations in NF1 patients. Data were extracted on demographics, age at NF1 and OPG diagnosis, tumour location (using the Dodge classification), and presenting symptoms. Sixteen studies met the inclusion criteria. RESULTS: Among 4 739 NF1 patients, 818 had OPGs, with prevalence ranging from 4.2% to 46.7%. The age at NF1 diagnosis ranged from 0 to 132 months (mean: 18-38 months), and at OPG diagnosis from 0-240 months (median: 29-58 months). Approximately 58.4% of OPGs were asymptomatic, and 25% were above the age of 5 years. Among symptomatic patients, the most frequent presentations included decreased visual acuity (62%), abnormal optic disc (45%), proptosis (20%), strabismus (12%), and visual field defects (7%). CONCLUSIONS: NF1-related OPGs typically present early within 6 years of age. Early ophthalmologic and/or radiologic screening at the time of NF1 diagnosis enhances the detection of silent OPGs.

Humans↗

PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗

PubMed2026

Beyond da Vinci: a systematic review of next-generation multiport robotic platforms in pediatric surgery.

As robotic surgery expands beyond the da Vinci platform, the relevance of new-generation systems to pediatric patients remains uncertain. This review examined the technical characteristics, applications, and perioperative outcomes of alternative multiport robotic platforms in children. PubMed/MEDLINE, Web of Science, Scopus, and the Cochrane Library were searched through 28 February 2026 in accordance with PRISMA 2020. Owing to clinical heterogeneity, findings were synthesized narratively, with Wilson 95% confidence intervals for key binary outcomes. Six studies reported 166 patients across 27 procedure types. Senhance accounted for 164 patients, while Hugo RAS and Hinotori were each represented by one patient. Ages ranged from 15 days to 17 years and weights from 3.8 to more than 100 kg. Senhance was the only platform used with 3-mm robotic instruments. Conversion or planned escalation occurred in 19 patients (11.4%; 95% CI, 7.5-17.2%). Four intraoperative complications were reported (2.4%; 95% CI, 0.9-6.0%). Across all reports, 20 patients experienced postoperative complications (12.0%; 95% CI, 7.9-17.9%). In the largest cohort, seven patients required reintervention (4.6%; 95% CI, 2.2-9.2%) and seven were readmitted (4.6%; 95% CI, 2.2-9.2%). No deaths or comparative pediatric studies were reported. Published experience remains sparse and is almost entirely limited to Senhance. Current evidence describes early clinical use without establishing comparative safety, effectiveness, or platform superiority. Prospective multicenter studies with standardized reporting are needed.

Humans↗

PubMed2026

Factors Impacting Overall Survival Post-Relapse in High-Risk Neuroblastoma: Children's Oncology Group Outcomes From 2000 to 2019.

PURPOSE: Prior studies of features impacting post-relapse survival in high-risk neuroblastoma (HRNB) evaluated patient cohorts that did not receive contemporary high-risk or relapse therapies. We describe overall survival (OS) after first progression or first relapse of HRNB in a modern cohort. METHODS: Patients with HRNB enrolled on COG ANBL00B1(NCT00904241) between 2000 and 2019, who had relapsed or progressive disease were eligible. Clinical and molecular risk factors at diagnosis, therapy era, clinical trial enrollment, and clinical features at relapse, including site of and time to relapse, were evaluated. OS post-relapse was compared between groups using log-rank tests and Cox models. RESULTS: Among 4253 eligible HRNB patients, 1616 had relapse or progression as a first event. Five-year OS post-relapse was 19.1 ± 1.1%. The risk group with the lowest post-relapse survival was observed in patients with INSS Stage 4 or 4S disease < 18 months of age at diagnosis with MYCN amplified (MYCN-A) tumors. The other significant most unfavorable factors at diagnosis included diagnosis 2000-2004, tumor MYCN-A, 1p loss of heterozygosity (LOH), and elevated LDH or ferritin. Unfavorable factors at relapse included the time to relapse < 36 months from diagnosis, and combined local and metastatic disease at relapse. Multivariable analysis indicated that those with tumors harboring 1p LOH, age ≤ 5 years at diagnosis, or earlier treatment therapy era (2000-2004) had a higher risk of post-relapse death. CONCLUSIONS: While the 5-year OS rate was low in this cohort, there are subsets of patients with relapsed HRNB who demonstrate long-term survival. TRIALS REGISTRATION: ClinicalTrials.gov identifier: NCT00904241.

Humans↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Volumetric bone marrow cellularity (VBMC) assessment from routinely processed trephines using three-dimensional x-ray histology and gaussian peak modelling.

Objective.Bone marrow cellularity is routinely estimated from a small number of two-dimensional histology sections, making assessment sensitive to section representativeness, processing artefacts and observer interpretation. Three-dimensional (3D) x-ray histology (XRH), using x-ray computed microtomography (µCT), enables non-destructive whole-block imaging of trephine biopsies. This study evaluated whether XRH combined with Gaussian peak modelling could provide a pragmatic whole-block volumetric bone marrow cellularity (VBMC) estimate from formalin-fixed paraffin-embedded (FFPE) trephine biopsy blocks.Approach.Six routinely processed FFPE bone marrow trephine blocks were imaged using µCT-based XRH at ∼15 µm spatial resolution. VBMC was defined as the red-marrow (RM) fraction of the marrow soft-tissue compartment, RM/(RM + intra-biopsy wax), with wax serving as the volumetric proxy for adipocyte/yellow marrow space. Whole-volume greyscale histograms were modelled using a three-peak Gaussian approach representing intra-biopsy wax, RM and demineralised trabecular matrix. Peak-height and area-under-the-curve metrics were compared with whole-volume 3D segmentation and clinical two-dimensional (2D) cellularity estimates.Main Results.Gaussian peak modelling successfully approximated the segmented tissue-phase distributions. The peak-height-derived VBMC metric showed the closest agreement with whole-volume 3D segmentation, with an average absolute percentage difference of 9.3%, compared with 18.6% for clinical expert 2D cellularity estimates. The area-under-the-curve metric followed similar trends but consistently overestimated VBMC. Clinical 2D cellularity broadly followed whole-biopsy trends but showed one discordant case not explained by slice-position sampling alone. XRH also enabled unrestricted virtual reslicing and visualisation of sectioning-associated artefacts prior to further microtomy.Significance.Pre-sectioning XRH combined with Gaussian peak modelling provides a rapid, segmentation-free route to volumetric cellularity estimation from intact clinical FFPE trephine blocks. The approach supports objective whole-biopsy assessment while remaining compatible with routine histopathology workflows, reflecting the expected limitations of section-based visual estimation despite its role as the current clinical standard. In the near term, it could provide a non-disruptive adjunct to conventional 2D cellularity reporting, pending larger validation studies.

Imaging, Three-Dimensional↗

PubMed2026

Upscaling Genotyping by Amplicon Sequencing With GBAS-GUI.

Genotyping by amplicon sequencing (GBAS) is a relatively low-cost approach for generating genotypic data compared with established genomic methods, making it highly scalable and particularly suitable for large-scale genetic monitoring projects. However, most existing analytical pipelines are either marker-specific, insufficiently scalable, or lacking efficient data management systems for the long-term integration of genotypic information, limiting the full potential of GBAS. Here, we address this gap by introducing GBAS-GUI (https://github.com/sonnenbe-dot/GBAS-GUI), a pipeline capable of generating GBAS-based genotypic data for a wide variety of loci at scale. GBAS-GUI integrates a graphical user interface with multiple checkpoints to improve accessibility and robustness. It implements multiprocessing architecture and a relational database that links genotypic data with associated sample metadata to enhance scalability and data management. The pipeline further enables marker screening through automated calculation of polymorphism information content (PIC) and implements a strategy to recover homologous genotypic information from paralogous loci with non-overlapping amplicon length ranges. Using multiple empirical datasets, we demonstrate substantial improvements in processing speed, database management and handling artefacts related to co-amplification of unspecific regions and duplicates of the same genomic region. We further show that incorporating the full sequence information captured by an amplicon increases marker information content beyond what is achievable with length-based genotyping alone and expands the analytical versatility of GBAS. Overall, GBAS-GUI provides a robust, scalable and versatile framework that unlocks the potential of GBAS for large-scale population genetic and phylogeographic studies.

Genotyping Techniques↗

PubMed2026

Malaria rapid diagnostic tests: performance, pitfalls, and progress.

PURPOSE OF REVIEW: Malaria rapid diagnostic tests (RDTs) have revolutionized malaria diagnosis in endemic settings. RDTs are simple to use and accurate for clinical cases, although sensitivity is reduced at parasite densities below 200 parasites/μl. However, increasing prevalence of hrp2/3 gene deletions in certain areas threaten utility of histidine-rich protein 2 (HRP2)-based RDTs, and lingering HRP2 antigenemia can generate false-positive results after parasite clearance. This review summarizes current performance of malaria RDTs, threats to their validity, and recent innovations to improve their performance and continued role in malaria diagnosis. RECENT FINDINGS: Most World Health Organization (WHO) prequalified RDTs perform well for clinical diagnosis, with only occasional exceptions, including a recently reported issue affecting several countries. RDT sensitivity is generally related to malaria transmission intensity, with higher proportions of false-negative results in lower-transmission areas. Newly prequalified lactate dehydrogenase (pLDH)-based RDTs perform well for both Plasmodium falciparum in areas with >5% hrp2/3 gene deletions and for Plasmodium vivax diagnosis. Several point-of-care alternatives to RDTs, including micro-fluidic devices, hemozoin-detecting devices, and automated hematology analyzers, have shown promising results in small studies, but require larger-scale trials before widespread use. SUMMARY: RDTs remain a critical tool in clinical diagnosis of malaria, and newer pLDH-based tests perform well in areas where hrp2/3 gene deletions threaten validity of HRP2-based RDTs.

Humans↗

PubMed2026

Alignment strategies in total knee arthroplasty and the patellofemoral joint: A systematic review.

BACKGROUND: Different alignment strategies in total knee arthroplasty (TKA) may affect the patellofemoral joint. Mechanical alignment (MA) is commonly used but may alter native anatomy. Newer strategies such as kinematic alignment (KA), restricted kinematic alignment (rKA), and functional alignment (FA) aim to better restore native joint mechanics. This study provides an overview of the effects of alignment strategies on patellofemoral outcomes after TKA. METHODS: A literature search in July 2025 identified studies comparing patellofemoral outcomes in TKA using different alignment strategies. Of 166 studies screened, eight met inclusion criteria. Three studies were considered medium quality and five studies low quality. RESULTS: KA and rKA more closely restored native trochlear morphology than MA and FA, reducing outliers in the anterior trochlear line compared with MA and FA. MA showed greater trochlear translation, suggesting worse patellar tracking. Trochlear angles were more anatomical in KA and rKA. However, KA was associated with increased internal femoral component rotation and more outliers beyond safe thresholds. FA showed more consistent rotational positioning, generally within safe limits. Lateral patellar shift and intraoperative lateral release rates did not differ significantly between KA and MA. Only one study reported patella-specific clinical outcome scores, finding no difference between FA and adjusted MA. CONCLUSION: KA and rKA better restore trochlear morphology, but risk excessive internal femoral component rotation. FA provides a more balanced approach. MA, while widely used, is linked to altered trochlear shape and worse patellar tracking. The clinical impact of these radiological differences remains unclear, and higher-quality studies are needed.

Humans↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

Diagnostic performance of panfungal PCR on tissue specimens for the diagnosis of invasive fungal diseases: a systematic review and meta-analysis of the Fungal PCR Initiative (FPCRI).

UNLABELLED: Invasive fungal diseases are difficult to diagnose because of the limited sensitivity of culture. Panfungal PCR amplicon sequencing assays (targeting ribosomal RNA, such as 18S, 28S, ITS) are recommended for fungal identification in histopathology samples showing fungal elements. However, data describing its overall performance and consistency are lacking. This systematic literature review and meta-analysis assessed the performance of panfungal PCR on formalin-fixed paraffin-embedded (FFPE) and non-fixed (fresh or frozen) tissue samples. A systematic literature search was performed to include studies reporting the use of panfungal PCR for fungal identification in FFPE or non-fixed tissue samples. PCR sensitivity and specificity were assessed using the reference standard of histopathology showing fungal elements. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates were obtained using random-effects meta-analysis. Twenty-eight studies were included. In FFPE samples (18 studies, 852 samples), sensitivity and specificity were 75.4% (95% confidence interval [CI], 59.2-86.6) and 93.5% (70.2-98.9), respectively. Sensitivity in non-fixed samples (13 studies, 207 samples) was 86.5% (74.7-93.3), while specificity could not be assessed (insufficient data). Comparative analyses showed a significantly higher sensitivity of panfungal PCR over culture (88.2%; 76-94.7 vs 52.2%; 39-65, P = 0.001). Sub-analyses could not demonstrate the superiority of one PCR target over another due to limited data. Panfungal PCR exhibited adequate sensitivity and good specificity in FFPE samples. Sensitivity was even higher in non-fixed samples and largely superior to culture. Nevertheless, large interstudy variability was observed, warranting interlaboratory studies to define the optimal PCR target and standardized protocols. IMPORTANCE: Invasive fungal diseases are difficult to diagnose because of the low sensitivity of culture. Panfungal PCRs are widely used for fungal identification in tissue specimens but suffer from heterogeneous procedures and performance. This meta-analysis shows an acceptable sensitivity (75.4% and 86.5% in fixed and non-fixed samples, respectively) and good specificity (93.5%) of panfungal PCR, supporting its use, not only on histopathology-positive fixed samples but also in non-fixed samples concomitantly with other diagnostic tools (cultures and fungal-specific PCRs if available). These results provide a strong basis for further standardization of panfungal PCR techniques via interlaboratory assays to assess reproducibility and optimize analytical protocols. CLINICAL TRIALS: This study is registered with PROSPERO as CRD42023461148.

Humans↗